Evaluation of sample preparation methods for rice geographic origin classification using laser-induced breakdown spectroscopy. (March 2018)

• Record Type: Journal Article
• Authors: Yang, Ping; Zhu, Yining; Yang, Xinyan; Li, Jiaming; Tang, Shisong; Hao, Zhongqi; Guo, Lianbo; Li, Xiangyou; Zeng, Xiaoyan; Lu, Yongfeng
• Is Part Of: Journal of cereal science. Volume 80(2018)
• Page Start: 111
• Page End: 118
• Publication Date: 2018-03
• Subjects: LIBS -- Rice geographic origin -- Sample preparation methods -- SVM; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2018.01.007
• Languages: English
• ISSNs: 0733-5210

Abstract

Abstract: The quality and safety of food is one of the most important issues in our life. In this work, four different sample preparation methods, i.e., rice powder pellet with boric acid (RPPBA), rice powder pellet (RPP), rice grain pellet (RGP) and rice grain (RG), were carried out to study the adulteration problem in food industry. 20 kinds of rice from different geographic origins were classified by laser-induced breakdown spectroscopy (LIBS) coupled with principal component analysis (PCA) and support vector machine (SVM). PCA was used to reduce the input variables of SVM, and the classification accuracies by PCA and SVM combination for the four sample preparation methods were 92.70%, 95.70%, 98.80%, and 99.20%, respectively. In addition, the sample preparation times were 15, 12, 10, and 1 min, respectively. These results show that RG was simpler and more efficient sample preparation method for distinguishing different geographical origin of agricultural products than the other preparing methods of RPPBA, RPP, and RG. Modeling efficiency of SVM could be improved by reducing its input variables using PCA. It can be concluded that the LIBS technique combined with chemometric method should be a promising tool to rapidly distinguish different rice geographic origins. Highlights: Rice classification according to their geographical origins using LIBS combined with support vector machine (SVM) was carried out. Rice grain preparing method was found to be more efficient than the other preparing methods of rice powder pellet with boric acid, rice powder pellet, and rice grain pellet. Modeling efficiency of SVM could be improved by reducing its input variables using principal component analysis.