León Ecay, SaraLópez Maestresalas, AinaraMurillo Arbizu, María TeresaBeriain Apesteguía, María JoséMendizábal Aizpuru, José AntonioArazuri Garín, SilviaJarén Ceballos, CarmenBass, Phillip D.Colle, Michael J.García, DavidRomano Moreno, MiguelInsausti Barrenetxea, Kizkitza2022-10-112022-10-112022León-Ecay, S., López-Maestresalas, A., Murillo-Arbizu, M. T., Beriain, M. J., Mendizabal, J. A., Arazuri, S., Jarén, C., Bass, P. D., Colle, M. J., García, D., Romano-Moreno, M., & Insausti, K. (2022). Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics. Foods, 11(19), 3105.2304-815810.3390/foods11193105https://academica-e.unavarra.es/handle/2454/44194Nowadays, the meat industry requires non-destructive, sustainable, and rapid methods that can provide objective and accurate quality assessment with little human intervention. Therefore, the present research aimed to create a model that can classify beef samples from longissimus thoracis muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples with WBSF < 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values 53 N. Then, classification models were created by applying the partial least squares discriminant analysis (PLS-DA) method. The best results were achieved by combining the following pre-processing algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and 67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI technology combined with chemometrics has the potential to differentiate and classify meat samples according to their textural characteristics.application/pdfeng© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseMeat qualityTextureHSIPLS-DAChemometricsArtículo / Artikulua2022-10-10Acceso abierto / Sarbide irekiainfo:eu-repo/semantics/openAccess