Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, United States.
Meat Sci. 2012 Jul;91(3):232-9. doi: 10.1016/j.meatsci.2012.01.017. Epub 2012 Jan 28.
Existing objective methods to predict sensory attributes of pork in general do not yield satisfactory correlation to panel evaluations, and their applications in meat industry are limited. In this study, a Raman spectroscopic method was developed to evaluate and predict tenderness, juiciness and chewiness of fresh, uncooked pork loins from 169 pigs. Partial Least Square Regression models were developed based on Raman spectroscopic characteristics of the pork loins to predict the values of the sensory attributes. Furthermore, binary barcodes were created based on spectroscopic characteristics of the pork loins, and subjected to multivariate statistical discriminant analysis (i.e., Support Vector Machine) to differentiate and classify pork loins into quality grades ("good" and "bad" in terms of tenderness and chewiness). Good agreement (>83% correct predictions) with sensory panel results was obtained. The method developed in this report has the potential to become a rapid objective assay for tenderness and chewiness of pork products that may find practical applications in pork industry.
现有的客观方法通常无法预测猪肉的感官属性与感官评价之间产生令人满意的相关性，其在肉类行业的应用受到限制。本研究建立了一种拉曼光谱方法，用于评估和预测 169 头新鲜未煮猪里脊肉的嫩度、多汁性和咀嚼性。基于猪里脊肉的拉曼光谱特征，建立了偏最小二乘回归模型，以预测感官属性的值。此外，基于猪里脊肉的光谱特征创建了二进制条码，并进行多元统计判别分析（即支持向量机），以区分和分类里脊肉的质量等级（嫩度和咀嚼性方面的“好”和“坏”）。与感官小组的结果具有很好的一致性（>83%的正确预测）。本报告中开发的方法有可能成为猪肉产品嫩度和咀嚼性的快速客观测定方法，在猪肉行业可能具有实际应用价值。
