title: Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit.
abstract: Tomato is an important health-stimulating fruit because of the antioxidant properties of its main bioactive compounds, dominantly lycopene and phenolic compounds. Nowadays, product differentiation in the fruit market requires an accurate evaluation of these value-added compounds. An experiment was conducted to simultaneously and non-destructively measure lycopene and phenolic compounds content in intact tomatoes using multispectral imaging combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) were applied to develop quantitative models. Compared with PLS and LS-SVM, BPNN model considerably improved the performance with coefficient of determination in prediction (RP(2))=0.938 and 0.965, residual predictive deviation (RPD)=4.590 and 9.335 for lycopene and total phenolics content prediction, respectively. It is concluded that multispectral imaging is an attractive alternative to the standard methods for determination of bioactive compounds content in intact tomatoes, providing a useful platform for infield fruit sorting/grading.
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
authors.forename: Changhong
authors.initials: C
authors.lastname: Liu
authors.affiliation: Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China. Electronic address: yjianbo@263.net.
authors.forename: Jianbo
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China. Electronic address: lzheng@hfut.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Zheng
mesh_terms: D002338:Carotenoids / Q000032:analysis; D005240:Feasibility Studies; D005638:Fruit / Q000737:chemistry; D016018:Least-Squares Analysis; D000077276:Lycopene; D018551:Solanum lycopersicum / Q000737:chemistry*; D016571:Neural Networks, Computer; D010636:Phenols / Q000032:analysis; D060388:Support Vector Machine
chemical_list: D010636:Phenols; D002338:Carotenoids; D000077276:Lycopene
keywords: Bioactive compounds; Chemometric; Multispectral imaging; Non-destructive determination; Tomato fruit