Discrimination of Waxy Wheats Using Near-Infrared Hyperspectral Spectroscopy

Abstract

Wheat (Triticum aestivum L.) carries three waxy loci (Wx-A1, Wx-B1, and Wx-D1) encoding granule-bound starch synthase (GBSS) which are related to amylose synthesis. The present study was to investigate the possibility of using near-infrared (NIR) hyperspectral spectroscopy to differentiate waxy wheat and three partial waxy wheats from wild-type wheat. Nearly 267 seeds from the same harvest year were used to obtain hyperspectral imaging maps in the near-infrared range (930–2548 nm), and then the data were analyzed based on chemometric methods. The first derivative, standard normal variable (SNV) transformation, and multivariate scattering correction (MSC) were used for spectral pretreatment. Support vector machine (SVM) and partial least square discriminant analysis (PLS-DA) and backpropagation neural network (BPNN) were applied to build discrimination models for wheat line classification. The results demonstrate that the highest classification accuracy of 98.51% for the prediction set was achieved by SVM model based on raw spectral data in full NIR region, and the classification accuracy of wild-type and partial waxy wheats all reached 100%. SVM models’ prediction accuracy (98.51%) is higher than PLS-DA and BPNN models’ (75.76% and 82.10%). The above results show that NIR hyperspectral spectroscopy combined SVM model was useful in identification of waxy and partial waxy wheats from wild-type wheat.