Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saeed, Maham | - |
dc.contributor.author | Kim, Jung-Seop | - |
dc.contributor.author | Kim, Seok-Young | - |
dc.contributor.author | Ryu, Ji Eun | - |
dc.contributor.author | Ko, JuHee | - |
dc.contributor.author | Zaidi, Syed Farhan Alam | - |
dc.contributor.author | Seo, Jeong-Ah | - |
dc.contributor.author | Kim, Young-Suk | - |
dc.contributor.author | Lee, Do Yup | - |
dc.contributor.author | Choi, Hyung-Kyoon | - |
dc.date.accessioned | 2023-03-23T06:40:06Z | - |
dc.date.available | 2023-03-23T06:40:06Z | - |
dc.date.created | 2023-02-27 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 2218-1989 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43488 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | METABOLITES | - |
dc.title | Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/metabo12111012 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | METABOLITES, v.12, no.11 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000881171700001 | - |
dc.identifier.scopusid | 2-s2.0-85141747601 | - |
dc.citation.number | 11 | - |
dc.citation.title | METABOLITES | - |
dc.citation.volume | 12 | - |
dc.contributor.affiliatedAuthor | Seo, Jeong-Ah | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | rice | - |
dc.subject.keywordAuthor | geographical origin | - |
dc.subject.keywordAuthor | NMR spectroscopy | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | prediction model | - |
dc.subject.keywordPlus | METABOLOMICS | - |
dc.subject.keywordPlus | DISCRIMINATION | - |
dc.subject.keywordPlus | MS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL UNIVERSITY, ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.