Lamb Quality Testing Electronic Nose And Electronic Tongue Based

The production of mutton in China has reached4.1million tons in2010, occupying a third of the world production of mutton. China has been a great nation of mutton production and consumption, with a growing consumption by an average growth rate of10%a year. Mutton is mostly frozen stored, transported and distributed to adjust meat market due to the geographical differences between producer and consumer market. The eating quality and economic value of mutton is greatly affected by the repeated thawing and freezing cycles caused by the imperfect conditions of cold chains in the processing, transportation and distribution process. What’s more, Adulteration of meat, involving the replacement of selected breeds, particular geographical region or particular traditional method with other cheaper animal proteins and even none meat proteins (soy proteins), has jeopardized the market regulation and consumers’ health, and religion belief of Muslims. Electronic nose and electronic tongue were employed to detect samples of mutton, pork, chicken, mutton with different freeze-thaw cycles, and minced mutton adulterated with different content of pork or chicken. Combined with pattern recognition methods, meat samples were classified according to their origin, and the physicochemical parameters and content of adulterants in minced meat were predicted using predictive models. The main conclusions are as follows:With the help of One-way analysis of variance (ANOVA) and principle component analysis (PCA), the optimum experimental parameters were acquired with15g of mutton sample extracted by100mL K.C1solution for electronic tongue,10g sample with30min headspace-generated time in250mL beaker with a flow rate of200mL/min for electronic nose. With the optimized experimental parameters, meat samples of pork, chicken and mutton were detected and analyzed by PCA and canonical discriminant analysis (CDA). Results were obtained that meat of different kinds could be discriminated both by electronic tongue and electronic nose.Electronic nose, electronic tongue and physicochemical parameters detection of total volatile basic nitrogen (TVB-N), pH, Color values of L*, a*and b*were employed to monitoring the effect of freeze-thaw cycles on mutton. Mutton samples with different freeze-thaw cycles were successfully differentiated by electronic nose, electronic tongue and their fusion data by PCA and CDA. Cycles of mutton freeze-thawed and the physicochemical parameter (except b*) were effectively predicted by methods of partial least squares regression (PLS), multiple linear regression (MLR) and principle component regression (PCR) using signals of electronic nose, electronic tongue. Electronic nose, electronic tongue and their fusion data have the ability in discrimination of mutton samples with different freeze-thaw cycles and in prediction of Cycles of mutton freeze-thawed and the physicochemical parameter. What’s more, better results were obtained by combining of electronic nose and electronic tongue.Fast authentication of meat was conducted by electronic nose and electronic tongue on minced mutton adulterated with pork, chicken at levels of0%,20%,40%,60%,80%and100%by weight, respectively. For electronic nose, the false rate and numbers of misclassified samples were used as evaluation criterion to estimate feature extraction methods, Principle component analysis, loading analysis and stepwise linear discriminant analysis (step-LDA) employed to optimize the data matrix of electronic nose. The adulterated mutton samples were successfully discriminated by PCA and CDA using data set extracted by step-LDA with correct accuracy as high as99.17%. Effective predictive models for the pork, chicken content in minced mutton built by PLS, MLR and Back propagation neural network (BPNN) were obtained with R2>0.9, the prediction error within10%.For electronic tongue, the adulterated mutton samples were grouped according to their content of chicken with few misclassified samples by means of PCA, and better classification were found by CDA with more samples correctly clustered. Predictive model for the pork, chicken content in minced mutton built by PLS, MLR and LS-SVM were obtained the highest R2of0.99, the lowest prediction error within3%. Adulterated mutton could be discriminated and successfully predicted both by electronic nose and electronic tongue by PLS, MLR and LS-SVM, and better results were obtained by signals of electronic nose.Data fusion methods were explored for integration of smell and taste information of minced mutton adulterated with pork and chicken obtained by electronic nose and electronic tongue by methods of combination of signals of electronic nose and electronic tongue data, normalization, data extraction by step-LDA and principle component analysis. The accuracy rate of discrimination and precision of prediction were all improved by the fusion of electronic nose and electronic tongue data.Integration of smell and taste information made the combination use of electronic nose and electronic tongue generating overall and comprehensive results of mutton samples, improving the identification accuracy and the validity of the quantitative prediction model.