Classification of Beef longissimus thoracis Muscle Tenderness Using Hyperspectral Imaging and Chemometrics

• Published in: Foods Vol. 11; no. 19; p. 3105
• Main Authors: León-Ecay, Sara, López-Maestresalas, Ainara, Murillo-Arbizu, María Teresa, Beriain, María José, Mendizabal, José Antonio, Arazuri, Silvia, Jarén, Carmen, Bass, Phillip D., Colle, Michael J., García, David, Romano-Moreno, Miguel, Insausti, Kizkitza
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06-10-2022; MDPI
• Subjects: Aging; Algorithms; Beef; Chemometrics; Classification; Collagen; Discriminant analysis; Food science; HSI; Hyperspectral imaging; Meat; Meat industry; Meat processing industry; Meat quality; Muscles; Nondestructive testing; PLS-DA; Quality assessment; Quality control; Software; Spectrum analysis; Starters; texture; United Kingdom > UK; Spain
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods11193105

Nowadays, 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.