Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork
Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork
复制标题
电子鼻与电子舌相结合的猪肉碎羊肉掺假检测
DOI：
10.1155/2019/4342509
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发表时间：
2019-04
影响因子：
3.3
通讯作者：
Wei Zhenbo
中科院分区：
文献类型：
--
作者：
Tian Xiaojing;Wang Jun;Ma Zhongren;Li Mingsheng;Wei Zhenbo
利用电子鼻和电子舌组成的电子面板对掺有不同比例猪肉的羊肉糜进行感官鉴别。同时，采用归一化、逐步线性判别分析（step-LDA）和主成分分析对电子鼻和电子舌的数据矩阵进行融合。采用典型判别分析（CDA）和贝叶斯判别分析（BAD）对判别结果进行评价和比较。结果表明，组合系统的识别能力（分类误差0%~ 1.67%）上级或等于两种仪器单独使用的识别能力，电子舌系统的识别准确率（电子舌的分类误差为0 ~ 2.5%）高于电子鼻系统（电子鼻的分类误差为0.83%~ 10.83%）。对于组合系统，6个电子鼻PC和5个电子舌PC的提取数据的组合被证明是最有效的方法。为了预测掺假羊肉中猪肉的比例，采用多元线性回归（MLR）、偏最小二乘（PLS）和反向传播神经网络（BPNN）回归模型，并对结果进行比较，旨在建立有效的预测模型。电子舌、电子鼻、电子鼻与电子舌融合数据与肉末中猪肉比例之间具有良好的相关性，在标定和验证数据集中相关系数均大于0.90。BPNN被证明是最有效的方法，用于预测猪肉比例的校正和验证数据集的R2均大于0.97。这些结果表明，电子鼻和电子舌的集成可以用于羊肉掺假的检测。
An E-panel, comprising an electronic nose (E-nose) and an electronic tongue (E-tongue), was used to distinguish the organoleptic characteristics of minced mutton adulterated with different proportions of pork. Meanwhile, the normalization, stepwise linear discriminant analysis (step-LDA), and principle component analysis were employed to merge the data matrix of E-nose and E-tongue. The discrimination results were evaluated and compared by canonical discriminant analysis (CDA) and Bayesian discriminant analysis (BAD). It was shown that the capability of discrimination of the combined system (classification error 0%∼1.67%) was superior or equable to that obtained with the two instruments separately, and E-tongue system (classification error for E-tongue 0∼2.5%) obtained higher accuracy than E-nose (classification error 0.83%∼10.83% for E-nose). For the combined system, the combination of extracted data of 6 PCs of E-nose and 5 PCs of E-tongue was proved to be the most effective method. In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. Good correlations were found between the signals obtained from E-tongue, E-nose, and fusion data of E-nose and E-tongue and proportions of pork in minced mutton with correlation coefficients higher than 0.90 in the calibration and validation data sets. And BPNN was proved to be the most effective method for the prediction of pork proportions with R2 higher than 0.97 both for the calibration and validation data set. These results indicated that integration of E-nose and E-tongue could be a useful tool for the detection of mutton adulteration.
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影响因子：
8.4
作者：
Di Natale, C;Paolesse, R;Vlasov, Y
通讯作者：
Vlasov, Y
影响因子：
5.5
作者：
Tian, Xiaojing;Wang, Jun;Cui, Shaoqing
通讯作者：
Cui, Shaoqing
影响因子：
7.1
作者：
Rahman, Md Mahfujur;Ali, Md Eaqub;Hanapi, Ummi Kalthum
通讯作者：
Hanapi, Ummi Kalthum
影响因子：
8.8
作者：
Cosio, M. S.;Ballabio, D.;Gigliotti, C.
通讯作者：
Gigliotti, C.
影响因子：
4.3
作者：
Rodríguez-Méndez, ML;Arrieta, AA;de Saja, JA
通讯作者：
de Saja, JA