Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques

Abstract

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.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

“…116,117 Previous studies revealed that the application of ML in the analysis of NMR data is not rare, but most of these applications tend to solve the classification problem based on the fingerprint information carried by NMR combined with the ML algorithms, and do not focus on the structure elucidation. 118–120 By conducting a comprehensive literature review, we have classified the ML algorithm-aided NMR data-based NPs structural analysis methods into three distinct categories: (i) ML-assisted annotation of functional groups; (ii) ML-assisted classification of NPs; and (iii) ML-assisted quantum chemical calculation NMR.…”