Tejerina David, Oliván Mamen, García-Torres Susana, Franco Daniel, Sierra Verónica
Centro de Investigaciones Científicas y Tecnológicas de Extremadura (CICYTEX-La Orden), Junta de Extremadura, Guadajira, 06187 Badajoz, Spain.
Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Carretera AS-267, PK 19, 33300 Villaviciosa, Spain.
Foods. 2022 Oct 20;11(20):3274. doi: 10.3390/foods11203274.
The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; = 50), Rubia Gallega (RG; = 37), and Retinta (RE; = 42) was assessed. The results obtained by partial least squares-discriminant analysis (PLS-DA) indicated successful discrimination between Normal and DFD samples of meat from AV and RG (with sensitivity over 93% for both and specificity of 100 and 72%, respectively), while RE and total sample sets showed poorer results. Soft independent modelling of class analogies (SIMCA) showed 100% sensitivity for DFD meat in total, AV, RG, and RE sample sets and over 90% specificity for AV, RG, and RE, while it was very low for the total sample set (19.8%). NIRS quantitative models by partial least squares regression (PLSR) allowed reliable prediction of color parameters (CIE L*, a*, b*, hue, chroma). Results from qualitative and quantitative assays are interesting in terms of early decision making in the meat production chain to avoid economic losses and food waste.
评估了近红外反射光谱（NIRS）鉴别正常牛肉和DFD（深色、坚硬和干燥）牛肉以及预测来自三个西班牙纯种牛品种（阿斯图里亚纳山谷牛（AV；n = 50）、加利西亚红牛（RG；n = 37）和雷廷塔牛（RE；n = 42））的129个胸最长肌（LT）样本质量性状的潜力。通过偏最小二乘判别分析（PLS-DA）获得的结果表明，AV和RG的正常肉与DFD肉样本之间能够成功鉴别（两者的灵敏度均超过93%，特异性分别为100%和72%），而RE和总样本集的结果较差。类类比软独立建模（SIMCA）显示，在总样本集、AV、RG和RE样本集中，DFD肉的灵敏度为100%，AV、RG和RE的特异性超过90%，而总样本集的特异性非常低（19.8%）。通过偏最小二乘回归（PLSR）建立的NIRS定量模型能够可靠地预测颜色参数（CIE L*、a*、b*、色调、色度）。定性和定量分析结果对于肉类生产链中的早期决策以避免经济损失和食物浪费而言很有意义。
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