Abstract
The processing and quality properties of rice are significantly influenced by its variety and region of origin. However, discriminating between varieties and geographic regions is an urgent but difficult and time-consuming endeavor in China. In this study, an effective and reliable identification method was established by combining Raman spectroscopy (RS) with multivariate data analysis methods. Numerous RS spectra were collected, and the sensitive fundamental vibrations of less polar groups and bonds in rice were analyzed. Principal component analysis (PCA) was used for preliminary identification. Subsequently, different modeling methods were compared and seemed to reliably identify rice types, varieties, and region of origin, with accuracies of between 80 and 100%. As a result, a soft independent modeling of class analogy (SIMCA) model was shown to be the superior model for rice identification. The SIMCA model can deliver high precision detection of adulterated rice (i.e., rice of high quality blended with rice of inferior quality), and this study lays the foundations for an advanced rice quality identification technology system.
Published Version
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