New Forests 25: 163–176, 2003. 2003 Kluwer Academic Publishers. Printed in the Netherlands.

Discrimination of viable and empty seeds of Pinuspatula Schiede & Deppe with near-infraredspectroscopy

´ *MULUALEM TIGABU and PER CHRISTER ODENDepartment of Silviculture, Forest Seed Science Center, Swedish University of Agricultural Sciences,

*˚Umea S-901 83, Sweden; Author for correspondence (e-mail: Mulualem.Tigabu@ssko.slu.se); (e-mail:per.christer.oden@ssko.slu.se; phone: 146 90 786 5904; fax: 146 90 786 58 96)

Received 13 December 2001; revised 15 January 2002; accepted in revised form 29 May 2002

Key words: NIR, NIRS, Partial Least Squares, Seed quality

Abstract. Sustainable forest production demands a continuous supply of high quality seeds for theproduction of seedlings in the nursery or for direct sowing. Here, we demonstrated the potential of nearinfrared spectroscopy as a rapid technique to discriminate viable and empty seeds of Pinus patulaSchiede & Deppe. Near infrared spectra were collected from single seeds in transmittance and reflectancemodes. To discriminate viable and empty seeds, multivariate classification models were developed withpartial least squares (PLS) regression using the digitized spectra as a regressor and a y-vector of artificialvalues (1 for viable and 21 for empty seeds) as a regressand. Viable and empty seeds were perfectlydistinguished by PLS models computed on full and selected transmittance spectroscopy data, while thosederived from ‘full’ NIR reflectance spectra recognized 96 % of viable and 88 % of empty seeds. Analysesmade on selected NIR reflectance spectra improved the classification rate of empty seeds to 100%.Difference spectra and PLS weights indicated that the origin of spectral differences between viable andempty seeds was attributed to differences in fatty acids and proteins that were totally absent in emptyseeds. The result shows the prospect of developing rapid filter-based sorting equipment that can easily beautomated.

Introduction

Sustainable forest production is the prime concern today because of wide spreadenvironmental problems, on the one hand, and an ever-increasing demand of woodand wood products, on the other. The latter is, especially immense in the tropicswhere much of the forests have been decimated by unprecedented rate of deforesta-tion (Food and Agricultural Organization 1997). This challenge can be met byprotecting the remaining relict patches as well as establishment of new plantationsof various forms. Among others, the success of afforestation / reforestation endeavorhinges on continuous supply of high quality seeds for the production of seedlings innurseries or for direct sowing out in the field. Seed quality often encompasses all thephysical, biological, pathological and genetic attributes that affect forest reproduc-tion (Basu 1994). In many conifers, for example, production of low filled-viableseed due to pollination failure or post-zygotic degeneration is a major problem thatreduces the quality of seed lots (e.g. Owens et al. (1990), El-Kassaby et al. (1993)).To ensure seed lot quality, several seed handling methods have been developed.

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For example, the Pressure-Vacuum (PRE-VAC) method was developed to removephysically- and insect damaged seeds (Lestander and Bergsten 1985), X-radiog-raphy for assessing filled, empty, insect-, and physically damaged seed (Internation-al Seed Testing Association 1999). The IDS (Incubation, Drying and Separation)technique has been used to upgrade the quality of seed lots by removing empty anddead-filled seeds (Simak 1981, 1984; Bergsten 1988; Bergsten and Sundberg 1990;Downie and Bergsten 1990; Downie and Wang 1992; Falleri and Pacella 1997).Wealso showed the feasibility of the IDS technique to remove such unproductive seedsfrom a seed lot of Pinus patula (Demelash et al. 2002). However, the success of theIDS method varies from species to species and complete separation is far from beingachieved. This could be due to differences in water uptake by dry seeds, waterholding capacity of individual seeds during drying, and the inadequacy of densitygradient during separation. Consequently, a better method is still highly needed that

´ensures complete separation. Lestander and Oden (2002) recently modified theS-step in IDS using near infrared transmittance spectroscopy and showed a completeseparation of dead-filled and filled-viable seeds of Pinus sylvestris.Near infrared spectroscopy (NIRS) is a technique that can detect and measure

chemical composition and moisture in biological materials based on the absorptionof near infrared (NIR) radiation by bonds between light atoms, such as C - H, O - Hand N - H. These bonds generally have high vibrational frequencies, which result inovertones and combination bands that are detectable in the NIR region, 780–2500nm (Osborne et al. 1993). The technique is unusually fast compared to otheranalytical techniques, nondestructive, often needs no or minimal sample prepara-tion, and detects the concentration of the analyte exceeding ca. 0.1% of the totalcomposition. It has become a popular analytical tool in various fields of study (e.g.Burns and Ciurczak (2001)). In crop production, for example, it has been used tocharacterize moisture content and chemical composition of grains (e.g. Norris andHart (1965), Ben-Gera and Norris (1968), Campbell et al. (1997), Pazdernik et al.(1997), Delwiche (1998), Muhammad and Abu-Bakar (1998), Sato et al. (1998),Velasco et al. (1998), Kays et al. (2000)). Soybean, rice and wheat varieties weresuccessfully classified by NIRS (Delwiche and Massie 1996; Kwon and Cho 1998;Turza et al. 1998). It has also been proven effective in detecting internal insectinfestation in kernels of wheat and rice (Ridgway and Chambers 1996, 1999;Ghaedian and Wehling 1997; Dowell et al. 1998, 1999; Baker et al. 1999).We alsodemonstrated the potential of NIRS to detect internal insect infestation in forest tree

´seeds (Tigabu and Oden 2002).Thus, the objective of our study was to evaluate the potential of NIRS to

distinguish viable and empty seeds of P. patula. The underlying hypothesis of thestudy was viable and empty seeds could be discriminated based on storagecompounds that are found only in viable seeds.

Material and methods

Seed samples

Seeds of Pinus patula were collected from a plantation in Southeast Ethiopia,

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transported to the Swedish University of Agricultural Sciences, Department of˚Silviculture, Forest Seed Science Centre, Umea, and stored at 5 8C and ca. 6%

moisture content in glass bottle until the study was carried out. Two seed lotsdiffering in time of collection were used in the present study. A sample of seedsfrom the seed lot collected in 1999 was used as calibration set to develop theclassification model while samples from those collected in 2000 as validation sets totest the prediction performance of fitted models. Seeds were identified as viable orempty by taking a series of X-ray images. Seeds with visible embryonic axis andmegagametophyte were recognized as viable seeds while empty seeds werecharacterized by absence of megagametophyte and embryo.The viable seed fractionwas also subjected to germination test after scanning and all of them weregerminated.

Acquisition of NIR spectra

NIR reflectance spectra, expressed in the form of log (1 /R), were collected fromsingle seed with NIRSystems Model 6500 spectrophotometer (NIRSystems Divi-sion of Perstorp Analytical, Silver Spring, MD) from 400 to 2498 nm at 2 nminterval. Individual seed was placed on a black metallic bar with an oval-shapeddepression (ca. 2 3 1 mm) and scanned by tightly screwing the fiber optic probeagainst each seed. Since the background metallic bar has a negligible reflectance,such an arrangement enabled us to collect reflectance from the individual seed only.The instrument measures diffuse reflectance and thirty-two monochromatic scanswere averaged from each seed. Reference measurements were taken on a ceramicplate after every 10 scans.NIR transmittance spectra, expressed in the form of log (1 /T), were collected

from single seed with 1225 Infratec analyzer (FOSS Tecator, Sweden) from 850 to1048 nm at 2 nm interval. Individual seeds were placed in single seed adapter at 20fixed positions and the average of 32 successive scans from each seed was taken.Prior to scanning of every sample set, reference measurement was taken on standardbuilt-in reference of the instrument.

Data pre-treatment

NIR spectroscopy data are not usually amenable for direct analysis due to unwantedsystematic variation emanating from light scattering, base line shift, instrumentaldrift, and path length differences. Such systematic noise should be removed from theraw spectral signals to prevent them from dominating over the chemical signals.Hence, the reflectance spectroscopy data set (log 1/R) was filtered with orthogonalsignal correction (OSC) method. OSC is a spectral filter that solves the problem ofunwanted systematic variation in the spectra by removing components, latentvariables, orthogonal to the response (y-variable) calibrated against. It is a PLS-typeof filter where the weights in OSC are calculated to minimizing the covariancebetween the spectral data (X) and the response (y). Components, orthogonal to y,containing unwanted systematic variation are then subtracted from the originalspectral data X, to produce a filtered descriptor matrix containing the variation of

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interest. Since no true y-values existed in our data set, an arbitrary value of 1 forsound and 21 for empty seeds was assigned. Consequently, one OSC-componentwas removed and the remaining sum of squares was 67.38%. A detail description ofOSC-algorithm can be found in Wold et al. (1998).Since the transmittance spectroscopy data set (log 1/T) does not satisfy the

conditions for OSC-filtering, standard normal variate transformation (SNV) wasused to remove the multiplicative effect of scatter and particle size on an individualobject basis (Barnes et al. 1989). The SNV transformation was performed accordingto the following formula:

]]]2¯O(x 2x )i i¯ ]]]x 5(x 2x ) /i, corr i i œ K21

where x 5 the filtered absorbance value for each object and wavelength i, x 5i, corr i¯the original absorbance value, x 5 the mean value across the object direction and Ki

5 number of X-variables (wavelength channels). The actual filtering can be referredto as mean centering and scaling to unit variance in the object direction.

Multivariate classification models

To discriminate viable and empty seeds, multivariate classification models weredeveloped with partial least squares (PLS) regression using the digitized spectra asregressor and a y-vector of artificial values (1 for viable and 21 for empty seeds) asregressand. Detail description of the PLS regression method can be found inMartens and Næs (1989) and Eriksson et al. (1999). For developing the finalclassification models, transmittance spectra (850–1048 nm) from 100 viable andempty seeds each and reflectance spectra (1100–2360 nm)) from 80 viable andempty seeds each were used. The shorter wavelength region of NIR reflectancespectra was discarded because it appeared to carry very little useful information.Since the longer wavelength region of the reflectance spectra is usually noisy, it wasnot included in the model too. PLS models were also calculated on selectedabsorption bands that showed interesting peaks in the full spectrum models. All PLSmodels were developed on mean-centered data sets, and the computation was madeusing Simca-P software (Anonymous (2000), Copyright: Umetrics AB, Sweden).The number of significant PLS factors to build the model was determined by

cross-validation. The cross validation method emulates to predict unknown samplesby using the training data set itself. To do this, one seventh of the training sets weretaken out from the data set at a time, a calibration was developed with the remainingsamples, and the removed samples were predicted. This was repeated for allsamples, and a calibration was selected using the number of significant factorsrecommended by the software. A factor was considered significant if the ratio of the

ˆ2prediction error sum of squares [PRESS5o(Y2Y) ] to the residual sum of squares

of the previous dimension (SSY) was statistically smaller than 1.0, or if the2predictive power, Q , (1.0–PRESS/SS) was larger than a significant limit.

Finally, the computed models were used to predict samples in the external test

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sets that were consisted of 25 viable and empty seeds each drawn from a separateseed lot collected in 2000. Seeds were considered viable if predicted values weregreater than a rejection threshold, and all others were considered empty. To simplifypresentation of results, the rejection threshold was set to 0.0 for all tests. Based onthe predicted values, the classification rate of viable and empty seeds in test sets wascomputed for each model. Classification rate is defined as the proportion of numberof viable and empty seeds predicted correctly to the total number of each fraction intest sets.

Analysis of NIR spectra

Spectral region sensitive to differences between viable and empty seeds wasdetermined from PLS weights and difference spectra. PLS weights indicate whichabsorption bands are contributing to the explanation of the variation between thetwo groups. Thus, weight plots can be compared to NIR absorption of specificfunctional groups to get the unique chemical information distinguishing viable andempty seeds. Difference spectra, computed by subtracting the average spectrum ofviable seeds from those of empty, also indicate regions of interest.

Results and discussion

Model overview

PLS models were calculated to classify viable and empty seeds of Pinus patulabased on NIR spectroscopy. The calculated PLS models described more than 85%

2of the spectral variation (R X) that, in turn, explained 78–93% of the variation2between viable and empty seeds (R Y) with 1 or 2 significant PLS factors according

to cross-validation (Table 1). The overall prediction ability for the calibration set2 2(Q cv) as well as for the external test sets (Q test) was also high for all models

(.75%). This indicates that the calculated PLS models were efficient to describe thevariation between viable and empty seeds of P. patula. The unexplained variation inlog 1/R data set was slightly larger than that of log 1/T data set. This could beattributed to some measurement errors during collection of reflectance spectra.Unlike the transmission spectroscopy scanning the entire surface of the seed, someseed samples could be partially probed because of difficulties in securing the fiberoptic probe fully on each seed during collection of NIR reflectance spectra. Ingeneral, the result showed that NIR spectroscopy data contain much informationthat can be used to distinguish viable and empty seeds of P. patula.PLS score plots for the first two factors showed a clear grouping of viable and

empty seeds in the calibration set (Figure 1). Note that the second factor in the log1/R data set was not significant; just for making scatter plot possible. Thecorresponding PLS weight plots are shown in Figure 2. In the case of transmittancespectroscopy, wavelength regions between 890–950 and 1000–1048 nm showedbroader absorption bands in both first and second PLS factors. The second factor

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Table 1. Description and statistical summary of PLS models computed based on NIR transmittance (log1/T) and reflectance (log 1/R) spectra.

Data set /Bands Model parametersa 2 b 2 c 2 d 2 eA R X R Y Q Qcv test

NIR transmittance spectra (log 1/T)850–1048 2 0.983 0.930 0.928 0.998890–950 2 0.993 0.922 0.919 0.9911000–1048 2 0.988 0.923 0.921 0.999NIR reflectance spectra (log 1/R)1100–2360 1 0.875 0.852 0.851 0.8601100–1300 2 0.999 0.782 0.778 0.9941300–1600 2 0.999 0.845 0.841 0.9151600–1850 1 0.997 0.802 0.800 0.9171850–2050 2 0.999 0.850 0.847 0.8972050–2360 2 0.998 0.826 0.823 0.808

a 2 bA 5 number of significant PLS factors to build the model, R X 5 the explained spectral variation (122 cSS(E) /SS(X)), R Y 5 the variation between viable and empty seeds explained by the model (1 22 d 2 eSS(F) /SS(Y)), Q 5 the predictive power of a model according to cross validation, Q 5 thecv test

predictive power of a model for external test set

especially described the phenomenon in 1000–1048 nm. The reflectance spec-troscopy data showed several absorption peaks along its wavelength range, notablyin 1100–1300, 1600–1850, 1850–2050, and 2050–2360 nm.

Classification rate

Viable and empty seeds were perfectly classified using both full and selected NIRtransmittance spectra (Table 2). In contrast, a PLS model computed based on ‘full’NIR reflectance spectra (1100–2360 nm) did not result in a clear-cut separation ofviable and empty seeds of P. patula in the test set (Table 3). Analyses made onselected bands of NIR reflectance spectra improved the classification rate of emptyseeds in the test set; especially in two regions, 1100–1300 and 1600–1850 nm(Table 3). The remaining absorption bands either reduced the proportion of viableand empty seeds or slightly increased the percentage of empty seeds classifiedcorrectly compared with the classification rate by the full spectrum model. Viableseeds that were misclassified might have smaller size, and hence failed to produce athreshold amount of spectral signal due to partial probing. On the other hand, thosemisclassified empty seeds might have produced strong signal due to the presence ofwrinkled rudimentary tissues that were observed from the cutting test made after thescanning. It was difficult to clearly see such seeds on the X-ray image as the imageappeared totally dark for all empty seeds. Despite the existence of such tissues,empty seeds were completely distinguished from viable seeds at some selectedabsorption bands. This could be related to differences in the relative concentrationof chemicals showing typical characteristic absorption at these regions.

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Figure 1. Score plots for the first two PLS factors (t versus t ) based on full spectrum models showing1 2

clustering pattern of viable and empty seeds in the calibration sets.

Interpretation of absorption bands

The difference spectra (Figure 3), which are analogous to PLS weight plots,indicated that sound seeds absorbed more of the incident radiation than empty seeds.The origin of spectral difference between the two fractions is, thus, attributed todifferences in the availability of reserve compounds. The major reserve compoundsin pine seeds are oil, protein and carbohydrate (mainly starch), which account 48, 35and 6% of the total seed composition respectively (Bewley and Black 1994; Miqueland Browse 1995). The dominant fatty acid compositions of the oil from Pinuspatula seeds are linoleic, 9,12–18:2 (46.85%), pinolenic, 5,9,12–18:3 (19.96%),and oleic, 9–18:1 (16.26%) acids (Wollf et al. 1997). The same authors alsoreported several D5-olefinic acids, the sum of which accounts ca. 26.33% of the total

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Figure 2. PLS weight plots indicating absorption bands contributed to the classification of viable andempty seeds of Pinus patula. Note that only one significant PLS factor was used to develop the modelbased on NIR reflectance spectra.

Table 2. Classification rates of viable and empty seeds of Pinus patula in external test set by PLS modelsderived from full and selected bands of NIR transmittance spectra (log 1/T).

Viable seeds Empty seedsRegion (nm) % member % non-member % member % non-member RMSEP*

850–1048 100 0.0 100 0.0 0.362890–950 100 0.0 100 0.0 0.4951000–1048 100 0.0 100 0.0 0.364

* RMSEP 5 root mean square error of prediction

fatty acids, and two major saturated acids, palmitic (16:0) and stearic (18:0) acids.NIR absorption bands have interpreted on the basis of this chemical background.The observed NIR absorption band in 890–950 nm, the center being at 926 nm, is

characteristic of the third overtone of C–H stretching vibration of various chemicalgroups, notably CH , CH , and the peak at 928 nm specifically assigned to oil3 2

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Table 3. Classification rates of viable and empty seeds of Pinus patula in external test set by PLS modelsderived from full and selected bands of NIR reflectance spectra (log 1/R).

Viable seed Empty seedRegion (nm) % member % non-member % member % non-member RMSEP*

1100–2360 96 4 88 12 0.6701100–1300 96 4 100 0 0.4431300–1600 92 8 88 12 0.6921600–1850 96 4 100 0 0.5151850–2050 92 8 92 8 0.6562050–2360 92 8 80 20 0.790

* RMSEP 5 root mean square of error of prediction

Figure 3. Difference spectra of viable and empty seeds of Pinus patula, indicating regions of interest.

(Osborne et al. 1993). Norris (1983) showed high correlation for fat in pork usingthe second derivative of log 1/T at 931 nm. The second important absorption band,1000–1048 nm with a peak centered on 1028, corresponds the combination of N–Hsecond overtone stretching vibration and C–H stretch and deformation (Osborne etal. 1993). Molecules responsible for absorption in this region are mainly proteinmoieties like ArNH (aromatic amino acids) and NH , but CH and oil also show2 2 3

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characteristic absorption. Thus, NIR transmittance spectroscopy has detected fattyacids and proteins, which in turn are used by the model to discriminate viable andempty seeds.The 1100–1300 nm region of the NIR reflectance spectra shows a single

absorption band centered near 1206 nm and a small bump in the vicinity of 1170nm. This absorption band is characteristic of the second overtone of C–H stretchingvibration of various functional group:–CH ,–CH ,–CH5 CH- (Shenk et al. 2001).2 3

Osborne et al. (1993) has described that the major absorption band in fat or oil is dueto a long chain fatty acid moiety that gives rise to CH second overtone at 1200 nm;2

and the band near 1180 nm has been assigned as the second overtone of thefundamental C–H absorption of pure fatty acids containing cis double bonds, e.g.oleic acid (Sato et al. 1991).The 1300–1600 nm regions presents two very weak bumps around 1394 and 1510

nm, which corresponds to C–H combination and first overtone of N–H stretchingvibration due to absorption by CH and protein moieties (Shenk et al. 2001).2

Hourant et al. (2000) reported similar weak bands centered near 1392 and 1414 nmthat contained little useful information for oil and fat classification. They argued thatthe absorption band of the first overtone of water highly perturbed other absorptionbands at this wavelength region. Protein moieties could be the possible source ofvariation for the detection of viable and empty seeds in this region in our study(Table 3).The 1600–1850 nm shows two main peaks in the vicinity of 1716 and 1760 nm.

The region is characteristic of the first overtone of the C–H stretching vibration ofmethyl and methylene groups (Shenk et al. 2001). Numerous authors have studied

¨this region of the NIR spectra (e.g. Reinhardt and Robbelen (1991), Sato et al.(1991),Velasco et al. (1996), Daun and Williams (1997), Sato et al. (1995, 1998),Velasco et al. (1997), Hourant et al. (2000)). Cho and Iwamoto (1989) correlated theabsorption bands at 1710 and 1725 nm to linoleic and oleic acids respectively whileSato et al. (1991) reported maximum peak for triolein (18:1) in the vicinity of 1725nm, for trilinolein (18:2) near 1717 nm, and for trilinolenin (18:3) near 1712 nm.The absorption bands observed in our study could, therefore, be correlated to thedominant fatty acids in P. patula seeds: linoleic, pinolenic and oleic acids (Wollf etal. 1997).The 1850–2050 nm region shows one absorption band, centered near 1926 nm

that arises from C 5 O stretch second overtone, combination of O–H stretch andHOH deformation, as well as O–H bend second overtone. Several compounds,notably protein, starch and water, show characteristic absorption in this region(Shenk et al. 2001). We believe that the absorption band in this region correlatesmore to water than to other compounds because viable seeds often retain morebound water than empty seeds. This phenomenon was the principle behind the IDS(Incubation, Drying and Separation) technique developed by Simak (1981, 1984) toremove empty and dead-filled seeds of pines.The last part of the reflectance spectrum, 2050–2360 nm, contains two main

bands near 2308 and 2346 nm as well as smaller bumps in the vicinity of 2144 nm.These absorption bands are characteristics of CH stretch bend combinations as well2

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as other vibrational modes (Osborne et al. 1993; Shenk et al. 2001). Hourant et al.(2000) showed a positive correlation between total polyunsaturated fatty acids (18:2and 18:3) in several oil crops and absorbance in the 2050–2230 nm region. Besides,the NIR spectra of groundnut oil and liquid paraffin showed typical absorptionbands at 2310 and 2345 nm (Osborne et al. 1993). Thus, the absorption bandsobserved in our study could be attributed to several polyunsaturated fatty acids, suchas D5-olefinic acids that account 26.33% of the total fatty acid compositions in P.patula seeds (Wollf et al. 1997).

Conclusions

Classification of viable and empty seeds of P. patula using near infrared transmitt-ance and reflectance spectroscopy was successful. The technique is rapid and moreefficient as it takes a fraction of a minute to scan a single seed, and no samplepreparation is needed unlike, for example, IDS technique. Besides it can easily beextended to other species as the principle is based on a universal phenomenon, i.e.,reserve compounds that are found only in viable seeds are detected by NIRS. Thehigh classification accuracy based on selected NIR absorption bands suggests theprospect of developing filter-type-sorting equipment that is less expensive thanmonochromatic grating. Therefore, a continued emphasis should be given towardsdeveloping an automated filter-based sorting instrument for large-scale seed clean-ing operations.

Acknowledgements

The research was financed by the Kempe foundation. Research Group for Chemo-˚metrics, Department of Chemistry, Umea University is acknowledged for allowing

us to use their NIRSystems Model 6500 spectrophotometer. Our special thanks go to¨ ¨Prof. Michael Sjostrom for reading and commenting the manuscript. Authors

convey their appreciation to the Ethiopian Forestry Research Directorate and thenational tree seed project for the assistance during seed collection. Finally, anony-mous reviewers are highly appreciated for the valuable and constructive commentsto improve the manuscript.

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