Document Detail


Classification and adulteration detection of vegetable oils based on fatty acid profiles.
MedLine Citation:
PMID:  25078260     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (Principal Component Analysis and Hierarchical Clustering Analysis), supervised (Random Forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by Random Forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.
Authors:
Liangxiao Zhang; Peiwu Li; Xiaoman Sun; Xuefang Wang; Baocheng Xu; Xiupin Wang; Fei Ma; Qi Zhang; Xiaoxia Ding; Wen Zhang
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-31
Journal Detail:
Title:  Journal of agricultural and food chemistry     Volume:  -     ISSN:  1520-5118     ISO Abbreviation:  J. Agric. Food Chem.     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-7-31     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0374755     Medline TA:  J Agric Food Chem     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  High-field liquid state NMR hyperpolarization: a combined DNP/NMRD approach.
Next Document:  Toxicity Endpoint Selections for a Simazine Risk Assessment.