Euphytica 90: 359-363, 1996 .

359© 1996 Kluwer Academic Publishers . Printed in the Netherlands .

An efficient method for screening seed colour in Ethiopian mustard usingvisible reflectance spectroscopy and multivariate analysis

Leonardo Velasco*, Jose M . Fernandez-Martinez & Antonio De HaroInstituto de Agricultura Sostenible (CSIC), Apartado 4084, E-14080 Cordoba, Spain; (*author vorcorrespondence)

Received 24 July ; accepted 14 February 1996

Key words: common factor analysis, Ethiopian mustard, seed colour, visible reflectance spectroscopy

Summary

Seed colour is considered as an important breeding objective in Ethiopian mustard and other Brassica species,but its measurement is usually subjective rather than objective . For this reason we studied the potential of visiblereflectance spectroscopy (VRS) combined with factorial analysis and multivariate calibration to predict seed colourin seeds of Ethiopian mustard . A total of 8331 samples were screened in the range 400-2500 nm with a nearinfra-red reflectance spectroscopy (NIRS) spectrophotometer . A common factor analysis was performed using1148 samples and spectral data from 400 to 700 run . It was found that four common factors explained 99 .9% ofthe variance. Three of these factors were associated with colour characteristics of the seeds . Factor 1 separatedyellow, yellow-brown and brown seeded samples, factor 3 separated brown from reddish-brown samples and factor4 separated complete yellow from greenish-yellow samples . Calibration equations were developed for each factor,which allowed seed colour in those samples not included in factor analysis to be predicted . This study demonstratedthat seed colour prediction can be incorporated into NIRS routine analysis with instruments that incorporate thevisible spectral region .

Introduction

Seed colour is one of the most important traits tak-en into account in breeding programmes for Brassicaoilseed crops, due to its influence on several aspects ofseed quality. On the one hand, dark seeds cause dark-coloured oil, lecithin and meal (Jonsson & Bengtsson,1970). On the other hand, yellow seeds have thinnerseed coats, which is associated with a higher oil con-tent in the seed, higher protein and lower fiber contentsin the meal (Stringam et al., 1974; Jonsson, 1977 ;Downey & Ri bbelen, 1989 ; Chen & Heneen, 1992 ;Rashid et al., 1994; Slominski et al., 1995). Thesefacts have meant that the development of yellow cul-tivars is currently an important breeding objective inthese species .

Seed colour variation from dark-brown to reddish-brown and yellow-brown to completely yellow arecommon in B. rapa, B . juncea andB. carinata (Zaman,1989). No naturally yellow-seeded forms of B. napus

exist, however (Rashid et al ., 1994) . Therefore, effortsto develop pure yellow-seeded cultivars of this specieswith good agronomic characteristics have been made inthe last years. As a result several yellow-seeded geno-types have been obtained (e .g . Liu, 1983 ; Bechyne,1987; Chen et al., 1988; Rashid et al ., 1994). Manyexperiments designed to study the mode of inheritanceof seed coat colour in several Brassica species havealso been made (e.g . Stringam, 1980 ; Getinet et al .,1987; Zaman, 1989 ; Rawat, 1989; Chen & Heneen,1992; Henderson & Pauls, 1992). Likewise, studies ontemperature effects on seed colour have recently beenperformed (Van Deynze et al., 1993).

Most of the breeding programmes carried out inorder to develop yellow-seeded genotypes, to studythe relationship between seed colour and nutritionalcharacteristics, or to determine the inheritance of seedcoat colour, have been based on the visual determina-tion of seed colour. Thus, seeds are visually classifiedas black, dark-brown, reddish-brown, yellow-brown,

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completely yellow, etc . This method is fast and use-ful for a rapid classification of a reasonable numberof samples, but when thousands of samples have tobe screened or when a simple classification is insuffi-cient, an accurate quantification is needed . For examplein genetic and nutritional studies or investigations ofenvironmental effects on seed colour, a rapid and moreobjective procedure is required .

An important step towards the fast and objec-tive determination of seed coat colour in Brassicahas been the work of Van Deynze & Pauls (1994)using reflectance spectroscopy to quantify seed colour .The total reflectance from 550 nm to 650 nm mea-sured in a scanning spectrophotometer was used toclassify samples . The highest reflectance values werefound in yellow-seeded samples and the lowest valueswere obtained in black samples, while brown sam-ples showed intermediate values . Total reflectance val-ues were used to calibrate a near infra-red reflectancespectroscopy (NIRS) filter instrument modified with670 and 710 nm filters . The main advantage of thismethod is that it can be incorporated into the routineanalysis of nutritional traits by LAIRS currently intro-duced in an increasing number of laboratories . Thismethod allows yellow, brown and black seeds to bedistinguished and to establish a gradation within eachclass. However, it does not detect other colours usu-ally present in some Brassica species, such as red-dish colours and also greenish coloration in seedsfrom plants harvested before full ripeness. Further-more two different instruments were necessary to pre-dict seed colour. Consequently, for a more completecharacterization of seed colour in seeds of Brassicausing reflectance spectroscopy, a second step is neces-sary. The present work has been directed towards thisobjective, developing a procedure that includes vis-ible reflectance spectroscopy (VRS), common factoranalysis and multivariate calibration .

Material and methods

The progenies of 8331 individual plants of Ethiopi-an mustard (Brassica carinata Braun) were scannedby LAIRS in the course of a breeding programmefocused on the development of double zero Ethiopi-an mustard genotypes . A high proportion of themwere yellow-seeded but considerable variation in seedcolour was visually apparent, including also yellow-brown, greenish-yellow, dull reddish-brown, lightreddish-brown and brown seeds . Furthermore a wide

gradation within each colour and segregating plantswith seeds of different colours was observed .

Approximately 3 g of intact seeds from each plantwere scanned in a near infra-red reflectance spec-troscopy monochromator instrument (LAIRS Systemmodel 6500) with a silicon detector that collects thevisible and the LAIR spectrum in the range 400-2500nm. A total of 1148 samples were selected to performa common factor analysis using the Select Samplesalgorithm .

Factor analysis was carried out using the SAS statis-tical programme (Statistical Analysis System InstituteInc. 1990). A hundred absorbance values in the spectralregion between 400 and 700 nm were used as observedvariables in this analysis. Factor extraction was per-formed using principal factor analysis . The maximumabsolute correlation of each variable with any othervariable was used as the prior communality estimates .Four common factors that accounted for 99 .9% of thecommon variance were retained . Varimax and Promaxrotations were tried .

After factor analysis, 200 out of the 1148 samplesevaluated were selected according to their values forthe new four variables (common factors) and they werevisually classified for seed colour in order to associatethe common factors with colour characteristics of theseeds . These samples were then gathered into a calibra-tion set and modified partial least squares regressionwithout either mathematical transformation of the orig-inal spectral data or scatter correction was performedusing the common factors as variables . These calibra-tions were made in order to predict the values of sam-ples not included in the factor analysis for the differ-ent factors, and consequently assess their colour . Seedcolour of one hundred of these samples was assessedvisually in order to test the reliability of this procedure .

Results and discussion

Four common factors, explaining 94.8, 3 .5, 1 .1 and0.5% respectively of the common variance, wereretained. These four factors together accounted for99.9% of variance. Factor scores for each sample werefirst computed without any factor rotation . Scores forthe 200 samples selected for visual classification ofseed colour are shown in Figures 1 and 2 . Factor1 allowed us to distinguish amongst yellow, yellow-brown and brown seeded samples. Values of factor 1lower than -0 .5 corresponded to yellow samples, valueshigher than 0 .5 resulted in brown samples and interme-

M

O

A

B

D • f

-4

I

I

1

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-3

-2

-1

0

FACTOR 1

E

I

1

Figure 1 . Plot of factor 1 vs factor 3 in a set of 200 samples .A-yellow seeded samples; B-yellow brown and segregating sam-ples; C-brown samples ; D,E-reddish-brown samples .

diate values were associated with both yellow-brownsamples and segregating samples with seeds of differ-ent colours .

Factor 1 was not only found to be useful to clas-sify samples with different seed colours but also toestablish levels within each group . Thus, within thegroup of yellow-seeded samples it was observed thatthe most negative values were associated with a lighteryellow colour. Therefore, dull yellow-seeded sampleshad values between -0.5 and -1 .5 while light yellow-seeded samples had values below -1 .5 .

No relationship was found between factor 2 andseed colour. However factor 3 was found to be use-ful in separating reddish-brown from brown samples .Reddish-brown samples had values of factor 3 lowerthan -1.8 (Figure 1) . Similarly to factor 1, a grada-tion was found in the relationship between factor 3 andred colour. Values from -1 .8 to -2 .5 corresponded todull reddish-brown seeds, and values lower than -2 .5corresponded to light reddish-brown samples .

Factor 4 was associated with green colour. Some ofthe samples selected for visual determination of theircolour were greenish-yellow, due to a high chlorophyllcontent in the seed resulting from plants being harvest-ed prior to full ripeness. These samples were separatedfrom the rest by factor 4 . All these samples had valueslower than -1.7 (Figure 2) .

After interpretation of factors without rotation, theywere rotated using both Varimax and Promax rotations .Neither rotation gave results easier to interpret thannon-rotated factors . Because of this we used the resultsof factor analysis without rotation for the rest of thestudy.

FACTOR I

Figure2 . Plot of factor 1 vs factor 4 in a set of 200 samples . A-yellowsamples ; B-yellow brown and segregating samples ; C-brown sam-ples; D-greenish-yellow samples.

As a result of common factor analysis it can beconcluded that using three factors it was possible toseparate the samples according to their seed colour.Factor 1 allowed us to separate the samples in threegroups: yellow-seeded samples with values lower than-0.5, yellow-brown samples with values between -0 .5and 0.5 and brown samples with values higher than 0 .5 .Within each group it was possible to detect reddish andgreenish samples by means of low values of factor 3and factor 4 respectively .

As factor analysis was performed on 1148 out ofthe 8331 samples of the initial set, it was necessaryto expand the results of this analysis to the remainingsamples. There were two possibilities : on the one hand,once we knew that factor analysis was reliable enoughto classify mustard samples according to their colour,we could repeat the analysis using all samples . On theother hand, taking into account that common factors arecombinations of original absorbance values at differ-ent wavelengths, calibrations for these factors shouldbe highly accurate in theory . The first option impliesthe repetition of the factor analysis when new samplesare scanned. Furthermore a large amount of computermemory is necessary to perform a factor analysis withsuch a large number of samples. As the 1148 samplesselected for factor analysis were representative of thetotal population we decided to calibrate LAIRS usingfactors 1, 3 and 4 as variables. This approach has twoprincipal advantages: on the one hand, new samplescan be predicted with little effort, while on the otherhand the incorporation of these new equations to rou-tine analysis allows seed colour in mustard samples

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-6 1

1

1 1 1

1

2

-4

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-2

-1

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1

2

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Table 1 . Modified partial least squares regression statistics for factors 1, 3 and 4

Variable

n

Mean

SD

Factor 1 200 -0.173 1 .164Factor 3 200 -0 .036 1 .233Factor 4

200

0.226

1 .535

to be estimated when they are being scanned for theanalysis of other quality traits .

Calibrations were performed in the range 400-700 nm using modified partial least squares regressionwithout either mathematical transformation or scattercorrection on a calibration set of 200 samples . Cal-ibration statistics were characterized by a coefficientof multiple determination (R2 ) higher than 0 .99 in allcases (Table 1) . The probable reason for these highR2 values is that common factors are really a com-bination of absorbance values from the same spectralrange in which calibration was performed . Thereforewe calibrated VRS data for variables that were in facta combination of the same VRS data, what minimizedthe calibration error.

A total of 7183 samples not included in factoranalysis were estimated for the 3 factors with the equa-tions obtained . In order to test the predictive ability forseed colour of the three factors estimated with NIRS,100 samples were randomly selected and their colourwas determined visually . Based on their factors val-ues these samples were classified into colour groups .There were 64 samples completely yellow, 14 yellow-brown, 3 .greenish-yellow, 13 dull reddish-brown, 3light reddish-brown and 3 brown. When both visualand factorial predictions were compared, total agree-ment was observed between the observed and predictedcolour of each sample.

The present research work shows the reliability ofreflectance spectroscopy for the assessment of seedcolour in B. carinata . This technique was used for theprediction of three seed colours (yellow, brown andblack) in B. napus (Van Deynze & Pauls, 1994) bydetermining total reflectance in the range from 550 to650 nm in a scanning spectrophotometer and the useof these values to calibrate a NIRS filter instrumentmodified with 670 and 710 nm filters. The methoddeveloped in this research introduces a new method-ology based on factor analysis in the range 400-700nm, that allowed us to classify samples of B. carinata

n- number of samples; SD- standard deviation ; R2-coefficient of multiple determination; SEC--standard error ofcalibration ; SECV-standard error of cross validation (four groups) ; I-VR=proportion of the total variance explainedby NIRS in cross validation .

according to seed colour, including reddish and green-ish colours that were not identified in previous work .The results obtained in factor analysis were employedto calibrate an NIRS instrument using only the visibleregion, while in the previous method calibration wasperformed using 17 wavelengths in the near infra-redregion and only one in the visible . One of the mostimportant advantages of the method presented in thiswork is that a NIRS analyzer covering the visible regionwas sufficient to predict seed colour, while in the previ-ous work an additional spectrophotometer was neededas primary source of reflectance data . Therefore, thismethod contributes with a new approach to the predic-tion of seed colour in seeds of Brassica .

It is concluded that visible reflectance spectroscopytogether with multivariate analysis can be used topredict seed colour in B. carinata, allowing a largenumber of seed samples to be screened in breed-ing programmes. With this method it is possible todistinguish amongst yellow, yellow-brown, greenish-yellow, reddish-brown and brown samples . If the LAIRSinstruments incorporate the visible spectral region,simultaneous information about seed colour and oth-er quality traits (oil, protein, glucosinolates) currentlydetermined in routine analyses of this species can beobtained .

Acknowledgements

The authors are grateful to Servicio CentralizadoNIRS, University of Cordoba, for facilities given inNIRS analysis. Financial support for this study wasprovided by CICYT (Project no . AGF92-225) .

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