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Related Experiment Video

Updated: Jun 29, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Establishment of a Daqu Grade Classification Model Based on Computer Vision and Machine Learning.

Mengke Zhao^1,2,3, Chaoyue Han^1,2,3, Tinghui Xue^1,2,3

¹College of Food Science and Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.

Foods (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces a computer vision and machine learning model for objective Daqu grading, improving efficiency and accuracy in Baijiu quality assessment.

Keywords:

evaluation model feature factors image segmentation light-flavor Daqu machine learning

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Area of Science:

Food Science and Technology
Computer Vision
Machine Learning

Background:

Daqu grade significantly impacts Baijiu quality.
Current Daqu grading methods are subjective, labor-intensive, and inefficient.

Purpose of the Study:

To develop an automated, objective Daqu grade evaluation system.
To enhance the efficiency and accuracy of Daqu classification for light-flavor Baijiu.

Main Methods:

Image segmentation techniques (thresholding, morphological fusion, K-means clustering) for Daqu image extraction.
Feature selection using Random Forest-Mean Decrease Accuracy (RF-MDA), RFE, LASSO, and ridge regression.
Machine learning models (SVM, LR, RF, KNN, stacking) for Daqu grade classification.

Main Results:

Morphological fusion achieved high image segmentation performance (96.67% accuracy).
Random Forest (RF) models excelled in classifying Daqu-P, Daqu-F, and Daqu-S (96.67% accuracy).
A combination of RF-MDA and a stacking model showed superior performance in distinguishing Daqu-P from Daqu-F (90.00% accuracy).

Conclusions:

The proposed computer vision and machine learning model offers an efficient and objective solution for Daqu grade evaluation.
This approach provides valuable theoretical and technical support for the Baijiu industry.
Automated Daqu grading can overcome limitations of traditional methods, ensuring consistent quality.