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Assessing traits in bread using image analysis and machine learning.

Maria Azmat¹, M Belén García-Gómez², Eva Cernadas¹

¹Centro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain.

Food Research International (Ottawa, Ont.)

|May 5, 2026

Summary

This summary is machine-generated.

A new computer system uses bread images to predict sensory quality, offering a faster, cheaper alternative to traditional methods. This automated approach shows promising results for the food industry in quality control.

Keywords:

Bread Bread quality Image analysis Machine learning Regression Sensory analysis Texture analysis

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Area of Science:

Food Science
Computer Vision
Machine Learning

Background:

Sensory assessment is crucial for food quality but is time-consuming and resource-intensive.
Traditional methods involve trained sensory or consumer panels.
The food industry seeks faster, cost-effective quality assessment techniques.

Purpose of the Study:

To develop and evaluate a computer system for predicting bread's sensory quality from digital images.
To automate and expedite the food quality assessment process.

Main Methods:

Image processing algorithms to segment bread crust and crumb.
Color texture feature extraction from images.
Machine learning models, including support vector regression, for sensory trait prediction.

Main Results:

The system achieved high reliability (R≥0.75) for 7 out of 24 sensory traits.
Moderate to good reliability (0.5≤R<0.75) was observed for 14 traits.
The computer prediction showed encouraging correlation with trained panel scores.

Conclusions:

The proposed computer vision system demonstrates potential for reliable bread quality prediction.
This technology offers a promising, efficient alternative for industrial quality control and product categorization.
Further validation with larger datasets is recommended to address current limitations.