Predicción del tiempo de almacenamiento de carne congelada usando modelado de redes neuronales artificiales con valores de color

• Saliha Lakehal University of Batna1, Institute of Veterinary Science and Agricultural Sciences, Department of Veterinary Sciences. Batna, Algeria
• Brahim Lakehal University of Batna2, Institute of Hygiene and Industrial Security. Batna, Algeria

DOI: https://doi.org/10.52973/rcfcv-e33268

Palabras clave: Carne de res, modelado ANN, parámetros de color, tiempo de almacenamiento

Resumen

Entre los diversos métodos disponibles para determinar el tiempo de almacenamiento de la carne congelada, incluidos los análisis basados en propiedades físicas y químicas, el análisis sensorial, en particular los cambios de color, es un aspecto importante de la aceptabilidad de la carne por parte de los consumidores. En este estudio, se empleó una red neuronal artificial (ANN) para predecir el tiempo de almacenamiento de la carne con base en el espacio de color CIELAB, representado por los valores Lab* (L*), (a*) y (b*) medidos por un sistema de visión artificial a intervalos de dos meses durante un período de hasta un año.La topología ANN se optimizó en función de los cambios en los coeficientes de correlación (R²) y los errores cuadráticos medios (MSE), lo que resultó en una red de 60 neuronas en una capa oculta (R² = 0,9762 y MSE = 0,0047). El rendimiento del modelo ANN se evaluó utilizando criterios como desviación absoluta media (MAD), MSE, error cuadrático medio (RMSE), R² y error absoluto medio (MAE), que resultaron ser 0,0344; 0,0047; 0,0687; 0,9762 y 0,0078, respectivamente. En general, estos resultados sugieren qu’el uso de un sistema basado en vision por computadora combinado con inteligencia artificial podría ser una técnica confiable y no destructiva para evaluar la calidad de la carne durante su tiempo de almacenamiento.

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