PREDICTION OF WOOD SPECIES AND - DiVA portal143949/FULLTEXT01.pdf-3- Title Prediction of Wood Species and Pulp Brightness from Roundwood Measurements Author David Nilsson, Department

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PREDICTION OF WOOD SPECIES AND

PULP BRIGHTNESS FROM

ROUNDWOOD MEASUREMENTS

by

David Nilsson

Akademisk avhandling Som med tillstånd av rektorsämbetet vid Umeå universitet för erhållande av Filosofie Doktorsexamen vid Teknisk–Naturvetenskapliga fakulteten, framlägges till offentlig granskning vid Kemiska institutionen, Umeå universitet, sal KB3A9, KBC, fredagen den 28:e oktober, kl 10.00, 2005. Fakultetsopponent: Prof. Olav Kvalheim, Department of Chemistry, University of Bergen, Allegaten 41, N-5007 Bergen, Norway.

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COPYRIGHT © 2005 DAVID NILSSON ISBN: 91-7305-959-5

PRINTED IN SWEDEN BY VMC–KBC UMEÅ UNIVERSITY, UMEÅ 2005

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Title Prediction of Wood Species and Pulp Brightness from Roundwood Measurements Author David Nilsson, Department of Chemistry, Organic Chemistry, Umeå University, SE–901 87, Umeå, Sweden. Abstract This thesis presents a number of studies, where a multivariate approach was taken to construct models that predict wood species and thermo mechanical pulp brightness from roundwood of Norway spruce and Scots pine. The first and second studies produced multivariate prediction models for wood species from the bark of spruce and pine. These models can be used for wood species classification and would replace the manual log assessment that takes place today. Principal Component Analysis, PCA, and Partial least squares projections to Latent Structures, PLS, were used to predict the wood species from multivariate measurements recorded from the bark of spruce and pine. Two different kinds of measurements were employed, near-infrared spectroscopy and digital imaging. Both methods showed that it was possible to predict the wood species with a high accuracy. The third and fourth studies of the thesis are related to the wood storage of roundwood and the deterioration of wood that occurs during the storage. The third study used an experimental design with five storage factors that provided different conditions for the analysed wood. The experimental design made it possible to identify the factors and the interaction between factors, which were important for the ISO brightness of peroxide and dithionite bleached thermo mechanical pulp, TMP. The final study of the thesis used NIR spectroscopy for predicting the ISO brightness of bleached TMP. Spectra recorded from stored wood were used to construct PLS prediction models. Keywords Design of Experiments, ISO brightness, Multivariate Image Analysis, Multivariate Modelling, NIR spectroscopy, Norway spruce, PCA, PLS, Roundwood, Scots pine.

ISBN: 91-7305-959-5

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CCoonntteennttss 1. List of Papers………………………………………………7 2. List of Abbreviations…………………………….……….. 8 3. Introduction…………………………………..…………… 9 4. Design of Experiments……………………………………. 13 5. Multivariate Modelling………………………………........ 19 5.1. Data Pre-treatment…………………………………. 20 5.2. Principal Component Analysis, PCA………………. 21

5.3. Partial least squares projections to Latent Structures, PLS……………………………... 24 5.4. Model Complexity and Validation…………. ……... 26

6. NIR Spectroscopy………………………………………….29 7. Modelling of Multivariate Images………………….……. 35 8. Roundwood Wood Species Classification……………….. 41

8.1. Roundwood species classification using NIR spectroscopy, Paper I…………………………… 41

8.2. Roundwood species classification using Multivariate Image Analysis, Paper II………………. 43

8.3. Comparison between the two methods…………….. 46 9. Wood Storage and its Relation to the Brightness of Bleached Thermo Mechanical Pulp……… 49

9.1. The Effect of Designed Wood storage on the Brightness of Bleached TMP, Paper III…………...... 51 9.2. Prediction of Bleached TMP Brightness using

NIR Spectroscopy on Wood Raw Material, Paper IV……... 56 10. Concluding Remarks……………………………………... 59 11. References…………………………………………………. 61 12. Acknowledgements………………………………………...71

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List of Papers

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This thesis is based on the papers listed below, which will be referred to in the text by the corresponding Roman numerals (I-IV). I Nilsson, D., E. Persson, Classification of the Softwood Species

Spruce and Pine using Near-infrared Reflectance Measurements on Bark combined with Multivariate Data Analysis, Submitted to Wood Science and Technology

II Nilsson, D., U. Edlund, “Pine and Spruce Roundwood Species Classification Using Multivariate Image Analysis on Bark”, to be published in Holzforschung. 59(6) (2005). Reprinted with kind permission from Walter de Gruyter, GmbH & Co KG, Berlin Germany.

III Nilsson, D., U. Edlund, K. Elg-Christofferson, M. Sjöström, R.

Agnemo (2003). “The effect of designed wood storage on the brightness of bleached and unbleached thermo mechanical pulp.” Nordic Pulp & Paper Research Journal. 18(4): 369-376. Reprinted with kind permission from Nordic Pulp and Paper Research Journal, Stockholm, Sweden.

IV Nilsson, D., U. Edlund, M. Sjöström, R. Agnemo (2005).

“Prediction of thermo mechanical pulp brightness using NIR spectroscopy on wood raw material.” Paperi Ja Puu – Paper and Timber. 87(2): 102-109. Reprinted with kind permission from Paperi Ja Puu Oy, Helsinki, Finland.

List of Abbreviations

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Abbreviation Meaning DFT Discrete Fourier Transformation DWT Discrete Wavelet Transformation GLCM Grey Level Co-occurrence Matrix MIA Multivariate Image Analysis MLR Multiple Linear Regression MSC Multiplicative Scatter Correction NIR Near InfraRed (Reflectance) O-PLS Orthogonal PLS (see PLS) OSC Orthogonal Signal Correction PCA Principal Component Analysis PLS Partial least squares projections to Latent

Structures PLS-DA PLS Discriminant Analysis RGB Red Green Blue RMSEP Root Mean Squared Error of Prediction TMP Thermo Mechanical Pulp

Introduction

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hermo mechanical pulp, TMP, is a high-yield pulp manufactured in many countries, among which Canada, USA,

Finland, Sweden, Japan and Norway are the largest producers. The difference between the thermo mechanical pulping process and the mechanical one is that the former heats the wood chips with steamed water prior to pulping. Unlike the cooked chemical sulphite and sulphate pulps, the mechanical pulping process refines wood into pulp that, with the exception of bark, consists of all wood raw material components including lignin. Lignin is a complex polymer that can be found in all types of wood where it serves as a binding material between the wood fibres. Lignin will react with the ultraviolet light of the sun and this will cause the paper produced from the TMP to yellow. Therefore, TMP is primarily used for low-cost and non-durable papers, i.e. in most cases newspaper. In Sweden TMP is produced from the two softwood species Norway spruce (Picea Abies) and Scots pine (Pinus Sylvestris). These are harvested and then transported to saw and pulp mills. Pulpwood logs are normally of poorer quality and lower diameter than sawlogs. Regardless of the quality of the used roundwood, tree harvesting and transportation to the mills will imply that the logs are stored at several locations before they enter the actual manufacturing process. Normally, the felled trees are stored temporarily at the roadside prior to transport to the saw or pulp mills. At the mills, the logs are then stored for a longer time to ensure a shortage-free production. The storage sometimes brings unwanted effects to the quality of the wood, especially at summertime when for example insect attacks and a high temperature can deteriorate the stored logs. Stored roundwood is generally sprinkled with water during the summer in order to keep the wood from drying. Consequently, sprinkling has a positive effect on the quality of sawlogs, but it may also have undesirable effects on the brightness of the stored wood. In TMP production, a darker wood will have a negative effect on the bleachability of the pulp.

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Introduction

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At the sawmill, the logs are manually classified as either spruce or pine prior to sawing. The residual parts of the logs not utilised as sawn wood are chopped into wood chips and transported to pulp mills. Thus, the wood used for TMP enters the process as either pulpwood or saw mill chips. The pulpwood logs are debarked and chopped into chips. After steaming the chips with water, the wood is refined into TMP. The pulp is then bleached in order to achieve acceptable brightness. This is normally done by treating the TMP with hydrogen peroxide or sodium dithionite. The bleaching can consist of a single or of two and even more stages. The bleachability of the pulp will be determined directly by the quality of the wood used. Wood of lesser quality may prevent the pulp from achieving acceptable brightness and may increase the amount of bleaching chemicals that has to be used. With access to powerful personal computers, wood and pulp have, in the last decade, been subject to a large number of investigations that characterise the material from multivariate data, i.e. data comprised of many variables. Analysing multivariate of data with traditional univariate statistical methods is troublesome. The sheer number of variables and also variable collinearity, i.e. several variables that give the same information, cause problems that are difficult to handle. Principal Component Analysis, PCA (Wold et al. 1987), and Partial least squares projections to Latent Structures, PLS (Sjöström et al. 1983; Geladi and Kowalski 1986), are two multivariate data analysis methods that compress the data into a few significant variables. These methods also deal with the collinearity problem. By using these methods, it is possible to construct models that can be used to predict the properties of the analysed material. The information content in the models might be increased by recording the data from a set of designed samples. Design of experiments is a way to set up an experiment in such a way that the information is maximised. This thesis presents results that are based on experimental data from two different analysis methods that generate multivariate data. The first is Near-Infrared Reflectance (NIR) spectroscopy (Osbourne et al. 1993), which is a fast analysis method that is used for quality control and monitoring of variables in various processes of today. The second method is Multivariate Image Analysis, MIA (Geladi et al 1989). This method treats a digital image in terms of multiple variables that can be processed by means of multivariate data analysis. The above mentioned methods are here used for recording data from logs of roundwood. The first property that is correlated from the data is the wood species, i.e. pine or spruce.

Introduction

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This property represents an early stage of the refinement of wood. The second property that is modelled from the data is the ISO brightness of bleached TMP. This property, on the other hand, is characteristic of the final paper product. The first aim of the thesis is to demonstrate how it is possible to construct models from roundwood measurements that predict the wood species and the ISO brightness. The second aim of the thesis is to show how design of experiments can be used to identify factors that affect the bleachability of TMP. The structure of the thesis will be the following; the fourth chapter will explain the concept of design of experiments and why its use is important. In chapter five to seven, the reader will in be familiarised with multivariate data analysis followed by NIR spectroscopy and multivariate image analysis. The last chapters, eight and nine, will deal with wood species classification and wood storage. Finally, there will be some concluding remarks.

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Design of Experiments

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esign of experiments involves a set of variables or factors that are varied in such a way that the information that can be

gathered is maximised. It should be understood that an experimental design comprises a number of experiments followed by the subsequent measuring of the outcome, where all factors are varied simultaneously in such a way that each of the experiments represents a set of factor settings that is not matched by any of the other experiments. This implies that the number of experiments needed is kept at a minimum. A designed experiment will always span the experimental samples more efficiently than if they are just picked randomly. With the use of an experimental design it is possible to determine interaction effects between the factors, making this approach far better than changing-one-variable at-a-time methodology, which is probably the most spontaneous way of performing experiments. To illustrate the difference between the two approaches, the model for dithionite-bleached TMP samples in Paper III can be used as an example. Figure 1 shows a contour plot for the decrease in ISO brightness for TMP produced from wood that has been stored for 14 weeks. Using the changing-one-variable-at-a-time approach, varying the light according to the horizontal line and then the watering according to the vertical line will eventually give the factor settings that correspond to the largest decrease in ISO brightness. Figure 2 shows the ISO brightness decrease when the response is also depending on the interaction between light and watering. It is clear that the approach of changing-one-variable-at-a-time will not give the settings that correspond to the largest decrease in ISO brightness. However, figure 3 shows how design of experiments can be used for varying the two factors at the same time. Obviously, this approach will have a better chance of giving the factor settings that provide the largest decrease in ISO brightness. The factors of light and watering are here varied in a full factorial design at two levels

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Figure 1. Contour plot from the dithionite model in Paper III. Changing the light and then the watering will eventually give the factor settings that correspond to the largest decrease in ISO brightness.

Figure 2. But if there exists an interaction, changing one variable at a time will not lead the experiments into the right direction.

Figure 3. Instead, it is better to change both variables at a time as this approach will have a better chance of reaching settings that corresponds to the largest decrease in ISO brightness.


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(Lundstedt et al. 1998). The number of experiments is given by nk, where n equals the number of levels and k is the number of variables or factors. In this example, both the number of levels and the number of factors is equal to two which equals 22 = 4 experiments. A design with 2 levels and 3 factors would be denoted 23 and equal 8 experiments. At two levels the factors are varied between the low setting usually denoted (-) and the high setting denoted (+). Full factorial design tables for two and three factors can be seen in table 1 and table 2. Also, a number of centre points are normally added to the designs in order to estimate the replicate error and give indications of model curvature. The centre level setting is usually denoted as (0).

Exp. No x1 x2

1 + + 2 - + 3 + - 4 - -

Table 1. The table for the full factorial design in two levels when the two factors x1 and x2 are varied in a total of four experimental runs.

Exp. No x1 x2 x3

1 + + + 2 - + + 3 + - + 4 - - + 5 + + - 6 - + - 7 - - 8 + - -

Table 2. The table for the full factorial design in two levels when the three factors, x1, x2 and x3 are varied in a total of eight experimental runs. A function y = f(x) can then be fitted between the measured response and the factors. Multiple Linear Regression (MLR) or PLS can be used for fitting the model. The simplest model for two factors will be the linear model fxbxbby +++= 22110


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where b0 is a constant, b1 and b2 the coefficients for the two factors x1 and x2 and f is the residual. Normally, there is a desire to determine interactions between the factors which gives the model fxxbxbxbby ++++= 211222110 where b12 is the coefficient that describes the interaction between the two factors. The number of possible experiments that can be carried out is a limiting factor in an investigation. The normal approach is that after a couple of preliminary trial experiments are performed, a screening design is set up in the purpose of getting a rough estimation of the measured response and the effect of the factors. A design can be constructed for optimisation purposes or just for examination how different factors affect the response. As the number of experiments required rises exponentially with the number of factors, it can be necessary to reduce a full factorial design to a fractional factorial design. This is done by generating the settings for one or more factors from interaction effects of the other factors. The main drawback of doing this is that these interactions cannot be resolved as they are confounded with the main factors. The least important interactions should therefore be discarded in this way. An example to this is the 24-1 fractional factorial design that requires eight experiments compared to 16 experiments for the corresponding full factorial design. The fourth factor is generated here from the interaction between the first three factors. This interaction is consequently lost, but all main factor coefficients and also some of the two-factor interactions can be resolved. The reader is referred to the following literature (Box and Draper 1987; Myers and Montgomery 1995; Montgomery 1997; Lundstedt et al. 1998) for a deeper understanding how fractional factorial designs can be generated and how additional trials in the experimental region can be constructed after a screening design has been performed. In addition to the full and fractional factorial designs there are other ways to construct an experimental design. The D-optimal design (Myers and Montgomery 1995) is a computer generated design that chooses the experimental runs from a set of possible experiments. The selection is made on basis of the size of the determinant of the chosen design matrix X. The use of a D-optimal design can be beneficial when the examined


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factors are of a qualitative nature with several levels. The use of D-optimality will in this case give a more efficient design with respect to the number of experimental runs compared to a factorial design. In this thesis, the design used for the wood storage discussed in Paper III was D-optimal. As several of the factors were quantitative, this type of design made it possible to carry out the experiments in the limited number of climate chambers that were available for the storage.

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Multivariate Modelling

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typical scientific study often generates large amounts of data that is gathered in a matrix, X. This matrix is comprised of N

observations (e.g. wood samples) as rows and K variables (e.g. NIR wavelengths) as columns (see figure 4). The size of the matrix is dependent on the scope of the study and how many variables are measured, but matrices exceeding a number of one thousand for the variables are common. The number of observations is, for most studies, more limited. Analysing a large matrix is problematic in several aspects and two questions need to be answered. First of all, how is it possible to gather information from so many variables, and secondly, what happens if some of the variables are correlated? It is obvious that neither of these two questions can be answered by means of traditional univariate statistical analysis where one variable is analysed at a time. Instead, it is better to use a multivariate method that analyses the data set with respect to all variables at the same time. Principal Component Analysis (Joliffe 1986; Wold et al. 1987), PCA, and Partial least squares projections to Latent Structures (Sjöström et al. 1983; Geladi and Kowalski 1986), PLS, are two methods used in multivariate analysis. The concept behind these methods is that they compress matrices into a few latent variables that explain most of the variation that can be found in the data. The calculation of PCA and PLS models makes it much simpler to obtain an overview of data and at the same time solves the problem with correlated variables.

Figure 4. The typical scientific study often generates data that can be assembled in a matrix, X. The rows, N, represent the number of studied samples and the columns, K, are the same as the measured variables.

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X

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he procedure for how multivariate methods are applied to data involves a data pre-treatment step where the matrix is

processed in such a way that it facilitates the modelling. The most common data pre-treatment is variable mean centering, which implies that the variable means are deducted from the variable values. Visually this means that the matrix is shifted towards the origin of coordinates. It is praxis that variable mean centering is always performed prior to PCA or PLS modelling. Sometimes variable scaling is also applied to the data set. The most frequently used scaling is unit-variance (UV) scaling where each variable is divided by its standard deviation. Scaling into unit variances implies that all variables have the same kind of variation and they therefore are equally important. For certain types of data (e.g. non spectral) this is essential for successful modelling but for other kinds of data (e.g. spectral data) unit variance scaling will lessen the signal-to-noise ratio which will negatively affect the multivariate modelling. In addition to different scaling techniques, a matrix can be pre-treated by means of applying a filter that removes unwanted information. Multiplicative Scatter Correction (Geladi et al. 1985), MSC, and Orthogonal Signal Correction (Wold et al. 1998), OSC, are two methods which are used for filtering X data. In the present thesis, these two methods have been applied to NIR data and this will be discussed more thoroughly in the next chapter.

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CA is a multivariate method that is applied to a data set or matrix, X, in order to reduce the dimensionality of the data. The

PCA algorithm calculates a set of principal components that are orthogonal to each other and explains most of the variation in the data. Each of the components is comprised of a score vector, t, and a loading vector, p. The first principal component can be seen as a line that goes through the origin of coordinates and is drawn through the K-dimensional space in such a way that it covers most of the variation of the data. Each observation is then projected down to the line resulting in a score value can be obtained for all observations. The next principal component also goes through the origin and is orthogonal to first component. In the same way, each observation is projected down to the line so that a score vector for the second component can be obtained. Here, the first and second components form a plane in the K-dimensional space. The loading vectors for the two components are derived from the way in which this plane is located in the K-dimensional space in relation to the original variables. Thus, the length of each score vector is equal to the number of observations while the length of each loading vector equals the number of variables. The PCA decomposition of the matrix X into scores and loadings gives a PCA model that can be written: X = TP’ + E Here, the scores are given by the matrix T and the loadings are given by the matrix P. The variation not explained by the model is given as the residual matrix E. The dimensionality or rank of the model is given by the numbers of score and loading vectors that are calculated. This value is referred to as the number of Principal Components (PCs). A powerful way to visualise the meaning of the PCA model is to plot vectors of scores or vectors of loadings against each other. A score plot from Paper II showing the separation between pine and spruce (figure 5) is a visualisation of two score vectors that represents a plane in the K-dimensional space. Which score vectors to plot can be chosen arbitrarily from the number of components calculated for the PCA model. Usually, it

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is most relevant to plot the score vectors that explain most of the variation in the data, i.e. the first and second components. Observations that have similar properties are positioned close to each other while observations of different properties are separated from each other and can normally be found on opposite sides of the plot. The score plots shown in the present thesis have been used for classification of samples. Here, observations that are related to each other often form clusters of objects or so-called groupings. Outliers, i.e. isolated observations that are located far away from the rest, can also be seen in the score plot.

Figure 5. Score plot from Paper II. Usually, it is relevant to plot principal components that explain most of the variation in the data. It is sometimes possible to detect groups of similar objects that are located close to each other.

The corresponding loading plot (figure 6) can then be constructed in order to interpret the score plot and to obtain an understanding of why observations are separated from each other. The loading plot shows variables important for the separation seen in the corresponding score plot. Observations located in a certain area of the score plot are highly correlated to the variables that can be located in the corresponding position in the loading plot. Generally, the most important variables are found far away from the origin of coordinates while variables that are close to origin have very little impact for the plotted principal components. While it is relevant to make two-dimensional loading plots for non-spectral variables, it is sometimes useful to construct a single-component loading plot for spectral data. Figure 7 shows a loading line plot where the values are plotted against their variable numbers, which in this case are the NIR wavelengths of spectra measured on ground wood in Paper IV. This type of plot resembling an original spectrum is used for determining important spectral regions for the plotted principal component.


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Figure 6. In the loading plot, it is possible to detect variables important for the separation seen in the corresponding score plot. Variables located far from the origin of coordinates are the most important.

Figure 7. The one-dimensional loading line plot can be relevant to use for spectral data in order to identify important regions. Figure taken from Paper IV.

PCA was developed by Pearson in 1901 (Pearson) and is now frequently used in studies dealing with multivariate data. PCA has been described in detail by Wold et al. (Wold 1987) and Massart and Heyden have very recently shown how PCA can be used for visualising data tables into plots (Massart and Heyden 2004; Massart and Heyden 2005). In forest applications, PCA has been used for the multivariate characterisation of sample material (Wallbäcks et al. 1991; Lennholm and Iversen 1995; Ferraz et al. 1998).


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LS (Sjöström et al. 1983; Geladi and Kowalski 1986) is a multivariate method used for finding the relationship between

two data matrices. In addition to the X matrix used in PCA, PLS regression also involves a Y matrix or a y vector that can be seen as response variable(s). The use of PLS implies that it is possible to obtain a model that predicts Y from X. In some cases, the y-variable will consist of a dummy variable that gives the class belonging of the corresponding sample. This type of PLS regression is usually referred to as PLS Discriminant Analysis, PLS-DA (Sjöström et al. 1986). As in the case of PCA, PLS reduces the multidimensional space into latent structures in the X block as well as in the Y block where scores for each block are calculated as t and u vectors. The scores are calculated in such a way that the co-variance between X and Y is maximised. For each calculated PLS component, a weight vector, w, is introduced that gives the maximum co-variance between the two blocks. PLS regression coefficients, B, can then be derived from the combined weight vectors, W, and from the loadings in X and Y that are denoted as P and C: B = W(P’W)-1C’ The regression coefficients are then used for constructing the prediction model: Y =XB + F It may be useful to construct a number of different plots for the interpretation of PLS models. The one-dimensional weight plot (figure 8) can be used to identify important variables in X that explain much of the variation in y. As in the case of NIR data, this plot taken from Paper I will resemble an original spectrum and the importance of specific spectral regions can be assessed. Another plot of great importance is the observed versus predicted plot (figure 9) for a certain y-variable. Here, observed ISO brightness values for test set samples in Paper IV are plotted against predicted values obtained from the PLS model.

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Figure 8. In PLS modelling, the one-dimensional weight plot can be used for finding variables that explain much of the variation in the response, Y. Plot taken from Paper I.

Figure 9. An observed vs predicted plot for test set samples in Paper IV can be constructed for the prediction of ISO brightness.


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ow well a model is estimated according to the variation in the data is given by the goodness-of-fit, R2, and can be calculated

for both X and Y data: R2X = 1 – SS(E) / SS(X) R2Y = 1 – SS(F) / SS(Y) Here, the variances for the matrices E, F, X and Y are given as the Sum of Squares (SS). A R2X or R2Y value close to one unit means that the model explains almost all the variation found in the data. The determination of the model complexity or rank, which is equal to the number of significant components is a crucial part of PCA and PLS modelling. Too few model components imply that the model is under-fitted and that there still is information left in the data than can be modelled. Too many components, on the other hand, means that the model is over-fitted and variation in the data that is due to noise is modelled. Both these scenarios are unwanted situations and the optimal model rank has to be decided. Cross-validation (Wold 1978) is one method to determine the number of significant model components. The cross-validation procedure implies that a number of samples are omitted during the model calculation and later predicted. The samples are usually divided into a number of cross-validation groups that are left out one at a time so that all samples have been kept out of the model estimation. The Predicted Residual Estimated Sum of Squares (PRESS) can then be calculated for the observations left out of the calculations. PRESS can be used for computing the goodness-of-fit for predicted values, Q2X or Q2Y: Q2X = 1 – PRESS / SS(X) Q2Y = 1 – PRESS / SS(Y) The latter of these two expressions is frequently used for determining both the PLS model complexity and how well the PLS model predicts left out samples. While the cross-validation gives a rough estimate of how well left out samples are predicted, it is better to use external test sets for the validation of the model. The use of an external test set implies that a number of

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samples not included in the model calculation at any time are predicted. The samples used for building the model are called the calibration set. The predicted values can then be compared with the observed or true values of the test set samples. It must be understood that the validation of external test sets should not be used for determining the model complexity. If this is the case, the model is adapted to a particular test set and it is possible that different results could have been obtained if another test set was to be used. Using the predicted test set samples it is possible to calculate the Root Mean Squared Error of Prediction, RMSEP, from the differences between the observed and predicted test set values:

NPRESSRMSEP TS /= where PRESSTS are the predicted residual sum of squares values of the test set samples and N is the number of predicted samples. It is convenient to use RMSEP as a measure of model prediction accuracy due to the fact that it has the same unit as the measured response.

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NIR Spectroscopy

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66.. NNIIRR SSppeeccttrroossccooppyy

ear-Infrared spectroscopy or, occasionally, Near-Infrared Reflectance (NIR) spectroscopy is a technique for measuring

transmitted or reflected light of wavelengths between 780 and 2500 nm. The NIR region is consequently positioned between the visual and the infrared regions of the light spectrum. NIR light is absorbed by bonds between hydrogen and the three atoms oxygen, carbon and nitrogen. The absorption is due to overtones or combinations of overtones of the vibrations that occur for the bonds between these atoms. The NIR region was originally discovered by Herschel (McClure 2003) and in the middle of last century Karl Norris lay most of the foundations for how we use NIR spectroscopy today (Butler and Norris 1960; Norris and Rowan 1962). Recently, NIR spectroscopy has frequently been used in research dealing with wood and forest products with many studies focusing on eucalyptus wood species (Schimleck et al. 1996; Michell and Schimleck 1996; Bailleres et al. 2002; Poke et al. 2004). It has also been used for the characterisation of lignin (Yeh et al. 2004) and for evaluating the resin content (Niemz et al.1994). NIR spectroscopy is also commonly used in the food industry for monitoring and quality control (Givens et al. 1997; Buning-Pfaue et al. 1998; McCaig 2002; Buning-Pfaue 2003; Wang et al. 2004; Svensson et al. 2004). The reason for the popularity of the use of NIR spectroscopy in various applications can be deduced from several things. First of all it is a non-destructive method which means that the measured sample is left intact. Secondly, no sample pre-treatment is normally required and, should one be required, it is usually a fairly simple produce. Finally, NIR spectroscopy is a fast and robust method that can be repeated with similar results. The typical NIR spectrum is comprised of numerous overlapping peaks that arise from the overtone absorption bands. This implies that for most NIR spectra it is impossible to make interpretations based on a single wavelength. The many overlapping peaks in combination with a strong dependency between adjacent wavelengths means that is favourable to

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analyse a matrix consisting of digitised NIR spectra with multivariate methods. Most studies that use NIR spectroscopy for the characterisation of wood related materials also perform the spectral interpretation by means of multivariate modelling (Borga et al. 1992; Antti et al. 1996; Hauksson et al. 2001; Tsuchikawa and Yamoto 2003; Kelley et al. 2004). Furthermore, NIR spectroscopy is used for determining moisture content (Tsuchikawa et al. 1996; Lestander and Geladi 2003; Blazquez et al. 2004, Nystrom and Dahlqvist 2004) as some of strongest absorption bands in the NIR region are due to bonds in water. The strong correlation between NIR light and water content makes the spectra sensitive to changes in temperature due to wavelength shifts of the hydroxyl absorption bands (Thygesena and Lundqvist 2000; Thygesenb and Lundqvist 2000) but other parts of the spectra are affected as well (Blanco and Valdes 2004). Figure 10 shows NIR spectra of ground wood samples in Paper IV, where the large absorptions around 1450 and 1950 nm are due to the sample water content. For more aspects regarding NIR theory and applications, the reader is referred to the literature (Osborne et al. 1993; So et al. 2004; McClure 2003).

Figure 10. The typical NIR spectra consist of broad peaks that often are heavily influenced by the water content of the measured samples which gives strong absorptions around 1450 and 1950 nm.

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For Paper I & IV, NIR spectra have been recorded from ground wood and from wood and bark surface. The measurements have been done with a NIR Systems 6500 spectrophotometer (figure 11). This instrument uses a Si based detector for the wavelengths between 400nm to 1098nm and PbS for the wavelengths between 1100 to 2500. Hence, it is possible to record spectra from the visual region with this instrument. The ground wood material has been packed into spinning cups (figure 12) that are inserted in the NIR instrument. The surface of the wood and bark has been measured with a fibre-optic probe (figure 12), which has been held against the analysed material.

Figure 11. The NIR spectrophotometer, NIR Systems 6500, which was used for the measurements described in the present thesis.

Figure 12. The equipment used with the spectrophotometer. The fibre-optic probe to the left and the spinning cup to the right.

Both these types of recordings measure the diffuse reflectance (Osborne et al.1993) from the sample. Here, the light is transmitted through the first sample layer where it undergoes absorption. The attenuated remaining light will then be diffused by reflections from other layers of the sample or it can be further transmitted and absorbed. For most samples the light will more or less be subject to scattering, i.e. it will be dispersed in all possible directions because the wavelength is considerably narrower than the size of the particles that the sample is comprised of. This will have a tangible influence on the recorded spectra where the scattering may cause

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additive as well as multiplicative effects. This can mainly be seen as a shift in baseline or slope. Often, an inconsistent sample treatment can cause large variations in the way in which the light is scattered. Figure 13 shows a score plot for the first two components of a PCA model calculated from NIR spectra of ground wood recorded by two different people using the spinning cup (unpublished results). It is evident that the largest variation in the data is caused by the person performing the measurements and, in particular, how that person presented the sample material in the spinning cup. Figure 13. The score plot for the first two components of a PCA model calculated from NIR spectra. Two persons recorded the measurements, which form two classes in the plot. Light scattering is the reason why it is beneficial to perform data pre-treatment on NIR spectra prior to multivariate modelling. Multiplicative Scatter Correction (Geladi et al. 1985), MSC, is a pre-treatment used for removing the additive and multiplicative effects found in spectral data. Without MSC, these effects normally add a few components to a PLS model. A model comprised of raw spectra normally has the same predictive properties as a model calculated from MSC treated spectra, albeit with a higher rank. An extension to the regular MSC, Extended Multiplicative Scatter Correction (Martens el al. 2003), EMSC, has been proposed for samples containing pure ingredients. Orthogonal Signal

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Correction (Wold et al. 1998), OSC, is another pre-treatment method used for filtering spectral and other kinds of data. The OSC is used in combination with PLS, where it removes variation in the X data that is orthogonal to the response variable(s) y (Y). Orthogonal PLS (Trygg and Wold 2002), O-PLS, is a PLS method with an integrated OSC filter. It divides the X data into orthogonal and non-orthogonal parts. The use of either OSC or O-PLS implies that a single component PLS model can be fitted between filtered NIR spectra and the response y. However, the total number of withdrawn orthogonal components must be taken into consideration when defining the rank of the model. Figure 14 (from Paper I) shows raw NIR spectra, the corresponding MSC treated spectra and the corresponding OSC treated spectra.

Figure 14. Raw NIR spectra (above) and the corresponding MSC (above right) and OSC treated spectra (right). (Paper I)

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Modelling of Multivariate Images

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77.. MMooddeelllliinngg ooff MMuullttiivvaarriiaattee IImmaaggeess

he digital representation of an image is in terms of rows and columns of pixels. The pixel properties are given by the

intensities of the colour channels or wavelengths that are recorded for the image. Thus, the image representation is a three-dimensional matrix consisting of rows, columns and colour channels (figure 15). Greyscale images only have a single colour channel whereas common RGB images have three (red, green and blue). Other types of images can have even more colours, e.g. NIR cameras (Geladi et al. 2004) can generate up to 80 different colour channels or wavelengths. The number of intensities or levels that is possible for each colour channel is defined by the channel bit depth. For normal RGB images this value is often set to 8 bit, which equals 28 = 256 different intensities. The total number of colours for such an image is 224 = 16777216.

Figure 15. The digital representation of an image consists of rows (N) and columns of pixels (K). Each pixel is then characterised by the intensities (I) in the recorded colour channels.

The three-dimensional data representing an image contains huge amounts of variation that is preferably analysed by multivariate modelling or by a combination of multivariate modelling and other techniques. Multivariate Image Analysis, MIA, introduced in the late 1980’s and early 1990’s, used PCA for decomposition of digitised images. These studies (Esbensen and Geladi 1989; Geladi 1989; Geladia et al. 1992; Geladib et al. 1992;

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Lindgren and Geladi 1992; Vanespen et al. 1992) compressed images into scores and loadings that visualised the image variation. Multivariate Image Regression (Esbensen et al. 1992; Grahn and Saaf 1992), MIR, was established as a technique where PLS is used for image decomposition according to a y-variable. At this time, MIA and MIR unfolded the image rows and columns to a pixel array (figure 16) that had the number of colour channels as variables.

Figure 16. The original multivariate image analysis unfolded the analysed image into a pixel array (N*K), which had the number of colour channels as variables (I).

Performing PCA or PLS directly on image data is convenient when one image at a time is analysed. However, when numerous images are compared it is often better to use some kind of feature extraction method that collects information from the image data and apply either PCA or PLS. There are methods that analyse the spectral image information, i.e. image colour, and there are also methods that extract spatial information, i.e. the image texture. The image texture can be defined as the pixel variation within the image and where this variation can be located in the image. Here, the local pixel variation will be denoted as frequency information while the location will be denoted as space information. A

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review covering most techniques that are being used for texture feature extraction has been written recently (Bharati et al. 2004). Analysing the spectral information of an image is traditionally done with histograms (Gonzalez and Woods 2002). The histogram construction implies that the pixels of each colour channel are binned according to their intensities. Thus, the number of bins equals 256 for images having 8 bit colour per channel. It has been shown that RGB histograms can be joined with other image features such as HSI (hue, saturation and intensity) values for the analysis of food colour (Antonelli et al. 2004). The calculation of grey level co-occurrence matrices, GLCM, is a statistical method that can be employed for feature extraction of an image. The co-occurrence matrix (Haralick 1973) is a two-dimensional table that uses pixel intensities for its X and Y axes. Each point in the matrix gives the probability that there are two pixels with the intensities given by the two axes at a certain distance. The distance is in pixels and is an arbitrary number that can be set according to the pixel variation of the analysed image. It is also necessary to give the direction for the distance. The distance can be set to horizontal, vertical or diagonal directions. Preferably, the direction should be set so that it corresponds to the structural features of the image texture. Compared with other methods, it has been shown that co-occurrence matrices can be used for efficient texture feature extraction (Ohanian and Dubes 1992). More studies have shown (Basset et al. 2000; Foucerot et al. 2004) that co-occurrence matrices can be used for analysing image texture. Furthermore, image transformations can be used for extracting features from images. The two-dimensional discrete Fourier Transform (2D DFT) can be used for capturing the frequency information of the image texture (Geladi 1992). This implies that information about space is lost as the 2D DFT transforms image data into the frequency domain. On the other hand, the two-dimensional Discrete Wavelet transform (2D DWT) is a transformation that can be applied to image data for capturing both frequency and space information. This transformation uses small waves, or so-called wavelets for the image decomposition (Daubechies


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1992). The wavelets are used for analysing images at many different resolutions, denoted as scales. The use of the wavelet transform on different kinds of data may serve different purposes. It can be done in order to perform noise-reduction (Mallat and Hwang 1992; Deng 1996; Cai et al. 2001; Cho and Bui 2005) and for data compression (Devore et al. 1992; Luo et al. 1997; Grgic et al. 2001; Trygg et al. 2001). Its use for capturing texture features has been proven in numerous studies (Laine and Fan 1993; Henkereed and Cheng 1993; Unser 1995; Van de Woewer et al. 1999; Bashar et al. 2003). The reader is referred to a textbook (Gonzalez and Woods 2002) for more information and theory about wavelet transformations. As mentioned earlier, the original MIA and the MIR techniques analysed one image at a time where each pixel represented an observation or sample in the constructed multivariate models. However, for classification of many images at a time, where each image is considered as an observation, it is more convenient to extract features from the images prior to calculating multivariate models. Recently, a number of studies that calculates multivariate models on extracted image data have been shown (Kvaal et al. 1998; Yu and MacGregor 2003, Antonelli et al 2004). This approach has also been used for classification of spruce and pine bark in Paper II that will be discussed in the next chapter. The extraction of textural features by calculating co-occurrence matrices was an optional step in the image processing prior to the 2D DWT and 2D DFT transformations. 2D DWT has been used for capturing and compressing the image information. 2D DFT has then been applied to each wavelet scale in order to remove space information. For the image classification conducted in this thesis only frequency information was considered important as the location of small sample variations within the image are not related to the wood species of either spruce or pine. It should be pointed out that the use of 2D DWT was paramount for the feature extraction in one aspect, namely that it was possible to summarise most of


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the image information in one vector consisting of wavelet coefficients obtained from the processed wavelet scales. Further, 2D DFT was not applied to the wavelet scales derived from the co-occurrence matrices. The information found in a co-occurrence matrix is related only to the image frequency information due to the fact that compared intensities disregard the exact location of image pixels. The extracted features from each image were thus given as one vector regardless of origin (figure 17). The vectors were then assembled as matrices comprised of spruce and pine samples as observations and histograms or wavelet coefficients as variables. PCA and PLS were then utilised for constructing multivariate models that was used for the classification of pine and spruce.

Figure 17. The image analysis in Paper III uses extraction techniques that compress the image information into one vector for each image. The vectors of many images are then assembled as a matrix that can be analysed by means of multivariate data analysis.

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Roundwood Species Classification

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88.. RRoouunnddwwoooodd SSppeecciieess CCllaassssiiffiiccaattiioonn

he two softwood species Norway spruce (Picea Abies) and Scots pine (Pinus Sylvestris) represent, according to the

Swedish sawmill inventory, 98% of the roundwood transported to the Swedish sawmills. Some mills have concentrated the production using only one species at a time but the majority of sawmills is, in fact, processing both species. When the two species are sawn at the same time, the incoming roundwood needs to be classified at some point in the production chain. This is normally done by manual assessment of the logs prior to the scaling operation. At the moment, no automatic methods are used for the species classification of roundwood. There are, however, techniques for automatic grading of logs such as gamma-ray (Hagman 1993) and X-ray (Oja et al. 2003). There are also techniques for the classification of sawn wood including FT-NIR spectroscopy (Brunner et al. 1996) and ultrasonic signalling (Jordan et al. 1998). In this thesis, two methods that could be used to automatically assess the softwood wood species are presented. These two methods, NIR spectroscopy and multivariate image analysis, have been used for analysing the bark of the samples. However, the wood surfaces of the samples have also been analysed as the tree felling and transportation sometimes involves that the bark is partly peeled off. 88..11 RRoouunnddwwoooodd SSppeecciieess CCllaassssiiffiiccaattiioonn uussiinngg NNIIRR ssppeeccttrroossccooppyy,, PPaappeerr II

he data set used in this study comprised of 53 wood discs that were collected from a thinning. NIR spectroscopy was used to

record spectra of bark and wood at wavelengths between 400 to 2500 nm. Every second wavelength was recorded and the spectra were assembled as two matrices sized 53x1050 and 48x1050. The score plot for the first

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three components (figure 18) of the PCA model calculated for the spectra recorded from bark showed that the spruce objects form a distinct group in the middle while the pine samples are divided into two smaller groups.

Figure 18. The score plot for the first three components of a PCA model calculated from NIR spectra of pine and spruce bark. The spruce samples form a group in the middle while the pine sample are scattered on both sides.

The reason why the pine samples were divided into two groups was due to the fact that the bark is of a smoother nature at the top of the stem while it is rougher at the bottom. The PLS model between the bark spectra and the wood species belonging as y-variable showed that it was possible to correctly classify all excluded test set samples. The corresponding PLS model based on wood spectra was, with a few wrongly predicted samples, not as accurate. This type of model could, however, prove to be important if the classified sample had the barked peeled off for some reason. An analysis of the PLS weights for the classification of the bark samples showed that the most important wavelength region was around 800nm, which corresponds to the upper visual part and the lower near-infrared part of the spectrum. It was also shown that the wavelengths around 2000 nm were important. Reduced PLS models, using either the lower part of the spectrum or the upper part of the spectrum only, proved to give as efficient predictions as the full model. This may allow for a simpler instrumentation setup for an automated classification system using NIR spectroscopy.


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88..22 RRoouunnddwwoooodd SSppeecciieess CCllaassssiiffiiccaattiioonn uussiinngg MMuullttiivvaarriiaattee IImmaaggee AAnnaallyyssiiss,, PPaappeerr IIII..

he sample material for this study consisted of 146 discs from spruce and pine logs collected from a clearing. RGB images

that contained bark, wood and a combination of bark and wood were recorded from the surface of each disc. An overview of the study can be seen in figure 19. Extraction of features as discussed in the chapter five was applied to the images. Extracted features included: 1. Coefficients from Fourier transformed wavelet compressed images 2. Coefficients from wavelet compressed GLCM data Overall, the best classification was obtained by using wavelet compression on GLCM data. The score plot for the first two components of a PCA model derived from GLCM of images containing bark (figure 5 in Chapter 5) shows that there is an obvious separation between the two classes. Only one out of 48 test set samples was wrongly predicted in the PLS model. The classification using images containing wood only was not as successful, with 73% prediction accuracy. In order to simulate a scenario that describes a real situation, the images containing both bark and wood were predicted. All three image types were divided into smaller sub-images that were used for building a PCA classification model. It can be seen in the score plot for the first two components (figure 20) that the bark is easily separable from the wood.

Figure 20. The score plot for the first two compo-nents of a PCA model derived from wood and bark images. The black objects are wood samples, the grey are bark samples and the light grey are images containing both bark and wood.

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Figure 19. An overview of the wood species classification with MIA. The percentages correctly classified observations are given next to the score plots.

Spruce bark Spruce bark+wood Spruce wood Pine bark Pine bark+wood Pine wood

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The sub-images originating from the images containing both bark and wood are spread among the two classes. These images were then used as an external test set but only those images that were classified as bark were predicted. The pure bark images was here used as calibration set for the PLS model that managed to correctly classify 92% of the samples in the test set. The pure wavelet classification could predict the unknown test set comprised of bark images with an accuracy of around 90%. The weight plot for the first two components (figure 21) of the PLS model calculated from the wavelet compressed images shows that only the coefficients accounting for the mean colour of the samples contributed to the separation between the two wood species. Evidently, it is difficult to construct prediction models that rely on structural information without extracting features prior to the modelling. Although the wavelet compression in combination with the Fourier transformation can be seen as a feature extraction method in itself, this approach merely summarises all the information that can be found in the images. Figure 21. The loading plot for the first two components of a PCA model calculated from wavelet compressed images. The only important wavelet coefficients (marked with arrows) are those that account for the mean colour information.


-46-

88..33 CCoommppaarriissoonn bbeettwweeeenn tthhee ttwwoo mmeetthhooddss

he results for the wood species classification show that the method using NIR spectroscopy has similar or even better

prediction accuracy than the method using multivariate image analysis. There are, however, a few circumstances that could favour the use of image analysis instead of NIR spectroscopy: 1. Instrumentation setup. The NIR instrumentation should be fairly simple. It has been shown that an NIR instrument can be used online for monitoring incoming sample material (Feldoff et al. 1997; Axrup et al. 2000; Anderson and Walker 2003; Kulcke et al. 2003). On the other hand, the camera setup could be even simpler. Using co-occurrence feature extraction, the present study has shown that low resolution greyscale images can successfully predict bark images. 2. Robustness of the instrumentation. Conditional variations such as distance to the measured sample material, variable light conditions and temperature affect the NIR instrumentation. While these problems may be solved, they definitely need to be taken into account. A camera setup is rather insensitive to the sample distance provided that a sufficient depth of field is being used. The camera should also be insensitive to changes in temperature. While changes in ambient light might cause problems, this can easily be solved by putting some kind of reference in the line of sight of the camera. 3. Future model improvements. The PLS prediction models based on NIR spectra have been constructed under laboratory conditions. While the number of included calibration set objects can be increased in order to cover a larger variation in sample material, real conditions might lessen the predictive ability. An advantage with the NIR classification is that the actual prediction models are very simple. This is also a drawback as it probably means that the models are optimal regarding model complexity and data pre-treatment. In other words, there is little room for model improvements. The multivariate image analysis on the other hand, uses more complex prediction models. These models should be largely uninfluenced by industrial conditions due to the robustness of the instrumentation. There should also be substantial room for improvements, as much of the image feature extraction is still unexplored.

T


-47-

For both methods, stepwise classification could be possible as they are sufficiently fast to record data from more than one point of the incoming log. The predictions made in the present thesis have used a cut-off (e.g. 0.5 or 1.5) that is exactly between the values giving the true class belonging (e.g.1 or 2). It might, however, be feasible to use cut-off values that are closer to the true values, where doubtful samples could be predicted once more. This next step could be another data recording or in case image analysis is being used, just another feature extraction method. It might even be possible to use a combination of both techniques in an instrumentation setup used for automatic wood species classification.

-48-

Wood Storage and its Relation to the Brightness of Bleached TMP

-49-

99.. WWoooodd SSttoorraaggee aanndd iittss RReellaattiioonn ttoo tthhee BBrriigghhttnneessss ooff BBlleeaacchheedd TTMMPP

ach year enormous quantities of conifer trees are felled in Sweden. In year 2000, over 33 million m3 of wood raw

material was transported to the saw mills alone according to the Swedish sawmill inventory. The total wood consumption where pulp mills are included is over 80 million m3. The logistics system for felled roundwood involves storage at different locations. Usually, roundwood is stored at the roadside for some time before the logs are transported to either a pulp or saw mill. In order to ensure a shortage-free production a certain wood supply has to be present at the mills. This type of storage normally takes place during a longer period of time compared to the roadside storage. As mentioned in the introduction, the storage often has undesirable effects on the quality of wood. This is a matter of great interest and has recently been dealt with in two doctoral theses (Liukko 1997, Persson 2003). Especially during summertime, the storage brings unwanted effects to the quality of the stored wood and this is due to several reasons. In order to avoid damage caused by drying, roundwood transported to the saw mills is usually sprinkled with water (Elowson and Liukko 1995; Liukko 1997; Persson 2001; Persson and Elowson 2001). Apart from apparent drying damages such as checking and debarking, the drying of wood makes it vulnerable to fungi, causing blue stain and to beetle attacks (Liukko 1995, Persson 2001). Sprinkling or wet storage can, however, cause unwelcome effects with the respect to brightness of wood (Lorås 1974; Persson and Elowson 2001, Persson et al. 2002). The risk of bacterial damage and fungi causing brown rot increases with a higher moisture content (Liukku 1997). One of the major drawbacks of wet storage or sprinkling is the discolouration of wood that occurs when organic compounds, mostly tannins, migrate from the bark into the sapwood (Lorås 1974; Wilhelmsen 1968; Persson et al. 2002). This so-called bark staining decreases the bleachability of pulp produced from the stored wood. Moreover, it is

E


-50-

generally considered that metal ions of Mn, Fe and Ca can migrate into the wood during wet storage. A high metal ion content in the wood may increase the rate of the decomposition of peroxide which is one of the chemicals most often used for bleaching of thermo mechanical pulp. However, a recent study (Persson and Lindeberg 2001) has shown that while there was a certain accumulation of metal ions during sprinkled storage, this increase was marginal and could not be related to large brightness reductions for bleached thermo mechanical pulp. The bleaching of thermo mechanical pulp is done in order to remove coloured compounds of the lignin (Dence and Reeve 1995). These compounds, sometimes called chromophores, react with the used bleaching agent which leads to a brightness increase of the bleached mechanical pulp. The chromophore groups mainly consist of carbonyl, ethylenic and phenolic rings that form conjugate systems and thus give rise to wood or pulp colouration (Dence and Reeve 1995). Two common agents used in mechanical pulping are hydrogen peroxide, H2O2, and sodium dithionite, Na2SO4. Hydrogen peroxide is used under alkaline conditions, which leads to the formation of the hydroperoxide ion:

OHHOOHOHOOH 2+⎯→←+ −− The hydroperoxide ion is a very strong nucleophile and reacts with lignin mostly in an oxidative manner. The known reactions of the chromophore eliminations are described in detail by Dence and Reeve (Dence and Reeve 1995). Furthermore, it is well known that hydrogen peroxide decomposes into water and oxygen:

2222 OOHHOOH +⎯→⎯ The decomposition of hydrogen peroxide is catalysed by metal ions such as Fe3+ and Mn2+. Various aspects of peroxide bleaching and its stability in presence of metal ions have been covered in many studies (Abbot and Hobbs 1992; Gierer and Jansbo1993; Brown and Abbot 1995). Owing to the catalytic decomposition of peroxide it is conventional that stabilising agents such as EDTA and DTPA are used as metal chelants for forming complexes with metal ions (Brown and Abbot 1995). Another reason to treat the pulp with chelants is due to the formation of coloured complexes


-51-

between metal ions and structures in the lignin (Ghosh and Ni 1998, Friman et al. 2004). Sodium dithionite is contrary to peroxide a reductive agent that during its reaction with lignin converts into the bisulphite ion (Dence and Reeve 1995):

−+−− ++⎯→⎯+ eHHSOOHOS 2222 322

42 Sodium dithionite or sometimes hydrosulphite will oxidise in the presence of oxygen and form a mixture of various sulphur compounds. The decomposition of dithionite has been investigated in a number of studies (Lister and Garvie 1959; Rinker 1965, Kilroy 1980). The efficiency of dithionite bleaching is affected by the pH value and this has also been a subject for some interest (Tredway 1979; Svensson 1998). Moreover, it is a well-known fact the brightness of mechanical pulp can also be degraded by ultraviolet or visible radiation that is absorbed by the chromophoric conjugated systems of lignin (Gellerstedt and Petterson 1978; Agnemo et al. 1991). The effect of intensity has been explored in a relative recent study (Andrady 1999) and the use of stabilising agents to retard this so-called photo-yellowing has also been investigated (Cook et al. 1996; Cook and Ragauskas 1997). In brief, there are many factors that might influence the final brightness of bleached mechanical pulp. The brightness is normally given in terms of ISO brightness which is a well-known standard in the Pulp and Paper industry. An elaboration of the meaning of ISO brightness can be obtained from a couple of papers published in the last decade (Bristow 1994, Bristow 1999). 99..11 TThhee EEffffeecctt ooff DDeessiiggnneedd WWoooodd ssttoorraaggee oonn tthhee BBrriigghhttnneessss ooff BBlleeaacchheedd TTMMPP,, PPaappeerr IIIIII

he wood storage described in Paper III was a designed study with five factors that controlled the storage conditions. Sixty

logs of Norway Spruce were collected in a forest district in the middle of Sweden and transported to climate chambers for storage. The five factors

T


-52-

light, temperature, watering, tree growth and level of debarking were varied according to a D-optimal design that gave different conditions for each of the 15 experiments. Samples (wood chunks) were collected in the beginning, in the middle, and at the end of the storage. These chunks were completely debarked and refined into 45 samples of thermo mechanical pulp. The TMP was bleached with either hydrogen peroxide or sodium dithionite so that the final sample material consisted of 45 unbleached, 45 peroxide bleached and 45 dithionite bleached pulps. The ISO brightness was then measured for each of the bleached pulps. The design table, X, was then fitted to the measured ISO brightness values, Y, using three PLS models which R2Y and Q2Y values are given in figure 22.

Figure 22. The R2Y and Q2Y values of the models calculated for unbleached, dithionite bleached and peroxide bleached TMP. The model for the peroxide bleached TMP has the highest degree of explanation among the three models.

The effect of time was added as an extra factor and the Y values were expressed as the loss in ISO brightness compared to brightness of the fresh samples. The PLS models could then be used for identification of the most important factors affecting the brightness and thus bleachability of unbleached, peroxide bleached and dithionite bleached TMP. An overview of the storage experiments, which includes Paper IV, can be seen in figure 23. The evaluation of the results showed that the brightness decrease for the unbleached pulp was around three ISO brightness units for three replicates and this is in line with what was reported in an earlier study (Persson et al. 2002). These replicates had been subjected to a relatively high level of watering so the degree of bark staining was substantial. The coefficient plot derived from the model calculated for all of the unbleached pulp samples (figure 24) showed that the most signifi-

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Unbleached TMP Dithionite BleachedTMP

Peroxide BleachedTMP

R2YQ2Y

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Unbleached TMP Dithionite BleachedTMP

Peroxide BleachedTMP

R2YQ2Y


-53-

Figure 23. An overview of the two wood storage studies of the thesis. Ultimately, PLS models that give the importance of the storage factors and that predict the ISO brightness of bleached TMP can be obtained.

-0,50

0,00

0,50

1,00

1,50

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Tim

wat*

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im

-0,50

0,00

0,50

1,00

1,50

wat Lig

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wat

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wat

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0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP

Peroxide bleached TMP

Dithionite bleached TMP

-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

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wat

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wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

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-0,50

0,00

0,50

1,00

1,50

wat Lig

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wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

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wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

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wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



Stand with fast growing spruce

Stand with slow growing spruce

Wood samples consisting of four logs each

Some logs were freed from outer bark

Storage in climate chamberSampling

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining

ISO brightness measurements

ISO brightness values

NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

Prediction of peroxide

bleached pulp

Prediction of dithionite bleached pulp

RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark

Coefficients for storage factors






400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark







400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark







400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark

Coefficients for storage factors-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

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wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

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wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP








400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark







400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark







400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark







400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

400 60080010001200140016001800200022002400-0.10

0.10.20.3

0.40.5

0.60.7

0.8

Wavelength nm

A

NIR spectra

ChippingTMP

Dithionite

Peroxide

Bleached pulp

NIR measurements

refining



NIR spectra

PLS modelling

samples

wavelengths

samples

X YPLS

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d

77 77.5 78 78.5 79 79.5 80 80.5 8177

77.5

78

78.5

79

79.5

80

80.5

81

Predicted

Obs

erve

d


bleached pulp


RMSEP = 0.96

RMSEP = 0.74

Storage

Variables

Watering

Temperature

Light

Tree Grow

th

Bark



-54-

-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



-0,50

0,00

0,50

1,00

1,50

wat

tem

p

Lig

Tim

wat

*Lig

wat

*Tim

-0,50

0,00

0,50

1,00

1,50

wat Lig

Tim

wat

*Lig

wat

*Tim

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

wat

tem

p

Unbleached TMP



cant term was the interaction between watering and light, followed by the terms for temperature and time. The terms for watering and the interaction between watering and time were close to being significant according to 95% confidence level. The term for light was insignificant but, in conjunction with watering, it is clear that these two terms account for a considerable brightness loss. The size of each of the coefficients can be translated into the brightness loss that occurs when the particular factor is varied from centre to high level. It is noticeable that the level of debarking and the tree growth had no to little effect on the brightness. It should be stated though; that a high debarking level corresponded to a removal of only outer bark. Moreover, the model fit of the PLS model indicated that much variation was not modelled. This is most likely due to uncertainties in the y- variable.

Figure 24. The coefficients of the models calculated from the three types of TMP samples. The unbleached and dithionite bleached pulp brightness is heavily dependent on the interaction between watering and light. The brightness of peroxide bleached pulp is on the other hand dependent on the effects of watering and temperature.


-55-

With the exception of temperature, the coefficient plot for the dithionite bleached pulps includes all the terms used for modelling the brightness loss of the unbleached pulps. The brightness of dithionite bleached TMP is, therefore, dependent on all these terms. Considering the fact that the coefficients are comparable in size to the ones obtained from the unbleached samples, this means that dithionite bleaching is unable to remove brightness losses caused by these terms. Dithionite bleaching can, however, be used for removing effects introduced by a high temperature. As seen in the coefficient plot for the peroxide bleached samples, only the terms for watering and temperature are significant. This implies that peroxide bleaching can be used for removing the brightness loss caused by the interaction of light and watering. It also means that bleachability of TMP did not decrease with increased storage time. Hence, most of the brightness reversion for the bleached TMP occurred during the first six weeks. Nevertheless, peroxide bleaching seems to be sensitive to brightness reversion introduced by watering and temperature. As mentioned earlier, the risks of wood storage regarding the bleachability of TMP are highly pronounced during summer-time. This has also been confirmed by the present study where watering, light and temperature cause a brightness reversion of bleached TMP. The most significant effect for the brightness of unbleached and dithionite bleached was the interaction between watering and light. Peroxide, on the other hand, is totally unaffected by the brightness loss caused by this interaction. The interaction between watering and light could be related to an increased activity of micro-organisms, which is affected by the accessibility of light and water. Furthermore, both peroxide and dithionite bleaching seem sensitive to the brightness reversion caused by watering, even though the coefficient for dithionite is not significant according to 95% confidence level. As shown in earlier studies (Lorås 1974; Persson et al. 2002) this effect is due to bark staining. Finally, it is noteworthy that dithionite can be used for removing the effect caused by temperature in contrary to peroxide.


-56-

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

67 68 69 70 71 72 73 7467

68

69

70

71

72

73

74

Predicted

Obs

erve

d

99..22 PPrreeddiiccttiioonn ooff BBlleeaacchheedd TTMMPP BBrriigghhttnneessss uussiinngg NNIIRR SSppeeccttrroossccooppyy,, PPaappeerr IIVV

his study is based on wood material collected from the logs during the wood storage explained in Paper III. These samples

were taken every second week and consisted of wood discs with a thickness of approximately 5 cm. NIR spectroscopy was then used for recording spectra from four different kinds of material: 1. Peripheral surface of each disc 2. Ground wood 3. Dried ground wood 4. Unbleached TMP The measurements also included the visual region of the spectra. PLS was then used for calculating prediction models between the spectral data and the ISO brightness of the bleached thermo mechanical pulp. The measurements on the unbleached TMP were performed just for the sake of comparison. It was seen that the brightness of dithionite bleached TMP could be predicted from the peripheral wood surface (figure 25), which was not the case for peroxide bleached TMP.

Figure 25. The observed vs predicted plot made for test set samples predicted from NIR spectra recorded from wood surface.

T


-57-

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavelength (nm)

PLS

Wei

ghts

w

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavelength (nm)

PLS

Wei

ghts

w

This consolidates the findings made in the previous paper where the strong interaction effect between watering and light played a dominating part in the bleachability of TMP when using dithionite as bleaching agent. This interaction should mostly affect the outer parts of the sapwood that is close to the bark. The models made for the ground wood material could be used for predicting the ISO brightness of both dithionite and peroxide bleached TMP. It was shown, however, that the predictive ability of the models that originated from the near-infrared region of the spectra was limited without drying the samples prior the spectral measurements. The analysis of PLS weights showed that wavelengths corresponding to the near-infrared region was highly influenced by absorptions related to bonds of water (figure 26). Moreover, the weight plots showed that the most important parts of the spectra corresponded to the visual region. It was also seen that much of the variation of the data was orthogonal to the predicted ISO brightness and could therefore be removed by means of OSC prior to the computing of PLS prediction models.

Figure 26. The analysis of PLS weights showed that the modelling was heavily influenced by the sample water content.

The modelling suggests that is should be possible to build prediction models from NIR spectra of stored wood that can be used for predicting a rough estimation of the ISO brightness achieved after dithionite bleaching. If a model is to be used for predicting the brightness obtained after a subsequent peroxide stage, the wood has to be ground prior the spectral recordings.

-58-

Concluding Remarks

-59-

1100.. CCoonncclluuddiinngg RReemmaarrkkss

he studies in this thesis have shown that the use of a multivariate approach is a prerequisite for finding different

properties of roundwood. The two methods used for the wood species classification generated large quantities of data that were efficiently handled by PCA and PLS models. The study using multivariate image analysis demonstrated that it was also necessary to extract certain image features prior to the multivariate modelling. The two classification studies showed that the wood species could be predicted from the bark of the analysed log. The measurements were, however, carried out in a laboratory environment which means that other results could have been obtained if an industrial environment was used. The use of experimental design in the brightness study, revealed that it was possible to identify factors that negatively affected the bleachability of TMP. This made it possible to identify the large interaction between watering and light that affected the bleaching of TMP using dithionite. It is virtually impossible to detect such interactions without employing a designed experiment. The use of experimental design also indicated that the samples used for constructing PLS prediction models for the ISO brightness of bleached TMP were as diverse as possible. Picking samples randomly often produces narrow data sets that cannot be used in a more general context.

T

-60-

References

-61-

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1122.. AAcckknnoowwlleeddggeemmeennttss Först och främst vill jag tack min huvudhandledare Ulf Edlund för den kloka vägledning jag fått, den noggrannhet du haft när du läst mina artiklar och den förståelse jag fått när jag behövt det. Jag vill även tacka min biträdande handledare Michael Sjöström för dina intelligenta kemometriska synpunkter, ibland när man allra minst anade det. Vidare vill jag tacka Roland Agnemo, Domsjö Fabriker, för det engagemang du visade när jag utförde mina blekningsförsök på dåvarande Modo. Du ska också ha tack för att jag fått fiska ett flertal gånger i de fina representationsfiskevattnen! Ett stort tack till min kära Josefina för att du stått ut med mig i ur och skur. Jag vill också tacka min mamma Berit, min pappa Bo och mina syskon Esbjörn och Olle för att ni finns där. Sen vill jag tacka min gamle följeslagare och barndomsvän sen lågstadiet, Johan ”Pinocchio” Trygg. Tack för dina synpunkter och alla goda råd jag fått angående min forskning. Tack för att du kallar mig ”pucko”. Tack till Erik Persson, SLU Uppsala, för dina idéer, ditt engagemang och för att du vidgat mina vyer när det gäller forskningen. Tack till Kristina Elg Christoffersson, min rumskompis under många år. Dina tankar och frågeställningar har fått mig att tänka till och analysera saker som jag annars inte hade gjort. Tack till Carina och Ann-Helèn för allt hjälp jag fått och för att ni sett till att allt flutit på. Tack till Bert Larsson för din vänskap och våra diskussioner angående olika mest tekniska saker.

Acknowledgements

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Tack till Thomas Jonsson, Umetrics AB, för alla dom gånger Simca har jävlats och du kommit som räddaren i nöden. Tack till mina gamla kompisar Per-Erik, Carl-Mikael, Stefan och Conny. Jag hoppas att vi fortfarande kan hålla kontakten och hitta på saker. Tack till Tobias Stenström och Dan Johnels för er vänskap och ert engagemang angående foto. Roligt att ha någon att diskutera hobbies med på jobbet. Jag vill också tacka fiskarna Ulf, Frippe, David, Mickael M, Mikael S, Hans och Tomas för alla trevliga fisketurer och allt fiskesnack. Tack till Jon Gabrielsson för alla intressanta pryldiskussioner. Tack till Freddan för du hållt uppe mitt musik och dataspelsintresse. Tack till Suss för våra diskussioner, hoppas vi ses mer i Innansjön! Tack till Anders B och Lissan. Ett stort tack till övriga kemometriker nu och då; Anton, Ing-Marie, Henrik, Anna, Max, Malin, Pär och Per. Jag hoppas jag inte glömde nån nu. Ett stort tack till övriga personer på Organisk Kemi som jag inte nämnt med namn! Tack till Agne, Eva, Hjalmar, Julia, Johanna, Karin, Daniel, Helena och Markus för att ni gör det lättare att vara ute i obygden. Tack Bim, Isa och Claes!

Classification of the Softwood Species Spruce and Pine using Near-infrared Reflectance Measurements on Bark combined with Multivariate Data Analysis David Nilssona,* and Erik Perssonb

aDepartment of Chemistry, Organic Chemistry, Umeå University, SE-901 87 Umeå bDepartment of Forest Products and Markets, P.O. Box 7060, SE-750-07 Uppsala

Abstract

Near-infrared reflectance (NIR) measurements were performed on the bark for a number of 25

discs of Norway Spruce and 28 discs of Scots Pine. Principal Component Analysis (PCA) was

applied to the spectral data in order to visually classify the pine and spruce objects. Partial

Least Squares (PLS) to latent structures were then used to find regression models between the

collected spectra and the roundwood species classification of the measured discs. Separate test

sets used for validation of the PLS models were selected from the spectral data and predicted.

It was found that PCA to some degree could be used to estimate the roundwood species. PLS

gave more accurate results and provided a simpler solution for the roundwood classification.

All predicted objects were classified correctly using the PLS modelling. The results showed

that measuring bark with NIR spectroscopy might be a viable method for automatic

classification of roundwood.

*Author to whom correspondence should be addressed Email address: [email protected]: +46 90 786 5999 Fax: +46 90 13 88 85

mailto:[email protected]

Introduction

In general, different species of wood are used for different end products with different pricing

and handling in the sawmill process. The roundwood delivered to the saw mills are still to this

date delivered as mixed species from the forest. As a result, the different species must be

separated at some stage in the production chain, i.e. from forest to sawn timber. This is also

true, to some extent, for the quality grading. While different end products have different

requirements of grade, the drying operations must be adapted to the end products. The sorting

or separation of different species and quality grades takes place at different stages in the

chain, which depend on the number of commercially available species, the structure of forests

or industries, traditions and the available technology.

In North America the lumber is often delivered as mixed softwood species to the sawmills and

the species or groups of species are separated after the sawing, prior to the drying operations.

In Sweden the two most important species used for timber production are Norway Spruce

(Picea abies (L.) Karst.) and Scots Pine (Pinus sylvestris). According to the Swedish sawmill

inventory for year 2000, these species represent more than 98% of the production. The

remaining 2% mainly consists of different hardwood species. The sawmills often use both

Scots Pine and Norway Spruce as raw material and the species are separated at the scaling

operation prior to sawing. The classification of species and the quality grading is done by

manual visual assessment of the logs. The situation is similar in other Scandinavian countries.

As a part of the general strive to increase the productivity and cut costs in the sawmill

industry, efforts are made to develop automatic systems for log scaling. Methods for

automatic measurement of length and diameter have been in use at sawmills for a long time.

The sorting and grading of logs for payment to the supplier is usually made manually, but the

use of different automatic methods is increasing. Techniques like gamma-ray (Hagman 1993),

X-ray (Oja et al. 2003; Grundberg and Gronlund 1997) and optical 3D (Gjerdum et al. 2001;

Oja et al. 2004) log scanning can facilitate the grading of logs. There are however very few

automatic methods for species classification of logs described in the literature or patent

databases. Existing methods for wood classification are instead based on sawn products and

the majority of them are using a spectroscopic approach. Wood identification has been done

by means of Fourier-Transform NIR (Brunner et al. 1996a; Brunner et al. 1996b), with Ion

Mobility Spectrometry (Lawrence 1989) and with Raman Spectrometry (Lewis et al. 1994;

Yang et al. 1999). There is also a method using ultrasonic signals (Jordan et al. 1998).

The present pilot study suggests a method for automatically classify wood species with near-

infrared reflectance (NIR) bark measurements in combination with multivariate data analysis.

NIR spectroscopy can be used in industrial applications for quality control and for

classification of raw material in the pharmaceutical- (Zhou et al. 1998; Columbano et al.

2002), food- (Wahlby and Skjoldebrand 2001) and pulp & paper (Wright et al. 1990;

Hauksson et al. 2001) industries. NIR spectroscopy gives information about both the chemical

composition (Osbourne et al. 1993) as well as the physical properties (Osbourne et al. 1993)

of a sample. The different species of wood differs both in chemical composition (Hon and

Shiraishi 1991), wood anatomy and colour. For unbarked logs, the different properties of the

bark could be utilised for classification. Since the wavelengths of any NIR spectrum are

highly correlated and consists of numerous underlying peaks (Osbourne et al. 1993),

multivariate data analysis methods such as Principal Component Analysis PCA (Wold et al.

1987) and Partial Least Squares (PLS) regression to latent structures (Geladi et al. 1986;

Hoskuldsson 1988) must be applied to the spectral data. Prior to calculating multivariate

models, the spectra are often processed with some kind of filtering in order to reduce

unwanted variation which facilitates the modelling. Multiplicative Scatter Correction (MSC)

(Geladi et al. 1985) and Orthogonal Signal Correction (OSC) (Wold et al. 1998) are two pre-

treatment methods viable for filtering NIR spectra.

Materials and methods

The used wood samples were discs of Scots Pine (Pinus sylvestris) and Norway Spruce (Picea

abies (L.) Karst.) collected in a pile of logs from a thinning made approximately one month

before the collection of samples. There were totally 53 discs, 25 of Norway spruce and 28 of

Scots pine.

The measurements were performed using a NIR Systems 6500 Spectrophotometer equipped

with a fibre-optic probe. Measurements were done both on the bark and on the sapwood.

During the measurements the probe was held against the surface of the bark or the radial

wood surface and the reflectance was measured. The sapwood measurements were

accomplished by putting the probe against the sawn surface of the disc. The radial surface had

the original properties created by chainsaw during the collection of samples.

Reflectance spectra of the 53 samples (48 sapwood samples) were collected in the wavelength

range 400-2500 nm with a spectral resolution of 2 nm. The scanned spectra were assembled

into two data matrices (53 objects and 1050 variables), one for the bark measurements and

one for the sapwood measurements.

Multivariate PCA and PLS models were calculated from raw, MSC-treated and OSC-treated

spectra. MSC pre-treatment implies that the spectra undergo a regression towards the average

spectrum and that each spectrum is corrected with respect to both baseline shift and slope.

The use of OSC is only feasible when a response variable is available. The OSC treatment

removes variation in the X matrix (NIR spectra) that is not correlated to the chosen response

or y-variable. In this study the y-variable was the species classification of the roundwood, i.e.

spruce or pine.

PCA was used to describe the multidimensional spectral variable space with a few significant

principal components that covered most of the variation. More generally, principal

components are comprised of scores and loadings, which, if they are plotted, can give

valuable information about the samples and variables in the data set. A score plot consists of

the score values for one up to three components and shows how the similarities or differences

between samples or objects. Objects with similar properties are lying closely to each other and

can often form a cluster. A loading plot is comprised of the loading values for up to three

principal components and reveals what variables or wavelengths that are important for the

separation of objects in the score plot.

PLS was used to find correlation between the spectral data and the roundwood species

classification variable. In the general case, PLS finds latent structures, or components, in the

X matrix and correlates these with components in the Y matrix. Often, the Y matrix consists

of only one variable, y. The data are usually divided into a calibration set that is used for

model building, and a test set that is used for validation. In order to obtain a good prediction

of the test set, an optimal number of PLS components has to be calculated. Too few

components will result in poor prediction due to the fact that the under-fitted model has not

included variation important for the correlation to the Y matrix. Too many components will

cause the model to become over-fitted and the superfluous components will mostly be

comprised of noise that will lower the prediction ability of the model. In this study, the cross-

validated Q2Y-value (Wold 1978) was used to find the number of significant components.

Results and discussion

The NIR spectra collected from bark of roundwood contain considerable amounts of variation

that is due to various scatter effects. These can arise from the properties of the measured log

surface such as irregularities in the bark or by a varying distance between the measured log

and the spectroscopic near-infrared probe. A plot (fig 1) of the fifty-three recorded spectra

measured in this study shows that a baseline shift as well as a variation in slope can be found

within the spectral data. The corresponding multiplicative scatter corrected spectra can be

seen in fig 2, which shows that the spectral variation has been reduced significantly. The OSC

treatment represents an even heavier reduction of the spectral variation and can be seen in fig

3.

Classification of roundwood using Principal Component Analysis

Principal Component Analysis was applied to the multiplicative scatter corrected spectra of

the measured roundwood. The wavelengths between 400 to 600 nm and 2250 to 2500 were

removed prior to modelling in order to exclude random noise that was found in these regions.

The five-component model derived from the spectra explained 99% of the variation in data.

The score plot for the first three components (fig 4) shows that the objects are grouped into

three different classes. The group in the middle corresponds to the spruce samples while the

two other groups are comprised of the pine samples. The reason for the separation of the pine

objects into two groups was that the measured logs had bark that differed with respect to the

surface smoothness. The bark of a living pine tree is often very rough and dark-coloured at the

base while the top of the tree has a brighter and smoother bark. The score plot for the second,

third and forth components (fig 5) divides the objects into two classes, pine and spruce. It is

noticeable that in both score plots there are objects that lie in the wrong class. The objects in

the spruce class are grouped tightly together, so the faulty classification is mainly caused by

the large spread of the pine samples. It is evident that much of the variation in the principal

components is due to the structure of the tree bark where the bark of spruce is more

homogenous. A PCA model was also calculated for the raw centred spectra but the score plot

for the first three components (not shown) had a worse separation between the objects

compared to the model using MSC treated spectra.

Based on the score plot for the first three components, two test sets of 15 objects each that

spanned the variation in data with respect to both structure and chemical properties were

selected. To ensure that representative test sets were used, the objects that were dubiously

classified in the score plots were not discriminated and several of these objects were included

in the test sets. The test sets were then used for external validation of the models created in

this study. In order to further examine the classification characteristics of the PCA modelling,

Cooman (Coomans et al. 1984) classification plots were created for the two test sets. In order

to exclude variation that originated from light scattering, the models were calculated from

MSC-treated spectra. The concept behind making these plots was that one PCA model was

made for each of the three groupings seen in the first score plot. It is noticeable that two

models had to be made for the pine objects since the variation within these objects was too

large. After that, the distance between the objects in the test sets and the three models could

be plotted. A short distance to one of the models implies that a certain object belongs to that

particular class and thus the object can be classified as either spruce or pine (rough or smooth

bark). Of course, separate models had to be made for the two test sets, since the corresponding

calibration sets were consisted of different samples. The results from the Cooman

classification of the first test set (fig 6 a,b,c) were rather ambiguous since several of the pine

samples with smooth bark were predicted erroneously. The two classes comprised of spruce

samples and pine samples with rough bark were however classified almost correctly. The

Cooman classification for the second test (not shown) set showed similar results compared to

the first set.

Classification of roundwood using Partial Least Squares regression to latent structures

Partial Least Squares Discriminant Analysis (PLS-DA) (Sjostrom et. Al) models using the

NIR spectra as X matrix and the roundwood species classification as y-variable were

developed. The y-variable consisted of a single column that was set either at “0” or “1”,

denoting pine or spruce class belonging. Prior to computing the regression models, the NIR

spectra were filtered using Orthogonal-PLS (Trygg and Wold 2002) (O-PLS), a variant of the

Orthogonal Signal Correction algorithm. The O-PLS treatment implied that the NIR spectra

were filtered from variation in the data that was not correlated to the roundwood species

classification. During the modelling prior to using O-PLS, it was found that a fairly large

number of latent variables was needed to describe the correlation between X and y. The O-

PLS managed to exclude the orthogonal variation within these components and PLS could

then be used to fit a final one-component model between the NIR spectra and the species

classification variable for each of the calibration sets. In any case, the same number of

components had to be calculated whether O-PLS was used or not, but the O-PLS utilisation

provided a greatly simplified solution for the final prediction models. The prediction of the

test sets is shown in fig 7 where it can be seen that all excluded objects are predicted correctly.

A predicted value below 0.5 denotes pine class belonging, while a predicted value above 0.5

denotes spruce class belonging. The correlation between X and y is represented by the PLS

loading weights (fig 8), which show that roundwood species classification correlates to whole

used spectral region, but especially to the wavelengths around 800 nm which corresponds to

the upper part of the visual region and lower part of the near-infrared region.

Moreover, PLS models between the NIR spectra and the roundwood species classification

were calculated using reduced data matrices. Models that were comprised of the wavelength

region between 600 to 1100 nm and models that only used the wavelengths between 1100 to

2250 nm were created. The modelling showed that is was possible to use either of the two

wavelength regions and yet obtain very similar prediction results to when using the whole

region. All predicted samples were classified correctly in the predicted vs observed plots (not

shown). The PLS weights for the models using the wavelength region between 600 to 1100

nm indicated that the species classification, as showed earlier, is strongly correlated to

wavelengths around 800 nm. The correlation to the high near-infrared region (fig 9) includes

almost all wavelengths but especially those that are close to 2000 nm. The reader is referred to

table 1 for a summary of the most important models built in this study.

Classification of roundwood from NIR spectra measured on sapwood surface

Multivariate models were also computed from spectra measured on the sawn wood surface of

each disc. This was done in order to examine how well this modelling compared to the models

created from spectra of bark. An initial PLS model using the whole spectral region between

400 and 2500 showed that the roundwood classification is much correlated to lower part of

the visual region (fig 10). A PCA model based on MSC treated spectra using the wavelengths

between 400 to 2250 nm was calculated and the score plot for the first three components (fig

11) reveals that it is hard to distinguish the spruce samples from the pine samples. One

possible outlier was also detected and removed before further modelling. A test set consisting

of 16 samples was excluded from the calculation of a final PLS model that was comprised of

the same wavelengths used in the PCA model. The prediction of the test set (fig 12) showed

that roundwood classification was estimated fairly accurately with two objects being wrongly

classified.

Conclusions

This study has shown that PCA can be applied to spectra collected from bark in order to some

degree classify spruce and pine roundwood. Much of the variation in the spectra is due to light

scatter effects, which are caused by irregularities of the measured roundwood. The scattering

is partly correlated to the measured roundwood species and much of the irrelevant variation

can be corrected using data pre-treatment methods such as Multiplicative Scatter Correction

or Orthogonal Signal Correction. It was not possible, however, to predict the roundwood

species classification using Cooman classification since the pine objects with smooth bark did

not form a well defined class. In any case, it is evident that the use of PLS regression will

allow for a much safer prediction of the roundwood species. The PLS modelling will find

components that are comprised of variation that arises from structural as well as chemical

differences in the measured material. This is not the case with the PCA modelling, where the

significant components mainly consists of variation due to various scattering effects. Another

advantage of using PLS instead of PCA is that the classification is much easier. The

utilisation of OPLS will provide parsimonious PLS models that can be used for reliable

predictions of the roundwood species.

Compared to the prediction from bark, the wood measurements failed to classify the

roundwood samples when using PCA. The classification using PLS was however much better

and proved to be almost as good as the bark classification. Though it is evident that there is a

larger separation between spruce and pine bark than between spruce and pine wood that is due

to both chemical and structural differences, the wood classification might prove to be useful

in those cases the bark is not present on the measured log.

The final conclusions that can be made is that the bark classification worked well under

laboratory conditions. There are however a number of disturbing factors such as temperature,

background light, distance between log and measuring probe and finally the limited time for

data collection that need to be taken into account in a possible industrial application. In order

to handle these factors, a robust calibration model that consists of a variety of samples that

cover all possible sample variation needs to be assembled. This may sound cumbersome, but

the advantage from using NIR spectroscopy is that the model creation will be a swift process

due to the speed of the near-infrared measurements combined the simplicity of the reference

variable. The outcome of this study suggests that the present method is not only applicable to

the spruce and pine species, but to other species as well, both coniferous and decidous.

Compared to other techniques, such as X-ray, NIR spectroscopy should prove itself to be a

feasible method due its low-cost, speed and predictive ability when combined with

multivariate data analysis.

Acknowledgements

Grants from The Swedish Council for Forestry are gratefully acknowledged.

References to the literature Brunner C, Maristany A, Butler D (1996) Wood species identification using spectral reflectance. Forest Products Journal 46(2):82-85 Brunner M, Eugster R, Trenka E, Bergamin-Strotz L (1996) FT-NIR Spectroscopy and wood identification. Holzforschung 50:130-134 Columbano A, Buckton G, Wikely P (2002) A study of the crystallisation of amorphous salbutamol sulphate using water vapour sorption and near infrared spectroscopy. International Journal of Pharmaceutics 237:171-178 Coomans D, Broeckaert I, Derde MP, Tassin A, Massart DL, Wold S (1984) Use of a microcomputer for the definition of multivariate confidence regions in medical diagnosis based on clinical laboratory profiles. Computers and Biomedical Research 17(1):1-14 Geladi P, Kowalski BR (1986) Partial Least Squares Regression: A Tutorial. Analytica Chimica Acta 185:1-17 Geladi P, MacDougall D, Martens H (1985) Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat. Applied Spectroscopy 39(3):491-500 Gjerdum P, Warensoj M, Nylinder M (2001) Classification of crook types for unbarked Norway spruce sawlogs by means of a 3D log scanner. Holz als Roh-und Werkstoff 59(5):374-379 Grundberg S, Gronlund A (1997) Simulated grading of logs with an X-ray log scanner – Grading accuracy compared with manual grading. Scandinavian Journal of Forest Research 12(1):70-76 Hagman POG (1993) Automatic sorting of Picea abies logs with a Gamma-ray log scanner. Scandinavian Journal of Forest Research 8(4):583-590 Hauksson JB, Bergqvist G, Bergsten U, Sjostrom M, Edlund U (2001) Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression. Wood Science and Technology 35:475-485 Hon DNS, Shiraishi N (1991) Wood and cellulosic chemistry. Marcel Dekker, New York Hoskuldsson A (1988) PLS regression Methods. Journal of Chemometrics 2:211-228 Jordan R, Feeney F, Nesbitt N, Evertsen JA (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36:219-222 Lawrence AH (1989) Rapid Characterization of Wood Species by Ion Mobility Spectrometry. Journal of Pulp and Paper Science 15(5):196-199 Lewis I, Daniel N, Chaffin N, Griffiths P (1994) Raman Spectrometry and neural networks for the classification of wood types. Spectrochimica Acta Part A: Molecular Spectroscopy 50(11):1943-1958 Oja J, Wallbacks L, Grundberg S, Hagerdal E, Gronlund A (2003) Automatic grading of Scots pine (Pinus sylvestris L.) sawlogs using an industrial X-ray log scanner. Computers and Electronics in Agriculture 41(1-3):63-75 Oja, J, Grundberg S, Fredriksson J, Berg P (2004) Automatic grading of sawlogs: A comparison between X-ray scanning, optical three dimensional scanning and combinations of both methods. 19(1):89-95 Osbourne BG, Fearn T, Hindle PH (1993) Practical NIR spectroscopy with applications in food and beverage analysis, 2nd edn. Longman Scientific & Technical, Harlow Sjostrom M, Wold S, Soderstrom B (1986) PLS discriminant plots. In: Pattern recognition in practice II. ES Gelsema and LN Kanal, Eds, Elsevier, Amsterdam, 486. Trygg J, Wold S (2002) Orthogonal projections to latent structures. Journal of Chemometrics 16:119-128

Wahlby U, Skjoldebrand C (2001) NIR-measurements of moisture changes in foods. Journal of Foof Engineering 47:303-312 Wold S (1978) Cross validatory estimation of the number of components in factor and principal components models. Technometrics 20:397-405 Wold S, Antti H, Lindgren F, Ohman J (1998) Orthogonal Signal Correction of Near-Infrared Spectra. Chemometrics and Intelligent Laboratory Systems 44:175-185 Wold S, Esbensen K, Geladi P (1987) Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems 2:37-52 Wright JA, Birkett MD, Gambino, MJT (1990) Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy. Tappi Journal Yang H, Lewis I, Griffiths P (1999) Raman spectrometry and neural networks for the classification of wood types. 2. Kohonen self-organizing maps. Spectrochimica Acta Part A 55:2783-2791 Zhou X, Hines P, Borer MW (1998) Moisture determination in hygroscopic drug substances by near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 17:219-225

Figure and table captions Table 1. Summary of the multivariate models calculated in this study. aModel Type. bSamples used for bulding the model. Bark=bark samples, Sapwood=sapwood samples, PR=Pine with rough bark, PS=pine with smooth bark, S=spruce, CS1=calibration set 1, CS2=calibration set 2, CSS=calibration set spruce. cWavelength region used (nm). dPre-treatment method used, Ctr=centring, MSC=Multiplicative Scatter Correction, OPLS=Orthogonal PLS. eNumber of components for calibration model. fNumber of components calculated for OPLS. gVariation in X removed by OPLS filtering. hVariation in X explained by model. iCross-validated value for internal model validation. jPer cent of samples correctly classified. Figure 1. Raw near-infrared spectra of bark. The spectra exhibit a baseline shift as well as differences in slope. Figure 2. MSC treated near-infrared spectra of bark. The MSC filtering has reduced the variation within the spectra significantly. Figure 3. OSC treated spectra of bark. The OSC filtering has removed around 95% of the spectral variation. Figure 4. Score plot for the first three components of the PCA model made for the near-infrared and MSC treated spectra of bark. Spruce samples are marked with spheres and pine samples with cubes. Pine samples with rough bark are found to the left and pine samples with smooth bark to the right. Figure 5. Score plot for component two, three and four of the PCA model made for the near-infrared spectra of bark. Spruce samples are marked with spheres and pine samples with cubes. Figure 6. Cooman classification plots for the three PCA models based on spectra of bark.

a) Normalised distances to the models made for spruce and pine with rough bark. Three test set objects are correctly classified as pine with rough bark and seven objects are correctly classified as spruce. One spruce object, no. 8, is wrongly classified. The pine objects with smooth bark are classified to neither of the groups. The classification used a significance level of 95%, which is represented by the vertical and horizontal line in the plot.

b) Normalised distances to models made for spruce and pine with smooth bark. All spruce objects with the exception of no. 8 are classified correctly. One pine object with smooth bark, no. 32, is almost classified correctly. The rest of the pine objects are classified to neither of the models using a significance level of 95%.

c) Normalised distances to models made for pine with smooth and rough bark. The three pine objects with rough bark are classified correctly. The rest of the objects are belonging to neither of the models according to a significance level of 95%.

Figure 7. Observed vs. predicted plot for the excluded objects in the two test sets. The objects were predicted using the PLS calibration models calculated from the spectra of bark. The wavelength region between 600 and 2250 nm were used in the modelling. All predicted objects were classified correctly. Figure 8. Loading weights for the one-component PLS model created from spectra of bark in the wavelength region. The loading weights represent the wavelengths in X that correlate to y. A strong correlation was found to the wavelengths around 800 nm. Figure 9. Loading weights for the one-component PLS model calculated from spectra of bark in the wavelength region of 1100 to 2250 nm. The correlation to y is particularly described by the wavelengths around 2000 nm. Figure 10. Loading weights for the PLS model calculated from spectra collected from sapwood. The wavelengths in the visual region are strongly correlated to the roundwood species classification. Figure 11. Score plot for the first three components of the PCA model computed from multiplicative scatter corrected spectra measured on sapwood. Apart from one outlier, the objects are not separated into different classes. Figure 12. The prediction of roundwood for the excluded test set during the modelling of spectra collected from sapwood measurements. Two spruce objects are classified incorrectly.

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PCA Bark 600-2250 Ctr, MSC 5 - - 0.99 - - PCA Bark, PR, CS1 600-2250 Ctr, MSC 3 - - 0.98 - 67 % PCA Bark, PS, CS1 600-2250 Ctr, MSC 5 - - 1.00 - 67 % PCA Bark, S, CS1 600-2250 Ctr, MSC 6 - - 1.00 - 67 % PLS Bark, CS1 600-2250 Ctr, OPLS 1 14 93.31 % 1.00 0.758 100 % PLS Bark, CS2 600-2250 Ctr, OPLS 1 14 99.02 % 1.00 0.737 100 % PLS Bark, CS1 600-1100 Ctr, OPLS 1 11 83.22 % 1.00 0.727 100 % PLS Bark, CS2 600-1100 Ctr, OPLS 1 11 94.55 % 1.00 0.673 100 % PLS Bark, CS1 1100-2250 Ctr, OPLS 1 12 95.79 % 1.00 0.772 100 % PLS Bark, CS2 1100-2250 Ctr, OPLS 1 12 99.95 % 1.00 0.832 100 % PCA Sapwood, CSS 400-2250 Ctr, MSC 6 - - 0.99 - - PLS Sapwood, CSS 400-2250 Ctr, OPLS 1 6 98.69 % 0.99 0.541 87.5 %

Holzforschung, Vol. xx, pp. xxx-xxx, 2005 • Copyright � by Walter de Gruyter • Berlin • New York. DOI 10.1515/HF.2005.XXX

Pine and spruce roundwood species classification usingmultivariate image analysis on bark

David Nilsson* and Ulf Edlund

Department of Chemistry, Organic Chemistry, UmeaUniversity, SE-901 87 Umea, Sweden

*Corresponding author.E-mail: [email protected]

Abstract

Wood discs from 67 pine and 79 spruce logs were col-lected from a forest clearing. Three different 24-bit red-green-blue (RGB) images were acquired from the radialsurface of each disc. The first image contained bark, thesecond image was a mixture of bark and wood surface,and the third image consisted only of wood surface. Theimage texture was compressed into vectors of Fourier-transformed wavelet coefficients. These were assembledin matrices and analysed by principal component ana-lysis (PCA) and partial least-squares projections to latentstructures (PLS). Classification using Fourier-transformedwavelet coefficients showed that the wood species couldbe predicted with 90% accuracy. A thorough examinationof this classification showed that the predicting power ofthese models was mostly due to wavelet coefficients thatrepresented the mean value of each colour channel. Theprediction accuracy that could be obtained from coeffi-cients representing image texture was generally low. Theuse of grey-level co-occurrence matrices prior to thewavelet transformation showed, however, that it is pos-sible to classify the wood species of pine and sprucewith an accuracy approaching 100%.

Keywords: bark; co-occurrence matrix; multivariateimage analysis; partial least squares projection to latentstructures (PLS); pine; spruce; wavelet; wood species.

Introduction

Norway spruce (Picea abies) and Scots pine (Pinus syl-vestris) are the two most common wood species in Swe-den and Scandinavia and account for nearly all wood thatis transported to sawmills. The majority of the Swedishsawmills, i.e., 75% according to the Swedish sawmillinventory, process both Norway spruce and Scots pine.Even though there is rough division of the species duringfelling and transportation, logs can arrive at the millsmixed. It is therefore necessary to visually classify thelogs at the scaling operation prior to sawing. While woodspecies classification is carried out manually, the lengthand diameter of logs are assessed automatically. Cur-rently there are no techniques available for species clas-sification of roundwood. There are, however, severaltechniques for automated sorting and grading of logs

such as X-ray scanning (Oja et al. 2003), gamma-rayscanning (Hagman 1993) and 3D log scanning (Gjerdrumet al. 2001). It is unclear at present if any of these meth-ods can be used for automatic roundwood species clas-sification. Sawn wood species, on the other hand, havebeen classified by a number of methods, including FT-NIR spectroscopy (Brunner et al. 1996), Raman spec-troscopy (Lewis et al. 1994), ion mobility spectrometry(Lawrence 1989) and ultrasonic signals (Jordan et al.1998). An automated method has also been proposed fordetecting defects in sawn wood using image segmen-tation (Funck et al. 2003). Again, it is not known if themethods available for sawn wood can be used for auto-matic roundwood species classification.

Multivariate image analysis

A multivariate digital image is a three-way data table inwhich the two first dimensions represent the phys-ical size and the third dimension, the number of colourchannels or wavelengths measured. A grey-scale imagereduces down to a 2D matrix, as the third dimension con-sists of a single channel. A red-green-blue (RGB) image(Gonzalez and Woods 2002) can consequently be viewedas a three-dimensional array (N,K,Is3), where N is thepixel rows, K the pixel columns and I the number of col-our channels, which in this case is three. Traditionalspectral analysis uses histograms to bin the image pixelsaccording to their intensities in each colour channel(Gonzalez and Woods 2002). While histogram analysis isa more powerful method than just looking at the meanvalues for each channel, spatial information is lost.Therefore, analysis of texture can be used as a comple-ment to colour evaluation. Combinatorial image analysisof colour and texture at the same time is, however, notnecessary (Maenpaa and Pietikainen 2004). A compre-hensive review (Bharati et al. 2004) of different methodsfor image texture analysis has been published. Further-more, analysis of image textures has been used to ana-lyse and classify various forms of food, including texturalanalysis of bread (Kvaal et al. 1998), evaluation of foodcolour (Antonelli et al. 2004) and classification of bovinemeat (Basset et al. 2000). The former two studies bothextracted image features assembled as matrices, whichwere analysed by means of multivariate data analysis.Unlike traditional multivariate image analysis (MIA)(Esbensen and Geladi 1989; Geladi et al. 1989), whichprocesses one image at a time, these studies used imagefeature extraction on a large number of images, followedby multivariate data analysis. The last food classificationstudy utilised grey-level co-occurrence matrices (GLCM)(Haralick et al. 1973), a method used for feature extrac-tion from textures. The resulting co-occurrence matrixfrom an 8-bit greyscale image will be a square, i=j, whereeach side represents 28s256 intensities. Each element

2 D. Nilsson and U. Edlund

Figure 1 The four types of images used in the present study.(a) ‘‘A’’ image containing bark; (b) ‘‘B’’ image with bark partlypeeled off; (c) ‘‘C’’ image containing wood only; and (d) visuali-sed co-occurrence matrix.

of the matrix gives the probability that two pixels existwith intensity given by the two axes at a certain distance,d. The distance is preferably set to a value that corre-sponds to the local structural variations seen in the imag-es. The distance can be measured at several angles, butcan be set to a direction that corresponds to the imagetexture variation. GLCM calculation gives detailed infor-mation about the grey-level variation in the image. Visua-lisation of a GLCM is a square-shaped symmetric image.If GLCM variation is mainly evident close to the diagonal,this means that the distance is wrongly set and corre-sponds poorly to the texture coarseness.

The fast-Fourier transform (FFT) (Gonzalez and Woods2002) is a widely used method that can be applied to animage to transfer information to the frequency domain.The use of a two-dimensional discrete FFT (2D DFT) hasrecently been used in image analysis (Wettimuny andPenumadu 2004). Moreover, to capture both space andfrequency information, a wavelet-based transformation(Gonzalez and Woods 2002) can be used. Wavelet tex-ture analysis (WTA) has been used for feature extractionin digital imaging (Van de Woewer et al. 1999; Antonelliet al. 2004). Here, the two-dimensional discrete wavelettransform (2D DWT) is used to analyse images at multipleresolutions. Wavelet functions are used to calculate thedifferences between the resolutions or so-called scales.Wavelet transformation also means that processed imag-es are compressed. Optimisation of the wavelet com-position while preserving image features has also beeninvestigated (Lo et al. 2003).

Multivariate techniques such as principal componentanalysis (PCA) (Wold et al. 1987) and partial least-squaresprojections to latent structures (PLS) (Geladi and Kowal-ski 1986) are then frequently used for characterisationand classification of multidimensional image data. Whilethese methods are directly applicable to multivariateimages, it is often better to extract image-texture featuresprior to multivariate modelling (Bharati et al. 2004).

Objective of the study

The current study was intended as a pilot study to inves-tigate the possibility of using multivariate image analysisas an automated method for the classification of soft-wood species of pine and spruce. The use of an auto-mated method for species classification might replacethe manual assessment of logs, which would facilitatethe sawing process and thus be beneficial from an eco-nomic perspective. The proposed method is based onimage acquisition through a digital camera coupled withmultivariate image analysis. Spatial information of theimage textures is extracted by 2D DWT. The waveletvariables containing both global space and frequencyinformation are then unaligned by 2D DFT to the fre-quency domain. The wavelet transform merely serves asa compression tool that considerably reduces the num-ber of variables, while conserving pixel variation found inthe image textures. Furthermore, the use of grey-levelco-occurrence matrices was also tested for the extrac-tion of further information from the bark texture. PCA andPLS are then utilised for species classification of theimages.

Experimental

Sampling

A total of 79 wood discs of Norway spruce and 67 discs of Scotspine were collected from a clearing in the Vasterbotten regionof Sweden. The clearing was made in a mature tree stand, wherethe age of the trees was 80 years. The 146 discs, with a thick-ness of approximately 50 mm, were cut from the thicker end ofeach log, and several discs could be collected from each uniquetree. Due to practical reasons, it was therefore impossible todetermine the actual number of trees studied. The diameter ofthe discs was between 95 and 245 mm for the spruce samples,and between 117 and 265 mm for the pine samples. In general,the discs collected were fairly representative of the thinnerdimensions of roundwood processed by Swedish sawmills. Thebark of spruce is quite homogenous compared to that of pine,which can be of a rougher nature at the bottom of the tree.Nevertheless, this variation was covered within the samplematerial, as 23 of the 67 pine discs had the rougher type of bark.

Digital image acquisition and processing

All wood discs were partly debarked and two images (size2048=3072 pixels) were acquired from each disc; one imagewith the bark left intact and one image with the bark partlypeeled off. The bark was manually peeled using a two-handedsplit-knife. All images also contained part of a reference greycard (RGB 128, 128, 128). It should be noted that the discs didnot cover the whole image frame. An image labelled ‘‘A’’ of300=600 pixels (equivalent to 22 mm=44 mm) containing barkwas captured from the first image. An image of 300=600 pixelslabelled ‘‘B’’ showing a mix of bark and wood was captured fromthe second image. An image of 200=300 pixels labelled ‘‘C’’containing pure wood was also captured from the secondimage. It should be noted that since the ‘‘B’’ and ‘‘C’’ imageswere captured from the same original image, the smaller size ofthe ‘‘C’’ images was to avoid these cropped areas coincidingwith the ‘‘B’’ images. This could, of course, be avoided by peel-ing the bark from a larger area. However, the size of the imagesdid not impact on subsequent analysis. Figure 1a–c gives a visu-al overview of the captured images. The images were acquiredwith a tripod-mounted Canon EOS Digital Rebel 6.3-megapixelsingle-lens reflex camera using a Canon 18–55-mm lens. Thefollowing camera settings were used during image acquisition:

Image analysis for species classification 3

ISO sensitivity, 100; aperture, f/16; shutter speed, 1/30 s; focallength, 55 mm; distance from object to lens, 500 mm; acquiredimage type, RAW. The built-in flash of the camera was used. Allimage acquisition used the same exposure, determined byexposing the reference grey card using the flash exposure lockfunction of the camera. The RAW images were then convertedto 24-bit TIFF format.

Texture analysis

Two different approaches were taken to analyse the images:

1. 2D DWT followed by 2D DFT; and2. GLCM calculations followed by 2D DWT.

The 2D discrete wavelet transform was used to transform rawRGB bark and wood images and the corresponding grey-levelco-occurrence matrices. Simca CoDec 1.02 software (UmetricsAB, Sweden) was used for the transformations, for which theSymmlet 8 wavelet basis function (Daubechies 1992) was utili-sed. The length or a height of an image sized N, where N is apower of 2, can be represented by log2Nq1 different resolutionsor wavelet scales. The lowest resolution is represented by 1=1pixels, which is merely a mean value of the actual image. Thisvalue will also serve as an offset, i.e., by how much a certainimage deviates in that particular colour channel. As the actualimages were sized 300=600 or 200=300 pixels, they were pad-ded with zeros to sizes 512=1024 or 256=512 pixels to achievedyadic compliance. No padding was necessary for the GLCMdata, as they were sized 256=256 pixels. For RGB images, eachof the three colour channels was treated as one image. Theimages were compressed into 5000 wavelet coefficients in eachcolour channel, which equals a compression ratio close to 100.The maximum number of wavelet coefficients that could beobtained from the RGB images was 512=1024s219s524 288.

Use of GLCM data was the second approach in the imageanalysis process. The pixel distance, d, was set to 20, as muchof the texture variation was rather fine. The angle, u, was set tothe horizontal direction, as this corresponded well to the barkstructure. Figure 1d shows a visualised GLCM. Matlab 6.5(Mathworks) was used for GLCM calculation.

The different resolutions, i.e., wavelet scales, of every imagewere Fourier-transformed using 2D DFT, with Fourier transfor-mation implying no change in matrix size. All images except theGLCM data were Fourier-transformed, as these data containedno global image space information.

Generating calibration and test sets

Prior to multivariate data analysis, the wavelet coefficients ofeach image were arranged as one vector, i.e., one observation.The observations were assembled in matrices, which were divid-ed into calibration sets (CS) and test sets (TS). The data matriceswere used to create the following models:

Model I: CSs98 ‘‘A’’ images, TSs48 ‘‘A’’ images, 5000 variablesin each colour channel;Model II: CSs98 ‘‘B’’ images, TSs48 ‘‘B’’ images, 5000variables in each colour channel;Model III: CSs98 ‘‘C’’ images, TSs48 ‘‘C’’ images, 5000variables in each colour channel;Model IV: CSs98 ‘‘A’’ GLCM, TSs48 ‘‘A’’ GLCM, 5000variables;Model V: CSs98 ‘‘B’’ CLCM, TSs48 ‘‘B’’ GLCM, 5000 variables;Model VI: CSs98 ‘‘C’’ GLCM, TSs48 ‘‘C’’ GLCM, 5000variables; andModel VII: CSs96 ‘‘A’’ GLCM, TSs50 ‘‘B’’ GLCM, 5000variables.

The last model (VII) was calculated from sub-images of theoriginal ‘‘A’’ images. The division into sub-images gave a higher

number of observations. It also meant that the images were ana-lysed in more detail and that several predictions could be madefor each original image.

Multivariate classification of DWT- and DFT-processed images

Principal component analysis (Wold et al. 1987) was used tocompress the wavelet coefficient matrices into score and load-ing vectors. PCA is a powerful method for reducing a multi-dimensional space into a few variables, or so-called principalcomponents. Each of the compressed matrices was expressedas:

XsTP9qEst p9 qt p9 q«qE (1)1 1 2 2

where X is the original matrix, T and P9 are the scores and load-ings matrices, and E is the error matrix. PCA was mainly per-formed to provide visualisation for the classification of pine andspruce observations. This was accomplished by plotting thescore vectors, e.g., t1 and t2, against each other. Briefly, thescore plots a visualisation of how observations are located inrelation to each other in the reduced variable space. Observa-tions of similar properties are located close to each other. Inaddition, loading plots were used to gain understanding aboutthe importance of the variables or, here, wavelet coefficients. Inthis type of plot, the loading vectors, e.g., p1 and p2, are plottedagainst each other. Variables of high importance are located farfrom the coordinate origin, while variables of low importance arelocated close to it.

To further classify the observations, PLS (Geladi and Kowalski1986) was used to create prediction models between the imagedata matrices and the wood species class. PLS is a commonlyused method to find covariance between two data matrices.Instead of using PLS, other techniques such as artificial neuralnetworks (Jain et al. 1996) or principal component regression(Geladi and Esbensen 1991) might be suggested. However, PLSwas chosen due to its ability to model the relationships studiedbetween X and Y in low-rank models with high interpretability.Each of the PLS models (I–VI) was written in the form:

ysXbqf (2)

where y is the wood species class, set to ‘‘1’’ for pine and ‘‘2’’for spruce, X is the original matrix, b the regression coefficientand f the error. For the last model (VII), two dummy variableswere used as a Y matrix to discriminate between the two clas-ses. The use of two dummy variables was required to calculatea mean prediction value for each original ‘‘B’’ image. Generally,separate dummy variables that denote class should always beused. A single variable can be used, however, for two classeswithout introducing incorrect proportionality between the twoclasses. As described earlier, observations of the modelledmatrices were divided into calibration sets that consisted of two-thirds of the observations and test sets that comprised theremaining one-third. Centring of both X and y variables was usedas data pre-treatment for all PLS and for the previouslydescribed PCA models. The number of significant PLS compo-nents was determined by cross-validation, along with its relatedQ2Y value (Wold 1978). A Q2Y value close to unity means thatthe model has optimal prediction properties, while a value lowerthan or equal to zero implies that the model has no predictionability whatsoever. The test sets were then validated externallyand the cut-off between the species was set to 1.5 for the sixfirst models. For the seventh model, the highest value, i.e., )0.5,denoted the species class. Observations that lay on the wrongside of the given cut-off values were considered to be wronglyclassified. Simca Pq 10.5 software (Umetrics AB) was used forcalculation of all the multivariate models.


Figure 2 Scores for the first two components, tw1x and tw2x, of the PCA model calculated from wavelet and Fourier-transformed ‘‘A’’images containing bark. There is strong separation between the spruce (%) and pine observations (s).

Figure 3 Loadings for the first two components, pw1x and pw2x, of the PCA model calculated from the DWT and DFT processed‘‘A’’ images containing bark shows that the wavelet offset coefficients (marked with arrows) mainly contribute to the separationbetween pine and spruce.


Classification using DWT and DFT on ‘‘A’’, ‘‘B’’ and‘‘C’’ images (models I–III)

The best separation of ‘‘A’’ images was observed in thescore plot for the first and second component (Figure 2)for a PCA model calculated from Fourier-transformedwavelet scales. The PCA loading plot for the first andsecond component (Figure 3) showed that the mostimportant wavelet coefficients were those denoting theoffset in each of the three colour channels, i.e., deviationfrom the mean value in the particular channel. A two-component PLS model with a Q2Y value of 0.64 was cal-culated and used for predicting the test set observations.Five observations were wrongly predicted, which corre-sponds to a prediction accuracy of 89.6%. This model

lost almost all of its prediction efficiency when the wave-let coefficients that represented colour offset wereremoved. Table 1 gives an overview of the seven PLSmodels constructed in this study. PLS models were alsoconstructed for the ‘‘B’’ and ‘‘C’’ images. These classifiedthe observations with an accuracy of 81.3% and 66.7%,respectively. Furthermore, both these models showedthat the most important variables were the wavelet coef-ficients accounting for colour offset.

Classification using DWT of GLCM data (modelsIV–VI)

The score plot for the first two components (Figure 4) ofthe PCA calculated for the ‘‘A’’ images shows obviousseparation between the pine and spruce objects. Theloading plot for the same components showed that many


Table 1 Overview of the seven PLS models used for roundwood classification.

PLS Data type N R2Y Q2Y Correctlymodel classified

(%)

I DWT ‘‘A’’ 2 0.75 0.64 89.6II DWT ‘‘B’’ 3 0.68 0.52 81.3III DWT ‘‘C’’ 3 0.28 0.12 66.7IV Cooc ‘‘A’’ 6 0.99 0.92 97.9V Cooc ‘‘B’’ 6 0.98 0.88 85.4VI Cooc ‘‘C’’ 8 0.98 0.69 72.9VII Cooc ‘‘A’’q‘‘B’’ 7 0.91 0.83 92.0

N, number of significant PLS components; R2Y, the explained variation in y or Y; Q2Y, the value given by cross-validation.

Figure 4 Scores for the first two components, tw1x and tw2x, of the PCA model derived from the co-occurrence matrices calculatedfrom the ‘‘A’’ images containing bark. The spruce (%) and pine observations (s) form two classes.

coefficients contributed to the separation. The PLS mod-el used for the prediction of the externally validated ‘‘A’’images had a Q2Y value of 0.92. Only one out of 48observations was wrongly predicted, which correspondsto a prediction accuracy of 97.9%. Prediction modelswere also calculated for the ‘‘B’’ and ‘‘C’’ observations.They were able to classify the external test sets with anaccuracy of 85.4% and 72.9%, respectively.

Classification using DWT of GLCM data from sub-images (model VII)

All ‘‘A’’, ‘‘B’’ and ‘‘C’’ images were divided into six sub-images each prior to calculating a PCA model fromGLCM data. The score plot for the first two components(Figure 5) of the PCA calculated for all sub-images showsthat observations containing bark (‘‘A’’) are easily distin-guishable from observations containing wood (‘‘C’’). Theobservations containing both bark and wood (‘‘B’’) arespread among the two classes. A calibration set solelyconsisting of sub-images from the ‘‘A’’ images was con-structed. A PLS model was then calculated and used forthe prediction of those ‘‘B’’ sub-images that were clas-sified as bark according to the previous PCA model.Thus, several predictions were averaged into one meanprediction for each original ‘‘B’’ image. The constructed

PLS model had seven components and a Q2Y value of0.83, and managed to predict 46 out of 50 observationscorrectly, which corresponds to 92%. Inarguably, manyof the predicted sub-images contained both wood andbark, so the number of sub-images should preferably begreater. Wavelet transformation of GLCM data fromnumerous sub-images can be rather time-consuming(i.e., )1 h) if thousands of observations are handled atthe same time. However, wavelet transformation of manyimages can be performed in several steps, which makesit possible to construct large calibrations sets. The cal-culation times for unknown observations that are pre-dicted should be of no concern, even if numerous sub-images are predicted from one original image.

Discussion

The present study has shown that multivariate imageanalysis can be used to accurately separate the speciesspruce and pine. With further developments, the meth-odology presented here could be used in an automatedsorting system for pine and spruce. In addition to instru-mentation set-up, software has to be written that firstdivides the acquired images into proper sub-images ifneeded. Secondly, suitable image extraction techniques


Figure 5 Scores for the first two components, tw1x and tw2x, of the PCA model from 1744 bark ‘‘A’’, barkqwood ‘‘B’’ and wood ‘‘C’’sub-images. The bark (j) is easily separated from the wood (q). The mixed ‘‘B’’ images (s) can either be bark or wood.

have to be applied, e.g., GLCM and wavelet transfor-mation; and thirdly, multivariate prediction models haveto be constructed. The images acquired should prefera-bly be representative of roundwood processed by theactual sawmill and they should be obtained on site. Itshould be noted that the images processed in the pres-ent study were acquired under laboratory conditions andreal images acquired on site could differ from these.Roundwood transportation and handling could cause atype of debarking that is more inhomogeneous comparedto manual debarking, which almost completely removedthe bark. Once the software is written, it could be pos-sible to construct unique models adapted for certainconditions and that take the differences in debarking intoaccount. To improve species classification, it might alsobe possible to perform the prediction in several steps,whereby doubtfully classified observations could bereclassified, i.e., an extra image could be acquired fromthe analysed log. In this case a higher cut-off limit couldbe used to discriminate uncertain observations. Insteadof using sub-images, it might also be possible to usesegmentation techniques (Gonzalez and Woods 2002) forseparating wood and bark in acquired images.

Compared to the other techniques mentioned in theIntroduction (X-ray scanning, FT-NIR and Raman spec-troscopy, ultrasonic signalling), a possible image analysisset-up using a digital camera would provide a convenientsolution for an automated wood-species classificationsystem. This is valid in both practical and economicterms. Digital image acquisition is a fast method, whichmakes it possible to acquire and process several imagesper second provided the computer hardware is fastenough. Digital imaging is also a robust method with highreproducibility. Unlike spectroscopic methods such asRaman and FT-NIR (Thygesen and Lundqvist 2000), it isunaffected by changes in temperature and humidity.Changes in ambient light could easily be handled byplacing a reference in the line of sight of the camera.

Texture analysis based on the wavelet and Fouriertransforms showed that classification depended solely

on the wavelet scale representing the mean value of eachcolour channel. Evidently, the texture of bark exhibits toomuch variation, which makes it difficult to classify the twowood species without further image processing. How-ever, the use of GLCM in conjunction with wavelet trans-formation shows that it is possible to classify pine andspruce using the bark texture. This classification is al-most error-free for images containing bark only.

Furthermore, it is necessary to determine the numberof significant PLS components of a possible predictionmodel. In the present study, the number of significantcomponents varied between two and eight. Essentially,a PLS model with a single y vector should only consistof one significant component. PLS modelling in generaltends to give models of higher rank due to orthogonalvariation in the X data. The first three models of the pres-ent study were of lower rank, mainly due to the fact thatthree X variables accounted for much of the modelledvariation. The higher-rank PLS models were constructedfrom GLCM data. The wavelet coefficients obtained fromthese data are not easily modelled in a few significantcomponents without removing orthogonal X variation.Therefore, data pre-treatment using orthogonal signalcorrection (OSC) (Wold et al. 1998) or orthogonal PLS (O-PLS) (Trygg and Wold 2002) might be suggested. Thisapproach was also tested, but results showed that eachorthogonal component removed accounted for little var-iation in X and a large number of components was need-ed to reduce X to variation that could be used toconstruct a low-rank PLS model. This shows that a largenumber of PLS components are needed and there is anapparent danger of under-fitting the models if only a fewcomponents are used. It was necessary to nearly exhaustthe variation in the y vector(s), as some models had R2Yvalues approaching unity (Table 1).

Acknowledgements

Grants from the Swedish Agriculture and Forestry ResearchCouncil are acknowledged. The authors are grateful for theassistance provided by Peter Christoffersson at Holmen Skog.


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Received March 2, 2005. Accepted July 8, 2005.

102 Paperi ja Puu – Paper and Timber Vol.87/No. 2/2005

David Nilsson1, Ulf Edlund1, Michael Sjöström1, Roland Agnemo2

Keywords: Near-infrared, spectroscopy, principal com-ponent analysis, partial least squares regres-sion to latent variables, thermo mechanical pulp, ISO brightness

Abstract

Partial Least Squares regression to latent structures was used to find a correlation between near-infrared reflectance spectra of samples of milled Norway Spruce (Picea abies) and ISO brightness of bleached ther-mo mechanical pulp. Logs of spruce were stored in climate chambers with different conditions for 14 weeks. A factorial design based on five variables was constructed to control the storage and to provide different brightness properties of the stored spruce logs. Wood samples were collected during the storage and they were analysed with near-infrared reflectance spectroscopy and later refined to thermo mechanical pulp. The pulp was bleached with both hydro-gen peroxide and sodium dithionite. The ISO brightness of the bleached pulp was correlated to the near-infrared spectra of the untreated samples using Partial Least Squares regression to latent structures. The modelling showed that it is possible to pre-dict the ISO brightness of bleached thermo mechanical pulp from near-infrared spectra of original wood raw material.

Introduction

Near-infrared reflectance (NIR) spectrosco-py in combination with multivariate statisti-cal techniques has proven to be a powerful tool to determine wood, pulp and paper properties. NIR spectroscopy is a fast and robust method and is therefore suitable to be implemented in various processes. Due to the low-resolved nature of NIR spectrosco-py, multivariate data analysis has to be used to extract variation from the spectral data. Multivariate methods such as Partial Least Squares regression to latent structures /1, 2/ (PLS) and Principal Component Analysis /3/ (PCA) can be applied for classification of material and for predicting properties

1. David Nilsson*, Ulf Edlund, Michael Sjöström, De-partment of Chemistry, Organic Chemistry, Umeå University, SE-901 87 Umeå, Sweden

2. Roland Agnemo, Domsjö Fabriker AB, SE-891 80 Örnsköldsvik, Sweden

*Corresponding author

Prediction of thermo mechanical pulp brightness using NIR spectroscopy on wood raw material

P E E R - R E V I E W E D

of interest. Pre-treatment /5/ methods are applied to NIR spectra in order to remove unwanted variation. Multiplicative Scatter Correction /4/ (MSC) is a method used for correcting NIR spectra from scatter effects such as baseline shifts and different slopes. Orthogonal Signal Correction /6, 7, 8/, (OSC), deducts variation in the data that is not correlated to the response variable of interest.

NIR spectroscopy has previously been used to predict the content of different spe-cies in a mixture /9/ and for prediction of strength parameters for softwood pulps /10/. Furthermore, it was used for estimation of lignin content in wood /11/, for characteri-sation of pulp /12/ and for examination of pine needle samples /13/ and several other applications /14/. NIR spectroscopy is a powerful technique for determination of moisture content within different sample materials /15, 16, 17, 18/.

The raw material used in the pulp and papermaking industry, i.e. wood, is often exposed to wet-storage which has been in-vestigated in a number of studies /19, 20, 21/. Wet-storage decreases the brightness of wood and the bleaching of pulp will be less efficient and higher quantities of bleach-ing chemicals have to be used. The main reason for the darkening of wood during wet-storage is bark staining which caus-es migration of tannins and other organic compounds from tree bark into sapwood /19/. In addition, storage often leads to wood degradation by bacteria causing an increased permeability of wood /22/. It has been proven several times that metal ions such as manganese and iron catalyse the decomposition /23, 24, 25/ of hydrogen peroxide, one of the most commonly used chemicals for bleaching of mechanical pulp. Additionally, metal ions can form com-plexes with organic compounds that can contribute to discolouration of wood /26/. The brightness of pulp is usually expressed in terms of ISO brightness /27, 28/, which is defined as the reflectance from a sheet of paper made from the pulp measured at the wavelength 457 nm.

During wood storage, structural and

chemical changes occur in the wood and a study by Borga et. al. /29/ showed that it is possible to use NIR spectroscopy to moni-tor these variations. They investigated the correlation between near-infrared spectra of wet-stored pine timber and the storage time in relation to the water quality. It was found that chemical changes in the wood during wet-storage did occur and these changes were also related to the bacterial damage of the wood. The ISO brightness of pulp prior to bleaching is expected to be corre-lated to the brightness of the bleached pulp but there are more factors that contribute to the bleaching outcome. For example, the bleachability of pulp can fluctuate due to the previous mentioned metal ion degrada-tion of hydrogen peroxide, varying degree of bacterial damage and also the reactivity of the used bleaching chemical. This topic is comprehensively covered by Lorås /19/ who developed two methods for determina-tion of bleachability of thermo mechanical pulp (TMP). It is in the scope of this work to prove that NIR spectroscopy can be used as an alternative method for predicting the bleaching outcome of mechanical pulp and to give an approximate value of the ISO brightness obtained after bleaching. The use of a fast method for prediction of wood quality in terms of brightness achieved af-ter bleaching could facilitate the corrections that have to be made for the bleaching to be more efficient. The prediction could also be used for discarding certain wood raw mate-rial not suitable for the manufacturing of TMP. In total, the use of NIR spectroscopy to predict ISO brightness could improve the TMP manufacturing process in both practi-cal and economical aspects.

Experimental

The wood material used in this study con-sisted of 60 spruce logs which were stored in climate chambers for 14 weeks. The storage was performed according to an experimental design with five variables that were assumed greatly influence the brightness of the stored wood. The design had 15 experimental runs (each consisting of four logs) that were sam-pled on three occasions. The actual number of samples was therefore 45, and was com-prised of 180 wood chunks and 180 wood discs. The chunks were later refined into TMP while the discs were measured with near-infrared reflectance spectroscopy.

The TMP was bleached with either hy-drogen peroxide or sodium dithionite fol-lowed by ISO brightness measurements. See Appendix 1 for full details of the bleaching procedure as given in earlier work /30/ by the authors. The reader is referred to the same work /30/ for more details about the wood storage and the refining of TMP.

Vis-Near-infrared reflectance measurementsA NIR Systems 6500 Spectrophotometer was used for the reflectance measurements. A fibre optic probe was used for measurements directly on the wood surface and a spinning cup for measuring on ground wood. This in-strument uses a Si-based detector for meas-uring in the wavelength region of 400 to 1100 nm and a PbS detector between 1100 to 2500 nm. Thus, the scanned region was 400 to 2500 nm with each spectrum con-sisting of an average of 32 scans. It is notice-able that the visual region was included in the measured range as well. The resolution of the instrument was limited to every even wavelength, which resulted in spectra that consisted of 1050 values.

Four different reflectance measurements were performed on the different stored spruce material;Method A. Measurements directly on the

wood surfaceMethod B. Measurements on ground

woodMethod C. Measurements on dried ground

woodMethod D. Measurements on thermo me-

chanical pulpPlease see Appendix 2. for a complete

description of the four measurements meth-ods.

Data treatmentThe NIR spectra collected according to the four methods were assembled into four dif-ferent matrices with 45 observations and

Nimetön 2 1 9/9/05 9:09:58 AM

103Paperi ja Puu – Paper and Timber Vol.87/No. 2/2005 103

☛

1. David Nilsson*, Ulf Edlund, Michael Sjöström, De-partment of Chemistry, Organic Chemistry, Umeå University, SE-901 87 Umeå, Sweden

2. Roland Agnemo, Domsjö Fabriker AB, SE-891 80 Örnsköldsvik, Sweden

*Corresponding author

Prediction of thermo mechanical pulp brightness using NIR spectroscopy on wood raw material

P E E R - R E V I E W E D

chemical changes occur in the wood and a study by Borga et. al. /29/ showed that it is possible to use NIR spectroscopy to moni-tor these variations. They investigated the correlation between near-infrared spectra of wet-stored pine timber and the storage time in relation to the water quality. It was found that chemical changes in the wood during wet-storage did occur and these changes were also related to the bacterial damage of the wood. The ISO brightness of pulp prior to bleaching is expected to be corre-lated to the brightness of the bleached pulp but there are more factors that contribute to the bleaching outcome. For example, the bleachability of pulp can fluctuate due to the previous mentioned metal ion degrada-tion of hydrogen peroxide, varying degree of bacterial damage and also the reactivity of the used bleaching chemical. This topic is comprehensively covered by Lorås /19/ who developed two methods for determina-tion of bleachability of thermo mechanical pulp (TMP). It is in the scope of this work to prove that NIR spectroscopy can be used as an alternative method for predicting the bleaching outcome of mechanical pulp and to give an approximate value of the ISO brightness obtained after bleaching. The use of a fast method for prediction of wood quality in terms of brightness achieved af-ter bleaching could facilitate the corrections that have to be made for the bleaching to be more efficient. The prediction could also be used for discarding certain wood raw mate-rial not suitable for the manufacturing of TMP. In total, the use of NIR spectroscopy to predict ISO brightness could improve the TMP manufacturing process in both practi-cal and economical aspects.

Experimental

The wood material used in this study con-sisted of 60 spruce logs which were stored in climate chambers for 14 weeks. The storage was performed according to an experimental design with five variables that were assumed greatly influence the brightness of the stored wood. The design had 15 experimental runs (each consisting of four logs) that were sam-pled on three occasions. The actual number of samples was therefore 45, and was com-prised of 180 wood chunks and 180 wood discs. The chunks were later refined into TMP while the discs were measured with near-infrared reflectance spectroscopy.

The TMP was bleached with either hy-drogen peroxide or sodium dithionite fol-lowed by ISO brightness measurements. See Appendix 1 for full details of the bleaching procedure as given in earlier work /30/ by the authors. The reader is referred to the same work /30/ for more details about the wood storage and the refining of TMP.

Vis-Near-infrared reflectance measurementsA NIR Systems 6500 Spectrophotometer was used for the reflectance measurements. A fibre optic probe was used for measurements directly on the wood surface and a spinning cup for measuring on ground wood. This in-strument uses a Si-based detector for meas-uring in the wavelength region of 400 to 1100 nm and a PbS detector between 1100 to 2500 nm. Thus, the scanned region was 400 to 2500 nm with each spectrum con-sisting of an average of 32 scans. It is notice-able that the visual region was included in the measured range as well. The resolution of the instrument was limited to every even wavelength, which resulted in spectra that consisted of 1050 values.

Four different reflectance measurements were performed on the different stored spruce material;Method A. Measurements directly on the

wood surfaceMethod B. Measurements on ground

woodMethod C. Measurements on dried ground

woodMethod D. Measurements on thermo me-

chanical pulpPlease see Appendix 2. for a complete

description of the four measurements meth-ods.

Data treatmentThe NIR spectra collected according to the four methods were assembled into four dif-ferent matrices with 45 observations and

1050 variables or wavelengths. The spectra were checked for outliers using visual in-spection of score plots created from PCA models. Prior to multivariate modelling, the NIR spectra were then pre-treated with Multiplictative Scatter Correction, MSC /4/. This pre-treatment gave overall the best predictions predictions for ISO bright-ness and unless stated, all prediction models discussed in the results section were based on MSC-treated spectra. Two other filter-ing techniques were also tested; the use of first-derivatives and Orthogonal Signal Correction, OSC /6/. OSC was primarily used in order to facilitate model interpreta-tion. Variable centring was the only scaling technique that was applied to the spectral data prior to the multivariate PCA and PLS modelling.

Multivariate modellingPrincipal Component Analysis was per-formed on the NIR spectra. The PCA scores and loadings were examined to find possible clusters and important wavelength regions. PCA was also used for outlier detection. PLS was then utilised to find correlation between NIR spectra and the ISO bright-ness of TMP. For validation, the objects were divided into two training sets and two test. The PLS models were validated externally by prediction of the excluded objects, i.e. the objects in the test sets. RMSEP, Root Mean Squared Error of Prediction, was calculated in order to estimate the predictive ability of the models. The number of PLS compo-nents was determined by internal cross vali-dation /31/ (Q2Y) and by visual inspection of loading plots.

The following formula was used for cal-culation of RMSEP;

�

��

��

�

�

��

��

�

�

(1)where y

ipred denotes the ith predicted

value, yiobs

the ith observed value and N the total number of objects in the predicted test sets. Since two test sets were excluded from each of the PLS model computations, the calculated RMSEP values were derived from the total number of objects in the two test sets instead of taking the mean RMSEP value for the test sets. Thus, the use of two externally validated test sets implied that two PLS models had to be calculated for each of the data pre-treatment methods. The number of PLS components was always the same for both test sets. It should also be noted that the test set objects were left out of the parameter estimation for MSC and OSC.

Computer software Matlab 5.1 /32/ and Unscrambler 7.5 /33/ were used for building the data sets and for pre-treatment of data. Simca-P 8.0 /34/ was used for pre-treat-ment of data and for computing multivari-ate models. Spotfire Pro 4.0 /35/ was used for making 3D plots.


The four types of measurements presented in this study represent different degrees of sample treatment. It is of interest that the samples are taken at an early stage in the paper production chain. This makes it pos-sible to make early predictions of the end-product properties, i.e. in this study the ISO brightness value.

The measured wood raw material origi-nated from an experimental wood storage design that produced samples of different properties regarding bleachability and ISO brightness values obtainable after bleaching. The use of experimental design implies that the prediction models for ISO brightness are more extensive than if the measured and bleached samples were picked randomly.

Reference value checkingThe reference values in this study consist-ed of ISO brightness values for peroxide bleached and dithionite bleached TMP. In total 90 values and thus 45 values for each bleaching approach were collected. The 45 samples were comprised of 15 logs that were sampled on three occasions. It was possible to detect abnormal ISO brightness values that were caused by deviating storage-time dependencies by plotting the 45 samples for each of the two bleaching approaches with the samples grouped together three and three. It was also possible to compare the values with the accessible ISO bright-ness of the corresponding unbleached pulp. The ISO brightness values for the dithionite bleached TMP (Fig. 1) reveal that there seem to be three clearly suspicious values, the six-week stored sample in S4, the fresh sample in S8 and the six-week stored sample in S15. These three values do not follow the general trend of decreased brightness with increased storage time. To further investigate the outliers, these values were predicted as an excluded test set in a PLS model between the NIR spectra obtained from Method A and the corresponding ISO brightness val-ues. Only differentiation was used as data pre-treatment in the model that had two significant components with a R2Y of 0.52 and a Q2Y of 0.439. The observed values for these three objects are clearly lower than ex-pected (Fig .2). Furthermore, a PCA model

Nimetön 2 2 9/9/05 9:10:00 AM


that were exposed to a high irrigation level correspond very well to those objects that were clearly influenced by bark staining ac-cording to a visual inspection. Furthermore, two objects in the top left corner are slight-ly separated from the rest of the objects. A common feature of these two objects is that they exhibited a perceptible yellowing on the wood surface. Overall, the three-di-mensional space described by the first three principal components gives an overview of differences between the wood samples that is due to various storage conditions. Based on the score plot for the first three components, two test sets that covered most of the sample variation were selected and used for exter-nal validation of the models predicting ISO brightness of dithionite bleached TMP. The same test sets were used for all data sets in order to allow for fair comparisons between the different sample treatments.

The two-component PLS models made for the prediction of the dithionite bleached samples, revealed a correlation between the NIR spectra and the ISO brightness with a RMSEP value of 0.96 and Q2Y values of 0.61 and 0.59. The RMSEP value should be compared to the standard deviation (1.49) for the observed ISO brightness values of the test set objects in order to get an esti-mation of how well the predictions can be made in the given interval. An observed vs. predicted plot for the prediction of the dithionite bleached test set objects can be seen in Fig. 4.

Fig. 1. ISO brightness values for dithionite bleached samples. The

three values that originated from one main sample were plotted

together in order to find outliers.

Fig. 2. Observed vs. Predicted plot for

dithionite bleached samples. All predicted

objects were included in the model that was

comprised on spectra measured on wood

surface.

was created using the brightness values as X-block. The distance to model plot for the first component showed that the three suspicious values had the largest deviation among all samples. Therefore, these values were excluded from further modelling of dithionite bleaching prediction. In the same way as described above, three ISO brightness

values for the peroxide bleached pulps were rejected as well.

Method A, Multivariate modelling of NIR spectra measured on wood surfacePCA was used to calculate a model from centred and multiplicative scatter corrected NIR spectra. The model had three signifi-cant components that explained 98.5% of the data with respect to R2X and 98.1% with respect to Q2X. The score plot for the first two components (Fig. 3) shows that the wood samples were clustered into two groups as labelled in the plot. One group is comprised of samples that were exposed to a high irrigation level during the wood storage. The other group consists of sam-ples that were subjected to a low irrigation level. The corresponding loading plot for the first three components showed that the first and third components primarily describe spectral variation in the visual region. The second component is comprised of the vari-ation in the visual region, but also contains variation that is due to hydrogen – oxygen deformation and stretching around the wavelength of 1950 nm, as well as varia-tion in the higher wavelengths of 2100 to 2300 nm. It is noticeable that the objects

Fig. 3. Score plot for the first three components of the PCA model

calculated from spectra measured on wood surface. The darker

objects were subjected to low levels of watering during storage.

Fig. 5. Loading plot for the first component

of the PCA model calculated from spectra

measured on ground wood. The first loading

was heavily influenced by variation in water

content.

Method1 Pre-Treatment2 A3 Q2Y TS-14 Q2Y TS-25 RMSEP6

A 1:st Deri. 2 0.515 0.467 1.036

MSC 2 0.605 0.592 0.958

OSC {1} 1 0.771 0.719 0.961

MSC, Visual 3 0.533 0.516 0.976

B 1:st Deri. 4 0.261 0.365 1.277

MSC 2 0.348 0.327 1.281

OSC {1} 1 0.387 0.709 1.138

MSC, Visual 3 0.194 0.406 1.297

C 1:st Deri. 6 0.759 0.701 1.024

MSC 3 0.492 0.520 1.132

OSC {1} 1 0.838 0.888 1.240

MSC, Visual 3 0.402 0.460 1.275

MSC, NIR 3 0.370 0.347 1.119

D 1:st Deri. 3 0.771 0.785 0.809

MSC 2 0.822 0.822 0.714

OSC {1} 1 0.836 0.873 0.962

MSC, Visual 3 0.811 0.818 0.754

1. The used sample pre-treatment method.2. The used data pre-treatment technique. The bracketed digit denotes the number of removed orthogonal components.3. The number of used PLS components.4. The cross-validated Q2Y-value of the first test set.5. The cross-validated Q2Y-value of the second test set.6. The Root Mean Squared Error of Prediction value.

Table 1. Summary of all PLS models made for prediction of dithionit

bleached TMP brightness.

Fig. 4. Prediction of test sets excluded from

PLS modelling between ISO brightness of

dithionite bleached samples and spectra

measured on wood surface.

Nimetön 2 3 9/9/05 9:10:13 AM

105Paperi ja Puu – Paper and Timber Vol.87/No. 2/2005

that were exposed to a high irrigation level correspond very well to those objects that were clearly influenced by bark staining ac-cording to a visual inspection. Furthermore, two objects in the top left corner are slight-ly separated from the rest of the objects. A common feature of these two objects is that they exhibited a perceptible yellowing on the wood surface. Overall, the three-di-mensional space described by the first three principal components gives an overview of differences between the wood samples that is due to various storage conditions. Based on the score plot for the first three components, two test sets that covered most of the sample variation were selected and used for exter-nal validation of the models predicting ISO brightness of dithionite bleached TMP. The same test sets were used for all data sets in order to allow for fair comparisons between the different sample treatments.

The two-component PLS models made for the prediction of the dithionite bleached samples, revealed a correlation between the NIR spectra and the ISO brightness with a RMSEP value of 0.96 and Q2Y values of 0.61 and 0.59. The RMSEP value should be compared to the standard deviation (1.49) for the observed ISO brightness values of the test set objects in order to get an esti-mation of how well the predictions can be made in the given interval. An observed vs. predicted plot for the prediction of the dithionite bleached test set objects can be seen in Fig. 4.

Fig. 3. Score plot for the first three components of the PCA model

calculated from spectra measured on wood surface. The darker

objects were subjected to low levels of watering during storage.

The predictions made for the peroxide bleached samples were, however, less suc-cessful than those for the dithionite bleach-ing. The PLS modelling found very little correlation between the X and y data even when using as many as ten components. This was also reflected in the RMSEP val-ue (1.32) and in the Q2Y values (~0). The RMSEP value is higher than the standard deviation of the test set objects (1.18) which proves that the models have no predictive ability. Hereafter, the Q2Y values will not be reported in the text and the number of PLS components will be presented more sparsely. The reader is referred to Table 1 and for a complete summary of the charac-teristics of all prediction models calculated in this study.

Method B, Multivariate modelling of NIR spectra measured on ground woodA PCA model with five components ex-plaining nearly 100% of the data was calcu-lated from MSC-treated spectra in order to get an overview of the milled samples. The score plot for the first three components (not shown) revealed that there is some separa-tion between different levels of irrigation. This grouping is however much less obvi-ous when compared to the object grouping measured according to Method A. As for method A, the effect of bark staining as well as wood yellowing could also be seen using a different object labelling. It is obvious that the effect of wood storage still is significant

in the sample material although it is much less prominent. The first component mainly contains variation that is due to water con-tent and this can be seen in the correspond-ing loading plot (Fig. 5) The loading plot of the second component (not shown) covered the variation found in the visual region of the spectra.

The PLS models calculated for the dithionite bleached samples revealed a mod-erate correlation that had a RMSEP value of 1.28. Compared to the standard deviation for the observed values in the test sets, the RMSEP value is just barely lower than the standard deviation for the observed values in the both test sets.

Fig. 5. Loading plot for the first component

of the PCA model calculated from spectra

measured on ground wood. The first loading

was heavily influenced by variation in water

content.

Fig. 6. Prediction of excluded test sets

objects for the modelling between spectra

of ground wood and ISO brightness of

peroxide bleached TMP.


A 1:st Deri. 2 0.515 0.467 1.036

MSC 2 0.605 0.592 0.958

OSC {1} 1 0.771 0.719 0.961

MSC, Visual 3 0.533 0.516 0.976

B 1:st Deri. 4 0.261 0.365 1.277

MSC 2 0.348 0.327 1.281

OSC {1} 1 0.387 0.709 1.138

MSC, Visual 3 0.194 0.406 1.297

C 1:st Deri. 6 0.759 0.701 1.024

MSC 3 0.492 0.520 1.132

OSC {1} 1 0.838 0.888 1.240

MSC, Visual 3 0.402 0.460 1.275

MSC, NIR 3 0.370 0.347 1.119

D 1:st Deri. 3 0.771 0.785 0.809

MSC 2 0.822 0.822 0.714

OSC {1} 1 0.836 0.873 0.962

MSC, Visual 3 0.811 0.818 0.754

1. The used sample pre-treatment method.2. The used data pre-treatment technique. The bracketed digit denotes the number of removed orthogonal components.3. The number of used PLS components.4. The cross-validated Q2Y-value of the first test set.5. The cross-validated Q2Y-value of the second test set.6. The Root Mean Squared Error of Prediction value.


A 1:st Deri. 10 0.073 0.399 1.390

MSC 10 ˜0 0.132 1.319

OSC {2} 1 0.699 0.539 1.453

MSC, Visual >10 ˜0 ˜0 N/A

B 1:st Deri. 3 0.517 0.313 0.955

MSC 5 0.668 0.429 0.788

OSC {1} 1 0.732 0.587 0.814

MSC, Visual 3 0.472 0.212 0.905

C 1:st Deri. 7 0.797 0.829 0.859

MSC 3 0.574 0.459 0.744

OSC {1} 1 0.925 0.972 0.776

MSC, Visual 3 0.521 0.552 0.732

MSC, NIR 3 0.533 0.472 0.846

D 1:st Deri. 2 0.512 0.413 0.866

MSC 2 0.477 0.472 0.951

OSC {1} 1 0.720 0.797 1.019

MSC, Visual 3 0.515 0.467 0.740

1. The used sample pre-treatment method.2. The used data pre-treatment technique. The bracketed digit denotes the number of removed OSC components.3. The number of used PLS components.4. The cross-validated Q2Y-value of the first test set.5. The cross-validated Q2Y-value of the second test set.6. The Root Mean Squared Error of Prediction value.

Table 1. Summary of all PLS models made for prediction of dithionit


Table 2. Summary of all PLS models made for prediction of peroxide


Fig. 4. Prediction of test sets excluded from

PLS modelling between ISO brightness of

dithionite bleached samples and spectra

measured on wood surface.

☛

Nimetön 2 4 9/9/05 9:10:23 AM


The prediction of ISO brightness of peroxide bleached TMP based on spectra measured on ground wood was significantly improved compared to the correlation made from the wood surface measurements. The RMSEP value calculated from the predicted test sets was 0.79.

Method C, Multivariate modelling of NIR spectra measured on dried ground woodThe reason for drying wood before meas-uring the samples with NIR spectroscopy was to limit the influence of varying mois-ture content within the sample material. The drawbacks of treating the samples this way could be oxidation and lignin colour changes. PCA was performed on the MSC-treated NIR matrix with a five-component model that explained nearly 99% of the vari-ance of the spectral data. The score plot for the first three components showed that the sample material still is separated due to the irrigation level during storage, which main-ly can be seen in the second component. The loading plot for the first three compo-nents confirmed that the second component depends on variation in the wavelengths around 1950 nm, which is due to the ear-lier mentioned hydrogen-oxygen stretching and deformation in water. The samples also differ with respect to variation in the visual region, which can be seen in the first and third component.

The PLS models made for the prediction of ISO brightness of the dithionite bleached pulp gave a RMSEP value of 1.02. The ISO brightness of the peroxide bleached TMP was also well modelled using spectra measured on the milled and dried wood. The external vali-dation gave a RMSEP value of 0.74, which is significantly lower than the standard de-viation of 1.18 calculated from the observed values of the test set objects. The prediction of ISO brightness for these models is accept-able given that the interval in ISO brightness is rather narrow (Fig. 6.).

Method D, Multivariate modelling of NIR spectra measured on TMPPerforming NIR measurements on TMP might not be a viable solution in practise, since the unbleached TMP samples repre-sent a stage in the production process that is close to the final product. These meas-urements were still included in the study in order to examine how well the predictions based on these spectra compared to the pre-dictions made from the earlier material. The PCA that was performed on the multiplica-tive scatter corrected TMP spectra found a five-component model that explained nearly

100% of the variation in data. Only a slight grouping due to the effect of bark staining was noticeable in the score plot for the first three components.

As expected, the ISO brightness was well predicted from the spectra measured on the

unbleached pulp samples. The validation of the dithionite bleached samples generated a RMSEP value of 0.71 while the peroxide bleached samples were slightly worse pre-dicted with a RMSEP value of 0.95.

Fig. 7. OSC loading plots for all sample pre-treatment methods. The OSC loadings were

taken from the first orthogonal component in each case, and represent the variation

orthogonal to ISO brightness

a) dithionite bleaching/Method A

b) peroxide bleaching/Method A

c) dithionite bleaching/Method B

d) peroxide bleaching/Method B

e) dithionite bleaching/Method C

f) peroxide bleaching/Method C

g) dithionite bleaching/Method D

h) peroxide bleaching/Method D

Interpretation of the PLS modelsThe OSC algorithm removes variation in the X data that is not correlated to y by cal-culating a set of score and loading vectors that are orthogonal to y and are used for filtering X. It is possible to get an under-standing of what spectral variation that was removed by plotting the loading of an OSC component. The orthogonal signal corrected spectra can then be correlated to y using a PLS model that only consists of one signifi-cant component. The PLS loading weights for that single component yields informa-tion on regions of the spectral data that are correlated to y.

The OSC loadings (Fig. 7a–h), denoted p, and the PLS weights (Fig. 8 a–h), denoted w, were plotted for all prediction models based on orthogonal signal corrected spec-tra. All different sample treatment methods were included, which resulted in that eight plots were made for each of the two bleach-ing procedures. It is important to remember that the size of the loadings or weights given by the scale on y-axis cannot be compared directly between two different plots since only w is normalised.

It can be seen in Fig. 7a and Fig. 7b that the variation removed prior to computing the models based on spectra recorded ac-cording to Method A, mainly lies in the visual region. The two peaks that are due to water absorption are also removed. The weight plots (Fig. 8a and Fig. 8b) show that there is no variation in these plots that origi-nates from the water peaks. Consequently, the difference between Fig. 7a and Fig. 7b lies in the visual region and the OSC has therefore removed more variation in the vis-ual region prior to computing the model us-ing peroxide bleached ISO brightness than it has prior to computing the dithionite-bleaching model.

The OSC loading plots made for the ground wood (Fig. 7c and Fig. 7d) show that the orthogonal component for both bleaching procedures is, as above, primarily composed of variation that is due to the wa-ter content. Once again, the ISO brightness of the dithionite-bleached TMP seems to be better correlated to variation in the visual region (Fig. 8c) compared to the brightness achieved after peroxide bleaching. Peroxide bleached samples better correlate to changes in the near-infrared region (Fig. 8d). For the dried samples, the loading plots for the OSC pre-treated spectra measured according to Method C (Fig. 7e and Fig. 7f ) showed as expected that that the contribution from the water peaks was minor. There is still some variation left in the PLS weights plots (Fig. 8e and Fig. 8f ) that originates from the wa-

Nimetön 2 5 9/9/05 9:10:27 AM

107Paperi ja Puu – Paper and Timber Vol.87/No. 2/2005 107

☛

unbleached pulp samples. The validation of the dithionite bleached samples generated a RMSEP value of 0.71 while the peroxide bleached samples were slightly worse pre-dicted with a RMSEP value of 0.95.

Fig. 7. OSC loading plots for all sample pre-treatment methods. The OSC loadings were

taken from the first orthogonal component in each case, and represent the variation

orthogonal to ISO brightness

e) dithionite bleaching/Method C

f) peroxide bleaching/Method C

g) dithionite bleaching/Method D

h) peroxide bleaching/Method D

Interpretation of the PLS modelsThe OSC algorithm removes variation in the X data that is not correlated to y by cal-culating a set of score and loading vectors that are orthogonal to y and are used for filtering X. It is possible to get an under-standing of what spectral variation that was removed by plotting the loading of an OSC component. The orthogonal signal corrected spectra can then be correlated to y using a PLS model that only consists of one signifi-cant component. The PLS loading weights for that single component yields informa-tion on regions of the spectral data that are correlated to y.

The OSC loadings (Fig. 7a–h), denoted p, and the PLS weights (Fig. 8 a–h), denoted w, were plotted for all prediction models based on orthogonal signal corrected spec-tra. All different sample treatment methods were included, which resulted in that eight plots were made for each of the two bleach-ing procedures. It is important to remember that the size of the loadings or weights given by the scale on y-axis cannot be compared directly between two different plots since only w is normalised.

It can be seen in Fig. 7a and Fig. 7b that the variation removed prior to computing the models based on spectra recorded ac-cording to Method A, mainly lies in the visual region. The two peaks that are due to water absorption are also removed. The weight plots (Fig. 8a and Fig. 8b) show that there is no variation in these plots that origi-nates from the water peaks. Consequently, the difference between Fig. 7a and Fig. 7b lies in the visual region and the OSC has therefore removed more variation in the vis-ual region prior to computing the model us-ing peroxide bleached ISO brightness than it has prior to computing the dithionite-bleaching model.

The OSC loading plots made for the ground wood (Fig. 7c and Fig. 7d) show that the orthogonal component for both bleaching procedures is, as above, primarily composed of variation that is due to the wa-ter content. Once again, the ISO brightness of the dithionite-bleached TMP seems to be better correlated to variation in the visual region (Fig. 8c) compared to the brightness achieved after peroxide bleaching. Peroxide bleached samples better correlate to changes in the near-infrared region (Fig. 8d). For the dried samples, the loading plots for the OSC pre-treated spectra measured according to Method C (Fig. 7e and Fig. 7f ) showed as expected that that the contribution from the water peaks was minor. There is still some variation left in the PLS weights plots (Fig. 8e and Fig. 8f ) that originates from the wa-

ter content but the peak around 1950 nm is less dominant, but sharper. The ISO bright-ness of dithionite bleached TMP appears once again to be more affected by the visual region of the spectra.

It is evident that the OSC pre-treat-ment has removed much of the variation that is due to varying water content within

the sample material (Fig. 7g and Fig. 7h). Still, there is a considerable amount of vari-ation in the water-influenced regions of the spectra measured on TMP (Method D) that is correlated to the ISO brightness of both peroxide- and dithionite-bleached TMP (Fig. 8g and Fig. 8h). Furthermore, it is clearly so that the dithionite bleached TMP

Fig. 8. PLS weights plots for all sample pre-treatment methods. The weights, w, were taken

from the first component calculated for the correlation between ISO brightness and

spectral data.

Nimetön 2 6 9/9/05 9:10:33 AM


is more sensitive to variation in the visual region the spectra, when compared to the peroxide bleached TMP.

PLS modelling using wavelengths between 400 to 1000 nmThe wavelengths between 1002 and 2500 nm were removed prior to PLS modelling in order to examine the model prediction ability when only using the wavelengths be-tween 400 to 1000 nm. The reduced models managed to model the ISO brightness of dithionite bleached TMP fairly well with RMSEP values (Table 1) somewhat higher than those achieved from the models using the whole spectral region. The modelling of the ISO brightness measured on perox-ide bleached TMP did even better and gave acceptable predictions for methods B, C and D. The models computed from spec-tra measured on TMP produced a RMSEP value of 0.74, which is lower than the cor-responding value for the full models.

PLS modelling using wavelengths between 1002 to 2500 nmThe wavelengths between 400 and 1000 nm were removed in order to find out if it is necessary to include the upper part of the near-infrared region. For the dithionite bleached samples, the models based on the spectra measured according to Method C gave a RMSEP value of 1.12. This is very similar to the corresponding value produced from the full models and somewhat better than the value given by the models made on the wavelengths between 400 to 1000 nm. The RMSEP value obtained from the models predicting peroxide bleached TMP brightness was 0.85, which is slightly higher than for the previous models. Any modelling based on the other sample pre-treatment methods gave very poor results.

Conclusions

The present modelling study showed that it is possible to build satisfactory PLS models between the ISO brightness of dithionite bleached TMP and the spectra measured directly on the wood surface. However, for peroxide bleached TMP this was not the case. This does illustrate how the two bleaching methods compare with regards to wood factors affecting the bleaching ef-ficiency. In a previous study /30/ 30, the fac-tors controlling wood storage were examined and it was found that the major difference between the efficiency of dithionite and per-oxide bleaching was due to the interaction between light and irrigation, i.e. a factor mainly affecting the outer sapwood mate-

rial. What that study showed, and what the present work manifests, is that the bleach-ing efficiency of dithionite is very depend-ent on the condition of the outer part of the sapwood, i.e. the wood closest to the bark. The efficiency of peroxide bleaching is less dependent on outer sapwood changes, such as bark staining, and the bleaching efficiency is more influenced by the overall wood qual-ity. These reported observations are only valid for the actual bleaching procedures, i.e. bleaching conditions closely reflecting those found in the pulp industry.

The ISO brightness for peroxide bleached as well as dithionite bleached TMP was well correlated to all types of spectra except for the modelling between the ISO brightness of peroxide bleached TMP and spectra recorded on the sapwood closest to the bark. The predictive ability that origi-nated from the near-infrared region was generally very low for the samples that had a significant variation in water content. Although much of the irrelevant variation could be removed by using orthogonal sig-nal correction, there was still variation left in the spectra that was due to dissimilarities in water binding. It was however possible to improve the correlation to the near-infrared region by drying the samples before per-forming the spectroscopic measurements. This lowered the prediction error signifi-cantly for the models predicting ISO bright-ness of dithionite bleached pulp from the near-infrared region of the spectra while the predictions of ISO brightness for peroxide bleached pulp based on the visual region of the spectra was almost kept at the same level as before.

The three different kinds of data pre-treatment techniques used in this study produced very similar results with respect to prediction error. In some cases, the models using OSC tended to give slightly higher RMSEP values compared to the other two data pre-treatment techniques. This is re-flected in the RMSEP values for the models based on the OSC pre-treated spectra of TMP, where model over fitting results in lower prediction ability.

The final conclusion that can be made from the PLS modelling is that the most important part of the spectra lies in the re-gion between 400 to 1000 nm. Prediction models can in most cases be calculated solely from this region. However, the inclusion of the wavelengths between 1000 to 2500 nm does not worsen the predictive ability but can in fact in most cases improve the mod-elling slightly, which is especially true for the prediction of dithionite bleached TMP brightness.

Acknowledgments

Grants from The Swedish Council for Forestry gratefully acknowledged. We are also grateful for the assistance provided by the staff at Modo Paper Research & Development, and would like to thank Curt Hägglund, Lars-Herman Sjöström and Bo Ahlqvist.

References

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2. Geladi, P., Kowalski, B.R.: Partial least squares regression – A tutorial. Analytica Chimica Acta 185(1986), 1–17.

3. Wold, S., Esbensen, K., Geladi P.: Principal Com-ponent Analysis. Chemometrics and Intelligent Laboratory Systems 2(1987), 37–52.

4. Geladi, P., MacDougall, D., Martens, H.: Lineariza-tion and scatter-correction for NIR reflectance spectra of meat. Applied Spectroscopy 39(1985):3, 491–500.

5. Geladi, P., Martens, H.: Data pretreatment and principal component regression using Matlab. Journal of Near Infrared Spectroscopy 4(1996), 225–242.

6. Wold, S., Antti, H., Lindgren, F., Öhman, J.: Or-thogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 44(1998), 175–185.

7. Trygg, J., Wold, S.: Orthogonal projections to latent structures, O-PLS. Journal of Chemometrics 16(2002), 119–128.

8. Fearn, T.: On orthogonal signal correction. Ch-emometrics and Intelligent Laboratory Systems 50(2000), 47–52.

9. Antti, H., Sjöström, M., Wallbäcks, L.: Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applications. Journal of Chemometrics 10(1996), 591–603.

10. Marklund, A., Paper, M., Hauksson, J.B., Sjöström, M.: Prediction of strength parameters for softwood kraft pulps. Multivariate data analysis based on orthogonal signal correction and near infrared spectroscopy. Nordic Pulp and Paper Research Journal. 14(1999):2, 140–148.

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12. Wallbäcks, L., Edlund, U., Nordén, B., Berglund, I.: Multivariate characterization of pulp using solid-state C13 NMR, FTIR, and NIR. Tappi Journal 74(1991):10, 201–206.

13. Hiukka, R.: A multivariate approach to the analysis of pine needle samples using NIR. Chemometrics and Intelligent Laboratory Systems 44(1998), 395–401.

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15. Osborne, B.G., Fearn, T., Hindle, P.H.: Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, Longman Group UK Limited, Harlow 1993.

16. Derksen, M., Van de Oetelaar, P., Maris, F.: Ef-ficient prediction of the residual moisture content specification of a freeze-dried product by using near-infrared reflectance spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 17(1998), 473–480.

17. Ren, G., Chen, F.: Determination of moisture con-tent of ginseng by near infra-red reflectance spec-troscopy. Food Chemistry 60(1997):3, 433–436.

18. Rantanen, J., Lehtola, S., Ramet, P., Mannermaa, J.P., Yliruusi, J.: On-line monitoring of moisture content in an instrumented fluidized bed granu-lator with a multi-channel NIR moisture sensor. Powder Technology 99(1998):2, 163–170.

19. Lorås, V.: Bleachability of mechanical pulp. Tappi Journal 57(1974):2, 98–102.

20. Elowsson, T., Liukko, K.: How to achieve effective wet storage of pine logs (Pinus sylvestris) with a minimum amount of water. Forest Production Journal 45(1995):11/12, 36–42.

21. Sorge, C., Schulten, H.-R.: Influence of wet storage of spruce wood on groundwater quality. Journal of Environmental Analytical Chemistry 57(1994), 1–8.

22. Clausen, C.A.: International Biodeterioration & Biodegradation 1996 p. 101.

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26. Leary, G., Giampaolo, D.: The darkening reactions of TMP and BTMP during alkaline peroxide bleaching. Journal of Pulp and Paper Science 25(1999):4, 141–147.

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29. Borga, P., Hämäläinen, M., Theander, O.: Cor-relations between near-infrared spectra of wet-stored timber and the storage time in relation to the water-quality. Holzforschung 46(1992):4, 299–303.

30. Nilsson, D., Edlund, U., Elg Kristofferson, K., Sjöström, M., Agnemo, R.: The effect of designed wood storage on the brightness of bleached and unbleached thermo mechanical pulp. Nordic Pulp and Paper Research Journal 18(2003):4, 369–376.

31. Wold, S.: Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 20(1978), 397–404.

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Received January 30, 2004

Appendix 1.

Bleaching300 g (dry weight) from each pulp sample was treated with EDTA. The chelation re-action was accomplished using plastic con-tainers heated with a water bath holding 70 °C. An excess of EDTA was used and

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109Paperi ja Puu – Paper and Timber Vol.87/No. 2/2005

Acknowledgments

Grants from The Swedish Council for Forestry gratefully acknowledged. We are also grateful for the assistance provided by the staff at Modo Paper Research & Development, and would like to thank Curt Hägglund, Lars-Herman Sjöström and Bo Ahlqvist.

References

1. Wold, S., Johansson, E., Cocchi, M.: 3D QSAR Drug Design; Theory Methods and Applications. Escom, Leiden 1993, p. 523.

2. Geladi, P., Kowalski, B.R.: Partial least squares regression – A tutorial. Analytica Chimica Acta 185(1986), 1–17.

3. Wold, S., Esbensen, K., Geladi P.: Principal Com-ponent Analysis. Chemometrics and Intelligent Laboratory Systems 2(1987), 37–52.

4. Geladi, P., MacDougall, D., Martens, H.: Lineariza-tion and scatter-correction for NIR reflectance spectra of meat. Applied Spectroscopy 39(1985):3, 491–500.

5. Geladi, P., Martens, H.: Data pretreatment and principal component regression using Matlab. Journal of Near Infrared Spectroscopy 4(1996), 225–242.

6. Wold, S., Antti, H., Lindgren, F., Öhman, J.: Or-thogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 44(1998), 175–185.

7. Trygg, J., Wold, S.: Orthogonal projections to latent structures, O-PLS. Journal of Chemometrics 16(2002), 119–128.

8. Fearn, T.: On orthogonal signal correction. Ch-emometrics and Intelligent Laboratory Systems 50(2000), 47–52.

9. Antti, H., Sjöström, M., Wallbäcks, L.: Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applications. Journal of Chemometrics 10(1996), 591–603.

10. Marklund, A., Paper, M., Hauksson, J.B., Sjöström, M.: Prediction of strength parameters for softwood kraft pulps. Multivariate data analysis based on orthogonal signal correction and near infrared spectroscopy. Nordic Pulp and Paper Research Journal. 14(1999):2, 140–148.

11. Malkavaara, P., Alén, R.: A spectroscopic method for determining lignin content of softwood and hardwood kraft pulps. Chemometrics and Intel-ligent Laboratory Systems 44(1998), 287–292.

12. Wallbäcks, L., Edlund, U., Nordén, B., Berglund, I.: Multivariate characterization of pulp using solid-state C13 NMR, FTIR, and NIR. Tappi Journal 74(1991):10, 201–206.

13. Hiukka, R.: A multivariate approach to the analysis of pine needle samples using NIR. Chemometrics and Intelligent Laboratory Systems 44(1998), 395–401.

14. Durán, N., Angelo, R.: Infrared microspectroscopy in the pulp and paper making industry. Applied Spectroscopy Reviews 33(1998):3, 219–236.

15. Osborne, B.G., Fearn, T., Hindle, P.H.: Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, Longman Group UK Limited, Harlow 1993.

16. Derksen, M., Van de Oetelaar, P., Maris, F.: Ef-ficient prediction of the residual moisture content specification of a freeze-dried product by using near-infrared reflectance spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 17(1998), 473–480.

17. Ren, G., Chen, F.: Determination of moisture con-tent of ginseng by near infra-red reflectance spec-troscopy. Food Chemistry 60(1997):3, 433–436.

18. Rantanen, J., Lehtola, S., Ramet, P., Mannermaa, J.P., Yliruusi, J.: On-line monitoring of moisture content in an instrumented fluidized bed granu-lator with a multi-channel NIR moisture sensor. Powder Technology 99(1998):2, 163–170.

19. Lorås, V.: Bleachability of mechanical pulp. Tappi Journal 57(1974):2, 98–102.

20. Elowsson, T., Liukko, K.: How to achieve effective wet storage of pine logs (Pinus sylvestris) with a minimum amount of water. Forest Production Journal 45(1995):11/12, 36–42.

21. Sorge, C., Schulten, H.-R.: Influence of wet storage of spruce wood on groundwater quality. Journal of Environmental Analytical Chemistry 57(1994), 1–8.

22. Clausen, C.A.: International Biodeterioration & Biodegradation 1996 p. 101.

23. Brown, D.G. and Abbot, J.: Effects of metal-ions and stabilizers on peroxide decomposition during bleaching. Journal of Wood Chemistry and Tech-nology 15(1995):1, 85–111.

24. Ju, Y., Ni, Y., Ohi, H.: Journal of Wood Chemistry and Technology 19(1999):4, 323–334.

25. Abbot, J.: Catalytic decomposition of alkaline hydrogen-peroxide in the presence of metal-ions – Binuclear complex formation. Journal of Pulp and Paper Science 17(1991):1, 10–17.

26. Leary, G., Giampaolo, D.: The darkening reactions of TMP and BTMP during alkaline peroxide bleaching. Journal of Pulp and Paper Science 25(1999):4, 141–147.

27. Bristow, J.A.: What is ISO brightness?. Tappi Jour-nal 77(1994):5, 174–178.

28. Bristow, J.A..: ISO brightness – A more complete definition. Tappi Journal 82(1999):10, 54–56.

29. Borga, P., Hämäläinen, M., Theander, O.: Cor-relations between near-infrared spectra of wet-stored timber and the storage time in relation to the water-quality. Holzforschung 46(1992):4, 299–303.

30. Nilsson, D., Edlund, U., Elg Kristofferson, K., Sjöström, M., Agnemo, R.: The effect of designed wood storage on the brightness of bleached and unbleached thermo mechanical pulp. Nordic Pulp and Paper Research Journal 18(2003):4, 369–376.

31. Wold, S.: Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 20(1978), 397–404.

32. Mathworks Inc. (www.mathworks.com), Natick, United States 1997.

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Received January 30, 2004

pH was buffered to 5.5 during the reaction. All pulp samples were washed and then bleached with hydrogen peroxide and sodi-um dithionite and each sample was bleached twice using only single-stage bleaching with either of the two methods at the same time. Thus, the bleached material consisted of 45 peroxide bleached samples and 45 dithionite bleached samples.

Conditions for bleaching with H2O

2

Bleached pulp quantity: 50g (dry weight)Temperature: 70°CPulp concentration: 20%Reaction time: 2hBleaching chemicals & conditions: H

2O

2

4%, NaOH 3.7%, silicate 2.6%, intial pH ~11, end pH ~9

The pulp was mixed with the bleaching chemicals in a mixer. The bleaching was car-ried out in waterproof plastic bags heated in a water bath holding 70 °C. After two hours of bleaching the pulp was washed in a centrifuge with 6 litres of water and buff-ered to pH 5.5.

Conditions for bleaching with Na2S

2O

4

Bleached pulp quantity: 35g (dry weight)Temperature: 70%Pulp concentration: 4%Reaction time: 30 minBleaching chemicals & conditions: Na

2S

2O

4

2%, NaOH 0.2 M, initial pH ~11, end pH ~7

A solution containing water, Na2S

2O

4

and NaOH was prepared prior to bleaching. A new solution was made for every third bleaching experiment to make sure that the dithionite concentration was kept at a constant level. The pulp, containing 96% water, was put in a glass beaker connected to a nitrogen evacuation system, preventing degradation of dithionite by oxygen. The lid of the container was equipped with an inlet and outlet for nitrogen gas and an inlet for filling the beaker with dithionite solution. The beaker was evacuated three times prior to bleaching in order to keep oxygen free environment. Each evacuation started with filling the beaker with nitrogen gas followed by evacuation of the beaker with a water suction apparatus. The dithionite solution was then added to the beaker and a gentle nitrogen gas flow was applied to the beak-er. The temperature was kept constant at 70 °C using a water bath covering the beak-er. The pulp was washed in a centrifuge with 6 litres of water after each bleaching experi-ment was complete. The solution was then buffered to pH 5.5.

Appendix 2.

Method A. Measurements directly on wood surfaceThese measurements were carried out us-ing a fibre optic reflectance probe that was held tightly against the debarked peripheral surface of the measured disc. Each disc was measured at three different spots, which re-sulted in that 12 measurements were done for every sample (each sample consisted of four discs). The wavelength regions between 2300 to 2500 nm were discarded due to noise caused by the fibre optic instrumen-tation. The 12 spectra measured for each sample were checked for outliers (see data treatment) and were then averaged to one spectrum.

Method B. Measurements on ground woodOne fourth of each of the four debarked discs in each sample was chopped into small-er pieces. All knots and any bark material were carefully avoided and excluded in the sample material. The wood pieces were then ground in a Retch 2000 heavy-duty knife mill with a rotational speed of 750 rpm. The ground wood was collected through a 1.5 mm sieve and put in plastic bags. The mill was thoroughly cleaned between every grinding procedure.

The NIR measurements were accom-plished using a spinning cup that was filled with ground wood and sealed with a card-board disc. All samples were measured three times and the cup was emptied and refilled before a new measurement was started. The replicate spectra were checked for outliers and were then averaged to mean spectra. After that, the four spectra of each sample were averaged to one spectrum.

Method C. Measurements on dried ground woodIn order to achieve the same moisture con-tent for all samples the ground wood was dried in an oven for four hours at 105 °C. The samples were then put in a dessicator and were allowed to cool to room tempera-ture. Apart from that, the dried samples were treated in the same way as for the milled samples described above.

Method D. Measurements on thermo mechanical pulpMeasurements were also performed on the produced thermo mechanical pulp samples. The TMP measurements were accomplished using the spinning cup as well.

Appendix 1.

Bleaching300 g (dry weight) from each pulp sample was treated with EDTA. The chelation re-action was accomplished using plastic con-tainers heated with a water bath holding 70 °C. An excess of EDTA was used and

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PREDICTION OF WOOD SPECIES AND - DiVA portal143949/FULLTEXT01.pdf-3- Title Prediction of Wood Species and Pulp Brightness from Roundwood Measurements Author David Nilsson, Department