title: Prediction of banana quality indices from color features using support vector regression.
abstract: Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN).
authors.affiliation: Biosystems Engineering Department, Shiraz University, Shiraz, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Sanaeifar
authors.affiliation: Biosystems Engineering Department, Shiraz University, Shiraz, Iran.
authors.forename: Adel
authors.initials: A
authors.lastname: Bakhshipour
authors.affiliation: Department of Analytical Chemistry, University of Valencia, 46100 Burjassot, Spain. Electronic address: Miguel.Delaguardia@uv.es.
authors.forename: Miguel
authors.initials: M
authors.lastname: de la Guardia
mesh_terms: D001185:Artificial Intelligence / Q000639:trends; D003116:Color; D005544:Forecasting; D028521:Musa / Q000737:chemistry*; D016571:Neural Networks, Computer; D010860:Pigments, Biological / Q000032:analysis*; D060388:Support Vector Machine / Q000639:trends
chemical_list: D010860:Pigments, Biological
keywords: Banana; Color features; Firmness; Support vector regression; Total soluble solids; pH