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Mycobiome analysis for distinguishing the geographical origins of sesame seeds

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dc.contributor.authorChun, Yoon Shik-
dc.contributor.authorKim, Seok-Young-
dc.contributor.authorKim, Minjoo-
dc.contributor.authorLim, Jae Yoon-
dc.contributor.authorShin, Byeong Kon-
dc.contributor.authorKim, Young-Suk-
dc.contributor.authorLee, Do Yup-
dc.contributor.authorSeo, Jeong-Ah-
dc.contributor.authorChoi, Hyung-Kyoon-
dc.date.accessioned2021-08-13T06:40:20Z-
dc.date.available2021-08-13T06:40:20Z-
dc.date.issued2021-05-
dc.identifier.issn0963-9969-
dc.identifier.issn1873-7145-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48378-
dc.description.abstractSesame (Sesamum indicum) is one of the most widely cultivated crops in Asia and Africa. The identification of the geographical origins of sesame seeds is important for the detection of fraudulent samples. This study was conducted to build a prediction model and suggest potential biomarkers for distinguishing the geographical origins of sesame seeds using mycobiome (fungal microbiome) analysis coupled with multivariate statistical analysis. Sesame seeds were collected from 25 cities in Korea, six cities in China, and five sites in other countries (Ethiopia, India, Nigeria, and Pakistan). According to the expression of fungal internal transcribed spacer (ITS) sequences in sesame seeds, 21 fungal genera were identified in sesame seeds from various countries. The optimal partial least squares-discriminant analysis model was established by applying two components with unit variance scaling. Based on seven-fold cross validation, the predictive model had 94.4% (Korea vs. China/other countries), 91.7% (China vs. Korea/other countries), and 88.9% (other countries vs. Korea/China) accuracy in determining the geographical origins of sesame seeds. Alternaria, Aspergillus, and Macrophomina were suggested as the potential fungal genera to differentiate the geographical origins of sesame seeds. This study demonstrated that mycobiome analysis could be used as a complementary method for distinguishing the geographical origins of raw sesame seeds. © 2021 Elsevier Ltd-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier Ltd-
dc.titleMycobiome analysis for distinguishing the geographical origins of sesame seeds-
dc.typeArticle-
dc.identifier.doi10.1016/j.foodres.2021.110271-
dc.identifier.bibliographicCitationFood Research International, v.143-
dc.description.isOpenAccessN-
dc.identifier.wosid000663303400002-
dc.identifier.scopusid2-s2.0-85102623647-
dc.citation.titleFood Research International-
dc.citation.volume143-
dc.type.docTypeArticle-
dc.publisher.location네델란드-
dc.subject.keywordAuthorBiomarker-
dc.subject.keywordAuthorFungal ITS sequence-
dc.subject.keywordAuthorGeographical origin-
dc.subject.keywordAuthorMycobiome analysis-
dc.subject.keywordAuthorSesame seeds-
dc.subject.keywordPlusDiscriminant analysis-
dc.subject.keywordPlusFungi-
dc.subject.keywordPlusLeast squares approximations-
dc.subject.keywordPlusMultivariant analysis-
dc.subject.keywordPlusCultivated crops-
dc.subject.keywordPlusFungal genera-
dc.subject.keywordPlusFungal internal transcribed spacer sequence-
dc.subject.keywordPlusGeographical origins-
dc.subject.keywordPlusMicrobiome-
dc.subject.keywordPlusMultivariate statistical analysis-
dc.subject.keywordPlusMycobiome analyse-
dc.subject.keywordPlusPrediction model-
dc.subject.keywordPlusSesame seed-
dc.subject.keywordPlusSesamum indicum-
dc.subject.keywordPlusBiomarkers-
dc.relation.journalResearchAreaFood Science & Technology-
dc.relation.journalWebOfScienceCategoryFood Science & Technology-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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