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A selection method for feature vectors of electronic nose signal based on wilks Λ–statistic

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Abstract

There are mainly two selection methods for different features of electronic nose (E-nose) which is used to identify different samples, namely visual inspection and correct rate of discrimination result. The visual inspection is not a quantitative method. Besides, when the correct rates of discrimination result are identical for different features, the identification difference of different features is not evaluated accurately and quantitatively. To get a better feature vector for identifying different samples, a selection method was studied in-depth in which Wilks Λ–statistic was employed as a selection index for different features. At the same time, three different kinds of Chinese vinegar and three of Chinese milk were taken and tested by an E-nose. Five different features were extracted from the E-nose signals which are variance value (VARV), integral value (INV), mean value of relative steady-state responses (MVRSR), mean-differential coefficient value (MDCV) and energy value of wavelet packet decomposition (WE). The best feature vectors of these five features were obtained using the selection method and its effectiveness was respectively proved by the visual inspection and Fisher discriminant analysis (FDA) correct rate of vinegar and milk samples.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 31171685, the authors acknowledge the support.

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Authors and Affiliations

  1. School of Food & Bioengineering, Henan University of Science & Technology, Luoyang, 471003, China

    Yong Yin, Bing Chu, Huichun Yu & Yujuan Xiao

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  1. Yong Yin
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  2. Bing Chu
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  3. Huichun Yu
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  4. Yujuan Xiao
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Correspondence to Yong Yin.

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Yin, Y., Chu, B., Yu, H. et al. A selection method for feature vectors of electronic nose signal based on wilks Λ–statistic. Food Measure 8, 29–35 (2014). https://doi.org/10.1007/s11694-013-9162-3

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  • DOI: https://doi.org/10.1007/s11694-013-9162-3

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