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Updated: Jun 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Image-Based Detection of Adulterants in Milk Using Convolutional Neural Network.

Adhyayan Mamgain1, Virkeshwar Kumar2, Susmita Dash1

  • 1Department of Mechanical Engineering, Indian Institute of Science, Bengaluru 560012, Karnataka, India.

ACS Omega
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

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Machine learning detects milk adulterants using evaporation patterns. A deep learning model achieved 98% accuracy in identifying common contaminants like urea, ammonium sulfate, and oil, offering a cost-effective solution.

Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Machine Learning Applications

Background:

  • Milk adulteration is a significant public health concern.
  • Conventional adulterant detection methods are often costly and impractical for rural settings.
  • Need for accessible and reliable milk quality assessment techniques.

Purpose of the Study:

  • To investigate the potential of machine learning for detecting milk adulterants.
  • To develop a deep learning model using evaporative milk deposit patterns.
  • To assess the efficacy of different regularization techniques for model accuracy.

Main Methods:

  • Collected image datasets of evaporative milk deposit patterns from adulterated milk samples.
  • Developed a Convolutional Neural Network (CNN) model for pattern classification.

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  • Applied implicit (data augmentation) and explicit regularization techniques for optimization.

    • Main Results:

    • CNN successfully classified distinct evaporation patterns corresponding to different adulterants and concentrations.
    • The method detected specific minimum concentrations: 5% urea, 2.4% ammonium sulfate, and 2% oil.
    • Implicit regularization via data augmentation yielded the highest testing accuracy of 98%.

    Conclusions:

    • Machine learning, specifically CNNs, can effectively detect common milk adulterants using evaporative patterns.
    • The developed technique offers a promising, low-cost alternative for milk quality control.
    • Data augmentation significantly enhances the accuracy of adulterant detection models.