The nutritional composition of the diet directly affects the final quality of tench meat (Tinca tinca L.). Thus, in recent years there has been a commitment to replace the protein component of feed with more sustainable vegetable alternatives. The experimental design from which this study is derived consisted of substituting organic fish meal with different percentages of organic soybean meal and pregerminated soybean meal. Therefore, the objective of this study was to evaluate the potential of Near infrared spectroscopy (NIRS) in categorizing tench according to the feed they received during its fattening phase and the quantification of the main nutritional parameters. Different spectral pretreatments were used previous to the partial least squares regressions for qualitative (PLS‑DA) and quantitative (PLSR) predictions. The best PLS-DA model showed an accuracy for classification of 97.5 % in cross‑validation; while the best PLSR model showed a good predictive capacity for dry matter (g/100 g), fat (g/100 g Dry Matter), and γ‑tocopherol (mg/g dry matter) (0.689 ≤ R2vc ≤ 0.804), suggesting the possibility of performing a rapid and in situ control of the traceability and quality of tench meat by means of NIRS technology.

Ortiz A., Fallola C., Labrador J., Martín‑Gallardo J., Rodríguez P., Trenzado C., Pérez‑Jiménez A., García‑Torres S., Tejerina D. (2024). Espectroscopia en el infrarrojo cercano (NIRS) como herramienta para el control de calidad y trazabilidad de la carne de tenca (Tinca tinca). ITEA‑Información Técnica Económica Agraria 120(1): 13-29. https://doi.org/10.12706/itea.2023.014