Beriain María José, Ibañez Francisco C, Beruete Edurne, Gómez Inmaculada, Beruete Miguel
Instituto de Innovación y Sostenibilidad en la Cadena Agroalimentaria, Universidad Pública de Navarra, 31006 Pamplona, Spain.
Departamento de Biotecnología y Ciencia de los Alimentos, Universidad de Burgos, 09001 Burgos, Spain.
Foods. 2021 Jan 13;10(1):155. doi: 10.3390/foods10010155.
The aim of this research was to estimate the fatty acid (FA) content of intramuscular fat from beef by Fourier transform mid-infrared (FT-MIR) spectroscopy. Four diets were supplemented in 10% linseed (LS) and/or 2% conjugated linoleic acid (CLA): CON (without L or CLA), LS, CLA, and LS+CLA. For each diet, 12 young Holstein bulls were allocated. The spectral response of the beef samples was analyzed applying FT-MIR spectroscopy (from 400 to 4000 cm) and predictive models were developed using partial least square regression with cross-validation. The obtained coefficients ( ) for some FA, such as α-linolenic acid with a = 0.96 or -3 polyunsaturated fatty acids (-3 PUFA) with = 0.93, demonstrate that FT-MIR spectroscopy is a valid technique to estimate the content of FA. In addition, samples were correctly classified according to the animal diet using discriminant analysis in the region 3000-1000 cm. The obtained results suggest that the FT-MIR spectroscopy could be a viable technique for routine use in quality control because it provides fast and sustainable analysis of FA content. Furthermore, this technique allows the rapid estimation of the FA composition, specifically -3 PUFA and CLA, of nutritional interest in meat. It also allows the classification of meat samples by the animal diet.
本研究的目的是通过傅里叶变换中红外（FT-MIR）光谱法估算牛肉肌内脂肪中的脂肪酸（FA）含量。四种日粮分别添加了10%的亚麻籽（LS）和/或2%的共轭亚油酸（CLA）：对照组（不添加L或CLA）、LS组、CLA组和LS+CLA组。每种日粮分配12头年轻的荷斯坦公牛。应用FT-MIR光谱法（400至4000厘米）分析牛肉样品的光谱响应，并使用带有交叉验证的偏最小二乘回归建立预测模型。一些脂肪酸（如α-亚麻酸，相关系数为0.96；或-3多不饱和脂肪酸（-3 PUFA），相关系数为0.93）所获得的系数表明，FT-MIR光谱法是估算脂肪酸含量的有效技术。此外，在3000-1000厘米区域使用判别分析，根据动物日粮对样品进行了正确分类。所得结果表明，FT-MIR光谱法可能是质量控制中常规使用的可行技术，因为它能对脂肪酸含量进行快速且可持续的分析。此外，该技术能够快速估算肉类中具有营养意义的脂肪酸组成，特别是-3 PUFA和CLA。它还能根据动物日粮对肉类样品进行分类。
