title: Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.
abstract: An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.
authors.affiliation: Department of Biosystems Engineering, Zhejiang University , 388 Yuhangtang Road, Hangzhou 310058, PR China.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Qiu
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Liping
authors.initials: L
authors.lastname: Gao
mesh_terms: D001628:Beverages / Q000032:analysis*; D016002:Discriminant Analysis; D062609:Electronic Nose; D005511:Food Handling; D031985:Fragaria / Q000737:chemistry*; D016018:Least-Squares Analysis; D011786:Quality Control; D060388:Support Vector Machine