Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

Abstract

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.