Możliwości użycia spektrometru NIR do pomiaru ekstremalnie niskiej zawartości glukozynolanów w nasionach w czasie hodowli i utrzymywaniu odmian rzepaku podwójnie ulepszonego

Krzysztof Michalski, Jan Krzymański

Plant Breeding and Acclimatization Institute – National Research Institute, Branch Office in Poznan Author for correspondence — K. Michalski, e-mail: km@nico.ihar.poznan.pl

The possibility of the use of NIR spectrometer

to measure the extremely low content

of glucosinolates in seeds during the breeding and

maintenance of double low oilseed rape varieties

Możliwości użycia spektrometru NIR do pomiaru ekstremalnie niskiej

zawartości glukozynolanów w nasionach w czasie hodowli

i utrzymywaniu odmian rzepaku podwójnie ulepszonego

Key words: total of glucosinolates, alkenyl glucosinolates, indolyl glucosinolates, gluconapin, glucobrassicanapin, progoitrin, napoleiferin, brassicin, 4OH-brassicin, NIRS measurement done on intact seed

Abstract

The basic factor to obtain reliable results by Near Infrared Spectrometry (NIRS) is a good robust calibration. Two calibration methods were investigated. The more universal method called LOCAL was compared to classical PCA method called GLOBAL.

LOCAL method is based on a large database from which the calibration set is chosen on the basis of the spectra similarity. This is done during each measurement. The data base for LOCAL method was collected from seed analyses of varieties and strains bred in Poland during last 15 years.

GLOBAL method uses constant calibration based on a set of samples with composition similar to this expected in samples which will be measured. Calibration equations for this method were calculated by Principal Component Analysis (PCA).

The results obtained by GLOBAL method for the content of both the total and individual glucosinolates in intact seeds were more similar to the results of the chemical analyses than the results obtained with LOCAL method. Glucosinolate content in seeds of double low rapeseed is so low that when the LOCAL method is searching for calibration set from the database, their spectra are dominated by the spectra of the major seed components, such as fat, protein, fiber or moisture. It is impossible to obtain proper calibration for extremely low glucosinolate content.

NIRS method with GLOBAL calibration allowed getting results in satisfactory accordance with the results of the chemical analysis for the total of the glucosinolates, the total of the alkenyl glucosinolates, progoitrin, gluconapin. The measurements of the glucosinolates whose content was even smaller, such as glucobrassicanapin, napoliferin or brassicin, show too big an error.

Słowa kluczowe: suma glukozynolanów, glukozynolany alkenowe, glukozynolany indolowe, gluko-napina, glukobrassicagluko-napina, progoitryna, brazycyna, 4OH-brazycyna, pomiar NIRS na nienaruszonych nasionach

Streszczenie

Dla hodowli odmian rzepaku o bardzo niskiej zawartości glukozynolanów potrzebne są szybkie i tanie metody ich analizy. Do tego celu najlepiej wykorzystać metodę analizy instrumentalnej, np. NIRS (Near Infrared Spectrometry). Podstawą do otrzymania wiarygodnych wyników tą metodą jest dobre równanie kalibracyjne. Istnieje kilka metod kalibracji wykorzystujących regresję liniową wielo-krotną, analizę głównych składowych albo sieci neuronowe. W pracy przebadano dwie metody, nazywane LOCAL oraz GLOBAL. Pomiaru NIRS dokonywano na nienaruszonych nasionach.

Metoda LOCAL jest bardziej uniwersalna. Opiera się ona na dużej bazie danych oraz wybieraniu z niej przy każdym pomiarze zestawu kalibracyjnego na podstawie podobieństwa widm. Baza danych dla metody LOCAL została zebrana na podstawie wyników analiz nasion odmian i rodów hodow-lanych badanych w Polsce w ciągu ostatnich 15 lat.

Metoda GLOBAL używa stałej kalibracji opartej na zestawie próbek o składzie podobnym do oczekiwanego w próbkach, które będą mierzone. Równania kalibracyjne dla metody GLOBAL zostały obliczone metodą analizy składników głównych (Principal Component Analysis – PCA) przy użyciu zestawu kalibracyjnego próbek rzepaku podwójnie ulepszonego o mocno obniżonej i zróżnicowanej zawartości glukozynolanów.

Wyniki otrzymane metodą GLOBAL dały pomiary zawartości tak sumy, jak i poszczególnych glukozynolanów, bardziej zgodne z wynikami analiz chemicznych niż pomiary metodą LOCAL, która okazała się nieprzydatna w hodowli rzepaku o ekstremalnie niskiej zawartości glukozynolanów. Zawartość glukozynolanów w nasionach polskiego rzepaku podwójnie ulepszonego jest tak niska, że metoda kalibracji LOCAL szukając w bazie danych do zestawu kalibracyjnego, dokonuje fałszywych wyborów. Widma glukozynolanów są bowiem zdominowane przez widma głównych komponentów nasion, takich jak tłuszcz, białko, włókno lub wilgotność.

Metoda NIRS z kalibracją GLOBAL daje wyniki zadowalająco zgodne z wynikami analizy che-micznej dla następujących składników: sumy glukozynolanów, sumy glukozynolanów alkenylowych, progoitryny, gluconapiny. Natomiast pomiar składników zawartych w jeszcze mniejszej ilości, takich jak glukobrassicanapina, napoliferyna czy brazycyna jest obarczony zbyt dużym błędem.

Introduction

Upper limit for glucosinolate content in double low rapeseed according Polish

standard is the lowest in the world. The limit only up to 15 micromoles of

glucosinolates per gram of seeds (about 0.6%) is accepted (Polskie Normy

PN-90/R-66151). This applies to the total of the alkenyl and indolyl glucosinolates.

This standard was determined on the basis of the results of Polish nutritional

experiments (Rakowska et al. 1979, 1981, 1984). So low glucosinolate content

provides good weight gains and reproduction of animals despite still increased the

thyroid gland. Therefore, further reduction of glucosinolate contents is very

desirable (European Food Safety Authority 2008). Breeding works for further

lowering of glucosinolate content needs fast and cheap, and accurate methods for

its analysis.

Breeding or maintenance of variety of oilseed rape of double low quality (zero

erucic and very low in glucosinolate content) requires a large number of accurate

chemical analyses in order to control the content and composition of glucosinolates

in seeds. This condition is more important in the breeding of new varieties of oilseed

rape for even more reduced glucosinolate content. Such analyses are expensive and

time consuming. The sample of seeds used in chemical analysis is destroyed.

Therefore, attempts were made to replace the chemical analyses with spectrometry

measurements done on intact seed in the near-infrared (NIRS). The advantages

of the NIRS measurement are as follows:

— at the time of one measurement several components can be determined

simultaneously depending on the spectrometer calibration (Starr et al. 1985,

Michalski and Kołodziej 2000, Petiscoa et al. 2010).

— tested sample of seeds is not destroyed and can be sown to obtain the next

generation of plants. It is very important because this procedure speeds up the

breeding of new varieties.

The disadvantage of the NIR methods is that they are based on correlations

between the NIR spectra and the contents of the glucosinolates in the measured

samples of seeds. These correlations can be affected by many factors, such as:

weather conditions during plant growing, mineral nutrition or chemical treatment,

seed moisture content (Byczyńska et al. 1970, 1981, Piętka et al. 2001, 2002, 2003,

2005, 2007, Byczyńska et al. 1970, 1981). Different complicated calibration

methods are used to get more stable and repeatable results. The accuracy of the

calibration depends on:

— precision of chemical analysis of seeds samples used as calibration set,

— the accuracy of the spectrum measurement,

— statistical methods used to calculate the calibration equation,

— the number of seed samples used for calibration and validation,

— the chemical variability in composition of seed samples used for calibration

and validation,

— the size of measured sample and its chemical homogeneity.

The accuracy and repeatability of the results of chemical analysis and of NIRS

measurement has a decisive influence on the progress of breeding works.

Heritability and breeding effectiveness decreases with an increase of error in the

estimation of selected trait or component (Allard 1960, 1968).

The literature that has been concerned with the glucosinolate content

measurements in seeds of rapeseed using NIRS method relates to a natural much

higher contents of these compounds or to canola standard (Biston et al. 1988, Daun

et al. 1994, Font et al. 2004, Hom et al. 2007, Michalski et al. 1987, Montes et al.

2007, Petiscoa et al. 2010, Renard et al. 1987, Velasco and Becker 1998, Velasco

and Mollers 1999, Welle et al. 2007, Zhang et al. 2013). The official definition

of canola is: “Seeds of the genus Brassica (Brassica napus, Brassica rapa or Brassica

juncea) from which the oil shall contain less than 2% erucic acid in its fatty acid

profile and the solid component shall contain less than 30 micromoles of any one

or any mixture of 3-butenyl glucosinolate, 4-pentenyl glucosinolate,

2-hydroxy-3-butenyl glucosinolate, and 2-hydroxy-4-pentenyl glucosinolate per gram of air-dry,

oil-free matter” (canola standard of Canola Council 2012)”. Polish standard is lower

than for canola and IHAR target in rapeseed breeding is also much lower. It was

necessary to check usability of NIRS-spectroscopy and its exactness in rapeseed

with this very low glucosinolate contents.

The basis to obtain reliable results by NIRS method is a good robust calibration

model. For calibration purposes couple mathematical methods (multiple regression,

principal component analysis (PCA), neural network) were developed (Mahalanobis

1936, Martens and Naes 1989, 1991, Shenk and Westerhaus 1997).

In the study a classical PCA method called GLOBAL (static equation) was

investigated and compared with more flexible and universal method called LOCAL

(dynamic method) (Shenk and Westerhaus 1997, WinISI™ 4). These two methods

of calibrating the NIR spectrometer were examined to find the method giving the

most accurate results of glucosinolate estimation in seeds of oilseed rape also with

extremely low glucosinolate content (Spasibionek et al. 2016)

Materials and methods

Materials

NIRS measurements and chemical analyses used 100 samples of air dry seeds

(moisture content about 5.7%) collected from the field trial with breeding strains

of double low oilseed rape (zero erucic and with very low content of glucosinolates).

The field trial was performed in the growing period 2012/13 on the field of Plant

Breeding Company Strzelce, in Borowo Station (Wielkopolska, N 52

07’, E 16

47').

The randomized complete blocks design with 4 replications was used. The size

of the four-row plots was 12 m

for harvest. 25 objects were examined in the trial:

23 inbred lines from research works conducted in Plant Breeding and

Acclimatization Institute – Division Poznań and two check varieties: Chagall (open

pollinated cultivar) and Monolit (Doubled Haploid cultivar).

16 inbred lines were taken from the breeding conducted with the use of

chemical mutagenesis. The aim of breeding was to increase the content of oleic

acid and to reduce the linolenic acid content in seed oil (Byczyńska et al. 1996,

Spasibionek 2002, 2005, 2006, 2013, Spasibionek et al. 1999, 2000, 2003, 2011).

Another set of 7 inbred lines was taken from the recombination breeding

which used the natural variability obtained by recurrent selection. Crosses were

made between many different varieties and breeding lines of double low oilseed

rape. The purpose was to decrease to minimum glucosinolate content in seeds

(Krzymański 1968, 1970, 1970a, Krzymański et al. 1999, Piętka et al. 2001, 2002,

2003, 2005, 2007, EFSA 2008) and to increase the content of oleic acid in seed oil

(Krzymański et al. 1983, 2004, Piętka et al. 2003). The inbred lines described

above are extremely low in the glucosinolate content (total alkenyl glucosinolate

range 1,88 to 3,25

μM/g of seeds).

The glucosinolate contents and their variabilities for examined seed samples

were shown in Table 1. All tested seed samples met the Polish requirements of double

low rapeseeds. The most differentiated glucosinolate contents were for alkenyl

glucosinolates. The variability of the content of indolyl glucosinolates was significantly

lower. The contents of glucobrassicanapin, napoleiferin and brassicin were very

low (about 0.02 percent).

Table 1. Statistics of database used for LOCAL calibration

Statystyczny opis bazy danych użytej do kalibracji metodą LOCAL

G luc ona p in G lu co b ras si can ap in P ro g o it ri n N ap o lei fer in G lu co b ras si ci n 4O H gl uc obr as si ci n G lu co sin o la te to ta l A lke nyl gl uc os inol at e to ta l Mean — Średnia 2.48 0.44 4.35 0.06 0.19 4.15 11.67 6.64 Standard error Błąd standardowy 0.03 0.01 0.07 0.00 0.00 0.03 0.11 0.11 Mediane — Mediana 2.10 0.30 3.50 0.00 0.10 4.10 10.70 5.60 Mode — Tryb 2.30 0.30 2.50 0.00 0.10 4.20 8.50 0.00 Standard deviation Odchylenie standardowe 1.54 0.41 3.49 0.10 0.18 1.34 5.19 5.16 Sample variation Wariancja próby 2.37 0.17 12.15 0.01 0.03 1.81 26.98 26.66 Kurtosis— Kurtoza 9.41 25.79 13.70 187.49 61.49 1.53 9.49 10.02 Scenes — Skośność 2.20 3.61 2.78 10.05 5.25 0.64 2.22 2.27 Range — Zakres 15.50 5.10 34.60 2.40 3.00 11.90 47.00 48.70 Minimum 0.20 0.00 0.00 0.00 0.00 0.00 3.40 0.00 Maximum 15.70 5.10 34.60 2.40 3.00 11.90 50.40 48.70 Sample number Liczba prób 2359 2359 2359 2359 2359 2359 2359 2359

Methods of analyses

Chemical analyses

The glucosinolate analyses in rapeseed were accomplished by gas liquid

chromatography of trimethylsilyl derivatives of desulfated glucosinolates according

to Raney method (Raney et al. 1990, Michalski et al. 1995). The method was

standardized with BC 190 reference material and gives results comparable with the

results of high-pressure liquid chromatography method (HPLC) (Wathelet 1987

PN-EN ISO 9167-1:1999).

NIRS measurements

The reflectance spectra, Log (1/R), of the samples were recorded on a NIRS

spectrometer model 6500 (Foss NIRSystems, Silver Springs, MD) with a spectral

range of 400–2.498 nm and 2-nm wavelength increments. Spectrometer was

equipped with spinning sample module. Samples were measured using ring cup

cuvette. Sample volume was 8 ml.

Sample cell construction of NIRS-spectrometers allows usually to measure

about 4–5 ml volume of seed sample although small samples or single seed were

also estimated (Velasco 1999a, Hom 2007, Zhang 2013). The increased volume of

a sample and its rotation allows obtaining better and more exact results.

NIRS – GLOBAL calibration

GLOBAL method is a static model. It uses constant calibration based on a set

of samples with chemical composition similar to those expected in samples which

will be measured. This calibration remains unchanged until a calibration update is

made. Calibration equations for GLOBAL were calculated by Modified Principal

Component Analysis (PCA) method using the calibration set of samples of double

low rapeseed (Burns 2007, Martens 1989, 1991, WinISI™ 4, Font et al. 2004,

Mahalanobis 1936). Equations were made for individual glucosinolates, total of

alkenyl glucosinolates, total of indolyl glucosinolates and total of both glucosinolates.

NIRS – LOCAL calibration

LOCAL calibrations are dynamic in contrast to GLOBAL calibrations.

LOCAL method can be used for different products in one model. LOCAL method

is based on a large database and from which a calibration set is chosen on the basis

of the similarity of the spectra to the measured one by applying Mahalanobis

distance method (Mahalanobis 1936). The prediction is based on those of the

spectra that most closely resemble the examined unknown sample. It can also

better cope with non-linear dependencies (Burns 2007, Martens and Naes 1989,

Martens 1990, 1991, Shenk and Westerhaus 1996, 1997, WinIsi™ 4, patent US

5798526). The database containing 2359 seed samples spectra was collected and

used for LOCAL method calibration (Tab. 1 and Fig. 1). The content of total

glucosinolates ranged from 4 to 50 μM/g of seed – The database consists of mainly

winter and summer rapeseed samples but there were present black and white

mustard samples as well (20%).

Statistical methods

Statistical analysis of the field trial was conducted by method of the

two-factor analysis with testing the significances of differences between objects. Analysis

ToolPak (Microsoft Office Exel) was used for statistical calculations. Measurements

and chemical analyses of seeds were done on two average seed samples per each

plot. More details can be found in paper by Spasibionek et al. 2016.

Fig. 1. Histogram of total glucosinolate content in database for LOCAL calibration

Histogram rozkładu sumy glukozynolanów w bazie danych dla kalibracji LOCAL

Statistical analyses of variance and regression for chemical analyses and for

NIRS-measurements were conducted for totals and individual glucosinolate

contents in seeds. These analyses were made to compare two studied glucosinolate

estimation methods. Standard deviations for glucosinolate determinations (errors)

were calculated as the square roots of the mean squares for error. The repeatability

of results for objects means were presented as heritability (internal correlation

coefficient in the analysis of variance for the trial (h

) calculated according to

Allard (1960, 1968). The repeatability (heritability) has been calculated for the

selection based on the means for objects.

Results and discussion

The first attempts of NIRS application to the analysis of the glucosinolate

content in Brassica seeds have been carried out by Starr (Starr et al. 1985) and then

by other researchers (Biston et al. 1988, Daun et al. 1994, Kumar et al. 2010).

Typically, applications for glucosinolates were based on measurements made on

different models of spectrophotometers measuring NIR spectrum covering the

1100–2500 nm or 400-range.

Described methods have been developed for higher glucosinolate contents and

higher of this trait than those with which we are dealing in Polish breeding of new

varieties or in the maintenance of the varieties. Therefore, we investigated two

NIRS calibration methods by checking their repeatability and conformity of the

results with the results of the chemical analyses.

Comparison of NIRS measurements with results of chemical analyses

Tables 2, 3 and 4 contain chemical description of rapeseed samples used in

research. Tables show the statistical parameters of glucosinolate content estimated

with both chemical analyses and NIRS measurements using two different calibration

methods: GLOBAL and LOCAL.

Table 2. Statistical description of results of chemical analyses of glucosinolate contents in the set of examined seed samples (μM/g of seed) — Opis statystyczny wyników analizy chemicznej

zawartości glukozynolanów w zbiorze badanych próbek nasion rzepaku (μM/g nasion) Component

Komponent

Mean

Średnia MedianaMedian

Variance Wariancja

Range

Zakres Minimum Maximum

Gluconapin 2.296 2.3 1.1343 4.6 0.7 5.3 Glucobrassicanapin 0.668 0.7 0.1378 1.7 0.1 1.8 Progoitrin 3.969 3.9 5.7525 9,0 0.5 9.5 Napoleiferin 0.071 0.1 0.0041 0.2 0 0.2 Brassicin 0.140 0.1 0.0099 0.4 0 0.4 4OH-brassicin 4.432 4.3 0.5628 3.5 2.7 6.2 Glucosinolate total 11.592 11.8 15.7987 15.2 4.7 19.9 Alkenyl glucosinolate total 7.018 7.0 13.9718 14.5 1.3 15.8 Indolyl glucosinolate total 4.572 4.5 0.6271 3.8 2.8 6.6

The results of the glucosinolate content measurements using the two NIRS

calibration methods were compared with the results of the chemical analyses.

These comparisons were shown in the Table 4 containing regression equations and

determination coefficients. These comparisons for gluconapin, progoitrin,

4OH-brassicin, and total of glucosinolate, total of alkenyl glucosinolate and total of

indolyl glucosinolate were shown also graphically in Figures 2–5. These drawings

contain also the regression equations and the coefficients of determination (R

).

Table 3. Statistical description of results of glucosinolate measurements with GLOBAL method in the set of examined seed samples (μM/g of seed) — Opis statystyczny wyników analizy

glukozynolanów za pomocą metody GLOBAL badanych próbek nasion (μM/g nasion) Component

Komponent

Mean

Średnia Mediana Median

Variance Wariancja

Range

Zakres Minimum Maximum

Gluconapin 2.8643 2.9431 1.0882 4.5391 0.8118 5.3509 Glucobrassicanapin 0.5999 0.6039 0.0544 0.9317 0.1446 1.0763 Progoitrin 4.5592 4.6715 6.9949 10.8727 -0.3510 10.5216 Napoleiferin 0.1229 0.1234 0.0005 0.0937 0.0802 0.1739 Brassicin 0.2043 0.2054 0.0002 0.0763 0.1637 0.2401 4OHbrassicin 4.3682 4.3041 0.2264 2.1223 3.2712 5.3935 Glucosinolate total 12.6138 12.8351 17.3756 17.2052 4.8669 22.0721 Alkenyl glucosinolate total 7.9809 8.0720 16.0538 17.0348 0.6588 17.6936 Indolyl glucosinolate total 4.5725 4.5094 0.2300 2.1548 3.4544 5.6092 Table 4. Statistical description of results of glucosinolate measurements with LOCAL method in the set of examined seed samples (μM/g of seed) — Opis statystyczny wyników analizy

glukozynolanów za pomocą metody LOCAL badanych próbek nasion (μM/g nasion) Component

Komponent

Mean

Średnia Mediana Median

Variance Wariancja

Range

Zakres Minimum Maximum

Gluconapin 2.3955 2.3575 0.3878 3.5150 0.3010 3.8160 Glucobrassicanapin 0.2832 0.3110 0.0270 0.8400 -0.1080 0.7320 Progoitrin 3.7887 3.8100 1.5927 7.1820 0.0000 7.1820 Napoleiferin 0.0679 0.0715 0.0009 0.1600 -0.0170 0.1430 Brassicin 0.1447 0.1475 0.0071 0.4010 -0.1100 0.2910 4OHbrassicin 3.4559 3.4640 0.7376 6.0150 0.0000 6.0150 Glucosinolate total 10.3024 10.2330 4.7808 16.7410 -0.4710 16.2700 Alkenyl glucosinolate total 7.3071 7.3390 3.7046 9.2180 2.0690 11.2870 Indolyl glucosinolate total 3.6006 3.5965 0.7906 6.1460 0.0370 6.1830

Table 5. Regression equations (Slope and Intercept) and determination coefficients (R2) between chemical analyses and NIRS measurements with GLOBAL and LOCAL calibrations

Równania regresji (nachylenie i stała) oraz współczynniki determinacji (R2) obliczone dla wyników analizy referencyjnej i pomiarów za pomocą NIRS dla kalibracji GLOBAL i LOCAL

Component Komponent

GLOBAL LOCAL

Slope Intercept R2 _{Slope Intercept R}2

Gluconapin 0.885 0.830 0.817 0.214 1.903 0.134 Glucobrassicanapin 0.395 0.335 0.396 0.171 0.168 0.149 Progoitrin 1.039 0.443 0.888 0.146 3.206 0.077 Napoleiferin 0.242 0.105 0.460 -0.042 0.070 0.008 Brassicin 0.013 0.202 0.010 0.119 0.161 0.019 4OHbrassicin 0.227 3.361 0.128 0.006 3.427 0.0001 Total of glucosinolates 0.973 1.330 0.861 0.193 8.055 0.124 Total of alkenyl glucosinolates 1.009 0.893 0.887 0.177 6.064 0.080 Total of indolyl glucosinolates 0.149 3.890 0.060 -0.057 3.862 0.002 R2 in bold were significant at p = 0.01 — R2wytłuszczone istotne na poziomie p = 0,01

Table 6. Correlation (r) and regression (b) coefficients between results of chemical analyses and results of NIRS measurements on glucosinolate contents in seeds of double low rapeseed

Współczynniki korelacji i regresji pomiędzy wynikami referencyjnymi i wynikami analizy metodą NIRS zawartości glukozynolanów w rzepaku podwójnie ulepszonym

Component Komponent GLOBAL LOCAL r b r b Gluconapin 0.9042 0.8857 0.3667 0.2144 Glucobrassicanapin 0.6298 0.3958 0.3869 0.1712 Progoitrin 0.9427 1.0395 0.2789 0.1468 Napoleiferin 0.6787 0.2425 -0.0922 -0.0422 Brassicin 0.1034 0.0139 -0.1411 -0.1194 4OHbrassicin 0.3582 0.2272 0.0055 0.0064 Total of glucosinolates 0.9282 0.9734 0.3524 0.1939

Total of alkenyl glucosinolates 0.9421 1.0099 0.2834 0.1774 Total of indolyl glucosinolates 0.2464 0.1493 -0.0510 -0.0572

Regression coefficients with reliable value for breeding purposes printed with bold letters Współczynniki regresji o wartościach wystarczających dla potrzeb hodowlanych podano

Fig. 2. The comparison of the NIRS measurements results (y) with chemical analyses (x) for gluconapin — Porównanie wyników pomiaru za pomocą analizy w NIRS (y) z wynikami

Fig. 3. The comparison of the NIRS measurements results (y) with chemical analyses (x) for progoitrin — Porównanie wyników pomiaru za pomocą analizy w NIRS (y) z wynikami

Fig. 4. The comparison of the NIRS measurements results (y) with chemical analyses (x) for total of glucosinolate — Porównanie wyników pomiaru za pomocą analizy w NIRS (y)

Fig. 5. The comparison of the NIRS measurements results (y) with chemical analyses (x) for total of alkenyl glucosinolate — Porównanie wyników pomiaru za pomocą analizy

The compatibility between results obtained with chemical analyses and results

obtained using LOCAL calibration was not acceptable and much worse than in the

case of GLOBAL calibration. Compatibility was also dependent on the genetic

variability of the examined components of seed samples. The best compliance was

for alkyl glucosinolates, the worst for indolyl glucosinolates.

Very low compliances were observed for the constituents present to a very

very small degree (napoleiferin, brassicin, glucobrassicanapin – bellow 0.02 per cent).

NIRS measurement using GLOBAL calibration can be used successfully for

estimation of gluconapin, progoitrin, total glucosinolate and total alkenyl glucosinolate

for breeding works purposes.

Repeatability and accuracy of NIRS measurement for seeds collected

from field trial

Variance analyses were performed for glucosinolate contents in seeds which

were collected from field trial. These analyses were calculated for the three

methods of glucosinolate estimation. Some parameters from these calculations

have been shown in Table 7. This Table contains:

— Standard deviations for object means (s

),

— Snedecor coefficient (F),

— Probability that mean values for object do not differ (p),

— Repeatability (heritability) for object means (h

),

— Standard deviation for single estimation (Stand. dev. of analyze).

The examined set of strains was very significantly differentiated for the

content of all individual glucosinolates and their totals in spite of their very low

content in seeds. The statistical significance of variability was the highest for

alkenyl glucosinolates. Indol glucosinolates were less variable and their heritability

values point to a lower proportion of genetic variability. Fig. 6 shows how the

determination coefficients depended on the variability of examined glucosinolates.

In case of very low glucosinolate contents and low variability the coefficients decrease

very quickly.

Table 7. Statistical parameters from variance analyses for glucosinolates contents in seeds from field trial estimated with two methods — Parametry statystyczne otrzymane z analizy wariancji

dla zawartości glukozynolanów w nasionach z doświadczenia polowego estymowane dwoma metodami Component Komponent Method Metoda s.ob F p h 2 Stand.dev. of analyze Gluconapin A 1.065 49.094 5.38E-34 0.9796 0.3039 B 0.966 15.980 3.99E-20 0.9374 0.4833 Glucobrassicanapin A 0.365 48.525 3.21E-35 0.9794 0.1048 B 0.210 11.450 3.85E-16 0.9127 0.1244 Progoitrin A 2.372 56.977 1.48E-37 0.9824 0.6284 B 2.447 15.083 2.06E-19 0.9337 1.2600 Napoleiferin A 0.061 20.953 1.38E-23 0.9523 0.0265 B 0.020 10.178 8.10E-15 0.9017 0.0128 Brassicin A 0.097 41.364 6.30E-33 0.9758 0.0303 B 0.011 7.724 6.45E-12 0.8705 0.0076 4OHbrassicin A 0.497 2.551 0.00120 0.6080 0.6223 B 0.403 7.559 1.06E-11 0.8677 0.2929

Glucosinolate total A 3.863 35.715 7.57E-31 _{0.9720 1.2928}

B 3.851 14.843 3.25E-19 0.9326 1.9990 Alkenyl glucosinolate total A 3.516 41.493 5.69E-33 _{0.9759 1.0917} B 3.704 15.118 1.94E-19 0.9339 1.9053 Indolyl glucosinolate total A 0.550 3.031 0.00015 0.6700 0.6318 B 0.403 7.188 3.29E-11 0.8609 0.3009

A — results of chemical analyses — wyniki analizy referencyjnej

B — results for NIRS measurements with GLOBAL calibration — wyniki estymacji metodą NIRS – kalibracja GLOBAL

Fig. 6. Results of comparisons of NIRS GLOBAL measurements with results of chemical analyses for individual glucosinolates and their totals (y = values of determination coefficients (R2), x = the standard deviations for the object means (sob) — Rezultat

porównania wyników otrzymanych metodą NIRS — kalibracja GLOBAL z wynikami referencyjnymi dla poszczególnych glukozynolanów oraz ich sum (y = wartości współ-czynników determinacji (R2), x = odchylenia standardowe dla średnich obiektowych (sob)

Errors of estimations for contents of individual glucosinolates or for their

totals were similar for chemical analyses and for NIRS estimations with GLOBAL

calibrations.

Conclusions

1.

The results obtained by GLOBAL method for the content of both the total and

individual glucosinolates were more in line with the results of the chemical

analyses than results obtained with LOCAL method. GLOBAL method can

be used with some limitations in oilseed rape breeding obtaining varieties

with extremely low glucosinolate content LOCAL method is not suitable

in this case.

2.

Glucosinolate content in seeds of double low rapeseed according to Polish

standard is so low (from below 0.6% for total of glucosinolate to below 0.01%

for some individual glucosinolates) that when LOCAL method is searching

for calibration set from the database, the spectra of the major seed components

dominate the spectra of glucosinolate. It is probably the reason that LOCAL

method is not suitable in the case of breeding oilseed rape for extremely low

glucosinolate content in seed (much below Polish standard for double low

quality).

3.

NIRS method with GLOBAL calibration allowed getting the results with

sufficient compliance to chemical analysis for the following components:

the total of the glucosinolates, the total of the alkenyl glucosinolates,

progoitrin and gluconapin.

4.

Probably selection algorithm for calibration set in LOCAL method prefers

spectra of major components (like protein, fat, fibre, polyphenols) and

relatively less variable glucosinolate spectra are neglected in selection process.

LOCAL calibration equations are much less representative and correct than

equations obtained by GLOBAL method.

Acknowledgments

Authors wish to thank MSC Teresa Piętka and Dr Sc Stanislaw Spasibionek

for sharing seeds from the field trial.

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