title: Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis.
abstract: This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil.
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China. Electronic address: pengdanhaut@gmail.com.
authors.forename: Dan
authors.initials: D
authors.lastname: Peng
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China.
authors.forename: Yanlan
authors.initials: Y
authors.lastname: Bi
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China.
authors.forename: Xiaona
authors.initials: X
authors.lastname: Ren
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China.
authors.forename: Guolong
authors.initials: G
authors.lastname: Yang
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China.
authors.forename: Shangde
authors.initials: S
authors.lastname: Sun
authors.affiliation: College of Grain Oil and Food Science, Henan University of Technology, PR China.
authors.forename: Xuede
authors.initials: X
authors.lastname: Wang
mesh_terms: D002849:Chromatography, Gas; D016000:Cluster Analysis; D005506:Food Contamination / Q000032:analysis*; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D010938:Plant Oils / Q000737:chemistry*; D025341:Principal Component Analysis; D012715:Sesame Oil / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D010938:Plant Oils; D012715:Sesame Oil
keywords: Adulterated sesame oil; Classification; Gas chromatography; Multivariate data analysis; Quantification