Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques
- 주제(키워드) rice , geographical origin , NMR spectroscopy , machine learning , prediction model
- 주제(기타) Biochemistry & Molecular Biology
- 설명문(일반) [Saeed, Maham; Kim, Jung-Seop; Kim, Seok-Young; Ryu, Ji Eun; Ko, JuHee; Choi, Hyung-Kyoon] Chung Ang Univ, Coll Pharm, Seoul 06974, South Korea; [Zaidi, Syed Farhan Alam] Chung Ang Univ, Dept Comp Sci & Engn, Seoul 06974, South Korea; [Seo, Jeong-Ah] Soongsil Univ, Sch Syst Biomed Sci, Seoul 06978, South Korea; [Kim, Young-Suk] Ewha Womans Univ, Dept Food Sci & Biotechnol, Seoul 03760, South Korea; [Lee, Do Yup] Seoul Natl Univ, Ctr Food & Bioconvergence, Res Inst Agr & Life Sci, Dept Agr Biotechnol, Seoul 08826, South Korea
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold
- 발행기관 MDPI
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000203084
- 본문언어 영어
- Published As https://doi.org/10.3390/metabo12111012
- PubMed https://pubmed.ncbi.nlm.nih.gov/36355095
초록/요약
Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares-discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice.
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