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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Related Experiment Video

Updated: May 23, 2026

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Baijiu Base Liquor Model Based on Variable Selection Neural Network Band Screening.

Guiyu Zhang1,2,3,4, Kuiting Li1,2,3,4

  • 1School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin, China.

Journal of Food Science
|May 22, 2026
PubMed
Summary

This study introduces a novel variable selection method for neural networks (VSNN) to analyze Chinese baijiu using near-infrared spectroscopy. This efficient technique accurately predicts baijiu quality without complex data preprocessing.

Keywords:
near‐infrared spectroscopystrong‐flavor baijiu base liquorvariable neural networkvariable selection

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Near-infrared (NIR) spectroscopy is crucial for analyzing complex mixtures like Chinese baijiu base liquor.
  • Traditional spectral analysis often requires extensive preprocessing, limiting efficiency.
  • Developing rapid and accurate analytical methods for baijiu quality control is essential.

Purpose of the Study:

  • To propose and validate a novel variable selection method for variable-length neural networks (VSNN) for NIR analysis of Chinese baijiu base liquor.
  • To eliminate the need for complex spectral preprocessing steps.
  • To establish efficient quantitative and qualitative models for baijiu analysis.

Main Methods:

  • Development of three vector mechanisms: spectral sensitivity (SV), gradient sensitivity (GV), and weighted gradient sensitivity (WGV) for variable selection within VSNN.
  • Implementation of nested five-fold cross-validation for dataset partitioning and model optimization.
  • Construction of quantitative prediction models for base liquor components and qualitative classification models for grade determination.

Main Results:

  • The VSNN method, utilizing GV and WGV, demonstrated high accuracy in quantitative analysis across three categories of baijiu, with R² values up to 0.9685.
  • The qualitative model, supported by a random forest classifier, achieved a prediction accuracy of 0.9403 using GV and WGV.
  • The proposed method effectively identified core spectral features, bypassing the need for smoothing and baseline correction.

Conclusions:

  • VSNN offers an efficient and convenient approach for the rapid quantitative and qualitative analysis of Chinese baijiu base liquor.
  • The spectral sensitivity vector mechanisms (GV and WGV) are effective in selecting relevant spectral features for accurate quality assessment.
  • This study provides a robust technical foundation for real-time quality monitoring in the baijiu industry.