Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state
This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assesse...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024027567 |
| _version_ | 1797259902859608064 |
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| author | Hee-Youn Chi Won-Ryeol Kim Ji-Ye Kim Seung-Hyun Kim |
| author_facet | Hee-Youn Chi Won-Ryeol Kim Ji-Ye Kim Seung-Hyun Kim |
| author_sort | Hee-Youn Chi |
| collection | DOAJ |
| description | This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model—incorporating δ13C, δ15N, δ18O, and δ34S in milled rice—yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain. |
| first_indexed | 2024-03-07T19:42:04Z |
| format | Article |
| id | doaj.art-f4db0b72dc674fdfa4cb13b0f5dd7fd8 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T23:16:49Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-f4db0b72dc674fdfa4cb13b0f5dd7fd82024-03-17T07:56:06ZengElsevierHeliyon2405-84402024-03-01105e26725Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling stateHee-Youn Chi0Won-Ryeol Kim1Ji-Ye Kim2Seung-Hyun Kim3Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of KoreaDepartment of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of KoreaDepartment of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of KoreaCorresponding author.; Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of KoreaThis study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model—incorporating δ13C, δ15N, δ18O, and δ34S in milled rice—yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.http://www.sciencedirect.com/science/article/pii/S2405844024027567RiceOrganic fraudMilling processMultiple stable isotope ratiosSupport vector machine |
| spellingShingle | Hee-Youn Chi Won-Ryeol Kim Ji-Ye Kim Seung-Hyun Kim Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state Heliyon Rice Organic fraud Milling process Multiple stable isotope ratios Support vector machine |
| title | Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state |
| title_full | Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state |
| title_fullStr | Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state |
| title_full_unstemmed | Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state |
| title_short | Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state |
| title_sort | improved organic and pesticide free rice oryza sativa l authentication based on multiple stable isotope ratio analysis and rice milling state |
| topic | Rice Organic fraud Milling process Multiple stable isotope ratios Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024027567 |
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