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      Improved Algorithms for the Classification of Rough Rice Using a Bionic Electronic Nose Based on PCA and the Wilks Distribution

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          Abstract

          Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic ( i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results also indicate that the electronic nose provides a non-destructive and rapid classification method for rough rice.

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          The Mahalanobis distance

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            Enhanced probabilistic neural network with local decision circles: A robust classifier

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              Rapid measuring and modelling flavour quality changes of oxidised chicken fat by electronic nose profiles through the partial least squares regression analysis.

              The objective of this study was to investigate whether an electronic nose, comprising 18 metal oxide semiconductor gas sensors, could be used for measuring and modelling flavour quality changes of refined chicken fat during controlled oxidation. Partial least squares regression (PLSR) was applied to determine the predictive relationships between the chemical parameters, GC-MS data, free fatty acid profiles and electronic nose responses for controlled oxidation of refined chicken fat. The results showed that peroxide value (PV) and acid value (AV) were significantly well predicted by the electronic nose responses, whereas p-anisidine value (p-AV) was found to be fairly well predicted especially for deeply oxidised chicken fat. Thus, this study gave evidence of the electronic nose system to be a promising device for future at- or on-line implementation in oxidation control of chicken fat for producing meat flavourings. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                March 2014
                19 March 2014
                : 14
                : 3
                : 5486-5501
                Affiliations
                [1 ] Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Ministry of Education, Guangzhou 510642, China; E-Mails: 294504658@ 123456qq.com (S.X.); huazlu@ 123456scau.edu.cn (H.L.); xwluo@ 123456scau.edu.cn (X.L.)
                [2 ] College of Engineering, South China Agricultural University, Guangzhou 510642, China
                [3 ] United States Department of Agriculture, Agricultural Research Service (USDA-ARS), College Station, TX 77845, USA; E-Mail: yubin.lan@ 123456ars.usda.gov
                Author notes

                Author Contributions: Zhiyan Zhou designed the reported study, evaluated the results, and prepared and reviewed the manuscript. Sai Xu conducted the whole experiment, analysed the results, developed the improved algorithm, and prepared the manuscript. Huazhong Lu and Xiwen Luo contributed to plan the reported research, evaluate the results, review and approve the manuscript. Yubin Lan helped in preparing the experimental setup, evaluated the system, and reviewed the manuscript. All authors read and approved the manuscript.

                [* ] Author to whom correspondence should be addressed; E-Mail: zyzhou@ 123456scau.edu.cn ; Tel.: +86-020-3867-6975; Fax: +86-020-8528-0158.
                Article
                sensors-14-05486
                10.3390/s140305486
                4004003
                24651725
                4a54c3b9-7f14-4fc3-a404-b146cb1d3353
                © 2014 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/.

                History
                : 11 January 2014
                : 22 February 2014
                : 11 March 2014
                Categories
                Article

                Biomedical engineering
                wilks distribution,principle component analysis (pca),bionic electronic nose,gas sensor,rough rice,classification and recognition,probabilistic neural networks

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