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An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterise strawberry juices based on different processes (i.e. Microwave Pasteurisation, Steam Blanching, High Temperature Short Time Pasteurisation, Frozen-Thawed, and Freshly Squeezed). Quality parameters (vitamin C, pH, total soluble solid, total acid and sugar/acid ratio) were detected by traditional measuring methods. For qualitative discrimination, E-tongue system reached higher accuracy than E-nose did, and lower than the simultaneous utilisation. For quantitative prediction, E-tongue system had better performance ( R 2 ranged from 0.7294 to 0.8622 for the calibration, and R 2 ranged from 0.7362 to 0.9527 for the validation) than E-nose did ( R 2 ranged from 0.5344 to 0.8741 for the calibration, and R 2 ranged from 0.4444 to 0.7258 for the validation), and worse than the simultaneous utilisation ( R 2 ranged from 0.9627 to 0.9834 for the calibration, and R 2 ranged from 0.8553 to 0.8959 for the validation). Either E-nose or E-tongue was suited to classify strawberry juice, but was insufficient to predict the values of quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio). This work indicates that the simultaneous utilisation of E-nose and E-tongue can discriminate processed juices and predict juice quality parameters successfully for the food industry. [ABSTRACT FROM AUTHOR]

Abstract: Various clustering algorithms have been developed since conventional hierarchical cluster analysis (HCA) and partitioning clustering algorithms have their own limitations and scopes of applications. However, in the area of e-nose where clustering is applied, the conventional algorithms (mostly HCA) still play a dominant role. In addition, comparison among different clustering methods or validation of clustering results was seldom mentioned. In this paper, we present a state-of-the-art clustering method – spectral clustering – and compare it with six conventional clustering methods: K-clustering (ISODATA, FCM and k-means) and HCA (single linkage, complete linkage and Ward's). Three external validation criteria – mutual information criteria (MI), precision and rand index (RI) – were used to evaluate clustering performances on three independent e-nose datasets. The spectral clustering outperforms with statistical significance (alpha=0.05) the performance of other methods, and the single linkage presents the worst (unacceptable) clustering result. In addition, the proposed approach – cluster validation criteria in combination with majority voting – in a way makes clustering a semi-supervised classification technique. Using this approach it is possible to compare clustering based semi-supervised methods with classification methods to find which method is better for discrimination of a certain e-nose dataset. [Copyright &y& Elsevier]

An electronic nose (E-nose) has been used to characterize five types of strawberry juices based on different processing approaches (i.e., Microwave Pasteurization, Steam Blanching, High Temperature Short Time Pasteurization, Frozen–Thawed, and Freshly Squeezed). Juice quality parameters (vitamin C and total acid) were detected by traditional measuring methods. Multivariate statistical methods (Principle Component Analysis, Linear Discriminant Analysis, Multiple Linear Regression, and Partial Least Squares Regression) and neural networks (Extreme Learning Machine (ELM), Learning Vector Quantization and Library Support Vector Machines) were employed for qualitative classification and quantitative regression. ELM showed best performances on classification and regression, indicating that ELM would be a good choice for E-nose data treatment. Results provide promising principles for the elaboration of E-nose which could be used to discriminate processed juices and to predict juice quality parameters based on appropriate algorithms for the beverage industry. [ABSTRACT FROM AUTHOR]