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- Nir spectroscopy for non-destructive quality evaluation of fishPublication . Rocha, Beatriz Alves da; Saeys, Wouter; Ferreira, António César da SilvaFish freshness is regarded as one major parameter for seafood quality. However, it is lost inevitably in practice after catching and fish death, owing to the natural autolysis process which, in turn, trigger the growth of microorganisms and, consequently, the progressive loss of food characteristics and quality. This phenomenon is perceptible by changes in the sensory characteristics such as appearance, odour, taste and texture of fresh fish as well as in chemical, biochemical and microbiological changes. Fish market prices is highly depended on accurately predict its freshness and shelf-life. Predicted storage time in ice is defined as the number of days that the fish has been stored in ice and it is possible to use these results to estimate the remaining shelf life. Quality Index Method (QIM) is currently the most wholesome and straightforward method of describing freshness. However, it is time consuming and subjective and it is not always suitable for largescale applications. NIR spectroscopy has been proven to be a rapidly and non-destructive method for evaluating fish components (moisture, protein, fat, …) as well as it has shown good predictions errors associated with fish storage time prediction. The purpose of this research is to test the possibility of using NIR spectroscopy for non-destructively predicting freshness levels of plaice fish in Flanders, Belgium. In the preliminary study, spectroscopic measurements were performed on tested plaice samples (n=10) subjected to different storage times assisted with Partial Least Squares Discriminant Analysis (PLSDA) indicated that NIR spectroscopy had great potential for nondestructive plaice freshness discrimination. In the next step, the main study employing NIR diffuse reflectance measurements for plaice samples (n=90) graded using commercial QIM scoring method at ILVO (Flanders, Belgium) together with Partial Least Squares (PLS) regression culminated on two different calibration models for predicting freshness expressed as storage days in ice (converted from the graded QIM scores): one for dark skin measurements with prediction performances of 1.82, 2.22 and 0.804 for RMSECV, RMSEP and R2p, respectively, using the selected wavelength range of 1400 to 1580 nm; and one for white skin measurements with those parameters of 2.356, 2.59 and 0.677 for RMSECV, RMSEP and R2p, respectively, using the full wavelength range studied of 940 to 1700 nm.