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  1. Home
  3. Image-based Machine Learning For Predicting Acceptability Limits In Frozen Pizza Shelf Life.
  1. Home
  3. Image-based Machine Learning For Predicting Acceptability Limits In Frozen Pizza Shelf Life.

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Image-Based Machine Learning for Predicting Acceptability Limits in Frozen Pizza Shelf Life.

Marika Valentino1, Giulia Varutti1, Sylvio Barbon Júnior2

  • 1Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via Sondrio 2/A, 33100 Udine, Italy.

Foods (Basel, Switzerland)
|May 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study uses image analysis and machine learning to predict frozen pizza acceptability by tracking tomato sauce color changes over time. This non-destructive method helps estimate shelf life and product quality decay effectively.

Keywords:
color saturationimage-processinglogistic regression classifiernon-destructive methodquality decay

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Area of Science:

  • Food Science
  • Computer Science
  • Sensory Science

Background:

  • Consumer acceptability of frozen foods is crucial for shelf life determination.
  • Quantifying the effects of storage duration and temperature fluctuations on food perception is complex.
  • Tomato sauce degradation is a key visual indicator of frozen pizza quality decline.

Purpose of the Study:

  • To develop a non-destructive, image-based method for estimating frozen pizza acceptability.
  • To utilize machine learning to predict product quality decay based on visual cues.
  • To identify tomato sauce saturation as a reliable indicator for assessing frozen food quality.

Main Methods:

  • An image processing pipeline was created to isolate tomato sauce regions.
  • Color extraction, specifically saturation in the HSV color space, was performed on sauce samples.
  • A polynomial regression model tracked saturation trends, and a logistic regression classifier predicted consumer acceptability using saturation and storage duration.

    • Main Results:

    • The regression model showed good performance with R² of 0.68 and RMSE of 12.8.
    • The logistic regression classifier achieved high accuracy (88.2%) and AUC (0.93) in predicting acceptability.
    • Tomato sauce saturation was confirmed as a primary driver of visual rejection by consumers (90% feedback).

    Conclusions:

    • The developed framework offers an early, non-invasive estimation of frozen food acceptability.
    • This approach has significant potential for practical application in the frozen food industry's shelf life studies.
    • Image-based analysis combined with machine learning provides a robust tool for quality assessment of frozen products.