Tenderness of PGI "Ternera de Navarra" Beef Samples Determined by FTIR-MIR Spectroscopy.

Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) "Ternera de Navarra", the primary goal of this study was to use Fourier transform infrared spectroscopy on the middle infrared region (FTIR-MIR) as a tool for the examination of meat tenderness evolution throughout ageing. Samples of the longissimus dorsi muscle of twenty young bulls were aged for 4, 6, 11, or 18 days at 4 °C. Animal carcass classification and sample proximate analysis were performed to check sample homogeneity. Raw aged steaks were analyzed by FTIR-MIR spectroscopy (4000-400 cm-1) to record the vibrational spectrum. Texture profile analysis was performed using a multiple compression test (compression rates of 20%, 80%, and 100%). Compression values were found to decrease notably between the fourth and sixth day of ageing for the three compression rates studied. This tendency continued until the 18th day for C20. For C80 and C100, there was not a clear change in the 11th and 18th days of the study. Regarding FTIR-MIR as a prediction method, it achieved an R2 lower than 40%. Using principal component analysis (PCA) of the results, the whole spectrum fingerprint was used in the discrimination of the starting and final ageing days with correct maturing time classifications. Combining the PCA treatment together with the discriminant analysis of spectral data allowed us to differentiate the samples between the initial and the final ageing points, but it did not single out the intermediate points.

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.

Indicators and Recommendations for Assessing Sustainable Healthy Diets.

Research coupling human nutrition and sustainability concerns is a rapidly developing field, which is essential to guide governments' policies. This critical and comprehensive review analyzes indicators and approaches to "sustainable healthy diets" published in the literature since this discipline's emergence a few years ago, identifying robust gauges and highlighting the flaws of the most commonly used models. The reviewed studies largely focus on one or two domains such as greenhouse gas emissions or water use, while overlooking potential impact shifts to other sectors or resources. The present study covers a comprehensive set of indicators from the health, environmental and socio-economic viewpoints. This assessment concludes that in order to identify the best food option in sustainability assessments and nutrition analysis of diets, some aspects such as the classification and disaggregation of food groups, the impacts of the rates of local food consumption and seasonality, preservation methods, agrobiodiversity and organic food and different production systems, together with consequences for low-income countries, require further analysis and consideration.

Raw-Cured Spanish Traditional Meat Product "Chistorra de Navarra": Sensory and Composition Quality Standards.

The aim of this work was to set the quality standards of the chistorra de Navarra, a raw-cured Spanish traditional meat product, through the study of its sensory and physicochemical features. The quality of chistorra samples, coming from 50 different artisan producers, were assessed during three sessions by expert assessors (n = 15). In the first session, instrumental colour (L*, a*, and b*) and appearance and odour parameters were evaluated in the raw products. In the second session, texture and flavour attributes were determined in cooked products. Finally, in the third session, the best 10 classified chistorras from the first and second sessions were sensorially evaluated and sampled for further analysis: texture (Warner Bratzler and texture profile analysis (TPA)), chemical composition, and fatty acid profile. The chistorras with the highest sensory scores had high shear force values, flavour intensity, and fat/hydroxyproline ratio. The average fatty acid profile obtained for chistorra de Navarra was: 42% saturated fatty acids (SFA), 45% monounsaturated fatty acids (MUFA), and 13% polyunsaturated fatty acids (PUFA), which was similar to the one found in other raw-cured sausages. Considering the sensory evaluation, chistorra was defined as a product with an intense orange colour, and with high resistance value in the initial bite. It was also characterised by a high juiciness and tenderness, aroma, and meat flavour. In mouth, the pork fat, one of the ingredients of chistorra, was balanced without any of the ingredients dominating. Chemically, the chistorra was characterised by a fat content close to 67% (dry matter), low hydroxyproline occurrence (≤0.6), and protein amount ranging 18-38%.