| Summary: | 76 pages, 27 figures, 8 tables [EN] The quality and freshness of fishery products has traditionally been determined by sensory evaluation methods, as well as by various analytical and instrumental techniques. However, all of them have several limitations. The first ones are subjective, as they depend on the personal judgment of a panel, while the second ones are generally invasive and destructive, requiring the selection of samples from the production batch and being unable to be applied to a complete production in an online way. Thus, a continuous effort is being made to find efficient methods and tools that allow the determination of quality and freshness in an objective, fast, reliable and non-destructive approach, in order to satisfy the expectations of consumers. In this regard, hyperspectral imaging (HSI) technology is one of the most promising in this field since, combined with multivariate analysis, it can result in a fast, non-invasive and non-destructive technique for the evaluation of fish quality indicators. However, most of the existing studies focus on processed fish, namely filets. In this work, correlation models using partial least squares regression (PLSR) have been developed to predict various quality indicators (quality index (QI) and colorimetric variables L, a and b of the CIELAB color space) in aquaculture whole turbot samples. Satisfactory results have been obtained for the prediction of QI using hyperspectral information collected from the dark side of turbots. This model was developed for 32 samples and 10 components and efficiently estimates the QI value of 9 samples of the external evaluation set, resulting in a normalized error of 1.0212 and a MSE of 8.7929. [ES] La calidad y frescura de los productos de la pesca se ha determinado tradicionalmente mediante métodos de evaluación sensorial, así como con diversas técnicas analíticas e instrumentales. Sin embargo, todos ellos adolecen de diversas limitaciones. Los primeros son subjetivos, pues dependen de la opinión personal de un ...
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