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Sensors 2015, 15(10), 26726-26742; doi:10.3390/s151026726

Comparison of Different Classification Methods for Analyzing Electronic Nose Data to Characterize Sesame Oils and Blends

1
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
2
Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: W. Rudolf Seitz
Received: 6 August 2015 / Accepted: 13 August 2015 / Published: 21 October 2015
(This article belongs to the Section State-of-the-Art Sensors Technologies)
View Full-Text   |   Download PDF [801 KB, uploaded 23 October 2015]   |  

Abstract

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. View Full-Text
Keywords: electronic nose; pressed sesame oil; linear discriminant analysis; k-nearest neighbor algorithm; partial least squares discriminant analysis; support vector machine; lasso; partial least squares regression electronic nose; pressed sesame oil; linear discriminant analysis; k-nearest neighbor algorithm; partial least squares discriminant analysis; support vector machine; lasso; partial least squares regression
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shao, X.; Li, H.; Wang, N.; Zhang, Q. Comparison of Different Classification Methods for Analyzing Electronic Nose Data to Characterize Sesame Oils and Blends. Sensors 2015, 15, 26726-26742.

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