Identification of rice varieties specialties in Vietnam using Raman spectroscopy

Abstract

The characteristics and quality of rice are significantly affected by its variety. However, discrimination between varieties is an urgent but difficult and time-consuming effort in Vietnam. In this study, an effective and reliable identification method was established by Raman spectroscopy (RS). Total Raman spectra of 32 rice samples were acquired from 400 to 1600 cm-1 and the sensitive fundamental vibrations of less polar groups and bonds in rice were analyzed. Initially, the raw Raman spectra were processed by standard normal variety (SNV) combined with Savitzky Golay (SG) smoothing algorithm. The positive influence of SNV-SGD2 on the ability to classify rice varieties has been confirmed by principal component analysis (PCA). Next, multivariate analysis methods included PCA, hierarchical cluster analysis (HCA), and K-nearest neighbor (KNN), that have been compared with each other on the ability to classify rice varieties. All three methods give the ability to classify four rice varieties very well. The PCA method identifies four main factors were starch chains, amylose, amylopectin, and protein contents which are used to distinguish among four rice varieties. While HCA only distinguishes well between rice with high and low amylopectin content and does not provide the main components.