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Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley–Sammon transformation

  • Xiao-Hong WuEmail author
  • Jin Zhu
  • Bin Wu
  • Da-Peng Huang
  • Jun Sun
  • Chun-Xia Dai
Original Article
  • 18 Downloads

Abstract

Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley–Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley–Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, K nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.

Keywords

E-nose Chinese vinegar Fuzzy Foley–Sammon transformation (FFST) LDA KNN 

Notes

Funding

This study was supported by the project funded by National Science Foundation of China (31471413), Natural Science Foundation of Anhui colleges (KJ2018ZD064), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0573) and Undergraduate Innovation and Entrepreneurship Training Program of Jiangsu University (201910299531X; 201810299274W).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Not applicable.

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Copyright information

© Association of Food Scientists & Technologists (India) 2019

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringJiangsu UniversityZhenjiangChina
  2. 2.Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery IndustryJiangsu UniversityZhenjiangChina
  3. 3.Department of Information EngineeringChuzhou Vocational and Technical CollegeChuzhouChina
  4. 4.School of Food and Biological EngineeringJiangsu UniversityZhenjiangChina

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