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Design of A Portable Electronic Nose system and Application in K Value Prediction for Large Yellow Croaker (Pseudosciaena crocea)

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Abstract

In this paper, a portable electronic nose (E-nose) system was designed, and its application in K value prediction for large yellow croaker (Pseudosciaena crocea) was also explored. E-nose responses to croaker samples were measured for 8 days. Microbiological and chemical indexes including total viable count (TVC), K value, and GC-MS were synchronously examined. Principal component analysis (PCA) and stochastic resonance (SR) were utilized for E-nose measurement data analysis. Results suggested that croaker’s K value increased rapidly due to microbial propagation. GC-MS results presented the information of volatile gas emitted by croaker samples, and provided reliable references for E-nose responses. PCA method could not discriminate all croaker samples, while SR signal-to-noise ratio (SNR) maximum (Max SNR ) values quantitatively discriminated all samples. Large yellow croaker K value predictive model was developed by linear fitting regression between K values and Max SNR values with a regression coefficient (R 2) of 0.96. Validating experiment results demonstrated that the forecasting accuracy of this model is 83 % the regression coefficient of the developed model was 0.83. E-nose is a useful tool for food quality examination. But, there are no such standard references for E-nose examination, which limits the applications of this technique. The method proposed here will promote the applications of E-nose in aquatic product quality rapid analysis.

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Acknowledgments

This work is supported by National Key Technology R&D Program (No. 2014BAD10B02-04), National Natural Science Foundation of China (Nos. 61272313, 61302185), Zhejiang National Natural Science Foundation (Nos. LQ14F020014), Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Talent Startup Project of Zhejiang A & F University (Nos.2015FR020).

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Authors and Affiliations

  1. School of Information Engineering, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Zhejiang, Linan, 311300, China

    Jian Li, Hailin Feng, Yuanyuan Gao & Guohua Hui

  2. Zhejiang Gongshang University, Zhejiang, Hangzhou, 310030, China

    Wei Liu

Authors
  1. Jian Li
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  2. Hailin Feng
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  3. Wei Liu
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  4. Yuanyuan Gao
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  5. Guohua Hui
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Funding

This work is supported by National Key Technology R&D Program (No. 2014BAD10B02-04), National Natural Science Foundation of China (Nos. 61272313, 61302185), Zhejiang National Natural Science Foundation (Nos. LQ14F020014), Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province (Nos. 100151402), Talent Startup Project of Zhejiang A & F University (Nos.2015FR020).

Conflict of Interest

Author Jian Li has received research grant from Zhejiang National Natural Science Foundation (Nos. LQ14F020014). Author Hailin Feng has received research grant from National Natural Science Foundation of China (Nos. 61272313, 61302185). Author Yuanyuan Gao has received research grant from  Talent Startup Project of Zhejiang A & F University (Nos. 2015FR020). Author Guohua Hui has received research grant from National Key Technology R&D Program (Nos. 2014BAD10B02-04), and Talent Startup Project of Zhejiang A & F University (Nos. 2015FR020).

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All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

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Additional information

> E-nose combined with non-linear stochastic resonance was proposed for large yellow croaker K value prediction.

> Stochastic resonance SNR eigen values presented better quantitative discriminating effects than PCA.

> The proposed model presented good predicting accuracy.

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Li, J., Feng, H., Liu, W. et al. 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). https://doi.org/10.1007/s12161-016-0431-8

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  • DOI: https://doi.org/10.1007/s12161-016-0431-8

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