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.
Highlights
With the advantages of small amount of the sample required, speed, simplicity, high sensitivity, and good correlation with data from sensory analyses, the use of the electronic sensory evaluation of electronic nose (E-nose) and E-tongue to evaluate the quality of meat has become more popular
By fusion of E-nose or E-tongue responses, the discrimination of meat adulteration could be improved by giving the overall sensory evaluation of meat, closer to human judgment. e combination of E-nose and E-tongue had been reported in the researches in geographical origin identification of potato creams [18] and virgin olive oil [19,20,21]; varieties and grade level discrimination of Chinese green tea [22], black tea [23], red wine [24], and fruit juice [25, 26]; cultivars discrimination and characterisation of Perilla frutescens [27] and species differentiation of coffee [28]; freshness evaluation of wine [29] and milk [30]; quality differentiation [31]; and authenticity assessment [32]. ese studies provided references for the in-depth study of the food quality inspection of E-nose and E-tongue
All the hind leg mutton samples detected by E-nose and E-tongue and used for determination of physical properties were obtained from the local logistics center for agricultural products, Hangzhou, China. e hind leg pork samples were purchased from Wal-Mart Stores, Hangzhou, China. e meat samples were brought to laboratory and stored at −18°C
Summary
E adulterated meat samples were brought to room temperature before being detected by E-nose and E-tongue. For detection of E-nose, the optimized detection parameters were as follows: 10 g of the minced meat was placed in a beaker of 250 mL at the temperature of 25°C ± 3°C, and the beaker was sealed by plastic for a headspace generation time of 30 min. For detection by E-tongue, the optimized detection parameters were as follows: the taste substances of minced meat samples were extracted using potassium chloride solution (0.1 mol/L) for 30 minutes at 4°C with a shaking (SKY2112B, SUKUN, China) rate of 1500 rpm. It shows that each sensor has a certain degree of affinity towards specific chemical or volatile compounds. W3S methane-aliph Reacts on high concentration >100 ppm, sometimes very selective (methane) CH4, 100 ppm
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