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Data-Driven Soft Sensing for Raw Milk Ethanol Stability Prediction.

Song Shen1, Xiaodong Song2, Haohan Ding1,3

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

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|February 13, 2026
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Summary
This summary is machine-generated.

A new soft sensing model predicts raw milk ethanol stability using routine quality indicators. This non-destructive method enhances dairy quality monitoring and heat-processing suitability assessments.

Keywords:
diffusion modelethanol stabilityraw milksoft sensingunbalanced data

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Area of Science:

  • Dairy Science
  • Food Quality Control
  • Machine Learning Applications

Background:

  • Ethanol stability is crucial for raw milk quality and heat-processing suitability.
  • Traditional testing methods are destructive and unsuitable for industrial-scale applications.
  • Routine compositional analysis (protein, fat, lactose) offers potential for non-destructive prediction.

Purpose of the Study:

  • To develop a non-destructive soft sensing model for predicting raw milk ethanol stability.
  • To leverage commonly monitored raw milk intake indicators for this prediction.
  • To improve the efficiency and applicability of ethanol stability testing in industrial settings.

Main Methods:

  • Utilized an autoencoder for feature correlation analysis and variable selection.
  • Employed TabNet for building the ethanol stability classification model.
  • Implemented a TabDDPM-based data generation technique to address class imbalance.

Main Results:

  • The developed soft sensing model achieved 92.57% accuracy in predicting ethanol stability.
  • The model demonstrated a recall of 90.26% for identifying ethanol-unstable raw milk samples.
  • The model was trained and validated on three years of industrial raw milk data.

Conclusions:

  • The soft sensing model shows significant potential for practical engineering applications in dairy quality monitoring.
  • This non-destructive approach offers a viable alternative to traditional destructive testing methods.
  • The study highlights the effectiveness of machine learning in predicting critical raw milk quality parameters.