Journal of Food Measurement and Characterization

, Volume 13, Issue 3, pp 2406–2416 | Cite as

A feature selection strategy of E-nose data based on PCA coupled with Wilks Λ-statistic for discrimination of vinegar samples

  • Yong YinEmail author
  • Yuzhen Zhao
Original Paper


In order to enhance the correct discrimination rate of six kinds of vinegar samples using electronic nose (E-nose), a feature selection strategy based on principal component analysis (PCA) coupled with Wilks Λ-statistic is put forward. PCA is used to generate principal component (PC) variables so as to eliminate the correlation between original feature variables; then some PC variables that are beneficial to identify the vinegar samples are selected by Wilks Λ-statistic. Considering that each PC variable is a linear combination of all original feature variables, so the sum of absolute values of combination coefficients of one original feature variable to the selected PC variables can be calculate, and some different original feature variable sets are formed by the sums from large to small, and the best variable set can be determined by further exploring their discrimination results. By the strategy, 51 original feature variables were selected as representational features of the E-nose data. In order to verify the effectiveness of the feature selection strategy, Fisher discriminant analysis (FDA) and radial basis function neural network (RBFNN) were employed to discriminate the six kinds of vinegar samples, and correct discrimination rates of training sets were 94% and 97%, respectively, and the correct discrimination rates of corresponding test sets were 90% and 92% at least, respectively. Moreover, Bhattacharyya distance (B-distance) was employed to explain the separability of these vinegar samples and the reliability of the FDA and RBFNN results.


Feature selection Vinegar Electronic nose Principal component analysis Wilks Λ-statistic 



This work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 31571923, the authors acknowledge the support.


  1. 1.
    N.H. Budak, E. Aykin, A.C. Seydim, A.K. Greene, Z.B. Guzel-Seydim, Functional properties of vinegar. J. Food Sci. 79, 757–764 (2014)CrossRefGoogle Scholar
  2. 2.
    C.W. Ho, A.M. Lazim, S. Fazry, U.K.H.H. Zaki, S.J. Lim, Varieties, production, composition and health benefits of vinegars: a review. Food Chem. 221, 1621–1630 (2017)CrossRefGoogle Scholar
  3. 3.
    X.B. Zou, J.Y. Shi, L.M. Hao, J.W. Zhao, Z.B. Sun, X.Y. Huang, Distinguishing four traditional vinegars by sensory analysis and colorimetric sensors. J. Texture Stud. 43, 413–419 (2012)CrossRefGoogle Scholar
  4. 4.
    P. Li, S. Li, L. Cheng, L. Luo, Analyzing the relation between the microbial diversity of DaQu and the turbidity spoilage of traditional Chinese vinegar. Appl. Microbiol. Biot. 98, 6073–6084 (2014)CrossRefGoogle Scholar
  5. 5.
    S. Li, P. Li, F. Feng, L.X. Luo, Microbial diversity and their roles in the vinegar fermentation process. Appl. Microbiol. Biot. 99, 4997–5024 (2015)CrossRefGoogle Scholar
  6. 6.
    D. Dong, W. Zheng, L. Jiao, Y. Lang, X. Zhao, Chinese vinegar classification via volatiles using long-optical-path infrared spectroscopy and chemometrics. Food Chem. 194, 95–100 (2016)CrossRefGoogle Scholar
  7. 7.
    Q. Chen, J. Ding, J. Cai, Z. Sun, J. Zhao, Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy. J. Food Sci. 77, 222–227 (2012)CrossRefGoogle Scholar
  8. 8.
    Z. Huang, C. Huang, J. Zhou, J. Li, G. Hui, Electronic nose system fabrication and application in large yellow croaker (Pseudosciaena crocea) fressness prediction. J. Food Meas. Charact. 1, 33–40 (2017)Google Scholar
  9. 9.
    J. Li, H. Feng, W. Liu, Y. Gao, G. Hui, Design of a portable electronic nose system and application in K value Prediction for Large yellow Croaker (Pseudosciaena crocea). Food Anal. Methods 9, 2943–2951 (2016)CrossRefGoogle Scholar
  10. 10.
    X. Jing, W. Liu, G. Hui, J. Fu, E-nose based rapid prediction of early mouldy grain using probabilistic neural networks. Bioengineered 4, 222–226 (2015)Google Scholar
  11. 11.
    H. Men, H. Liu, L. Wang, X. Zhou, Optimization of electronic nose sensor array and its application in the classification of vinegar. Adv. Mater. Res. 121–122, 27–32 (2010)CrossRefGoogle Scholar
  12. 12.
    Y. Jo, N. Chung, S. Park, B.S. Noh, Y. Jeong, J.H. Kwon, Application of E-tongue, E-nose, and MS-E-nose for discriminating aged vinegars based on taste and aroma profiles. Food Sci. Biotechnol. 25, 1313–1318 (2016)CrossRefGoogle Scholar
  13. 13.
    Y. Dai, R. Zhi, L. Zhao, H. Gao, B. Shi, H. Wang, Longjing tea quality classification by fusion of features collected from E-nose. Chemometr. Intell. Lab. 144, 63–70 (2015)CrossRefGoogle Scholar
  14. 14.
    Y. Yin, H. Yu, H. Zhang, A feature extraction method based on wavelet packet analysis for discrimination of Chinese vinegars using a gas sensors array. Sens. Actuators B Chem. 134, 1005–1009 (2008)CrossRefGoogle Scholar
  15. 15.
    J. Lozano, J.P. Santos, M.C. Horrillo, Enrichment sampling methods for wine discrimination with gas sensors. J. Food Compos. Anal. 21, 716–723 (2008)CrossRefGoogle Scholar
  16. 16.
    S. Omatu, M. Yano, E-nose system by using neural networks. Neurocomputing. 172, 394–398 (2016)CrossRefGoogle Scholar
  17. 17.
    M. Russo, D. Serra, F. Suraci, R.D. Sanzo, S. Fuda, S. Postorino, The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots. Food Chem. 165, 467–474 (2014)CrossRefGoogle Scholar
  18. 18.
    H. Yu, J. Wang, H. Zhang, Y. Yu, C. Yao, Identification of green tea grade using different feature of response signal from E-nose sensors. Sens. Actuators B Chem. 128, 455–461 (2008)CrossRefGoogle Scholar
  19. 19.
    H. Wu, T. Yue, Z. Xu, C. Zhang, Sensor array optimization and discrimination of apple juices according to variety by an electronic nose. Anal. Methods 9, 921–928 (2017)CrossRefGoogle Scholar
  20. 20.
    Y. Jing, Q. Meng, P. Qi, M. Zeng, W. Li, S. Ma, Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification. Rev. Sci. Instrum. 85, 055004 (2014)CrossRefGoogle Scholar
  21. 21.
    L. Xu, X. Yu, L. Liu, R. Zhang, A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food Chem. 202, 229–235 (2016)CrossRefGoogle Scholar
  22. 22.
    H. Sun, F. Tian, Z. Liang, T. Sun, B. Yu, S.X. Yang, Q. He, L. Zhang, X. Liu, Sensor array optimization of electronic nose for detection of bacteria in wound infection. IEEE Trans. Ind. Electron. 64, 7350–7358 (2017)CrossRefGoogle Scholar
  23. 23.
    K. Xu, J. Wang, Z. Wei, F. Deng, Y. Wang, S. Cheng, An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality. J. Food Eng. 203, 25–31 (2017)CrossRefGoogle Scholar
  24. 24.
    A. Bekker, J.J.J. Roux, M. Arashi, Exact nonnull distribution of Wilks’ statistic: the ratio and product of independent components. J. Multivariate Anal. 102, 619–628 (2011)CrossRefGoogle Scholar
  25. 25.
    M. Falasconi, M. Pardo, G. Sberveglieri, I. Riccò, A. Bresciani, The novel EOS835 electronic nose and data analysis for evaluating coffee ripening. Sens. Actuators B Chem. 110, 73–80 (2005)CrossRefGoogle Scholar
  26. 26.
    T. Chen, E. Martin, G. Montague, Robust probabilistic PCA with missing data and contribution analysis for outlier detection. Comput. Stat. Data Anal. 53, 3706–3716 (2009)CrossRefGoogle Scholar
  27. 27.
    A. Savitzky, M.J.E. Golay, Smoothing and differentiation of data by simplified least-squares procedures. Anal. Chem. 36, 1627–1639 (1964)CrossRefGoogle Scholar
  28. 28.
    Y. Yin, B. Chu, H. Yu, Y. Xiao, A selection method for feature vectors of electronic nose signal based on wilks Λ-statistic. J. Food Meas. Charact. 8, 29–35 (2014)CrossRefGoogle Scholar
  29. 29.
    S. Zhang, X. Xia, C. Xie, S. Cai, H. Li, D. Zeng, A method of feature extraction on recovery curves for fast recognition application with metal oxide gas sensor array. IEEE Sens. J. 9, 1705–1710 (2009)CrossRefGoogle Scholar
  30. 30.
    Q. He, R. Du, F. Kong, Phase space feature based on independent component analysis for machine health diagnosis. J. Vib. Acoust. 134, 021014 (2012)CrossRefGoogle Scholar
  31. 31.
    H. Gao, Applied Multivariate Statistical Analysis (Peking University Press, Beijing, 2005), pp. 63–66Google Scholar
  32. 32.
    X. Peng, L. Zhang, F. Tian, D. Zhang, A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants. Sens. Actuators A Physical. 234, 143–149 (2015)CrossRefGoogle Scholar
  33. 33.
    Y. Xiong, X. Xiao, X. Yang, D. Yan, C. Zhang, H. Zou, H. Lin, L. Peng, X. Xiao, Y. Yan, Quality control of Lonicera japonica stored for different months by electronic nose. J. Pharm. Biomed. 91, 68–72 (2014)CrossRefGoogle Scholar
  34. 34.
    L. Zhang, X. Li, Q. Tao, Feature Extraction and Classification for Hyperspectral Remote Sensing Images (Surveying and Mapping Press, Beijing, 2012), pp. 102–104Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Food & BioengineeringHenan University of Science and TechnologyLuoyangChina

Personalised recommendations