{"title":"The quality of EVOOs through the determination of VOCs coupled with SIMCA analysis","authors":"Simona Violino , Cosimo Taiti , Elettra Marone , Corrado Costa , Stefano Mancuso","doi":"10.1016/j.meafoo.2024.100159","DOIUrl":null,"url":null,"abstract":"<div><p>Determining the quality of the oil is essential to discriminate EVOOs from oils labeled as such but not having the chemical and physical characteristics to be EVOOs. The purpose of this study was to determine a metric based on multivariate Soft Independent Modeling of Class Analogy (SIMCA), to identify “Premium quality” versus “Standard quality”, 792 EVOO samples throughout VOCs and sensory analysis data. The result of SIMCA showed a correct classification of the “Premium quality” samples and only few samples were excluded from the SIMCA model (95.4 % sensitivity). The combination of VOCs collected by PTR-ToF-MS and SIMCA analysis allowed us to perfectly distinguish EVOO oils from non-EVOO oils. The proposed method could allow a secure, quick and reliable control of the EVOO quality within the global market.</p></div>","PeriodicalId":100898,"journal":{"name":"Measurement: Food","volume":"14 ","pages":"Article 100159"},"PeriodicalIF":3.6000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772275924000261/pdfft?md5=6092104d38fb7ae82c861792bfcb95a6&pid=1-s2.0-S2772275924000261-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement: Food","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772275924000261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Determining the quality of the oil is essential to discriminate EVOOs from oils labeled as such but not having the chemical and physical characteristics to be EVOOs. The purpose of this study was to determine a metric based on multivariate Soft Independent Modeling of Class Analogy (SIMCA), to identify “Premium quality” versus “Standard quality”, 792 EVOO samples throughout VOCs and sensory analysis data. The result of SIMCA showed a correct classification of the “Premium quality” samples and only few samples were excluded from the SIMCA model (95.4 % sensitivity). The combination of VOCs collected by PTR-ToF-MS and SIMCA analysis allowed us to perfectly distinguish EVOO oils from non-EVOO oils. The proposed method could allow a secure, quick and reliable control of the EVOO quality within the global market.
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