6533b7d8fe1ef96bd126a44f
RESEARCH PRODUCT
Prediction of banana quality indices from color features using support vector regression
Alireza SanaeifarAdel BakhshipourMiguel De La Guardiasubject
Support Vector Machinemedia_common.quotation_subjectOrganolepticColorHSL and HSVColor space01 natural sciencesAnalytical Chemistry0404 agricultural biotechnologyArtificial IntelligenceQuality (business)Radial basis functionmedia_commonArtificial neural networkChemistrybusiness.industry010401 analytical chemistryMusaPattern recognitionPigments Biological04 agricultural and veterinary sciences040401 food science0104 chemical sciencesSupport vector machineRGB color modelNeural Networks ComputerArtificial intelligencebusinessForecastingdescription
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).
year | journal | country | edition | language |
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2015-09-09 | Talanta |