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The Unscrambler
A Handy Tool for Doing Chemometrics
Prof. Waltraud Kessler
Prof. Dr. Rudolf Kessler
Hochschule Reutlingen, School of Applied Chemistry
Steinbeistransferzentrum Prozesskontrolle und DatenanalyseCamo Process AS
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Topics The Unscrambler by Camo
Many possibilities for Analysing Data Examples
NIR-Spectra Fluorescence Exitation Emission Spectra
Life Demonstration 3-way Data Handling
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The Unscrambler
Main FeaturesExploratory Analysis
Descriptive statistics
Principal Component Analysis (PCA)Multivariate Regression Analysis
Partial Least Squares regression (PLS)
Principal Component Regression (PCR)
Multiple Linear Regression (MLR)
Prediction
ClassificationSoft Independent Modeling of Class Analogies (SIMCA)
PLS-Discriminant Analysis
Experimental DesignFractional and full factorial designs, Placket-Burmann,Box Behnken, Central Composite, Classical mixturedesigns, D-optimal designs
ANOVA, Response Surface ANOVA, PLS-R
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The Unscrambler
Also Features
Raw data checks Data preprocessing
Over 100 pre-defined plots
Automatic outlier detection
Automatic variable selection
and more
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Example: Fiber Board Production
In-situ Measurements of Fibres in Blowpipe
Blowpipe:
~ 180C
~ 5 bar
~ velocity of fibres ~ 20 m/s
NIR FOSS Process
Spectrometer
with fibre bundle and
diffuse reflectance probe
400 - 2200 nm
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-0.05
0
0.05
-0.10 -0.05 0 0.05 0.10
RESULT8, X-expl: 72%,24%
oRfoRfoRfoRf oRf
oRf
oRf
oRf
oRfoRfoRfoRfoRfoRfoRfoRfoRf
oRfoRf
oRfoRf
oRf
oRf
oRf
oRfoRf oRfoRfoRfoRf
oRg
oRgoRg
oRoRgoRgoRg
oRgoRgoRg
oRg
oRgoRgoRg
oRgoRgoRg
oRgoRg
oRg
oRgoRgoRgoRg
oRg
oRgoRgoRgoRgoRgoRg
oRg
oRg
oRg
oRgoRg
mRf
mRf
mRf
mRf
mmRf
mRf
mRfmRfmRf
mRf
mRf
mRfmRfmRfmRf
mRf
mRfmRfmRf
mRfmRf mRf
mRf
mRf
mRfmRfmRf
mRf
mRf
mRfmRfmRf
mRg
mRg
mRg
mRg
mRg
mRgmRg
mRgmRgmRg
mRgmRg
mRgmRgmRgmRgmRg
mRgmRgmRgmRgmRg
mRg
mRgmRgmRgmRgmRg
mRgmRg
mRgmRg
mRg
mRgmRg
mRgmRg
mRg
PC1
PC2 Scores
Principal Component Analysis
Separate the Overlapping Information
PC2=
Fine
ness
co
arse
fine
PC1 = kind of wood: Spruce Spruce with bark
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-0.1
0
0.1
500 1000 1500 2000
RESULT9, PC(X-expl):1(72%)
X-variables
X-loadings
PC1:Kind of wood
Principal Component Analysis
Scores and Loadings for PC1 and PC2
-0.10
-0.05
0
0.05
06:21:49_13.11. 11:43:52_13.11. 07:05:02_14.11. 14:15:56_14.11.
RESULT2, PC(X-expl): 1(95%)
Samples
Scores
-0.1
0
0.1
500 1000 1500 2000
RESULT9, PC(X-expl):2(24%)
X-variables
X-loadings
-0.05
0
0.05
06:21:49_13.11. 10:19:54_13.11. 14:37:28_13.11. 08:47:24_14.11. 14:06:48_14.11.
RESULT5, PC(X-expl): 2(24%)
Samples
Scores
PC2:FinenessSpruce
with bark
Spruce
fine
coarse
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PLS Regression
Degradation of Lignin for Spruce
-0.02
-0.01
0
0.01
0.02
-0.06 -0.03 0 0.03 0.06
RESULT16, X-expl: 80%,5% Y-expl: 88%,6%
2.22.22.2
2.2
2.5
2.52.5
2.5
2.52.52.52.5
2.52.52.52.5
2.5
2.9 2.92.92.9
2.9
2.9
2.9 2.9
2.92.9
2.92.9
2.9
PC1
PC2 Scores
2.4
2.7
3.0
2.1 2.4 2.7 3.0
RESULT16, (Y-var, PC): (SFC,2)
Elements:
Slope:
Offset:
Correlation:RMSEP:
SEP:
Bias:
30
0.910025
0.237915
0.9416800.085069
0.086517
0.000981
Measured
Predicted Y
0
0.5E-06
0.0000010
0.0000015
0.0000020
10 20 30
RESULT16, PC:2 2
Samples
X-variance Residual Sample Variance
0
0.01
0.02
0.03
0.04
10 20 30
RESULT16, PC:2 2
Sample
Y-variance Residual Sample Variance
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Analysing Three-Way Data
Mode 2
Mode
1
Mode3
I
L
KMode 2
Mode
1
Mode3
I
L
K
Two different types of modes
are distinguished:
Sample mode -usually first mode
Variable mode -usually second and/or third mode
Sample mode - O
Variable mode - V
OV2 or O2V
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Substructures in Three-way Arrays
L f rontal slices I horizontal slices
K vertical slices
L f rontal slices I horizontal slices
K vertical slices
Three-way arrays can be divided into different slicesDecide which slices are put together to form a two-dimensional array
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Samples: 32 fibres from steam treated and ground woodchips
X-Data: Fluorescence Excitation-Emission spectra
(250 - 575 nm) x (300 - 600 nm)
Y-Data: Kind of wood (beech and spruce)
Severity of treatment (a combination of time and temperature)
Age of wood (fresh and old)
Plate gap of grinding ( fine and coarse).
Three-way Data Example:
Fluorescence Excitation Emission Spectra
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Three-way Data Example:
Fluorescence Excitation Emission Spectra
Beech
Spruce
Treatment: low middle severe
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Fluorescence Excitation Emission Spectra
Results of N-PLSR
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Fluorescence Excitation Emission Spectra
x1 and x2 Loading Weights
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3D Data Import: ASCII, Excel, JCAMP-DX, Matlab
Swapping: toggle freely between the 6 OV2 and 6 O2V
layouts of a 3D table
Matrix plots: Contour and landscape plots of the samples
Variable sets: Create Primary variable sets and
Secondary Variables sets
Possibilities for Three-way Data in
The Unscrambler
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Easy to make models Easy to interpret results
High user-friendliness
Less time spent doing data analysis,
more information extracted from your data
Faster decision making
The Unscrambler
Benefits
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Fully functioning version
Includes the Unscrambler user manual
Includes 7 tutorial exercises and associated files
Includes 3 demonstration tours
Try The Unscrambler
9.2 for 30 daysFree trial version available on www.camo.com
CAMO Software India Pvt. Ltd.,
14 -15, Krishna Reddy Colony, Domlur Layout,
Bangalore - 560071, INDIA
For details, contact: