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  3. Developing Machine Learning Models For Fluid Milk Spoilage Classification.
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  3. Developing Machine Learning Models For Fluid Milk Spoilage Classification.

Related Experiment Videos

Developing machine learning models for fluid milk spoilage classification.

YeonJin Jung1, Chenhao Qian1, Aljosa Trmcic1

  • 1Department of Food Science, Cornell University, Ithaca, NY 14853.

Journal of Dairy Science
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence models can now classify fluid milk spoilage using microbiological data, reducing the need for extensive shelf-life testing. This technology helps optimize testing schemes and identify spoilage patterns in the dairy industry.

Keywords:
artificial intelligencecontaminationmicroorganismshelf-life

Related Experiment Videos

Area of Science:

  • Food Science
  • Microbiology
  • Artificial Intelligence

Background:

  • The dairy industry faces knowledge gaps due to expert retirement.
  • Identifying fluid milk spoilage patterns is crucial for effective control strategies.
  • Traditional spoilage classification relies on human experts.

Purpose of the Study:

  • To develop a machine-learning-based digital expert system for classifying fluid milk spoilage.
  • To assess the potential for optimizing microbiological testing schemes in the dairy industry.

Main Methods:

  • A machine-learning model was developed using microbiological data from 770 fluid milk samples.
  • Expert-assigned spoilage types (Gram-negative bacteria, sporeformers, no spoilage) were used for training and validation.
  • Multiple models were trained and tested using subsets of data representing optimized testing scenarios.

    • Main Results:

    • The baseline model achieved 96.4% classification accuracy on the test set.
    • Optimized models using reduced data sets (e.g., specific microbial counts on day 14 and 21) reached 94.2% testing accuracy.
    • The developed system can identify predominant spoilage patterns and aid in root-cause investigations.

    Conclusions:

    • Machine-learning models offer a viable solution for classifying fluid milk spoilage.
    • Optimized testing schemes can reduce costs and resources while maintaining high accuracy.
    • This digital expert system can support targeted interventions and improve quality control in the dairy sector.