Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; Laboratory of Bio-Sensing Engineering, Division of Environmental Science & Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan; College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China.
Laboratory of Bio-Sensing Engineering, Division of Environmental Science & Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan.
Food Chem. 2019 Jul 30;287:369-374. doi: 10.1016/j.foodchem.2019.02.119. Epub 2019 Mar 2.
We investigated three-dimensional (3-D) fluorescence spectroscopy for its potential to evaluate beef quality deteriorative changes and freshness. The fluorescence characteristics of heme, conjugated Schiff base and amino acids, could be indicators of internal biochemical reactions associated with beef deterioration, including color changes, lipid oxidation, and protein degradation, as well as a measure of freshness decline. To classify beef quality in terms of color (sensory index) and pH (chemical index), cluster analysis method (CA) was used. Three classes were identified: "fresh", "acceptable", "spoiled". We then developed a qualitative model to classify stored beef into these three classes using 3-D front-face excitation-emission matrices (EEMs) of fat tissue, combined with a parallel factor analysis (PARAFAC) algorithm. The resulting model had calibration and validation accuracies of 95.56% and 93.33%, respectively. These results demonstrate the potential of fluorescence spectroscopy to accurately and non-destructively monitor beef quality decline.
我们研究了三维（3-D）荧光光谱法，以评估其评估牛肉质量恶化和新鲜度的潜力。血红素、共轭希夫碱和氨基酸的荧光特性可以作为与牛肉变质相关的内部生化反应的指标，包括颜色变化、脂质氧化和蛋白质降解，以及衡量新鲜度下降的指标。为了根据颜色（感官指数）和 pH 值（化学指数）对牛肉质量进行分类，我们使用了聚类分析方法（CA）。确定了三个类别：“新鲜”、“可接受”、“变质”。然后，我们使用脂肪组织的三维前向激发-发射矩阵（EEM）结合平行因子分析（PARAFAC）算法，开发了一种定性模型，将储存的牛肉分类为这三个类别。所得模型的校准和验证精度分别为 95.56%和 93.33%。这些结果表明荧光光谱法具有准确、非破坏性监测牛肉质量下降的潜力。
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