Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends.
An electronic nose (e-nose) was used to characterize sesame oils processed by three different methods (hot-pressed, cold-pressed, and refined), as well as blends of the sesame oils and soybean oil. Seven classification and prediction methods, namely PCA, LDA, PLS, KNN, SVM, LASSO and RF, were used to analyze the e-nose data. The classification accuracy and MAUC were employed to evaluate the performance of these methods. The results indicated that sesame oils processed with different methods resulted in different sensor responses, with cold-pressed sesame oil producing the strongest sensor signals, followed by the hot-pressed sesame oil. The blends of pressed sesame oils with refined sesame oil were more difficult to be distinguished than the blends of pressed sesame oils and refined soybean oil. LDA, KNN, and SVM outperformed the other classification methods in distinguishing sesame oil blends. KNN, LASSO, PLS, and SVM (with linear kernel), and RF models could adequately predict the adulteration level (% of added soybean oil) in the sesame oil blends. Among the prediction models, KNN with k = 1 and 2 yielded the best prediction results.
College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation, Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China. sxlion2@gmail.com.
Xiaolong
X
Shao
College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation, Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China. lihui0104@foxmail.com.
Hui
H
Li
College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation, Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China. wangnan0910@163.com.
Nan
N
Wang
Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada. Qiang.Zhang@UManitoba.ca.
Qiang
Q
Zhang
D016002:Discriminant Analysis; D062609:Electronic Nose / Q000145:classification*; D005511:Food Handling; D016018:Least-Squares Analysis; D012715:Sesame Oil / Q000737:chemistry* / Q000145:classification*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
D012715:Sesame Oil
electronic nose; k-nearest neighbor algorithm; lasso; linear discriminant analysis; partial least squares discriminant analysis; partial least squares regression; pressed sesame oil; support vector machine