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The aim of this research was to find a model based on hyperspectral images to predict the freshness of Sciaena delicious. In the investigation, samples of Sciaena deliciosa “lorna” were used, which after being captured, were placed in a cooler with ice and transported to the laboratory for their respective analysis. For the sensory analysis the sensory scheme proposed by Imarpe (Peru) for fatty species was used. Regarding hyperspectral images, each specimen was placed in the equipment to analyze the vitreous humor of the eye and obtain the corresponding spectral footprint. For the refractive index, the vitreous humor of the eye was removed and placed in the refractometer to quantify the degree of alteration. All these results were correlated to predict freshness, looking for the most appropriate model. The prediction model obtained for the freshness of fish had an R2 = 0.6359, adequate value to consider it as a model of possible practical use.

Prediction of the freshness of Sciaena delicious "lorna" using hyperspectral images