Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut (Arachis hypogaea L.)

Abstract

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding. In this study, Near Infrared Spectroscopy (NIRS) was applied to rapidly assess germplasm variability from whole seed of 699 samples, field-collected and assembled in four genetic and environment-based sets: one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population, evaluated in three environments in a large spatial scale of two countries, Mbalmayo and Bafia in Cameroon and Nioro in Senegal, under rainfed conditions. NIR elemental spectra were gathered on six subsets of seeds of each sample, after three rotation scans, with a spectral resolution of 16 cm^-1 over the spectral range of 867 nm to 2530 ​nm. Spectra were then processed by principal component analysis (PCA) coupled with Partial least squares-discriminant analysis (PLS-DA). As results, a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments. The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 ​nm, usually related to oil content and fatty acid composition. PCA yielded the most chemical attributes in three significant PCs (i.e., eigenvalues >10), which together captured 93% of the total variation, revealing genetic and environment structure of varieties and genotypes into four clusters, corresponding to the four samples sets. The pattern of genetic variability of the interspecific population covers, remarkably half of spectrum of the core-collection, turning out to be the largest. Interestingly, a PLS-DA model was developed and a strong accuracy of 99.6% was achieved for the four sets, aiming to classify each seed sample according to environment origin. The confusion matrix achieved for the two sets of Bafia and Nioro showed 100% of instances classified correctly with 100% at both sensitivity and specificity, confirming that their seed quality was different from each other and all other samples. Overall, NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection, as a source of nutritional diversity, to support the breeding efforts.