Application of machine vision in drying process modeling of carrot slices

Abstract

In this current research, the drying characteristics of carrot slices dried in a convective hot-air dryer are analyzed employing image analysis to determine the most significant factor. From the acquired images, nine parameters viz. redness (R), greenness (G), blueness (B), lightness (L), redness (a), yellowness (b), energy, entropy, and upper surface area of carrot slices were calculated using the algorithm developed in MATLAB 2015a. Boruta feature selection algorithm in the R console showed lightness, redness, and energy were the most significant features among calculated parameters. Additionally, single-layer feed-forward artificial neural network (ANN) architecture with three inputs (hot air temperature, thickness of slices, drying time), and outputs namely lightness, redness, and energy with one hidden layer was used to model input variables to that of responses. Multiple regression models are employed to optimize the drying condition by further assessing the behavior of response variables with hot air temperature and thickness of slices as inputs and lightness, redness, and energy as outputs. The lightness and redness of samples are found to be decreasing with an increase in temperature and a decrease in thickness. Whereas, the effect of these input parameters on energy, the measure of homogeneity of the product surface, is found to be reversed to that of the effect on lightness and redness. Lightness and redness are set to be highest, whereas energy was kept to be lowest. Convective hot air temperature of 60 ℃ and 7 mm thickness sample was found to provide the best quality product within the experiment range.