Food and Fermentation Industries >

2025 , Vol. 51 >Issue 24: 75 - 85

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.041311

Machine algorithm combined with spectral principal component feature fusion for discriminative study of Qingke liquor

  • ZHAO Yuxia ,
  • WANG Ru ,
  • ZHANG Shizhi ,
  • YIN Bo ,
  • ZHANG Mingjin
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  • 1(College of Chemistry and Chemical Engineering, Qinghai Normal University, Xining 810016, China)
    2(College of Chemistry and Chemical Engineering, Qinghai University for Nationalities, Xining 810016, China)
    3(Qinghai Key Laboratory of Advanced Technology and Application of Environmental Functional Materials, Xining 810016, China)

Received date: 2024-10-15

  Revised date: 2025-04-30

  Online published: 2026-01-12

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Abstract

This study established a qualitative analysis model based on spectral fusion to achieve rapid identification of the protected geographical indication product “Huzhu” Qingke liquor. Ultraviolet (UV) and near-infrared (NIR) spectra of Baijiu were collected and preprocessed using four methods. Principal component feature extraction was employed to integrate multispectral information through data layer and feature layer strategies. The modeling effectiveness was evaluated by comparing the performance metrics of partial least square-discriminant analysis (PLS-DA), random forest (RF), back propagation neural network (BPNN), and radial basis function neural network (RBF-NN) models. Results indicated that the PLS-DA model built with variables derived from second derivative preprocessing and principal component feature extraction performed the best, achieving sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) of 1.000, 0.966 7, and 0.962 4 in the prediction set, respectively. The RF model optimized by principal component feature extraction of raw spectra and Savitzky-Golay smooth (SG) spectra achieved the highest classification accuracy of 100% in both training and prediction sets. The BPNN model established with principal component variables from raw UV spectra and SG-preprocessed spectra demonstrated the best recognition performance, with a prediction set classification accuracy of 100% and a prediction coefficient of determination of 1, while the mean squared error (MSE) was less than 0.03. Principal component analysis-radial basis function neural network (PCA-RBF-NN) classification yielded optimal results, achieving 100% classification accuracy in both training and prediction sets. The RBF-NN model built from full NIR spectra after SNV preprocessing also produced the best classification results, with 100% accuracy in both training and test sets. The UV-NIR LF raw spectra and SG-preprocessed spectra classification results were the most optimal, achieving 100% classification accuracy in both training and test sets. Consequently, the spectral data variables used for principal component feature extraction modeling were significantly reduced, effectively simplifying the classification model while maintaining performance parity with models built using full wavelengths. This study provides a feasible method for rapid, non-destructive identification of “Huzhu” Qingke liquor.

Key words： Chinese Huzhu Qingke liquor; principal component feature extraction; partial least square-discriminant analysis; back propagation neural network; random forest; radial basis function neural network

Cite this article

ZHAO Yuxia , WANG Ru , ZHANG Shizhi , YIN Bo , ZHANG Mingjin . Machine algorithm combined with spectral principal component feature fusion for discriminative study of Qingke liquor[J]. Food and Fermentation Industries, 2025 , 51(24) : 75 -85 . DOI: 10.13995/j.cnki.11-1802/ts.041311

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