Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China.
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
Food Chem. 2020 Aug 15;321:126628. doi: 10.1016/j.foodchem.2020.126628. Epub 2020 Mar 17.
We investigated the potential of front-face synchronous fluorescence spectroscopy (△λ = 75 nm) to non-destructively evaluate beef freshness and quality decline during chilled storage. The total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and total viable count (TVC) values were used as standard freshness indicators. The fluorescent substances, including amino acids, collagen and conjugated Schiff bases, were highly correlated with the chemical and microbial deterioration of the beef. Quantitative models for simultaneously predicting the three freshness indicators were built combined with partial least squares (PLS) algorithm and showed good reliability. For TVB-N and TBARS values, R and R were both above 0.900, and for TVC values R and R were 0.912 and 0.871, respectively. The qualitative model established by partial least squares discriminant analysis (PLS-DA) algorithm could accurately classify beef samples as fresh, acceptable or spoiled. The accuracy of the calibration and validation sets were 92.54% and 86.96%, respectively.
我们研究了前向同步荧光光谱（△λ=75nm）在无损评估冷藏过程中牛肉新鲜度和质量下降方面的潜力。总挥发性碱性氮（TVB-N）、硫代巴比妥酸反应物（TBARS）和总活菌数（TVC）值被用作标准新鲜度指标。荧光物质，包括氨基酸、胶原蛋白和共轭希夫碱，与牛肉的化学和微生物恶化高度相关。结合偏最小二乘法（PLS）算法，建立了同时预测这三个新鲜度指标的定量模型，具有良好的可靠性。对于 TVB-N 和 TBARS 值，R 和 R 均高于 0.900，对于 TVC 值，R 和 R 分别为 0.912 和 0.871。偏最小二乘判别分析（PLS-DA）算法建立的定性模型可以准确地将牛肉样品分类为新鲜、可接受或变质。校准集和验证集的准确率分别为 92.54%和 86.96%。
