Mutia Nurulhusna Hussain, M. F. Abdul Khir, M. H. Hisham, Zalhan Md Yusof
{"title":"Feasibility study of detecting canola oil adulteration with palm oil using NIR spectroscopy and multivariate analysis","authors":"Mutia Nurulhusna Hussain, M. F. Abdul Khir, M. H. Hisham, Zalhan Md Yusof","doi":"10.1109/ICTS.2014.7010567","DOIUrl":null,"url":null,"abstract":"Detection of adulteration in food is one of the most important issues in food industry today. In this study, the feasibility of classifying canola oil samples from the one adulterated with palm oil using NIR spectroscopy in combination with multivariate analysis is investigated. An experiment to obtain the NIR spectra was conducted and analyzed using multivariate analysis. The result using open source R software has shown that adulterated oil samples could be detected with an overall correct classification rate of 100 % with minimum detection level of 3.23 %.","PeriodicalId":325095,"journal":{"name":"Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTS.2014.7010567","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Detection of adulteration in food is one of the most important issues in food industry today. In this study, the feasibility of classifying canola oil samples from the one adulterated with palm oil using NIR spectroscopy in combination with multivariate analysis is investigated. An experiment to obtain the NIR spectra was conducted and analyzed using multivariate analysis. The result using open source R software has shown that adulterated oil samples could be detected with an overall correct classification rate of 100 % with minimum detection level of 3.23 %.