Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors
Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors
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DOI：
10.1016/j.jfoodeng.2013.07.004
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发表时间：
2013-12-01
影响因子：
5.5
通讯作者：
Cui, Shaoqing
中科院分区：
文献类型：
--
作者：
Tian, Xiaojing;Wang, Jun;Cui, Shaoqing
目的是应用传统方法(pH和色泽评价)和电子鼻检测羊肉中的掺假现象，建立预测羊肉中猪肉含量的模型。金属氧化物传感器的电子鼻被用来收集样品中的挥发物。采用特征提取、主成分分析、载荷分析和逐步线性判别分析等方法对数据矩阵进行优化。用判别分析方法对结果进行评价，发现逐步LDA法是最有效的方法。然后利用典型判别分析(CDA)作为模式识别技术对肉类进行鉴定。利用偏最小二乘分析(PLS)、多元线性回归(MLR)和反向传播神经网络(BPNN)建立了羊肉脯中猪肉含量的预测模型。与偏最小二乘法和最大似然回归法相比，BP神经网络建立的模型能更准确地预测掺假。(C)2013爱思唯尔有限公司。保留所有权利。
The aims were to detect the adulteration of mutton by applying traditional methods (pH and color evaluation) and the E-nose, to build a model for prediction of the content of pork in minced mutton. An E-nose of metal oxide sensors was used for the collection of volatiles presented in the samples. Feature extraction methods, Principle component analysis (PCA), loading analysis and Stepwise linear discriminant analysis (step-LDA) were employed to optimize the data matrix. The results were evaluated by discriminant analysis methods, finding that step-LDA was the most effective method. Then Canonical discriminant analysis (CDA) was used as pattern recognition techniques for the authentication of meat. Partial least square analysis (PLS), Multiple Linear Regression (MLR) and Back propagation neural network (BPNN) were used to build a predictive model for the pork content in minced mutton. The model built by BPNN could predict the adulteration more precisely than PLS and MLR do. (C) 2013 Elsevier Ltd. All rights reserved.