Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice

Abstract

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.