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

• Published in: Analytical letters 2023-03, Vol.56 (5), p.730-743
• Main Authors: Dai, Yuanfeng, Dai, Zuoxiao, Guo, Guangzhi, Wang, Boran
• Format: Article
• Language: English
• Subjects: Classification; Data integration; Discriminant analysis; Feature extraction; Fraud; Fuzzy set theory; Fuzzy sets; Infrared spectroscopy; Military strategy; Near infrared radiation; Near-infrared spectroscopy (NIRS); partial least squares-discriminant analysis (PLS-DA); rice; Spectra; support vector machine (SVM); Support vector machines
• ISSN: 0003-2719; 1532-236X
• DOI: 10.1080/00032719.2022.2101060

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.