The Experts below are selected from a list of 5652 Experts worldwide ranked by ideXlab platform
Xiaojing Tian - One of the best experts on this subject based on the ideXlab platform.
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Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork
Hindawi-Wiley, 2019Co-Authors: Xiaojing Tian, Jun Wang, Zhenbo WeiAbstract: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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discrimination of pork chicken adulteration in minced Mutton by electronic taste system
International Journal of Food Science and Technology, 2018Co-Authors: Xiaojing Tian, Jun Wang, Ruiqian ShenAbstract:The adulteration of minced Mutton was studied by an electronic taste system with cross‐sensitive sensor array, providing a global liquid and taste perception to soluble flavour compounds in meat. The responses of taste sensors to adulterated Mutton were collected, and analysed by multivariate data analysis methods. For discrimination of meat species and adulterated Mutton with different content of pork/chicken, canonical discriminant analysis (CDA) and bayes discriminant analysis (BDA) and principle component analysis (PCA) were employed. The PCA and CDA results showed that meat of different species could be distinguished by E‐tongue responses. The CDA and BDA results showed effective classification results. For prediction of pork/chicken content in adulterated Mutton, Multiple linear regression (MLR), Partial least square analysis (PLS) and Least Squares Support Vector Machines (LS‐SVM) were used, and the results were compared, finding that LS‐SVM was proved to be the most effective method for the prediction of pork/chicken content.
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analysis of pork adulteration in minced Mutton using electronic nose of metal oxide sensors
Journal of Food Engineering, 2013Co-Authors: Xiaojing Tian, Jun Wang, Shaoqing CuiAbstract:Abstract 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.
N. Requena - One of the best experts on this subject based on the ideXlab platform.
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Historical and contemporary evidence of a Mutton snapper (Lutjanus analis Cuvier, 1828) spawning aggregation fishery in decline
Coral Reefs, 2008Co-Authors: R. T. Graham, R. Carcamo, K. L. Rhodes, C. M. Roberts, N. RequenaAbstract:Scientific information on reef fish spawning aggregation fisheries is sparse in light of numerous regional declines and extirpations from overexploitation. Fisher interviews of the small-scale commercial Mutton snapper ( Lutjanus analis ) spawning aggregation fishery at Gladden Spit, Belize, suggests a historic decadal decline. The reported trend is supported by analysis of inter-seasonal catch and effort and yield (2000–2002) that reveals a 59% decline in catch per unit effort (CPUE) and a 22% decrease in mean landings per boat. Declining population-level trends are also supported by a significant decrease in inter-annual median lengths of Mutton snappers (2000–2006). These findings demonstrate the need for additional life history information that includes length-associated age and details on growth to provide clearer support of the effects on, and responses by, populations following fishing. In view of the historical changes to Mutton snapper CPUE and landings at Gladden Spit and the fishery-associated declines in fish spawning aggregations observed globally, a precautionary approach to spawning aggregation management is warranted that provides full protection from fishing to enhance population persistence. The findings also highlight the need for substantially greater enforcement and long-term fisheries monitoring under a comprehensive regional management strategy.
R. T. Graham - One of the best experts on this subject based on the ideXlab platform.
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Historical and contemporary evidence of a Mutton snapper (Lutjanus analis Cuvier, 1828) spawning aggregation fishery in decline
Coral Reefs, 2008Co-Authors: R. T. Graham, R. Carcamo, K. L. Rhodes, C. M. Roberts, N. RequenaAbstract:Scientific information on reef fish spawning aggregation fisheries is sparse in light of numerous regional declines and extirpations from overexploitation. Fisher interviews of the small-scale commercial Mutton snapper ( Lutjanus analis ) spawning aggregation fishery at Gladden Spit, Belize, suggests a historic decadal decline. The reported trend is supported by analysis of inter-seasonal catch and effort and yield (2000–2002) that reveals a 59% decline in catch per unit effort (CPUE) and a 22% decrease in mean landings per boat. Declining population-level trends are also supported by a significant decrease in inter-annual median lengths of Mutton snappers (2000–2006). These findings demonstrate the need for additional life history information that includes length-associated age and details on growth to provide clearer support of the effects on, and responses by, populations following fishing. In view of the historical changes to Mutton snapper CPUE and landings at Gladden Spit and the fishery-associated declines in fish spawning aggregations observed globally, a precautionary approach to spawning aggregation management is warranted that provides full protection from fishing to enhance population persistence. The findings also highlight the need for substantially greater enforcement and long-term fisheries monitoring under a comprehensive regional management strategy.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork
Hindawi-Wiley, 2019Co-Authors: Xiaojing Tian, Jun Wang, Zhenbo WeiAbstract: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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discrimination of pork chicken adulteration in minced Mutton by electronic taste system
International Journal of Food Science and Technology, 2018Co-Authors: Xiaojing Tian, Jun Wang, Ruiqian ShenAbstract:The adulteration of minced Mutton was studied by an electronic taste system with cross‐sensitive sensor array, providing a global liquid and taste perception to soluble flavour compounds in meat. The responses of taste sensors to adulterated Mutton were collected, and analysed by multivariate data analysis methods. For discrimination of meat species and adulterated Mutton with different content of pork/chicken, canonical discriminant analysis (CDA) and bayes discriminant analysis (BDA) and principle component analysis (PCA) were employed. The PCA and CDA results showed that meat of different species could be distinguished by E‐tongue responses. The CDA and BDA results showed effective classification results. For prediction of pork/chicken content in adulterated Mutton, Multiple linear regression (MLR), Partial least square analysis (PLS) and Least Squares Support Vector Machines (LS‐SVM) were used, and the results were compared, finding that LS‐SVM was proved to be the most effective method for the prediction of pork/chicken content.
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analysis of pork adulteration in minced Mutton using electronic nose of metal oxide sensors
Journal of Food Engineering, 2013Co-Authors: Xiaojing Tian, Jun Wang, Shaoqing CuiAbstract:Abstract 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. M. Roberts - One of the best experts on this subject based on the ideXlab platform.
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Historical and contemporary evidence of a Mutton snapper (Lutjanus analis Cuvier, 1828) spawning aggregation fishery in decline
Coral Reefs, 2008Co-Authors: R. T. Graham, R. Carcamo, K. L. Rhodes, C. M. Roberts, N. RequenaAbstract:Scientific information on reef fish spawning aggregation fisheries is sparse in light of numerous regional declines and extirpations from overexploitation. Fisher interviews of the small-scale commercial Mutton snapper ( Lutjanus analis ) spawning aggregation fishery at Gladden Spit, Belize, suggests a historic decadal decline. The reported trend is supported by analysis of inter-seasonal catch and effort and yield (2000–2002) that reveals a 59% decline in catch per unit effort (CPUE) and a 22% decrease in mean landings per boat. Declining population-level trends are also supported by a significant decrease in inter-annual median lengths of Mutton snappers (2000–2006). These findings demonstrate the need for additional life history information that includes length-associated age and details on growth to provide clearer support of the effects on, and responses by, populations following fishing. In view of the historical changes to Mutton snapper CPUE and landings at Gladden Spit and the fishery-associated declines in fish spawning aggregations observed globally, a precautionary approach to spawning aggregation management is warranted that provides full protection from fishing to enhance population persistence. The findings also highlight the need for substantially greater enforcement and long-term fisheries monitoring under a comprehensive regional management strategy.
