Nutritional quality analysis

All the genotypes of GSP were subjected to phenotyping for protein content, oil content and four major fatty acid viz., palmitic acid (16:0), stearic acid (18:0), oleic acid (18:1) and linoleic acid (18:2) using near-infrared reflectance spectroscopy (NIRS). The oleic/linoleic acid (O/L ratio), an important oil quality determining parameter was calculated as follows.

O/L ratio = Oleic acid content (%) (18: 1) Linoleic acid content (%) (18: 2) Near-infrared reflectance spectroscopy (NIRS)

Near infrared spectroscopy either in reflectance (NIRS) or transmittance mode (NITS) is a multi-trait technique of large scale applications in the analysis of nutritional quality traits of food and agricultural commodities (Shenk and Wasterhaus, 1995). In present study, oil and protein content along with four major fatty acids measured for each genotype using NIRS. The details about principle, spectral measurement, calibration of prediction equation, data analysis and validation of calibrated equation of NIRS are given below.

Principle

The detection and measurement of chemical composition of biological material based on the vibrational response of chemical bonds to NIR radiation. Spectral measurement of NIR

All the samples were scanned on an NIR Systems model XDS monochromator (model XDS RCA, FOSS Analytical AB, Sweden, Denmark). Reflectance spectra (log1/R) from 400 to 2498 nm were recorded at 2 nm intervals. Each sample was subsequently scanned 32 times and the average spectrum was collected.

Data analysis

For analysis, about 30 to 60 g (depending on amount of seed available) of sound mature seed sample of each genotype was scanned in a rectangular cup. The cup was filled up sufficiently to allow good absorption of the incident light. In each scan, NIR light was allowed to fall on the bottom of the sample

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holder containing the groundnut seeds, where it penetrated and interacted with the samples. The reflected energy spectrum over the wavelength range of 400-2,498 nm that carried absorption information of the samples was collected. The instrument diagnostics was carried out to test the response of instrument, wavelength and NIR repeatability to avoid the effects of surrounding environment on the instrument performance. Mathematical procedures on the spectral information were carried out with WinISI II project manager software version 4.3 (Infrasoft International, Port Matilda, PA, USA). Calibration

Before using NIR spectrophotometer for any quantitative analysis it has to be calibrated using chemical reference method with the application of multivariate regression models to interpret chemical information encoded in the spectral data. Original reflectance spectra were corrected prior to calibration by applying first and second derivative information, standard normal variate transformation, de-trend scatter correlation and four passes were used to eliminate outliers. Calibrations were performed based on spectral data from 400 to 2498 nm with an interval of 2 nm, to obtain the regression equations for fatty acid contents between spectral data and laboratory reference values using modified partial least squares (MPLS). For performing MPLS the number of parameters was set to ‘default’ and the number of cross-validation groups set to 8; with samples with a ‘H’ value larger than 4 (spectral outliers) and a (Student) ‘T’ value larger than 2.5 (sample which did not fit the calibration model) being eliminated (Shenk and Westerhaus, 1995).

Different mathematical pretreatment methods were tested on the calibration set and the best method was chosen based on the optimum results obtained for R2 _{(determination coefficient of calibration) and 1-VR (coefficient}

of determination in cross-validation). Three mathematical treatments were used viz., the raw data or the first or second derivatives of log 1/R data to remove background differences, combined with gap sizes in data points over which the derivative was calculated for enhancing spectral differences and a smoothing algorithm to reduces random noise in the spectral data (Savitzky

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and Golay, 1964). For example in the treatment 1,4,4,1 the first number indicates the order of derivative function (one is the first derivative of log 1/R); the second number is the gap (the length in nm); the third number represents the number of data points (segment length) used in the first smoothing and the fourth number is the number of data points in the second smoothing which is normally set at 1 for no second smoothing (Shenk and Westerhaus, 1993).

Calibrations were performed with five different mathematical treatments (1,2,2,1; 1,4,4,1; 1,8,8,1; 2,4,4,1; 2,8,8,1) using SNV + D (Standard Normal Variate + De-trend) scatter correction option. Scatter corrections are useful in reducing differences in the spectra related to physical characteristics such as particle size and path length of reflectance from the particle surface (Shenk and Westerhaus 1993). Four cycles of outlier elimination were allowed. Calibration models were assessed using statistics that included the standard error of calibration (SEC), the coefficient of determination in calibrations (R2_),

the standard error of cross-validation (SECV), and the coefficient of determination in cross-validation (1-VR) (Shenk and Westerhaus, 1993). The optimum calibration equations were obtained based on the highest R2 _{or 1-VR}

and the lowest SEC or SECV values.

Using the above-described procedure, calibration equations were developed using the readouts of wet chemistry methods. For developing calibration equations for oil content, 142 genotypes with varying oil content were analyzed using Soxhlet method. Similarly the oleic, linoleic and palmitic acid content in 208 F2:3 population of the cross ICGV 06420 × SunOleic 95R

was estimated using gas chromatography (GC). For developing calibration equation for protein content around 114 samples was estimated using Autoanalyzer for protein content. Different mathematical treatments were tested to identify the best calibration equation based on their coefficient of determination in calibration (R2_{) and coefficient of determination of cross-}

validation (1-VR) values. The mathematical equation used, and the RSQ (R2₎

and I-VR values of the developed equations is given in Table 3.4. The RSQ values for oil, protein and palmitic acid was 0.83, 0.87 and 0.88, respectively,

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while that for oleic and linoleic acid was 0.97. Similarly, the 1-VR values for the samples used in cross-validation of the developed equation were ranged from 0.75 for protein to 0.95 for oleic and linoleic acid indicating the suitability of the equation for predicting oil, protein, palmitic acid, oleic acid and linoleic acid in whole seed kernels. This equation is being routinely utilized at ICRISAT to analyze around 25,000-30,000 samples/season for the above quality traits and to screen samples based on their seed quality profile. The absorption spectrum of NIRS for two samples differing in their oleic acid contents is depicted in Figure 3.5.

Table 3.4 Calibration equations for predicting oil, protein and fatty acids (palmitic, oleic and linoleic acid) content in whole groundnut kernels using NIRS

*N- Number of samples in calibration; **RSQ- coefficient of determination in calibrations; #_{1-VR- coefficient of} determination in cross-validation

Validation of calibration statistics

The accuracy and precision of the selected calibration equation was monitored with the WinISI software using the external validation set (Windham et al., 1989). The indicators for external validation were standard error of prediction (SEP); coefficient of determination in external validation (R2_{) and}

SEP/SD values, which is the ratio of the standard error of prediction to standard deviation (SD) for the validation samples. The best-calibrated equation was used to phenotype GSP for nutritional quality traits.

Constituent N Mean Range Mathematical

treatment RSQ 1-VR Oil (%) 142 48.69 40.08-57.31 1,4,4,1 0.83 0.80 Protein (%) 114 27.68 19.73-35.64 4,6,6,1 0.87 0.75 Palmitic acid (%) 208 11.42 6.77-16.06 2,4,4,1 0.88 0.80 Oleic acid (%) 208 52.12 23.44-80.79 2,4,4,1 0.97 0.95 Linoleic acid (%) 208 27.12 2.77-51.46 2,4,4,1 0.97 0.95

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Figure 3.5 Raw absorption spectra of groundnut kernels representing two extreme oleic acid values

3.5 Molecular diversity, allelic richness and marker-traits association