Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies

Abstract

The accurate classification of rice is important to effectively identify food fraud, especially in China, where rice is widely used as a staple. This study explored the application of near-infrared (NIR) and Raman spectroscopies and low-level and mid-level data fusion for the classification of four types of rice from similar origins. Classification models were constructed on separate data blocks after different preprocessing methods using partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). Low-level data fusion spliced the two types of spectra and a classification technique was applied. Mid-level data fusion consisted of band selection and feature extraction from the spectra from each technique and the establishment of classification models. Based on fuzzy set theory, high-level data fusion was carried out for the results of PLS-DA models based on a single spectrum. The results show that mid-level data fusion is effective because its performance was superior to the use of one instrument. The highest accuracy was 87.5% for both NIR and Raman and 100% for the SVM model based on the mid-level data fusion. Mid-level data fusion of Raman and NIR spectroscopies is effective for the nondestructive identification of rice.