Vegetable By-Products as Alternative and Sustainable Raw Materials for Ruminant Feeding: Nutritive Evaluation and Their Inclusion in a Novel Ration for Calf Fattening.

This research aimed to evaluate the nutritional composition, in vitro digestibility, and gas production kinetics of 15 vegetable by-products generated by the agri-food industry compared with corn silage as a reference raw material. Nutritional characterization and in vitro ruminal fermentation tests were performed to determine in vitro organic matter digestibility and digestible energy values, short-chain fatty acids, and the gas production profile. Results indicate that vegetable by-products were more degradable, more extensively fermented, and fermented at a faster rate than corn silage. Going one step further in the valorization of these by-products in animal feed, the second part of the research aimed to compare the novel ration designed for calf fattening with a conventional one. An artificial rumen unit was used to obtain nutrient disappearance, rumen fermentation parameters, and gas production of rumen digesta. Very slight differences were observed between both experimental rations, with their composition being the main difference. Most of the unitary vegetable by-products and all mixes, as real examples of by-product generation in the agri-food industry, have higher digestibility and a greater nutritional value than corn silage. These by-products showed the potential to be used in ruminant-ensiled rations and could replace part of the ingredients in conventional diets.

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

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.