Classification of different kinds of rice in the north of iran using IR spectroscopy combined with multivariate analysis

Rice agriculture plays an important role in the country’s economy. Rice is the main foodstuff for about half of the world s population. For the last decades, rice consumption has been expanding beyond the traditional rice-grown areas particularly in Asia and Europe [1]. Many of the traditional methods of analysis for determining the physical, chemical and mechanical properties to ensure the quality of rice are time consuming, destructive, require expensive harmful reagents [2]. The desire is to replace the traditional methods to find its quality with rapid, non-destructive, noninvasive methods. for this purpose, four kinds of rice were selected from the agricultural lands in the tonekabon in the north of Iran. Ten samples from each kind of rice were prepared. The objective of this study is to classify rice samples based on the fingerprints of the IR region by using the Infrared Spectroscopy (IRs). Therefore, thirty spectra were recorded such as ten samples with three replicates. IR spectra were taken on every four kinds of rice in the range of 400 to 4000 cm-1. All the spectral data were processed statistically and resulting, the rice samples were classified using Principle Component Analysis (PCA). Application of principal component analysis (PCA) to our experimental data resulted in satisfactory classifications for all kinds of rice. Rice samples were effectively distinguished in four discrete groups.