Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies. (24th March 2023)

• Record Type: Journal Article
• Authors: Dai, Yuanfeng; Dai, Zuoxiao; Guo, Guangzhi; Wang, Boran
• Is Part Of: Analytical letters. Volume 56:Number 5(2023)
• Page Start: 730
• Page End: 743
• Publication Date: 2023-03-24
• Subjects: Near-infrared spectroscopy (NIRS) -- partial least squares-discriminant analysis (PLS-DA) -- rice -- support vector machine (SVM); Chemistry, Analytic -- Periodicals; Chemistry, Analytic -- Abstracts; 543
• DOI: 10.1080/00032719.2022.2101060
• Languages: English
• ISSNs: 0003-2719

Abstract

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.