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当前位置: 首页 > 文章 > 近红外光谱技术对猪肉注水、注胶的快速检测 食品科学 2014,35 (8) 299-303
Position: Home > Articles > Rapid Detection of Meat Injected with Water or Gum by Near Infrared Spectroscopy FOOD SCIENCE 2014,35 (8) 299-303

近红外光谱技术对猪肉注水、注胶的快速检测

作  者：
孟一;张玉华;许丽丹;陈东杰;张应龙;张咏梅
单  位：
山东商业职业技术学院山东省农产品贮运保鲜技术重点实验室
关键词：
近红外光谱;注水肉;注胶肉;判别分析;偏最小二乘法
摘  要：
采用近红外光谱(near infrared spectroscopy,NIR)结合主成分分析(principal component analysis,PCA)和判别分析法建立了注水肉、注胶肉和正常肉的定性判别模型。注水肉中注水量的多少对判别准确率产生影响,当注水量为1.25%~20%时,3种肉的总体判别准确率为94.23%;当注水量为3.75%~20%时,判别准确率提高至96.96%。模型对所有预测集样品的总体判别准确率为94.92%。表明NIR结合PCA法、判别分析法判别注水肉、注胶肉和正常肉具有可行性。采用偏最小二乘法(partial least squares,PLS)结合PCA分别建立了注水量和注胶量的定量分析模型,经验证,两种模型对预测集样品的预测均方差分别为4.01%和3.87%,预测值与实测值间的相关系数(r)分别为0.904 2和0.912 8。表明两种模型的预测性能良好。
译  名：
Rapid Detection of Meat Injected with Water or Gum by Near Infrared Spectroscopy
作  者：
MENG Yi;ZHANG Yu-hua;XU Li-dan;CHEN Dong-jie;ZHANG Ying-long;ZHANG Yong-mei;Shandong Key Laboratory of Storage and Transportation Technology of Agricultural Products, Shandong Institute of Commerce and Technology;National Engineering Research Center for Agricultural Products Logistics;
关键词：
near infrared spectroscopy(NIR);;water-injected meat;;gum-injected meat;;discriminant analysis;;partial least squares
摘  要：
A qualitative model for discriminating water-injected meat, gum-injected meat from normal meat was established by near infrared spectroscopy(NIR) combined with principal component analysis(PCA) and discriminant analysis. The amount of water injection had an impact on the discrimination accuracy. The overall discrimination accuracy between normal and adulterated meat was 94.23% when the amount of water injection was 1.25%–20%, and was increased to 96.96% upon water injection at levels between 3.75% and 20%. The overall discrimination accuracy for all samples in the prediction set was 94.92%. These results show that NIR combined with PCA and discriminant analysis is feasible to discriminate water-injected meat, gum-injected meat from normal meat. Quantitative analysis models of water injection and gum injection were established using partial least squares(PLS) combined with PCA. On the basis of verification, the root mean square errors of prediction(RMSEP) from the two models were 4.01% and 3.87%, respectively, and the correlation coefficients(r) between the predicted values and the actual values were 0.904 2 and 0.912 8, respectively. Therefore, both models have good prediction performance.

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