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Counterfeit detection of bulk Baijiu based on fluorescence hyperspectral technology and machine learning

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Abstract

Baijiu is a unique distilled spirit in China. The bulk Baijiu market has been experiencing issues related to counterfeit and substandard products, raising concerns about food safety. Detecting liquor adulteration is crucial for eliminating fraud in the bulk Baijiu market. In this study, we proposed using fluorescence hyperspectral Technology (FH) combined with machine learning (ML) to detect Baijiu adulteration quickly and non-destructive. Due to the similarity of fluorescence spectral features between adulterated Baijiu and real Baijiu, it was difficult to distinguish them based on the fluorescence feature parameters alone. The data preprocessing methods were used and then principal component analysis (PCA) was adapted. The principal components were used as inputs to ML models to establish the qualitative and quantitative detection models. In the qualitative detection models, the Adaptive Boosting (AdaBoost) model demonstrated the best performance with 98.08% precision, 100% recall and 99.03% F1-score. In the quantitative detection models of adulterations concentration, the AdaBoost model after Wavelet denoising(WDS) processing yielded the best results with R2 of 0.9740 and RMSEP of 0.0247. The results demonstrated that the combination of FH and ML can efficiently detect adulterated bulk Baijiu, showing promising applications and feasibility in the nondestructive detection of adulterated substances.

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Data availability

The raw data on which the study is based were accessed from a repository and are available for downloading through the following link. https://github.com/wuyouli123/Baijiu.git

Abbreviations

FH:

Fluorescence hyperspectral technology

ML:

Machine learning

HS-SPME-GC-MS:

Headspace solid-phase microextraction gas chromatography-mass spectrometry

NMR:

Nuclear magnetic resonance technology

PCA:

Principal component analysis

MSC:

Multiplicative scatter correction

WDS:

Wavelet denoising

MSC-WDS:

Multiplicative scatter correction & wavelet denoising

AdaBoost:

Adaptive boosting

XGBoost:

Extreme gradient boosting

RF:

Random forests

EL:

Ensemble learning

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Funding

This work was supported by the subject double support program of Sichuan Agricultural University (Grant NO. 035-1921993093).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an, 625014, China

    Youli Wu, Lijia Xu, Rongsheng Fan, Yi Lin, Chunyi Zhan & Zhiliang Kang

  2. Sichuan Intelligent Agriculture Engineering Technology Research Center, Ya’an, 625014, China

    Lijia Xu & Zhiliang Kang

  3. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China

    Xiaoli Li

Authors
  1. Youli Wu
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  2. Xiaoli Li
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  3. Lijia Xu
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  4. Rongsheng Fan
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  5. Yi Lin
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  6. Chunyi Zhan
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Contributions

YW: Conceptualization; Resources; Software; Formal analysis; Writing—review and editing Visualization; Roles/Writing—original draft; ZK: Funding acquisition; Supervision; XL: Methodology; RF: Validation; LY: Data curation; CZ: Investigation; LX: Project administration.

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Correspondence to Zhiliang Kang.

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Wu, Y., Li, X., Xu, L. et al. Counterfeit detection of bulk Baijiu based on fluorescence hyperspectral technology and machine learning. Food Measure 18, 3032–3041 (2024). https://doi.org/10.1007/s11694-024-02384-2

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  • DOI: https://doi.org/10.1007/s11694-024-02384-2

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