Rapid Identification of Coix Seed Varieties by Near Infrared Spectroscopy

Unsupervised learning algorithm-principal component analysis (PCA), and supervised learning algorithm-learning vector quantization (LVQ) neural network and support vector machine (SVM) were used to carry out qualitative discriminant analysis of different varieties of coix seed from different regions. Since nutrient compositions of different varieties coix seed samples from different origins were complex and the contents were similar, characteristic variables of two kinds of coix seed were alike, the scores plot of their principal components seriously overlapped and the categories of coix seed were difficult to distinguish. While satisfactory results were obtained by LVQ neural network and SVM. The accuracy of LVQ neural network prediction is 90. 91%, while the classification accuracy of SVM, whose penalty parameter and kernel function parameter were optimized, can be up to 100%. The results show that NIRS combined with chemometrics can be used as a rapid, nondestructive and reliable method to identify coix seed varieties and provide technical reference for market regulation.