Prediction of Frozen Chicken Meat Storage Time through Artificial Neural Network Analysis of Colour Parameters

Mounia Megaache; Hafsa Akkari

doi:10.46419/cvj.57.5.6

Prediction of Frozen Chicken Meat Storage Time through Artificial Neural Network Analysis of Colour Parameters

Authors

M. Megaache Laboratory of Health, Animal Production and Environment, Department of Veterinary Sciences, Institute of Veterinary and Agricultural Sciences, University of Batna 1, Batna, Algeria Author https://orcid.org/0000-0003-4980-6067
H. Akkari Department of Veterinary Sciences, Faculty of SNV-STU, University of Biskra, Biskra, Algeria Author https://orcid.org/0009-0002-0264-9519

DOI:

https://doi.org/10.46419/cvj.57.5.6

Keywords:

colourimetry, MLP neural network, frozen poultry, storage prediction, non-destructive analysis

Abstract

Poultry meat colour deteriorates during prolonged frozen storage, affecting consumer perception and product value. This study investigated changes in colour parameters (L*, a*, b*) of frozen chicken breast meat, and developed an artificial neural network to predict storage time non-destructively. Ten chicken breast samples were analysed at different freezing storage times (0, 3, 6, 9, and 12 weeks) at -18°C, with colour measurements performed directly on each sample. Lightness (L*) increased from 51.82 ± 1.1 to 58.21 ± 1.0 units, redness (a*) decreased from 3.25 ± 0.2 to 2.12 ± 0.2 units, and yellowness (b*) increased from 8.12 ± 0.5 to 9.91 ± 0.6 units, resulting in a total colour difference of ΔE = 6.87, and all changes were statistically significant. The multilayer perceptron artificial neural network showed good agreement between predicted and actual storage times (R² ≈ 0.98), demonstrating the potential of colour-based modelling as a rapid, non-destructive tool for monitoring frozen chicken meat quality.

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Published

2026-09-15

Issue

Vol. 57 No. 5 (2026)

Section

Articles

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