Estimation of Pyrrolizidine Alkaloids in native and invasive weed species of the Netherlands

using reflectance spectroscopy

Fatemeh Eghbali Moghaddam January, 2010

Course Title: Geo-Information Science and Earth Observation for Environmental Modelling and Management

Level: Master of Science (Msc) Course Duration: April 2008 - January 2010 Consortium partners: University of Southampton (UK)

Lund University (Sweden) University of Warsaw (Poland) International Institute for Geo-Information Science and Earth Observation (ITC) (The Netherlands)

GEM thesis number: 2010-05

Estimation of Pyrrolizidine Alkaloids in native and invasive weed species of the Netherlands using reflectance spectroscopy

by

Fatemeh Eghbali Moghaddam

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation for Environmental Modelling and Management Thesis Assessment Board Chair: Prof. Dr. Andrew Skidmore External Examiner: Prof. Dr. Terry Dawson  

First Supervisor: Dr. Martin Schlerf Second Supervisor: Prof. Dr. Andrew Skidmore Member : M.Sc. Andre Kooiman Advisor: M.Sc. Sabrina Carvalho

International Institute for Geo-Information Science and Earth Observation Enschede, the Netherlands

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Dedicated to my dearest husband Ehsan For his continuous support

With love and gratitude

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Abstract

Pyrrolizidine Alkaloids (PAs) are secondary metabolites which are frequently found in some genera of genus Senecionea. Senecio species have been studied extensively for their alkaloid content, which are generally hazardous to herbivores, and to the fact that some of these species are becoming a plague in The Netherlands and becoming invasive abroad. Linking plant population biochemistry variation with differences in spectral reflectance properties is a new and promising tool to understand and monitor plant population dynamics and plant – soil interactions because these interactions affect the plants chemical variation. The overall aim of this research is to find the Pyrrolizidine Alkaloids spectral signals and correlate PAs concentration from the leaf spectral reflectance. Different water and nutrient treatments were fashioned on 200 samples of Jacobaea vulgaris, Senecio inaequidens and Senecio erucifolius to find the potential effects of the water/nutrients on the reflectance pattern and concentration of alkaloids. An ASD Integrating Sphere was used to collect the spectral data of their leaves in the spectroscopy lab in July and August 2009. The ANOVA test and T-test were applied on the PAs and spectral data to check the nutrient effects on the PAs concentration and spectral reflectance of the species. To study the relationship between the species spectral reflectance and the leaves PA concentrations, PLSR (Partial Least Squares Regression) method was used to find the important wavelengths related to Pyrrolizidine Alkaloids. The results of ANOVA test indicated that the PAs concentrations and PAs chemotypes were significantly different in the species. The results of the T-test showed that increasing the nutrient led to increasing the absorption of light in visible domain in both fresh and dry levels thus a shift in red edge to the increased wavelengths. The results of the PLSR indicated that, there were possibly two wavebands (2105 nm and 2355 nm) identified by PLSR B coefficients as the indicators of PAs in Jacobaea vulgaris and one wavelength (710 nm) as indicator for PAs in Senecio inaequidens. In fresh level, the best model for Jacobaea vulgaris achieved the highest R2 =0.64 with an RMSE of 42%, the best model for Senecio inaequidens achieved only R2= 0.14 with an RMSE of 66%. In dry samples, the best model for Jacobaea vulgaris achieved an R2=0.60 with an RMSE of 44%, the best model for Senecio inaequidens the highest R2=0.81 with an RMSE of 31%. Key words: Pyrrolizidine Alkaloids, nutrient effects, Jacobaea vulgaris, Senecio inaequidens, Senecio erucifolius, T-test, ANOVA test, PLSR.

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Acknowledgements

All the worship and adoration to almighty God, the beneficent, the merciful, Without His divine grace the successful accomplishment of this research was impossible. I am greatly indebted to the European Union through the Erasmus Mundus scheme for awarding me the scholarship to undertake this study. What a splendid opportunity I had to study in four European countries. The course would not have been effective without the commitment of the consortium leaders Prof. Terry Dawson of Southampton University, Prof. Petter Pilesjo of Lund University, Prof. Katarzyna Dabrowska of Warsaw University and Prof. Andrew Skidmore of ITC. The lecturers in the four universities can not go unnoticed for their dedication to the professionalism. My utmost gratitude goes to my supervisors: Dr. Martin Schlerf and Prof. Andrew Skidmore, for their invaluable inputs, expert guidance and moral support. All your great ideas helped me maintain the focus during all phases of the research for which I thank you. My deep gratitude goes to my advisor Sabrina Carvalho. She gave me invaluable advice and help on the design of the experiment, the lab-work and statistical analyzing. I am grateful to Prof. Wim Van Der Putten and Dr. Mirka Macel from NIOO for critical advises, coordinating to fieldwork and sharing their knowledge in plants-ecology. I specially thank GEM-2008 classmates for their lovely friendship. You were wonderful classmates indeed. Living, studding and travelling together yielded comradeship, fellow-feeling cordiality and unity. My heartfelt thanks go to my husband for his dedicated love, encouragement and support, without him all these would not have been achievable. Let it herewith be officially recorded that you are the best.

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Table of contents

1. Introduction ............................................................................................ 1 1.1. Invasive species ............................................................................ 1

1.1.1. Toxic content ............................................................................ 1 1.1.2. Natural Enemies of invasive plants .......................................... 2

1.2. The role of Pyrrolizidine Alkaloids and how thay are affected by nutrient supply ............................................................................................ 2

1.2.1. Spectroscopy as tool to detect PAs ........................................... 4 1.3. Research Objectives ...................................................................... 6

1.3.1. General Objective ..................................................................... 6 1.3.2. Specific Objectives ................................................................... 6

1.4. Research Questions and Hypothesis ............................................. 6 2. Methods and Materials ........................................................................... 8

2.1. Research workflow and the steps .................................................. 9 2.2. Study area ..................................................................................... 9 2.3. Fertilizing experimant ................................................................. 10

2.3.1. Soil collection and sterilization .............................................. 10 2.3.2. Seed germination .................................................................... 10 2.3.3. Plants growing phase in greenhouse ....................................... 11 2.3.4. Leaf spectral measurements with ASD Integrating Sphere .... 11

2.4. PAs concentration measurements ............................................... 16 2.5. Data preprocessing and Statistical analysis ................................ 17

2.5.1. Deletion of the noisy bands .................................................... 17 2.5.2. Testing the outliers ................................................................. 17 2.5.3. Testing the Normality of PA data ........................................... 17

2.6. Two-way ANOVA ...................................................................... 17 2.7. T-test 2 ........................................................................................ 18 2.8. Partial Least Squares Regression (PLSR) ................................... 18

2.8.1. Determination of optimum PLS factors .................................. 19 2.8.2. Selection of spectral processing methods ............................... 20 2.8.3. Important wavelengths for predicting PAs ............................. 22

2.9. Employed software ..................................................................... 22 3. Results .................................................................................................. 23

3.1. PAs measurments ........................................................................ 24

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3.1.1. Summery statistics of the PA .................................................. 24 3.1.2. Testing the outliers ................................................................. 24 3.1.3. Testing the normality of the PA data ...................................... 25

3.2. Leaf spectral measurements ........................................................ 25 3.3. Soil nutrient effects on leaf PAs concentrations ......................... 27 3.4. Soil nutrient affects on leaf reflectance ....................................... 30

3.4.1. Fresh samples ......................................................................... 30 3.4.2. Dry samples ............................................................................ 34

3.5. Relations between leaf spectral reflectance and leaf PAs ........... 38 3.5.1. PLSR models accuracy ........................................................... 38 3.5.2. Determination of important wavelength on PLSR models ..... 42

4. Discussion ............................................................................................ 46 4.1. Soil nutrient effects on leaf PAs concentrations ......................... 46 4.2. Soil nutrient effects on leaf spectral reflectance ......................... 47

4.2.1. Fresh samples ......................................................................... 47 4.2.2. Dry samples ............................................................................ 48

4.3. Relation between the leaf spectral reflectance and the leaf PAs . 49 4.3.1. Comparison of PLSR model performances for three species at the fresh and dry levels ......................................................................... 49 4.3.2. Important wavelengths for predicting of PAs in Jacobaea vulgaris ……………………………………………………………….50 4.3.3. Important wavelengths for predicting of PAs in Senecio inaequidens ........................................................................................... 51

4.4. Assumptions and source of errors ............................................... 52 5. Conclusions and Recommendations ..................................................... 53

5.1. Conclusions ................................................................................. 53 5.2. Recommendations ....................................................................... 55

6. References ............................................................................................ 56 7. Appendices ........................................................................................... 62

7.1. Appendix I: Shrub and leaf structure of the studied plants. ........ 63 7.2. Appendix II: Testing the outliers. ............................................... 64 7.3. Appendix III: PA data. ................................................................ 67 7.4. Appendix IV: The spectral reflectances of all samples in fresh and dry level. ................................................................................................... 70 7.5. Appendix V: Nutrient effects on fresh samples, group one. ....... 71

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7.6. Appendix VI: Nutrient effects on fresh samples, group two. ..... 72 7.7. Appendix VII: Nutrient effects on dry samples, group one. ....... 73 7.8. Aappendix VIII: Nutrient effects dry samples group two. .......... 74 7.9. Appendix IX: leave one out cross validation results. ................. 75

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List of figures

Figure 1-1: the structure of alkaloid known in Jacobaea vulgaris ................. 4 Figure 2-1: Research workflow and the steps. ............................................... 9 Figure 2-2: Leaves spectral measurements with Integrating Sphere. ........... 12 Figure 2-3: The effect of additive reflectance by multiple layers of leaves on the effective reflectance from vegetation. .................................................... 13 Figure 2-4: Reflectance from combination of cotton leaves stacked up to six layers (adapted from Hoffer 1978). .............................................................. 14 Figure 2-5: The effect of moisture content of leaves. .................................. 15 Figure 2-6: PLSR factors versus RMSE. ...................................................... 20 Figure 3-1: Response of Jacobaea vulgaris to nutrient treatment................ 26 Figure 3-2: Different PAs chemotypes concentrations in three species. ...... 28 Figure 3-3: Nutrient effects on fresh samples with high water treatment. ... 31 Figure 3-4: Nutrient effects on fresh samples with low water treatment. .... 33 Figure 3-5: Nutrient effects on dry samples with high water treatment. ...... 35 Figure 3-6: Nutrient effects on dry samples with low water treatment. ....... 37 Figure 3-7: Scatter plots of observed and predicted values of PA data of fresh samples. ............................................................................................... 40 Figure 3-8: Scatter plots of observed and predicted values of PA data of dry samples. ........................................................................................................ 41 Figure 3-9: B-coefficients associated to the PLSR models for PA in fresh plants. ........................................................................................................... 43 Figure 3-10: B-coefficients associated to the PLSR models for PA in dry plants. ........................................................................................................... 44

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List of tables

Table 2-1: The total number of seeds which were planted for this research.10 Table 2-2: The division of data for running the T-test2.…………...………18 Table 2-3: Total number of samples of each species used for the PLSR…..19 Table 3-1: Summery statistics for PA concentrations.……….…….………24 Table 3-2: Two-way ANOVA test.….………………………………..……29 Table 3-3: The results of leave 10 out cross validation.……………………39 Table 3-4: The B-coefficients standard deviations in fresh and dry samples. …………………………………………………………………...…………42 Table 3-5: Important wavelengths for predicting PA in fresh samples….…45 Table 3-6: Important wavelengths for predicting PA in dry samples………45 Table 4-1: The best PLSR models performance for the three species for both fresh and dry levels…………………………………………………………49 Table 5-1: The hypothesis regarding the nutrient effects on fresh samples and their conditions…………………………………………………………54 Table 5-2: The hypothesis regarding the nutrient effects on dry samples and their conditions. ........................................................……………..........................….………54

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Acronyms J.v : Jacobaea vulgaris JVHN : Jacobaea vulgaris, high nutrient JVLN : Jacobaea vulgaris, low nutrient NIOO : Netherlands Institute of Ecology NIR : Near Infra Red PAs : Pyrrolizidine Alkaloids RIKILT : Institute of Food Safety in the Netherlands RS : Remote Sensing S.e : Senecio erucifolius SEHN : Senecio erucifolius, high nutrient SELN : Senecio erucifolius, low nutrient S.i : Senecio inaequidens SIHN : Senecio inaequidens, high nutrient SILN : Senecio inaequidens, low nutrient SWIR : Short Wave Infra Red

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1. Introduction

1.1. Invasive species

Plant species may become particularly competitive and invasive in new

environments, thus being a treat to local biodiversity through biodiversity failure, having negative impacts on ecosystem and displeasing the local landscape beauties (Henderson et al. 2006; Garcia-Serrano et al. 2009). Moreover invader plants are costly to be coped with (Pimentel 2002; Henderson et al. 2006), the costs of the US economy raised dramatically from US $1.1 billion to US $138 billion yearly be cause of underestimations of dealing with plants invasion (Pimentel et al. 2002). The invasion success of invasive plants is determined by their ability to deal with new environmental restrictions (Garcia-Serrano et al. 2009; Henderson et al. 2006). Researchers believe that due to climate change and land use mismanagement some previously marginal plant species have became invasive and noxious, such as Jacobaea vulgaris, Senecio inaequidens. Jacobaea vulgaris is a monocarpic biennial with long flowering period from June to November (Van Der Meijden & Van Der Waals-Kooi 1979) native to the Netherlands and invasive elsewhere. Senecio inaequidens is widespread throughout north and west of Europe and during the last 20 years is expanding towards the south and east of Europe continent (Garsia-Serrano et al. 2008). It is an exotic perennial species (Bossdorf et al. 2008) native from South-Africa (Van Der Meiden 2005). Senecio erucifolious is a rare native from the Netherlands morphologically similar to Jacobaea vulgaris (Van Der Meiden 2005), it is not invasive and in this research it is the control plant. Appendix I shows the three species’ shrub and their leaves shape.

1.1.1. Toxic content

Livestock production (e.g. meat) has always been a costly and important

issue to think about in each society. Meat quality depends on the grazed grass quality which herbivores consume. These invasive weeds are a threat to herbivores due to their toxic contents (Garcia-Serrano et al. 2009). Eating Senecio inaequidens may lead to hepatotoxic diseases (Macel et al. 2004) and death of livestock (Hartmann & Zimmer 1986). Thus, hepatotoxic and carcinogenic characteristics to

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human world-wide have been reported (Hartmann & Zimmer 1986) due to consumption of the contaminated herbivores meat by the toxic weeds. A plant is classified hepatotoxic when damages the liver of mammals. Such problems increase the livestock productions costs (Di Tomaso 2002). Therefore monitoring the grasslands for their toxicity is vital, since the introduced weed species colonise heavily in grasslands (Garcia-Serrano et al. 2009). Therefore, monitoring the invasive plants frequently has been requested from many stakeholders (Neobiota European conference, 2008) and studding the plant invasions has became ‘one of the hottest current topics in ecology’ (Sol 2001; Henderson et al. 2006).

1.1.2. Natural Enemies of invasive plants

A highly debated issue is whether these invasive species are affected in

different ways by natural enemies compared to the native species (Kardol et al. 2006). If the invasive species are affected by natural enemies then the biotic resistance theory has happened (Karol et al. 2006). In other words, the native habitat itself fights against the invasive species. In this case then the level of PAs or other biochemicals in the invasive species is expected to increase to the higher level in order to protect the plants. Where native habitat is fighting the invasive plant it would be preferable to protect instead of cleaning the area so that it would promote the adaption of native habitats to the invaders. To find out such areas, it is believed to be something in the field of remote sensing. Assessing the chemical properties of Senecio species through spectroscopy could be extended in the future towards imaging techniques and would in principle allow the monitoring of chemical changes from remote sensing platforms. Thus, it would enable us to evaluate whether or not the invasive species are under attack from natural enemies.

By studying the Jacobaea vulgaris versus Senecio inaequidens it will be possible to study the sensitivity of RS platforms to chemical variation in relation to ecosystems dynamics such as the trophic interactions plant-soil, plant-herbivores.

1.2. The role of Pyrrolizidine Alkaloids and how thay are affected by nutrient supply

Pyrrolizidine Alkaloids (PAs) are secondary metabolites which can only be

found in the angiosperms. Angiosperms are plants whose seeds are contained in an ovary or fruit. PAs are frequently found in some genera of genus Senecioneae, Eupatorieae, many genera of the Boraginaceae and many more families (Pelser et

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al. 2005). About 100 different kinds of PAs are found in different plants of Senecionea or Asteraceae family (Hartmann & Witte 1995; Macel et al. 2004; Pelser et al. 2005)

Pyrrolizidine alkaloids are species-specific (Hartmann & Dierich 1998; Macel et al. 2004) which vary in type but all of them share some specific characteristics (Figure 1-1) i) they all have double bond between C-atom 1 and C-atom 2 ii) esterified allylic hydroxyl group at C-9 and iii) second esterified alcoholic hydroxyl at C-7. Jacobaea vulgaris can contain more than 10 senecionine related alkaloids. Senecionine N-oxide has been identified as the primary product of biosynthesis (Toppel et al. 1987; Pelser et al. 2005) and can be considered as the back bone structure of most PAs. It is synthesized in the roots and transported to the shoots via the phloem-path where it is transformed in to the species specific PA profile (Pelser et al. 2005). Phloem is the living tissue that carries organic nutrients. Phloem is mainly concerned with the transport of soluble organic material made during photosynthesis. Depending on the plant organ the concentration of the PAs varies and generally the flower heads have the highest amount of the PAs (Hartmann & Dierich 1998). A variation in pyrrolizidine alkaloids concentrations seem to exist in these plants, not only between organs but also between plants, the total pyrrolizidine alkaloid concentrations found in some studies varied between 0.1 and 6 mg. g-1. dry weight (Hol et al. 2003; Macel et al. 2004). These alkaloid concentration variations are believed to be influenced by nutrient availability and above and belowground damaged tissue as found by Hol et al. (2003) in shoots of Jacobaea vulgaris. The results found, show that Jacobaea vulgaris grows larger with higher nutrient conditions while maintains the productions of the alkaloids, which resulted in a dilution effect, i.e. in larger plants with the same alkaloid productions resulting in a generalized lower concentration (Hol et al. 2003).

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Figure 1-1: the structure of alkaloid known in Jacobaea vulgaris

1.2.1. Spectroscopy as tool to detect PAs

Reflectance spectroscopy has been applied to find the biochemical

composition of vegetation since 1970s (Hymowitz et al. 1974; Norris et al. 1976; Card et al. 1988; Curran et al. 2001; Cho & Skidmore 2006; Li et al. 2008; Abdel-Abdel-Rahman et al. 2009; Schlerf et al. 2010). The reflectance and transmittance of vegetation are sensitive to the chemical properties of its leaves. This is caused by leaf biochemicals, which often have strong energy absorption capacity in the near and mid infrared as a result of molecular vibrations of the C-H, N-H, C-O and O-H bonds within organic components, (Curren et al. 2000; Kumar et al. 2001; Huang et al. 2004; Li et al. 2008). For instance, Nitrogen (N) is relatively small part of leaf dry weight, (Kokaly et al. 2009), covering the weight range from 0.26% in some grasslands to 3.5% in broadleaf species (Kokaly et al. 2009). Foliar N is strongly linked to photosynthesis and net primary production (Schimel et al. 1997; Smith et al. 2002). And it has been quantified at leaf and canopy scales using reflectance measurements (Wessman et al. 1988; Curran et al. 1992; Smith et al. 2002). Nitrogen is a part of the structure of the proteins and chlorophylls in the leaf cells (Kokaly et al. 2009). Spectroscopy may therefore, offer the possibility to study the PAs concentration by their spectral reflectance patterns.

What should be considered is that there are so many factors which would mask the signals of leaf biochemical on spectral reflectance, for instance, from leaf level to canopy level confusing factors might be introduced, which affect the

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biochemical reflectance. For instance canopy structure, (leaf area and stem area, leaf and stem orientation), soil reflectance illumination condition (Asner 1998; Huang et al. 2004; Schlerf et al. 2010). While, up scaling to airborne and spaceborne imagery are influenced by BRDF and atmospheric effects (Schlerf et al. 2010). Regarding all problematic factors, scientists would try to find out the features like biochemicals first from leaf level to reduce the confusing factors. This research aims to find the PAs concentration and their effects on reflectance spectroscopy in leaf level.

Another issue to be noted is the minor absorption features of organic components are difficult to detect in the spectra of fresh leaves however. Because water masks the spectral reflectance of the organic components, the chemicals may be seen in the spectra if the leaf is dried (Peterson et al. 1988). By the year 1970s the relationship between 42 absorption features in visible and near-infrared wavebands and the concentration of some organic compounds in dried leaves had been established by researchers from USDA (Elvidge 1990). Kokaly and Clark (1999) suggested that, it is very important to control water influence within 10% in order to enable an accurate foliar prediction in fresh leaf.

Hyper-spectral remote sensing is a recent tool which may provide the possibility to detect the chemical components of vegetation over a wide range of scales (Asner & Martin. 2008; Ferwerda & Skidmore. 2007) and is mainly based on near infrared spectrometry (NIR) (Curran et al. 2001). The results of the present research project may provide the first step for field spectroscopy and air/spaceborne remote sensing in order to detect the PA concentration in different species for food security and for multitrophic interaction mechanisms within the exotic plants.

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1.3. Research Objectives

1.3.1. General Objective

The overall aim of this research is to find the Pyrrolizidine Alkaloids spectral signals and correlate PAs concentration from spectral reflectance.

1.3.2. Specific Objectives

1. To study the soil nutrient effects on the leaf PAs concentrations. 2. To study the soil nutrient effects on the leaf spectral reflectance. 3. To build predictive models which estimate the concentration of PAs using

reflectance spectroscopy. 4. To test the accuracy of the predicted model.

1.4. Research Questions and Hypothesis

1. How does soil nutrient affect the Pyrrolizidine Alkaloids concentrations?

Hypothesis 1.1: Three species have significantly different PAs concentrations due to nutrient effects.

H0 = Nutrient has no effects on species PAs concentrations. 2. How does soil nutrient affect the leaf spectral reflectance of the species?

Hypothesis 2.1: High nutrient samples have lower reflectance in visible domain due to nutrient effects.

H0= High and low nutrient samples have the same spectral reflectance in visible domain.

Hypothesis 2.2: Shift in red edge inflection point to longer wavelength due to nutrient effects.

H0: No shift in red edge inflection point to longer wavelength due to nutrient effects.

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Hypothesis 2.3: The higher the nutrient available in leaves the lower the SWIR reflectance.

H0= Nutrient has no effect on lowering the spectral reflectance in NIR and SWIR.

3. Do PAs affect spectral reflectance of the species?

Hypothesis 3.1: The higher the PAs concentration in the leaves the higher the absorption and the lower the reflectance in SWIR wavelength. H0 = There is no relationship between PAs and the leaf spectral

reflectance.

4. What is the accuracy to predict PA concentration?

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2. Methods and Materials

This Chapter describes the materials and methods of the research and these are presented are follows:

1. Research workflow and steps 2. Study area 3. Fertilising experiment 4. Leaf spectral measurements 5. PAs concentration measurements 6. Statistical analysis and modelling

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2.1. Research workflow and the steps

Research workflow

Labo

rato

ry w

ork

Post

labo

rato

ry w

ork

Pre‐

labo

rato

ry

wor

kSoil Sampling Seed Germination Grown Plants

Measuring the PAs concentrations 

T‐test  

PLSR

Lab data analysis

Measuring the spectral reflectance of fresh and 

dry samplesFertilizing experiment

Lab Data

Deletion of noisy bands

1st and 2nd derivative 

ANOVA test

PAs outlier test 

PAs normality test

Predictive models/ validation

Figure 2-1: Research workflow and the steps.

2.2. Study area

The Netherlands has become a target to invasive weeds such as Senecio

inaequidens. The main study area used for the present study is located in the centre of the Netherlands. This area is characterized by sandy grassland fields discarded from agricultural processes several years or decades ago and now main habitat for Senecio inaequidens and Jacobaea species.

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2.3. Fertilizing experimant

2.3.1. Soil collection and sterilization

Approximately 800 kg of poor nutrient soil were collected from Veluwe national park in Netherlands and sieved for soil homogenization. Thereafter was sterilized in a private company by radiation for 2 weeks.

2.3.2. Seed germination

200 seeds of each the introduced species (Jacobaea vulgaris, Senecio inaequidens and Senecio erucifolius) from Senecionea family were sterilized in a light bleach solution (10%) for 2 minutes and were cleaned with demineralised water. The seeds were sown in glass pearls substrate and stored in a chamber at 15-20C temperature for three weeks to germinate. Afterwards 80 seeds of each species were individually transferred to a 1,3L pots with the sterilized soil and kept in the green house, the 80 seeds were then divided in 4 groups of 20 to apply 4 different water/nutrient treatments. From this amount some plants died prior to start of the measurements. The experiment was run by 200 samples in total (Table 2-1). Table 2-1: The total number of seeds which were planted for this research.

Nutrient treatment Total Rich Poor

Wat

er tr

eatm

ent

Low

19 S. inaequidens 20 J. vulgaris 10 S. erucifolious

20 S. inaequidens 18 J. vulgaris 13 S. erucifolious 100

High

19 S. inaequidens 19 J. vulgaris 12 S. erucifolious

20 S. inaequidens 19 J. vulgaris 11 S. erucifolious 100

Total Plants 99 101 200

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2.3.3. Plants growing phase in greenhouse

Plants were kept in the greenhouse of NIOO, with 16 hours of light with standard moisture condition. Different treatments were fashioned to test the potential effects of the water/nutrients on the reflectance patterns and concentration of alkaloids. During 6 weeks the plants were grown with the same water and nutrient conditions: 50mL of water 3 days a week in which the second watering time had a 0.5 % Hoagland nutrient solution incorporated. In the 4 weeks thereafter 4 treatments were applied to each species: • Low water-Low nutrients (20-30mL demi water). • Low water-High nutrients (20-30mL 0.5 % Hoagland nutrient solution). • High water-Low Nutrient (40-50ml demi water). • High water-High Nutrient (40-50ml 0.5 % Hoagland nutrient solution). Extra water was added when found necessary.

2.3.4. Leaf spectral measurements with ASD Integrating Sphere

Leaf spectral measurement experiment was carried out in NIOO spectrometry laboratory. Measurements were performed with Integrating Sphere equipment together with an ASD FeildSpec® spectrometer from 6th of July 2009 till 7th of August 2009. Radiation balance and plant canopy modeling studies often require measurement of hemispherical reflectance and transmittance values of real world sample (Griffin 2006), thus many other complicating factors like, complexity of canopy structure, atmospheric and background effects found by (Huang et al. 2004; Matson et al. 1994; Yoder & Pettigrew-Crosby 1995). The integrating sphere is preferably suited for these measurements, without interfere of confusing factors, because it collects all the radiation reflected from, or transmitted through, a sample and spreads the energy over the entire surface area of the sphere in a very even distribution (Griffin 2006). The base sphere assembly includes six ports designed to accept supplied sample holders, light source assembly, fiber optic adapter, light trap, and port plugs: the Reflectance comparison and the Reflectance Sample. Two 99% reference standards, one calibrated and one uncalibrated for absolute or relative spectral reflectance measurements (Integrating Sphere user manual). In this research the

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absolute reflectance were collected, and the equipment was calibrated after each measurement with calibrated reference standard (white plug), (Figure 2-2).

(a) (b)

Figure 2-2: Leaves spectral measurements with Integrating Sphere. a) Calibration of equipment, by placing the calibrated reference standard

(white plug) in Reflectance Sample port and the sample in Reflectance Comparison port.

b) Measuring the reflectance of a sample, by placing the calibrated reference standard (white plug) in its place (Reflectance comparison port) and the sample in Reflectance Sample port.

2.3.4.1. Spectral measurement of fresh samples

The plants were cut from the above soil baseline. An average stack of 8 leaves in high nutrient condition samples and 4 in low nutrient condition samples were stacked together. Since the low nutrient samples had less biomass, the number of measured leaves was lower. Stacking of leaves was done for reaching the ‘NIR infinite reflectance’ (Kumar et al. 2001) which is the level at which the near-infrared reflectance does not increase with increasing the leaves to the addition (Belward 1991). By doing so we guarantee that the response was not due to leaf thickness or biomass but due to nutrient and biochemical contents of the leaves, (Figure 2-3). Much of the NIR energy which transmitted through the upper leaf is reflected from the lower leaves to enhance the NIR reflectance (Figure 2-4). The spectra of each

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sample unit were recorded 10 times and averaged in a single representative measurement to reduce noise effect.

Figure 2-3: The effect of additive reflectance by multiple layers of leaves on the effective reflectance from vegetation. I = Incoming energy. T = Transmitted energy. R = Reflected energy (adapted from Kumar et al. 2006), the Part of the radiation which is transmitted by the first leaf is reflected back by the subsequent leaf layers.

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Figure 2-4: Reflectance from combination of cotton leaves stacked up to six layers (adapted from Hoffer 1978).

2.3.4.2. Spectral measurement of dry samples

After measuring the spectra of fresh leaves, plants were frozen in -20 degrees Celsius at least for 2 hours and freeze-dried for 4 days. When the leaves became dry, the spectral reflectance of the dry samples was measured with the ASD Integrating Sphere. An average 8 number of leaves were considered as one stack in high nutrient samples, and an average of 4 leaves in low nutrient samples considered as one stack as for fresh samples. A black tick clipboard was used for fixing the dry leaves exactly in front of the light source: without using the black clipboard (Figure 2-5, part a), it was difficult to place the leaves in front of the light source because they were fragile and the pressure from the holder of the sphere could have broken them. The 10 spectra recorded for each sample were averaged to produce a single spectrum. The spectral reflectance of the dry samples was measured, since the minor absorption features of organic components are difficult to be detected in fresh leaf spectra. Water masks absorption features of the organic components in a green leaf, while they maybe seen in a dry leaf spectrum since the strong water absorption features are reduced. (Peterson et al. 1988) As the moisture content of leaves

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decreases, reflectance in the SWIR spectrum increases noticeably and absorption features of other chemicals become visible (Hoffer, 1978), (Figure 2-5).

(a)

(b)

Figure 2-5: The effect of moisture content of leaves. a) The schematic black clipboard, which was used for holding the samples in

front of the light source of ASD Integrating Sphere. b) The effect of moisture content of corn leaves (adapted from Hoffer 1978).

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2.4. PAs concentration measurements

After measuring the spectral reflectance of the dry leaves, leaves were brought to wet chemistry laboratory for PAs extraction according to RIKILT (Institute of Food Safety in Netherlands) protocol as fallow. PA extraction with 2%CH2O2

1) Weight 10 mg well dried, finely ground and well homogenized plant material in a 2ml epp.

2) Add 1.0 ml 2%CH2O2 (incl. 1mg intern standards /l formic acid) and vortex 10 seconds at maximum speed.

3) Shake all epps upright on the tumbling machine for 1/2 hour but vortex every 1/4 hour in between.

4) Centrifuge at maximum speed for 5 minutes. 5) Put the supernatant in a syringe and filter with an Acrodisc. 6) Also make at least one blanco samples: put 1ml of formic acid (incl. IS) in

a epp. Alkaloid dilution for LC-MS readings (RIKILT):

1) Dilution is made in glass vials (broad opening LC/GC vials). 2) Add 25µl of the alkaloid extraction solution and 975µl of water. 3) Randomize the position of the vials in racks of 6 (vertical) x 8 (horizontal)

where the 2 middle columns are empty*. Readings of the LC are made in vertical order.

4) Racks are set in the LC-MS in optimized conditions.

17

2.5. Data preprocessing and Statistical analysis

2.5.1. Deletion of the noisy bands

To minimize the noise in the measured reflectance spectra, the 10 spectra of each sample subplot were averaged. Wavebands below 400 nm and above 2400 nm displayed very high levels of noise and were excluded.

2.5.2. Testing the outliers

Outliers are the observations that appear to be inconsistent with the reminder of the collected data (Iglewicz & Hoaglin 1993). Different methods are available to the investigator for checking the outliers, for instance, z-score method and boxplot method (Iglewicz & Hoaglin 1993). Both z-score and boxplot methods were performed to check the possible outliers. In z-score method the data with a z-scores value greater that three or maximum three and half is considered as outlier (Fallen & Spada 1997). In boxplot method, observation suspected outlier if it falls more than 1.5*IQR (Interquartile Range) above the third quartile or below the first quartile (Fallen & Spada 1997). Both z-score and boxplot were performed in SPSS.

2.5.3. Testing the Normality of PA data

The normality of the PA data was tested in SPSS. Different methods of transformation were used for testing the normality of PA data, among those, Log10 and LN. Thereafter, the Shapiro-Wilk and the Kolmogorov-Smitnov were applied to check the normality of the PA data. Normality of the data is a pre-requirement for running the PLSR (Mevik & Cederkvist 2004).

2.6. Two-way ANOVA

The two-way ANOVA was performed to analyse:

18

1) Considering the nutrient treatments as first effect: to check how adding and not adding nutrient solution had an effect on the PAs concentration in the species.

2) Considering the species as second main effect: to check if there were any variations in PA chemotypes in the species.

3) Considering both nutrient treatment and the species together an effect: to check if there were any differentiation in PA concentrations levels and any variations in PA types in the three species. The two-way ANOVA was performed in SPSS.

2.7. T-test 2

T-test2 was performed to find out the effects of the nutrient on the reflectance properties of the leaves. The T-test2 tests the hypothesis that two independent samples, in the vectors X and Y, come from distributions with equal means, and returns the result of the test in H. H=0 indicates that the null hypothesis ‘means are equal’ (Matlab help) cannot be rejected at for example 5% significance level. H=1 indicates that the null hypothesis can be rejected at for example 5% level. T-test2 was performed in Matlab software. The data was divided in to two main groups according to their applied water treatment on them (Table 2-2). Table 2-2: The division of data for running the T-test2. Group one the water treatment is high while in group 2 the water treatment is low and the nutrient treatment varies.

T-test2 data division Group one; high water high water, low nutrient high water, high nutrient

Group two; low water low water, low nutrient low water, high nutrient

2.8. Partial Least Squares Regression (PLSR)

PLS regression combines the features of principal component analysis and multiple regressions (Huang et al. 2004). It compresses a large number of variables to a few latent variables (PLS factors). This regression relies on mathematical treatments of the full spectra (Kokaly et al. 2009). PLSR reduces the problem of

19

overfitting found with multiple regressions, (usually when more wavebands than sample size are used), (Card et al. 1988; Curran 1989; Curran et al. 2001; Kokaly et al. 2009). PLSR has become a standard method for modeling linear relationships between multivariate measurements in the field of chemometrics (Dejong 1993) and has been applied to other fields like remote sensing, (Chen et al. 2006; Hansen & Schjoerring 2003; Luypaert et al. 2003). PLS regression was processed using ParLes3.1 software (Rossel 2007). Cross validation is a generally applicable way to predict the performance of a model on a validation set and has been used in many other studies e.g., Schlerf et al. (2009) and Li et al. (2008). Leave-10-out cross validation was performed to assess the performance of the PLSR models. Generally the results of leave ten out cross validation are similar to those by leave-one-out cross validation (appendix IX), hence provide good indications of the number of factors to select for PLSR modelling (Rossel 2008). It randomly spitted the dataset into training (e.g. for Jacobaea vulgaris: n=63) data and validation (n=10) data. For each such split, the model was fit to the training data, and predictive accuracy was assessed using the validation data. The results were then averaged over the splits. The performance of the PLSR models were examined by the coefficient of determination (R2) the root mean square error (RMSE). A good model should have low RMSE and a high R2. The total number of samples which were used for PLSR in this research is presented in (Table 2-3). Table 2-3: Total number of samples of each species used for the PLSR

Species Total samples Fresh samples Jacobaea vulgaris 73

Senecio inaequidens 74

Senecio erucifolius 44 Dry samples Jacobaea vulgaris 73

Senecio inaequidens 73

Senecio erucifolius 43

2.8.1. Determination of optimum PLS factors

The determination of PLS factors plays an important role in the performance of a PLSR model. In this study, the optimum number of factors was chosen according to the lowest RMSE and AIC (Akaike Information Criterion) information, (Rossel 2008). Figure 2-6 exemplarily shows the relationship between

20

the numbers of PLS factors and RMSE of dry Jacobaea vulgaris with leave 10 out cross validation method.

2

111 )(

N

YYRMSE

N

i∑=

−=

Where Y1 is the predicted value and Y1 is the observed value.

MRMSENLOGAIC 2)( +=

Where N is the sample size and m is the number of model parameters in this case of factors.

Facotrs versus RMSE leave 10 out

0

1

2

3

4

5

6

0 5 10 15 20 25

Factors

RMSE

Figure 2-6: PLSR factors versus RMSE. Dry mean centre Jacobaea vulgaris with leave 10 out cross validation method.

2.8.2. Selection of spectral processing methods

Spectral processing is important to enhance features present in reflectance spectra. In this research, several pre-processing methods have been applied, and among those which delivered best results were chosen. The applied methods were: mean centre, first and second derivative transformation after Savitzky-Golay smoothing.

• Mean centre transformation: It enhance the absorption features and minimize the atmospheric effects. It calculates the average reflectance for

21

each wavelength and subtracts the average from the reflectance of each individual wavelength:

χχχ −= ijmij ,

Where mij ,χis the mean centre transformation reflectance of wavelength j

in spectrum i, ijχis the original reflectance, and χ is the average reflectance for

wavelength j across the spectra. It is a normalization method which enhances the absorption features and minimizes the atmospheric effects.

• Savitzky-Goly smoothing: The Savitzky-Golay filter was used to reduce the effect of random noise (Savitzky & Golay, 1964). The Savitzky-Goly filter uses a moving polynomial window of any order and the size of filter consists of (2n+1) points, where n is the half-width of the smoothing window, in this research n=3.

• First derivative and second derivative transformation: Derivative transformations enhance the minor absorption features but amplify the noise, so they are usually performed after smoothing. A first derivative reflectance calculates the slope values from the reflectance and the calculation is:

)()(

1

11

nn

nnst RRd

λλ −−

=+

+

Where d1st is the first derivative reflectance, n is the band number, Rn+1 is the reflectance at waveband n+1, Rn is the reflectance at the waveband n and λ is the wavelength (nm). • The equation for the second derivative reflectance:

)(5.0)(

2

1112

nn

stn

stnnd dd

dλλ −×

−=

+

+

Where d2nd is the second derivative approximation, n is the band number, d1st is the first derivative reflectance, and λ is the wavelength (nm).

22

2.8.3. Important wavelengths for predicting PAs

The calibration equation coefficients (B-coefficient) were used to determine the importance of spectral bands in PLSR calibration (Haaland & Thomas, 1988; Wold et al. 2001). B coefficients in PLSR models represent the contribution of each predictor (wavebands) to the model. The signs (plus or minus) of B-coefficients determine the direction of the relationship between independent variables (spectral reflectance) and dependent variable (biochemical concentration). The larger the absolute value of B-coefficients the stronger the relation. In this study, B-coefficients were obtained from PLSR models. Thresholds for B-coefficients were established based on B-coefficients standard deviations, (Gomez et al. 2008) and the wavebands greater than the thresholds (Table 3-4) considered significant. The wavelength selection was performed for all three species for fresh and dry levels (Table 3-5 and Table 3-6).

2.9. Employed software

The following technical software application were employed in this research under the ITC authorized licences on Windows XP platform, ViewSpecPro 5.6.10, (ASD Inc), Matlab 7.8, (The MathWorks Inc, 2009), SPSS 15 (SPSS Inc, 2006), ParLes 3.1 (2007), Statistica 7.1(StatSoft Inc, 2006), MS office.

23

3. Results

This chapter describes the main findings of the research. This includes:

1. Results of the PA measurements 2. Results of leaf spectral measurements 3. Results of soil nutrient effects on leaf PAs concentrations 4. Results of soil nutrient effects on leaf reflectance 5. Relations between leaf spectral reflectance and leaf PAs

24

3.1. PAs measurments

3.1.1. Summery statistics of the PA

Table 3-1 shows the detailed information about the measured PA biochemical concentrations in Jacobaea vulgaris, Senecio inaequidens and Senecio erucifolius. The summery statistics of the PA concentrations of the Jacobaea vulgaris was identical in fresh and dry levels, while it was different for Senecio inaequidens and Senecio erucifolius, because in dry level the number of samples were one less than the dry level. Table 3-1: Summery statistics for PA concentrations. PA concentrations are in mg g-1 of dry matter. Coefficient of variation is the standard deviation divided by the mean value.

Name level n Mean Std.dev. Min Max Var.coef. Jacobaea vulgaris

fresh 73 6.421 4.49 0.272 16.72 0.69 dry 73 6.421 4.49 0.272 16.72 0.69

Senecio inaequidens

fresh 74 5.408 3.93 0.41 15.632 0.71 dry 73 5.54 4.042 0.41 15.632 0.72

Senecio erucifolius

fresh 44 1.472 2.41 0.005 9.664 1.64 dry 43 1.66 2.76 0.005 9.664 1.66

3.1.2. Testing the outliers

The results of testing the outlier in SPSS with z-score and boxplot showed that there were no outliers in the PA data (Appendix II). The results of the z-score testing showed that all the PAs z-score values were within -3 to +3. And the result of the boxplot showed that there was no value between the 1.5 Interquartile Range (IQR) and 3 Interquartile Range (IQR) from the end of the box to be considered as a possible outlier.

25

3.1.3. Testing the normality of the PA data

Testing the normality of the PA data showed that: PA data of Jacobaea vulgaris and Senecio inaequidens are normally distributed, while Senecio erucifolius PA data is not normally distributed, and it did not become normally distributed even after applying the transformation methods (P value>0.05). Appendix III, part (i) shows the original PA data which was tested for normality and part (ii) shows the PA data of Senecio erucifolius which is not normally distributed.

3.2. Leaf spectral measurements

In all three species more nutrient supply led to more biomass compare to

low nutrient supplied plants. Plants with high nutrient treatment were dark green whereas plants with low nutrient supply were mostly pails with pinkish pigments in their leaves (Figure 3-1, part b). While the spectral reflectance data was being collected, some spectra were unusual like having picks in green and red regions or unusual red edge. It was seen mostly in samples with low nutrient treatments, (Figure 3-1, part b). Appendix IV Shows the spectral reflectances of all samples of Senecio inaequidens and Senecio erucifolius in fresh and dry levels.

26

(a) (b)

(c) (d)

(e) (f)

Figure 3-1: Response of Jacobaea vulgaris to nutrient treatment.

a) Jacobaea vulgaris appearance with high nutrient treatment. b) Same species appearance with low nutrient treatment.

27

c) The typical spectral reflectance of a sample of Jacobaea vulgaris with high nutrient and water treatments. Fresh level (the green solid curve) and dry level (the blue solid curve).

d) The unusual spectral reflectance of the one of the samples of Jacobaea vulgaris with low nutrient treatments. Fresh level (the green solid curve) and dry level (the blue solid curve).

e) The spectral reflectance of all samples of Jacobaea vulgaris in fresh level. f) The spectral reflectance of all samples of Jacobaea vulgaris in dry level.

3.3. Soil nutrient effects on leaf PAs concentrations

Jacobaea vulgaris had the highest and Senecio erucifolius the lowest concentrations (Figure 3-2). Moreover, increasing the nutrient treatment led to increase of PAs in Jacobaea vulgaris and Senecio erucifolius, while increasing of the nutrient treatment decreased the PAs concentration of Senecio inaequidens. There were some values out of Interquartile Range in (Table 3-2 part a), they were PA values away from the mean. But they were not statistically outliers. The two way ANOVA showed, nutrient treatments as the first factor had a significant effect on PAs concentrations in each species, (p<0.001). Considering the species as the second factor, the test showed; PAs chemotypes variations between the species, significant (P<0.001) (Table 3-2). And finally the interaction of the two factors also had significant effects on the PAs concentrations and PAs chemotypes. (Figure 3-2 and Table 3-2, p<0.001). The Jacobaea vulgaris dominant PA was Tertiary-amine, while Senecio erucifolius and Senecio inaequidens dominant PA was N-oxides (Figure 3-2) The conclusion from this test was the concentrations of the PAs were significantly different within and between the groups and the PAs chemotypes were also significantly different within and between the groups (p<0.001).

28

(a)

(b) Figure 3-2: Different PAs chemotypes concentrations in three species.

a) Blue boxplot shows high nutrient treatment and green boxplot shows low nutrient treatment.

b) Variation in PAs concentrations in the three species, Jacobaea vulgaris high and low nutrient treatments (two left columns), Senecio erucifolius high and low nutrient treatments (two middle columns) and Senecio inaequidens high and low nutrient treatments (two right columns). The

29

black colour shows the Tertiary-amine, the gray colour shows the N-oxides and white colour shows the Otonecines. Names of the species and their nutrient treatments are in abbreviations in the graph, e.g. JVHN stands for Jacobaea vulgaris high nutrient.

Table 3-2: Two-way ANOVA test.

a) Univariate Analysis of Variance. It shows the number of observations for each level of both factors.

b) The results of the two-way ANOVA. The (Type III) sum of the squares and the mean square are given. The ‘d.f.’ column gives the degree of freedom, which is 1 less than the number of factor levels. The mean square is the sum of the squares/degree of freedom. The ‘f’ column is the F-ratio, is the factors mean square/error mean square. The P-value is labelled ‘sig.’

Between-Subjects Factors N

nutrient treatment 1,00 103

2,00 104

species JV 77

SE 51

SI 79(a)

Tests of Between-Subjects Effects (Two-way ANOVA)

Dependent Variable: Total PA mgg

Source Type III Sum of Squares df

Mean Square F Sig.

nutrient treatment 168.621 1 168.621 14.518 ,000

species 704.825 2 352.413 30.343 ,000

nutrient treatment * species 658.086 2 329.043 28.331 ,000

a. R Squared = ,397 (Adjusted R Squared = ,382) (b)

30

3.4. Soil nutrient affects on leaf reflectance

The aim of this step was to study the differences of the spectral reflectance of Jacobaea vulgaris, Senecio inaequidens and Senecio erucifolius in the visible, NIR and SWIR domains due to nutrient effects.

3.4.1. Fresh samples

The results of nutrient effects of fresh samples are presented according to Table 2-2. The fresh and dry samples were divided in to two groups each. Group one contained samples with high water treatment and different nutrient treatments. Group two contained samples with low water treatment and different nutrient treatments.

3.4.1.1. Fresh samples, group one

The fresh high water treatment samples: In all three species high nutrient plants compared to low nutrient plants clearly showed a lower reflectance in visible domain. In addition a shift was observed in the red edge inflection point to increased wavelength (α=0.05), (Figure 3-3). The red edge is characterized by the shift between the chlorophyll low reflectance zone to the high reflectance around 680-750 nm region, highly correlated with chlorophyll, (Fillella & Penuelas. 1994; Mutanga & Skidmore. 2007; Kumar et al. 2001). In NIR spectrum high nutrient samples had higher reflectance than low nutrient samples (Jacobaea vulgaris and Senecio inaequidens, α=0.05).In some parts of the SWIR, high nutrient plants had a lower reflectance (Senecio inaequidens and Senecio erucifolius, α=0.05). Appendix V shows the mean spectral reflectances, their standard deviations and the significant bands.

31

(a)

(b)

(c) Figure 3-3: Nutrient effects on fresh samples with high water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red curve: mean spectral reflectance of plants with high nutrient and high water treatment. Blue curve: mean spectral reflectance of plants with low nutrient and high water treatment. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

32

3.4.1.2. Fresh samples, group two

The fresh low water treatment samples: In all three species high nutrient plants compared to low nutrient plants clearly showed a lower reflectance in the visible domain. In addition, a shift was observed in the red edge inflection point to increase wavelengths (α=0.05) (Figure 3-4). In NIR spectrum high nutrient samples had higher reflectance than low nutrient samples (Jacobaea vulgaris and Senecio inaequidens, α=0.05). In some parts of SWIR, high nutrient samples had a lower reflectance than low nutrient samples (Senecio erucifolius, α=0.05). Appendix VI shows the mean spectral reflectances, their standard deviations and the significant bands.

33

(a)

(b)

(c) Figure 3-4: Nutrient effects on fresh samples with low water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red curve: mean spectral reflectance of plants with high nutrient and low water treatment. Blue curve: mean spectral reflectance of plants with low nutrient and low water treatment. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

34

3.4.2. Dry samples

3.4.2.1. Dry samples group one

The dry high water treatment samples: In all three species, high nutrient plants compared to low nutrient plants clearly showed a lower reflectance in the visible domain. In addition, a shift was observed in the red edge inflection point to increased wavelengths (α=0.05), (Figure 3-5). In some parts of the NIR and SWIR, high nutrient plants had a lower reflectance than low nutrient samples (Jacobaea vulgaris and Senecio erucifolius, α=0.05). Appendix VII shows the mean spectral reflectances, their standard deviations and the significant bands.

35

(a)

(b)

(c) Figure 3-5: Nutrient effects on dry samples with high water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red curve: mean spectral reflectance of plants with high nutrient and high water treatment. Blue curve: mean spectral reflectance of plants with low nutrient and high water treatment. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

36

3.4.2.2. Dry samples group two

The dry low water treatment samples: In all three species, high nutrient plants compared to low nutrient plants clearly showed lower reflectance in some parts of the visible domain. In addition, a shift in the red edge inflection point to increased wavelengths (α=0.05), (Figure 3-6). Appendix VIII shows the mean spectral reflectances, their standard deviations and the significant bands.

37

(a)

(b)

(c) Figure 3-6: Nutrient effects on dry samples with low water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red curve: mean spectral reflectance of plants with high nutrient and low water treatment. Blue curve: mean spectral reflectance of plants with low nutrient and low water treatment. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

38

3.5. Relations between leaf spectral reflectance and leaf PAs

3.5.1. PLSR models accuracy

A model was considered good when it achieved the low RMSE and the high R2 with the optimum number of factors. The PLS factors shown, are the optimum number of factors chosen in accordance with the lowest RMSE. Root mean square error explains how accurate a derived model is and coefficient of determination (R2) shows how strong the relationship between predicted and measured concentration is. In general, Jacobaea vulgaris and Senecio inaequidens produced PLSR models with RMSE values between 30% and 66% (Table 3-3). In fresh level, the best model for Jacobaea vulgaris achieved the highest R2 =0.64 with an RMSE of 42%, the best model for Senecio inaequidens achieved only R2= 0.14 with an RMSE of 66%, models for Senecio erucifolius showed a large errors (>100%) In dry level, the best model for Jacobaea vulgaris achieved an R2=0.60 with an RMSE of 44%, the best model for Senecio inaequidens the highest R2=0.81 with an RMSE of 31%, models for Senecio erucifolius showed large errors (>100%). As a principals for running the PLS regression, the data should be normally distributed (Mevik & Cederkvist 2004). The scatter plots (Figure 3-7 and Figure 3-8) show linear relationships between observed and predicted values of PA concentrations of three species in two levels of fresh and dry. First and second derivative transformations after Savitzky-Colay smoothing are abbreviated as SG+1st derivative and SG+2nd derivative respectively.

39

Table 3-3: The results of leave 10 out cross validation.

Leave 10 out cross validation Name Methods n factors R2 RMSE

Fres

h Sa

mpl

es

Jacobaea vulgaris

No preprocessing 73 9 0.64 42% Mean centre 73 7 0.46 52%

SG +1st derivative 73 3 0.51 43% SG +2nd derivative 73 2 0.38 56%

Senecio inaequidens

No preprocessing 74 2 0.08 69% Mean centre 74 2 0.14 66%

SG +1st derivative 74 2 0.09 69% SG +2nd derivative 74 1 0.11 71%

Senecio erucifolius

No preprocessing 44 2 0.09 107% Mean centre 44 6 0.45 111%

SG +1st derivative 44 2 0.5 106% SG +2nd derivative 44 2 0.25 150%

Dry

sam

ples

Jacobaea vulgaris

No preprocessing 73 7 0.54 47% Mean centre 73 7 0.6 44%

SG +1st derivative 73 2 0.34 56% SG +2nd derivative 73 1 0.098 82%

Senecio inaequidens

No preprocessing 73 7 0.48 51% Mean centre 73 11 0.81 31%

SG +1st derivative 73 4 0.73 49% SG +2nd derivative 73 5 0.74 36%

Senecio erucifolius

No preprocessing 43 1 0.05 156% Mean centre 43 1 0.06 148%

SG +1st derivative 43 1 0.02 155% SG +2nd derivative 43 1 0.13 146%

40

(a)

Fresh Jacobaea vulgaris with no pre-processing

R2 = 0.6434

-2

0

2

4

6

8

10

12

14

0 5 10 15 20

ObservedPr

edic

ted

Relative RMSE=42%

(b)

Fresh Senecio inaequidens mean center

R2 = 0.1436

0123456789

0 5 10 15 20

Observed

Pred

icte

d

Relative RMSE=66%

(c)

Fresh Senecio erucifolius 1st derivative

R2 = 0.6039

-4

-2

0

2

4

6

8

10

0 2 4 6 8 10 12

Observed

Pred

icte

d

Ralative RMSE= 106%

Figure 3-7: Scatter plots of observed and predicted values of PA data of fresh samples.

a) Jacobaea vulgaris with no pre-processing method. b) Senecio inaequidens mean centre transformation. c) Senecio erucifolius 1st derivative transformation.

41

(a)

Dry Jacobaea vulgaris mean centre

R2 = 0.5429

0

2

4

6

8

10

12

14

16

0 5 10 15 20

ObservedPr

edic

ted

Relative RMSE=40%

(b)

Dry Senecio inaequidens mean center

R2 = 0.8102

-2

0

2

4

68

10

12

14

16

0 5 10 15 20

Observed

Pred

icte

d

Relative RMSE=31%

(c)

Dry Senecio erucifolius 2nd darivative

R2 = 0.1375

0

0.5

1

1.5

2

2.5

3

0 2 4 6 8 10 12

Observed

Pred

icte

d

Relative RMSE= 146%

Figure 3-8: Scatter plots of observed and predicted values of PA data of dry samples.

a) Jacobaea vulgaris mean centre transformation. b) Senecio inaequidens mean centre transformation. c) Senecio erucifolius 2nd derivative transformation.

42

3.5.2. Determination of important wavelength on PLSR models

The calibration equation coefficients (B-coefficient) were used to determine the importance of spectral bands in PLS calibration (Haaland & Thomas, 1988). B-coefficients were driven from PLSR models, using different methods of processing in different species, (Figure 3-9 and Figure 3-10). The thresholds for finding important wavelengths were determined based on the B-coefficients standard deviations (Gomes et al. 2008), (Table 3-4). The wavelengths were considered significant (Table 3-5 and Table 3-6) if the corresponding B-coefficient were bigger than the thresholds. Table 3-4: The B-coefficients standard deviations in fresh and dry samples.

Species B-coefficients standard deviation Fresh samples Jacobaea vulgaris 2.689

Senecio inaequidens 0.028

Senecio erucifolius 0.5506 Dry samples Jacobaea vulgaris 1.04

Senecio inaequidens 3.062

Senecio erucifolius 50.95

43

(a)

(b)

(c) Figure 3-9: B-coefficients associated to the PLSR models for PA in fresh plants. (a) Jacobaea vulgaris with no pre-processing method. (b) Senecio inaequidens mean centre transformation. (c) Senecio erucifolius 1st derivative transformation.

44

(a)

(b)

(c) Figure 3-10: B-coefficients associated to the PLSR models for PA in dry plants. (a) Jacobaea vulgaris mean centre transformation. (b) Senecio inaequidens mean centre transformation. (c) Senecio erucifolius 2nd derivative transformation.

45

Table 3-5: Important wavelengths for predicting PA in fresh samples PA fresh samples Positive Negative Jacobaea vulgaris 440, 445, 691, 698, 1020,

1050, 2045, 2105, 2200, 2355, 2362

590, 650, 740, 990, 1000, 1855, 2100, 2140, 2290, 2265, 2298, 2350

Senecio inaequidens 640, 710 900, 1100

Senecio erucifolius 710, 1071, 1098, 1398, 1500, 2055, 2145, 2230, 2290, 2310, 2355

490, 463, 650, 751, 720, 740, 1700, 1710, 1858, 1768, 1940, 2178, 2270, 2300, 2310, 2330

Table 3-6: Important wavelengths for predicting PA in dry samples

PA fresh samples Jacobaea vulgaris 598, 600, 700, 1940, 2000,

2075, 2105, 2120, 2160, 2189, 2355

600, 700, 1980, 2183, 2277, 2335, 2340, 2350

Senecio inaequidens 460, 485, 585, 710, 1840, 1980, 2075, 2080, 2100, 2105, 2355

460, 608, 615, 710, 997, 1840, 1980, 2075, 2080, 2100, 2105, 2355

Senecio erucifolius 700, 2160, 2190, 2320, 2345, 2350, 2355

2140, 2155, 2185, 2278, 2334, 2348, 2363, 2375

46

4. Discussion

4.1. Soil nutrient effects on leaf PAs concentrations

The results of soil nutrient effects on PAs concentrations proved that

nutrient has significant effects on PAs concentration within and between the groups of the species (P<0.001). This result is in agreement with the study of Hol et al. (2003) that such alkaloids concentration variations are believed to be influenced by nutrient availability in shoots of Jacobaea vulgaris. Nutrients have been found to indirectly affect the concentration of alkaloids (Hol et al. 2003). Jacobaea vulgaris grows bigger with higher nutrient conditions while maintains the productions of the alkaloids at the same level (Hol et al .2003). This leads to a dilution effect, i.e. larger plants with the same alkaloid productions as smaller plants, generally have a lower concentration in leaf PA (Hol et al. 2003). This research identified that with increasing the nutrient the PAs concentration diluted in Senecio inaequidens. But, the dilution effect did not occur in Jacobaea vulgaris either Senecio erucifolius. Such result is contrary to our expectations. We are unaware of other work in the Pyrrolizidine Alkaloids directly linking nitrogen to PAs concentration in the introduced species and why the dilution effect did not accrued in Jacobaea vulgaris and Senecio erucifolius remained unclear in this research.

Moreover, the variations in PAs concentrations between the species are also due to genetic basis, which is proved by the study of Macel et al. (2004). They found that, the concentrations of the PAs in the species of family Senecionea are related to their PAs chemotypes. The PAs chemotypes are different within the species of family Senecionea and some of which has higher concentration than the others. It is due to genetic basis of the species (Macel et al. 2004).

47

4.2. Soil nutrient effects on leaf spectral reflectance

4.2.1. Fresh samples

The results of the T-test2 for testing the nutrient effects on the fresh samples signified that, samples with high nutrient treatment had significantly (α=0.05) lower reflectance in visible part of the spectrum, than low nutrient samples. Since increasing the nutrient in particular nitrogen leads to higher chlorophyll concentration in the leaves, and chlorophyll pigment is a strong absorber of visible spectra, this result is in agreement with the studies of (Martin & Aber 1997; Serrano et al. 2002; Mutanga et al. 2003; Kokaly et al. 2009). Moreover, this research proved a shift in red edge inflection point to longer wavelength in high nutrient samples. This result is in agreement with the study of Mutanga & Skidmore (2007), which proved that, with increasing the nutrient treatment, red edge shifts to longer wavelength. According to Mutanga et al. (2003) who found that nitrogen supply affects mesophyll cell structure, resulting in higher reflectance with an increase in nitrogen supply, in some of the species increasing the nutrient led to increasing the reflectance in NIR for instance: 1) Jacobaea vulgaris, both high water samples and low water samples: plants with

high nutrient treatment had higher reflectance in NIR than plants with low nutrient treatment, (Figure 3-3, part a, and Figure 3-4, part a respectively).

2) Senecio inaequidens, both high water samples and low water samples: high nutrient plants had higher reflectance in NIR than low nutrient plants, (Figure 3-3 part b, and Figure 3-4 part b respectively).

3) Senecio erucifolius both high water samples and low water samples: nutrient supply had no significant effects on NIR reflectance, (Figure 3-3, part c, and Figure 3-4, part c respectively).

According to Curran (1989) and Schmidt (2003) nitrogen in leaves (mainly as protein) absorbs light in specific wavebands in the SWIR, e.g. At 1510, 1690, 1730, 1940, 1980, 2060, 2130, 2180, 2300 and 2350 nm; this study realized that: 1) Jacobaea vulgaris, high water samples: high nutrient plants had significantly

(α=0.05) higher reflectance than the low nutrient plants between 1580 nm and

48

1820 nm (Figure 3-3, part a). Low water samples: high nutrient plants had lower reflectance in SWIR between 1300 nm and 2400 nm (Figure 3-4, part a).

2) Senecio inaequidens, high water samples: high nutrient plants had significantly lower reflectance (α=0.05) between 2150 nm and 2400 nm, (Figure 3-3, part b). Low water samples: the spectral reflectances of the high nutrient plants and low nutrient plants were not significantly different (Figure 3-4, part b).

3) Senecio erucifolius, high water samples: high nutrient plants had significantly (α=0.05) lower reflectance than low nutrient plants throughout the SWIR (Figure 3-3, part c). Low water samples: high nutrient plants had significantly (α=0.05) lower reflectance than low nutrient plants between 1900 nm and 2000 nm, (Figure 3-4, part c).

Increasing the nutrient should lower the reflectance in the specific channels of SWIR as introduced earlier, as they are in connection with nutrient supply. With S.e, nutrient supply shows the expected effects on SWIR reflectance. However, the results of J.v and S.i are contrary to our expectations and expected effects did not happen this might be due to the effects of other factors like leaf water and leaf structure, which dominate the SWIR reflectance.

4.2.2. Dry samples

For cancelling out the influence of leaf water on leaf reflectance, the tests were repeated on dried leaves. The results of the T-test2 for testing the nutrient effects on spectral reflectance of the fresh samples signified that, samples with high nutrient treatment had significantly (α=0.05) lower reflectance in visible part of the spectrum, than low nutrient samples. Since increasing the nutrient in particular nitrogen leads to higher chlorophyll concentration in the leaves, and chlorophyll pigment is a strong absorber of light in visible spectra. This result is in agreement with the studies of (Martin & Aber 1997; Serrano et al. 2002; Mutanga et al. 2003; Kokaly et al. 2009). In addition, this research proved a shift in red edge inflection point to longer wavelength in dry high nutrient samples. This is also proved by the study of Mutanga and Skidmore (2007), with increasing the nutrient treatment, red edge shifts to longer wavelength. According to Mutanga et al (2003), who found that nitrogen supply affects mesophyll cell structure, resulting in higher reflectance with an increase in nitrogen supply, this feature could be seen just in Jacobaea vulgaris high water samples: high nutrient plants had higher reflectance in NIR than low nutrient plants, (Figure 3-5

49

part a). For the rest of the species increasing the nutrient had no significant effects on NIR spectrum. Increasing the nutrient should lower the reflectance in the specific channels of SWIR as introduced earlier, as they are in connection with nutrient supply. With J.v, S.i and S.e the expected effects did not happen, this might be due to the effects of other factors like, leaf internal structure, which dominate the SWIR reflectance.

4.3. Relation between the leaf spectral reflectance and the leaf PAs

4.3.1. Comparison of PLSR model performances for three species at the fresh and dry levels

To make a direct comparison of the performance of fresh and dry levels, Table 4-1 was created for best performance of PLSR for the three species. The PLSR models performances were determined by their R2 and RMSE. Table 4-1: The best PLSR models performance for the three species for both fresh and dry levels

PLSR performance for fresh level Species Jacobaea vulgaris Senecio inaequidens Senecio erucifolius Method Not pre-processed Mean centre Mean Centre Factors 9 2 6 R2 0.64 0.14 45 RMSE 42% 66% 111% PLSR performance for dry level Species Jacobaea vulgaris Senecio inaequidens Senecio erucifolius Method Mean Centre Mean Centre 2nd derivative Factors 7 11 1 R2 0.6 0.81 0.13 RMSE 44% 31% 146%

Overall the performance of the PLSR in fresh level was successful for

Jacobaea vulgaris with R2 = 0.64 and RMES = 42%, while the PLSR model for Senecio inaequidens was week with R2 = 0.14 and an RMSE of 66%. The overall limited accuracy of the PLSR models may be attributed to several factors.

50

1) The concentrations of the measured biochemical, in this research (PAs concentrations) were very low in all three species. Jacobaea vulgaris had the highest amount of PAs, mean = 6.241 mg per g, of dry weight, Senecio inaequidens had the moderate PAs concentration, mean = 5.482 mg per g, of dry weight and Senecio erucifolius had the lowest concentration, mean = 1.472 mg per g, of dry weight, taking in account many of the samples of Senecio erucifolius had almost 0 mg per g PA in their dry weights, (Table 3-1 and Appendix III). Other leaf biochemicals that have been investigated in other research have a much larger concentration. For instance, Rong (2009) found accurate model prediction for tea polyphenols, were the concentration of tea polyphenols was up to 30 percent of the leaf dry weight. To ensure the good performance of the model, the range of biochemical concentration should be as large as possible within the normal range (Chen et al. 2007), while there were so many samples with zero PAs concentration. The small variation of PA may result in minor spectral change, which makes it more difficult for PLSR models to detect chemical differences.

2) The poor accuracy of the PLSR may also be caused by the different chemotypes of PAs (Figure 3-2, part b). In fact, PAs are different in structure like jacobine, senecionine, eruciflorine, jacoline, jaconine and many more different chemotypes (Macel et al. 2004).

3) The third reason for having limited PLSR models accuracy maybe attributed to the unusual reflectances of plants with low nutrient treatment, like either low absorptions of blue and red light. (Figure 3-1 and appendix IV).

4) The data for running the PLSR must be normally distributed (Mevik & Cederkvist 2004). The PA data of Senecio erucifolius is not normally distributed. That is another reason for having high errors.

In dry samples the performance of the Senecio inaequidens improved in terms of R2 = 0.81 and the low RMSE 31%, while dry Jacobaea vulgaris remains more or less the same as the fresh level, the R2= 0.6 and RMSE of 44%.

4.3.2. Important wavelengths for predicting of PAs in Jacobaea vulgaris

The wavelengths which consistently showed positive relations with PAs concentration in fresh Jacobaea vulgaris were: 445, 590, 698, 990, 1020, 1855, 2045, 2105, 2200, 2265, 2290 and 2355 nm (Table 3-5) and for dry Jacobaea vulgaris were: 600, 700, 1940, 2000, 2075, 2105, 2120, 2160, 2183, 2189, 2277, 2335, 2340, 2350 and 2355 nm (Table 3-6).

51

Some of the selected wavelengths are sensitive to other known leaf biochemicals. For instance, 445 nm in fresh level and 600 and 700 nm in dry level are sensitive to leaf chlorophyll content (Schmidt 2003). Chlorophyll pigments are in direct relation with nutrient particularly nitrogen content of the leaves (Kokaly et al. 2009) and since PAs have N in their structure (Macel et al. 2004), there is maybe an indirect relation between the explained wavelengths and the PAs of Jacobaea vulgaris. 1020 nm in fresh level and 2350 nm in dry level are linked to protein, nitrogen and cellulose content of the leaves, and because most of the PAs have N in their structure and proteins also have up to 50 % of leaves nitrogen (Kokaly et al. 2009) there is probably another indirect relation between the explained wavelengths and the PAs. The significant and common wavelengths in fresh and dry levels possibly are 2105 and 2355 nm.

4.3.3. Important wavelengths for predicting of PAs in Senecio inaequidens

The wavelengths which consistently showed positive relations with PAs concentrations in fresh Senecio inaequidens were, 640, 710, 900 and 1100 nm (Table 3-5), and in dry Senecio inaequidens were 460, 585, 670, 710, 990, 1700, 1840, 1890, 1938, 1980, 2075, 2080, 2100, 2105, 2155, 2200, 2245, 2320 and 2355 nm (Table 3-6). Some of the selected wavelengths are related to other known leaf biochemicals. For instance, 640 nm in fresh level and 460 nm in dry level are linked to leaf chlorophyll content, (Schmidt 2003), and leaf chlorophyll is linked to leaf nitrogen and PAs have N in their structure (Macel et al. 2004), this is maybe an indirect link to the selected wavelengths and the PAs. 710 nm is in the red edge inflection point (Fillella & Penuelas 1994; Mutanga & Skidmore. 2007; Kumar et al. 2006). Red edge is highly correlated to leaf chlorophyll content (Mutanga & Skidmore. 2007), chlorophyll pigments are related to leaf nitrogen, (Kokaly et al. 2009) and PAs have N in their structure (Macel et al. 2004), so in Senecio inaequidens exists perhaps another indirect relations between the 710 nm wavelength and the its PAs. The significant and common wavelength in fresh and dry levels is possibly 710 nm.

52

4.4. Assumptions and source of errors

Operational source of error goes to the lab-work; using integrating Sphere for measuring the spectral reflectance of the dry leaves, especially Senecio inaequidens as the leaves were linear, 0.3 cm (Garcia-Serrano et al. 2009).

53

5. Conclusions and Recommendations

5.1. Conclusions

This work has explored for the three native and invasive weeds of the Netherlands the effects of nutrient fertilisation on spectral leaf reflectance and reflectance spectroscopy of the Pyrrolizidine Alkaloids. The first objective aimed at finding if the soil nutrient had effects on PAs concentrations within and between the groups of the three species. This research found that nutrient had a significant effect on PAs concentrations within the species and between the groups. However, the dilution effect on PAs concentration in Senecio inaequidens by increasing the nutrient. But the expected effect did not occur in Jacobaea vulgaris and Senecio erucifolius, this result are contrary to our expectations and the study of (Hol et al. 2003). The second objective tried to find the effects of soil nutrient on leaf spectral reflectance. The satisfactory results of this research revealed that, increasing the nutrient led to decreasing the reflectance of the leaves in visible domain and at the same time, in high nutrient plants the red edge inflection point shifted toward longer wavelengths. The first null hypothesis and second null hypothesis regarding the nutrient effects on spectral reflectance were rejected at α = 0.05, the third null hypothesis rejected in some cases (Table 5-1 and Table 5-2).

54

Table 5-1: The hypothesis regarding the nutrient effects on fresh samples and their conditions

species 1: H0= High and low nutrient samples have same spectral reflectance in visible spectra.

2: H0= No shift in red edge inflection point to longer wavelength due to nutrient available in leaves.

3: H0= Nutrient has no effect on lowering the spectral reflectance in SWIR.

Hig

h w

ater

pla

nts Jacobaea

vulgaris Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio inaequidens

Rejected at α = 0.05

Rejected at α = 0.05

Rejected at α = 0.05

Senecio erucifolius

Rejected at α = 0.05

Rejected at α = 0.05

Rejected at α = 0.05

Low

wat

er p

lant

s Jacobaea vulgaris

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio inaequidens

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio erucifolius

Rejected at α = 0.05

Rejected at α = 0.05

conditional

Table 5-2: The hypothesis regarding the nutrient effects on dry samples and their conditions.

species 1: H0= High and low nutrient samples have same spectral reflectance in visible spectra.

2: H0= No shift in red edge infection point to longer wavelength due to nutrient available in leaves.

3: H0= Nutrient has no effect on lowering the spectral reflectance in SWIR.

Hig

h w

ater

pla

nts

Jacobaea vulgaris

Rejected at α = 0.05

Rejected at α = 0.05

Rejected at α = 0.05

Senecio inaequidens

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio erucifolius

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

55

Low

wat

er p

lant

s Jacobaea vulgaris

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio inaequidens

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

Senecio erucifolius

Rejected at α = 0.05

Rejected at α = 0.05

Not rejected

The third and the fourth objectives of this research were to build predictive models which estimate the concentration of PAs using reflectance spectroscopy and to test the accuracy of these models. This study concluded that, there are many specific wavelengths affected by PAs, however, it should be noted that most of the affected wavelengths are also affected by other known biochemicals (mostly affected by nitrogen, proteins and starch). These selected wavelengths are indirect relation with the PAs present in the leaves. There are two commonly selected wavelengths by PLSR models in fresh and dry Jacobaea vulgaris, which are possibly 2105 and 2355 nm wavelengths. There is one commonly selected by PLSR models in fresh and dry Senecio inaequidens, which is possibly 710 nm. The two commonly selected wavelengths in Jacobaea vulgaris are also common in dry Senecio inaequidens. Overall the results of this study showed that the accuracy of the predicted models was mostly week, it is possibly because of: 1) Very low concentrations of the Pyrrolizidine Alkaloids. PAs seem have a very

small contribution to the leaf spectral reflectances of the three species. 2) Different PA chemotypes present in the species. 3) Unusual reflectances of low nutrient plants. 4) The non normal data of Senecio erucifolius.

5.2. Recommendations

The following suggestions are made by this research. • Although ASD Integrating Sphere is a proper tool for measuring the

spectral reflectance of the plants in leaf level, but it should be taken in to account that measuring the dry leaves with this equipment is tricky. Since the pressure of the holder can break the dry leaves.

• Mean centre transformation improved the performance of PLSR models in some cases. More transformation techniques can be explored in future study, such as continuum removal and band depth normalization.

56

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7. Appendices

63

7.1. Appendix I: Shrub and leaf structure of the studied plants.

(a) Jacobaea vulgaris

(b) Senecio erucifolius

(c) Senecio inaequidens

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7.2. Appendix II: Testing the outliers.

Table i) Testing the outliers, z-scores values between the -3 to +3 are not outlier.

Code True value Z-score Code

True value Z-score

JVB01 469 -878 SEW07 0.009 -1.124 JVB02 4.843 -0.001 SEW08 0.013 -1.123 JVB03 13.527 2.018 SEW09 0.710 -0.961 JVB04 0.687 -0.966 SEW11 0.020 -1.121 JVB05 9.108 0.991 SEW12 0.010 -1.124 JVB06 0.272 -1.063 SEW13 0.957 -0.904 JVB07 3.555 -0.300 SEW14 0.010 -1.124 JVB08 2.644 -0.512 SEW15 0.377 -1.039 JVB09 2.693 -0.500 SEW17 0.011 -1.124 JVB10 0.631 -0.980 SEW18 0.013 -1.123 JVB11 0.581 -0.991 SEW20 0.010 -1.124 JVB12 1.210 -0.845 SEY01 0.352 -1.044 JVB13 1.373 -0.807 SEY02 2.535 -0.537 JVB14 7.339 0.580 SEY03 0.235 -1.071 JVB15 0.596 -0.988 SEY04 0.713 -0.960 JVB16 3.040 -0.419 SEY05 0.021 -1.121 JVB17 2.438 -0.559 SEY08 5.381 0.125 JVB18 0.548 -0.999 SEY12 2.614 -0.519 JVB19 3.064 -0.414 SEY15 0.248 -1.069 JVB20 0.845 -0.930 SEY16 0.977 -0.899 JVR01 5.319 0.110 SEY18 6.957 0.491 JVR02 10.404 1.292 SIB01 7.535 0.625 JVR03 6.433 0.369 SIB02 6.441 0.371 JVR05 2.897 -0.453 SIB03 5.004 0.037 JVR06 12.519 1.784 SIB04 12.653 1.815 JVR07 13.065 1.910 SIB05 9.314 1.039 JVR08 8.670 0.889 SIB06 5.282 0.101 JVR09 11.925 1.646 SIB07 0.789 -0.943 JVR10 8.246 0.790 SIB08 11.282 1.496 JVR11 11.105 1.455 SIB09 6.121 0.296 JVR12 9.112 0.992 SIB10 4.762 -0.019 JVR13 7.783 0.683 SIB11 7.714 0.667 JVR14 13.909 2.107 SIB12 12.596 1.802 JVR15 9.156 1.002 SIB13 4.473 -0.086 JVR16 10.051 1.210 SIB14 3.594 -0.291

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JVR17 12.715 1.829 SIB15 2.617 -0.518 JVR18 11.023 1.436 SIB16 4.046 -0.186 JVR19 8.247 0.791 SIB17 5.087 0.056 JVR20 9.585 1.102 SIB18 4.247 -0.139 JVW01 1.045 -0.883 SIB19 14.934 2.345 JVW02 1.178 -0.852 SIB20 10.998 1.430 JVW03 0.603 -0.986 SIR01 1.125 -0.865 JVW05 11.939 1.649 SIR02 1.307 -0.822 JVW06 4.867 0.005 SIR03 1.915 -0.681 JVW07 5.428 0.135 SIR04 3.545 -0.302 JVW08 5.492 0.150 SIR05 4.390 -0.106 JVW10 16.721 2.760 SIR06 0.622 -0.982 JVW11 2.341 -0.582 SIR07 3.773 -0.249 JVW12 0.310 -1.054 SIR08 2.104 -0.637 JVW13 0.702 -0.963 SIR09 0.410 -1.031 JVW14 1.516 -0.774 SIR10 6.962 0.492 JVW17 1.128 -0.864 SIR11 1.392 -0.803 JVW18 3.670 -0.273 SIR12 1.066 -0.878 JVW20 4.421 -0.099 SIR13 4.323 -0.121 JVY01 11.968 1.656 SIR14 0.607 -0.985 JVY02 9.819 1.156 SIR15 3.554 -0.300 JVY03 4.860 0.003 SIR16 3.303 -0.358 JVY04 4.809 -0.009 SIR18 3.943 -0.210 JVY05 8.943 0.952 SIR19 2.675 -0.504 JVY06 10.578 1.333 SIR20 2.135 -0.630 JVY07 12.416 1.760 SIW01 6.965 0.493 JVY08 6.320 0.343 SIW02 6.130 0.299 JVY09 15.937 2.578 SIW03 9.789 1.149 JVY10 10.902 1.408 SIW04 1.976 -0.667 JVY11 2.045 -0.651 SIW05 9.410 1.061 JVY12 4.838 -0.002 SIW06 1.837 -0.699 JVY13 5.860 0.236 SIW07 6.161 0.306 JVY14 6.254 0.327 SIW08 5.436 0.137 JVY15 11.510 1.549 SIW09 4.065 -0.181 JVY16 8.857 0.932 SIW10 12.470 1.772 JVY17 9.565 1.097 SIW11 2.082 -0.642 JVY18 10.444 1.301 SIW12 3.335 -0.351 JVY19 5.358 0.119 SIW13 4.524 -0.075 JVY20 5.039 0.045 SIW14 6.863 0.469 SEB02 0.010 -1.124 SIW15 5.688 0.196 SEB04 0.025 -1.120 SIW18 4.857 0.003 SEB08 0.037 -1.118 SIW19 15.633 2.507

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SEB09 0.033 -1.118 SIW20 12.603 1.803 SEB10 0.005 -1.125 SIY01 3.172 -0.389 SEB12 0.010 -1.124 SIY02 13.412 1.991 SEB14 6.577 0.403 SIY03 2.347 -0.581 SEB15 0.011 -1.124 SIY05 5.937 0.254 SEB16 0.010 -1.124 SIY06 1.052 -0.882 SEB19 0.015 -1.123 SIY07 4.948 0.024 SEB20 0.012 -1.123 SIY08 9.567 1.097 SER01 0.852 -0.928 SIY09 0.911 -0.914 SER02 4.432 -0.096 SIY10 2.058 -0.648 SER03 6.241 0.324 SIY11 1.222 -0.842 SER04 5.684 0.195 SIY12 14.167 2.167 SER05 0.587 -0.990 SIY13 13.458 2.002 SER07 1.053 -0.881 SIY14 3.892 -0.222 SER08 4.731 -0.026 SIY15 2.563 -0.531 SER12 0.212 -1.077 SIY16 4.541 -0.071 SER14 3.023 -0.424 SIY17 12.272 1.726 SER15 0.015 -1.123 SIY18 4.270 -0.134 SER16 0.378 -1.038 SIY19 3.381 -0.340 SER17 9.665 1.120 SIY20 5.374 0.123 SER18 0.956 -0.904 SIY16 4.541 -0.071 SER20 5.940 0.255 SIY17 12.272 1.726 SEW01 0.011 -1.124 SIY18 4.270 -0.134 SEW04 0.007 -1.125 SIY19 3.381 -0.340 SEW05 0.013 -1.123 SEW06 0.006 -1.125

Figure i) The boxplot of the PA values, for testing the outliers.

PA

20.00

15.00

10.00

5.00

0.00

Boxplot of PA values

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7.3. Appendix III: PA data.

Table i) The original PA data. Jacobaea vulgaris

PA mg/g

Senecio erucifolius

PA mg/g

Senecio inaequidens

PA mg/g

JVB01 0.469 SEB02 0.010 SIB01 7.535 JVB02 4.843 SEB04 0.025 SIB02 6.441 JVB03 13.527 SEB08 0.037 SIB03 5.004 JVB04 0.687 SEB09 0.033 SIB04 12.653 JVB05 9.108 SEB10 0.005 SIB05 9.314 JVB06 0.272 SEB12 0.010 SIB06 5.282 JVB07 3.555 SEB14 6.577 SIB07 0.789 JVB08 2.644 SEB15 0.011 SIB08 11.282 JVB09 2.693 SEB16 0.010 SIB09 6.121 JVB10 0.631 SEB19 0.015 SIB10 4.762 JVB11 0.581 SEB20 0.012 SIB11 7.714 JVB12 1.210 SER01 0.852 SIB12 12.596 JVB13 1.373 SER02 4.432 SIB13 4.473 JVB14 7.339 SER03 6.241 SIB14 3.594 JVB15 0.596 SER04 5.684 SIB15 2.617 JVB16 3.040 SER05 0.587 SIB16 4.046 JVB17 2.438 SER07 1.053 SIB17 5.087 JVB18 0.548 SER08 4.731 SIB18 4.247 JVB19 3.064 SER12 0.212 SIB19 14.934 JVB20 0.845 SER14 3.023 SIB20 10.998 JVR01 5.319 SER15 0.015 SIR01 1.125 JVR02 10.404 SER16 0.378 SIR02 1.307 JVR03 6.433 SER17 9.665 SIR03 1.915 JVR05 2.897 SER18 0.956 SIR04 3.545 JVR06 12.519 SER20 5.940 SIR05 4.390 JVR07 13.065 SEW01 0.011 SIR06 0.622 JVR08 8.670 SEW04 0.007 SIR07 3.773 JVR09 11.925 SEW05 0.013 SIR08 2.104 JVR10 8.246 SEW06 0.006 SIR09 0.410 JVR11 11.105 SEW07 0.009 SIR10 6.962 JVR12 9.112 SEW08 0.013 SIR11 1.392 JVR13 7.783 SEW09 0.710 SIR12 1.066 JVR14 13.909 SEW11 0.020 SIR13 4.323 JVR15 9.156 SEW12 0.010 SIR14 0.607 JVR16 10.051 SEW13 0.957 SIR15 3.554 JVR17 12.715 SEW14 0.010 SIR16 3.303

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JVR18 11.023 SEW15 0.377 SIR18 3.943 JVR19 8.247 SEW17 0.011 SIR19 2.675 JVR20 9.585 SEW18 0.013 SIR20 2.135 JVW01 1.045 SEW20 0.010 SIW01 6.965 JVW02 1.178 SEY01 0.352 SIW02 6.130 JVW03 0.603 SEY02 2.535 SIW03 9.789 JVW05 11.939 SEY03 0.235 SIW04 1.976 JVW06 4.867 SEY04 0.713 SIW05 9.410 JVW07 5.428 SEY05 0.021 SIW06 1.837 JVW08 5.492 SEY08 5.381 SIW07 6.161 JVW10 16.721 SEY12 2.614 SIW08 5.436 JVW11 2.341 SEY15 0.248 SIW09 4.065 JVW12 0.310 SEY16 0.977 SIW10 12.470 JVW13 0.702 SEY18 6.957 SIW11 2.082 JVW14 1.516 SIW12 3.335 JVW17 1.128 SIW13 4.524 JVW18 3.670 SIW14 6.863 JVW20 4.421 SIW15 5.688 JVY01 11.968 SIW18 4.857 JVY02 9.819 SIW19 15.633 JVY03 4.860 SIW20 12.603 JVY04 4.809 SIY01 3.172 JVY05 8.943 SIY02 13.412 JVY06 10.578 SIY03 2.347 JVY07 12.416 SIY05 5.937 JVY08 6.320 SIY06 1.052 JVY09 15.937 SIY07 4.948 JVY10 10.902 SIY08 9.567 JVY11 2.045 SIY09 0.911 JVY12 4.838 SIY10 2.058 JVY13 5.860 SIY11 1.222 JVY14 6.254 SIY12 14.167 JVY15 11.510 SIY13 13.458 JVY16 8.857 SIY14 3.892 JVY17 9.565 SIY15 2.563 JVY18 10.444 SIY16 4.541 JVY19 5.358 SIY17 12.272 JVY20 5.039 SIY18 4.270 SIY19 3.381 SIY20 5.374 SIY16 4.541

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SIY17 12.272 SIY18 4.270 SIY19 3.381

Table ii) Test on the normality of Senecio erucifolius.

Tests of normality of Senecio erucifolius Code Kolmogorov-Smirnov(a) Shapiro-Wilk Statistics df Sig Statistics df Sig SEB 0.3293 11 0.001508 0.6231 11 4.95E SER 0.1759 14 0.001508 0.8745 14 0.04861 SEW 0.3700 15 5.25E-06 0.6644 15 0.000106 SEY 0.15100 10 0.2 0.9392 10 0.544307

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7.4. Appendix IV: The spectral reflectances of all samples in fresh and dry level.

a) All samples of fresh Senecio inaequidens. b) All samples of dry Senecio inaequidens. c) All samples of fresh Senecio erucifolius. d) All samples of dry Senecio inaequidens.

(a) (b)

(c) (d)

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7.5. Appendix V: Nutrient effects on fresh samples, group one.

(a)

(b)

(c) Nutrient effects on fresh samples with high water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red curve: mean spectral reflectance of plants with high nutrient and high water treatment. Red dotted curves: its standard deviations. Blue curve: mean spectral reflectance of plants with low nutrient and high water treatment. Blue dotted curve: its standard deviations. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

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7.6. Appendix VI: Nutrient effects on fresh samples, group two.

(a)

(b)

(c) Nutrient effects on fresh samples with low water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red solid curve: mean spectral reflectance of plants with high nutrient and low water treatment. Red dotted curves: its standard deviations. Blue curve: mean spectral reflectance of plants with low nutrient and low water treatment. Blue dotted curves: its standard deviations. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

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7.7. Appendix VII: Nutrient effects on dry samples, group one.

(a)

(b)

(c) Nutrient effects on dry samples with high water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red solid curve: mean spectral reflectance of plants with high nutrient and high water treatment. Red dotted curves: its standard deviations. Blue solid curve: mean spectral reflectance of plants with low nutrient and high water treatment. Blue dotted curves: its standard deviations. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

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7.8. Aappendix VIII: Nutrient effects dry samples group two.

(a)

(b)

(c) Nutrient effects on dry samples with low water treatment. (a) Jacobaea vulgaris, (b) Senecio inaequidens and (c) Senecio erucifolius. Red solid curve: mean spectral reflectance of plants with high nutrient and low water treatment. Red dotted curves: its standard deviations. Blue solid curve: mean spectral reflectance of plants with low nutrient and low water treatment. Blue dotted curves: its standard deviations. Level of significance α =0.05: significant bands are marked with 1; non significant bands are marked with 0.

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7.9. Appendix IX: leave one out cross validation results.

Leave 1 out cross validation

Name Methods n factors R2 RMSE

Fres

h Sa

mpl

es

Jacobaea vulgaris

No preprocessing 73 4 0.32 32% Mean centre 73 4 0.33 58%

SG +1st derivative 73 2 0.23 54% SG +2nd derivative 73 2 0.38 56%

Senecio inaequidens

No preprocessing 74 2 0.08 69% Mean centre 74 2 0.14 66%

SG +1st derivative 74 2 0.09 69% SG +2nd derivative 74 1 0.11 71%

Senecio erucifolius

No preprocessing 44 2 0.09 141% Mean centre 44 2 0.12 140%

SG +1st derivative 44 2 0.19 141% SG +2nd derivative 44 1 0.07 147%

Dry

sam

ples

Jacobaea vulgaris

No preprocessing 73 8 0.61 43% Mean centre 73 5 0.33 57%

SG +1st derivative 73 2 0.34 56% SG +2nd derivative 73 1 0.05 69%

Senecio inaequidens

No preprocessing 73 1 0.009 73% Mean centre 73 1 0.09 71%

SG +1st derivative 73 1 0.007 73% SG +2nd derivative 73 1 0.01 72%

Senecio erucifolius

No preprocessing 43 1 0.05 156% Mean centre 43 1 0.06 148%

SG +1st derivative 43 1 0.02 155% SG +2nd derivative 43 1 0.13 146%