Predicción del pH en filetes de caballa salazonada usando imágenes hiperespectrales y quimiometría
DOI:
https://doi.org/10.57063/ricay.v1i1.10Palavras-chave:
calidad del pescado, conservación por salazón, perfiles espectrales, aprendizaje automáticoResumo
El objetivo de este estudio fue predecir el pH de la caballa salazonada, como indicador de calidad, mediante la tecnología de las imágenes hiperespectrales acopladas a técnicas quimiométricas. Se adquirieron 35 caballas frescas en un mercado local de Sullana, Perú, estas fueron lavadas, evisceradas y fileteadas para obtener dos filetes sin piel por cada ejemplar, los mismos se sometieron a un proceso de salazón por inmersión en salmuera al 28% y se almacenaron en refrigeración por 6 días. Las evaluaciones de pH y adquisición de espectros se realizaron con potenciómetro y sistema de imágenes hiperespectrales NIR, respectivamente en los días 0, 1, 2, 3, y 6. Las imágenes fueron corregidas, luego se extrajeron los perfiles de la muestra por umbralizado y estos fueron pretratados con el filtro Savitzky-Golay, seguidamente, se implementó el modelo de regresión de mínimos cuadrados parciales (PLSR) con las longitudes de onda completas y optimizadas. Para validar el modelo se aplicaron 30 repeticiones con validación cruzada (K-fold = 5). El mejor rendimiento se obtuvo con PLSR optimizado con 9 variables laten- tes, logrando un R2 superior a 0.85 y un RMSE de 0.904. Por tanto, es viable el uso de HSI NIR con PLSR para monitoreo del pH en pescado salazonado.
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