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Prediction of warmed-over flavour development in cooked chicken by colorimetric sensor array

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dc.contributor.authorKim, Su-Yeon-
dc.contributor.authorLi, Jinglei-
dc.contributor.authorLim, Na-Ri-
dc.contributor.authorKang, Bo-Sik-
dc.contributor.authorPark, Hyun-Jin-
dc.date.accessioned2021-09-03T16:55:32Z-
dc.date.available2021-09-03T16:55:32Z-
dc.date.created2021-06-16-
dc.date.issued2016-11-15-
dc.identifier.issn0308-8146-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/86817-
dc.description.abstractThe aim of this study was to develop a simple and rapid method based on colorimetric sensor array (CSA) for evaluation of warmed-over flavour (WOF) in cooked chicken. All samples were classified according to storage time by CSA coupled with principle component analysis (PCA) or hierarchical cluster analysis (HCA). The CSA data were used to establish prediction models with thiobarbituric acid reactive substances (TBARS), pentanal, hexanal, or heptanal associated with WOF by partial least square regression (PLSR). For the TBARS model, the coefficient of determination (r(p)(2)) was 0.9997 in the prediction range of 0.28-0.69 mg/kg. In each of the models for pentanal, hexanal, and heptanal, all r(p)(2) were higher than 0.960 in the range of 0.58-2.10 mg/kg, 5.50-11.69 mg/kg, and 0.09-0.16 mg/kg, respectively. These results demonstrate that the CSA was able to predict WOF development and to distinguish between each storage time. (C) 2016 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subjectLIPID OXIDATION-
dc.subjectOPTOELECTRONIC NOSE-
dc.subjectELECTRONIC NOSE-
dc.subjectTURKEY MEAT-
dc.subjectSHELF-LIFE-
dc.subjectBEEF-
dc.subjectSAUSAGE-
dc.subjectCOOKING-
dc.subjectSTORAGE-
dc.subjectDISCRIMINATION-
dc.titlePrediction of warmed-over flavour development in cooked chicken by colorimetric sensor array-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Su-Yeon-
dc.contributor.affiliatedAuthorPark, Hyun-Jin-
dc.identifier.doi10.1016/j.foodchem.2016.05.084-
dc.identifier.scopusid2-s2.0-84969581174-
dc.identifier.wosid000377543500054-
dc.identifier.bibliographicCitationFOOD CHEMISTRY, v.211, pp.440 - 447-
dc.relation.isPartOfFOOD CHEMISTRY-
dc.citation.titleFOOD CHEMISTRY-
dc.citation.volume211-
dc.citation.startPage440-
dc.citation.endPage447-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaFood Science & Technology-
dc.relation.journalResearchAreaNutrition & Dietetics-
dc.relation.journalWebOfScienceCategoryChemistry, Applied-
dc.relation.journalWebOfScienceCategoryFood Science & Technology-
dc.relation.journalWebOfScienceCategoryNutrition & Dietetics-
dc.subject.keywordPlusLIPID OXIDATION-
dc.subject.keywordPlusOPTOELECTRONIC NOSE-
dc.subject.keywordPlusELECTRONIC NOSE-
dc.subject.keywordPlusTURKEY MEAT-
dc.subject.keywordPlusSHELF-LIFE-
dc.subject.keywordPlusBEEF-
dc.subject.keywordPlusSAUSAGE-
dc.subject.keywordPlusCOOKING-
dc.subject.keywordPlusSTORAGE-
dc.subject.keywordPlusDISCRIMINATION-
dc.subject.keywordAuthorOptoelectronic nose-
dc.subject.keywordAuthorColorimetric artificial nose-
dc.subject.keywordAuthorAldehyde-
dc.subject.keywordAuthorTBARS-
dc.subject.keywordAuthorMultivariate analysis-
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