Indirect Determination of Basic Tomato Quality Parameters Using Color Digital Images

Abstract

The indirect determination of basic tomato quality parameters using color digital images is presented in the paper. A database of images of tomatoes of the Clarosa variety was formed. The six main chemical parameters, dry matter, ascorbic acid, titratable organic acids, total dyes, lycopene, and beta-carotene, were measured as referent values. Image processing techniques were used for the extraction of the color components, to remove the background, and for color spaces transformation. The values of the color components of four color models, RGB, HSV, XYZ, and Lab, were obtained in the MATLAB environment. Statistical approaches were used for assessing the relation between the main chemical parameters of tomatoes and the color components. The results of the correlation analysis showed that there were the following relationships between the color components and the chemical indicators: the dry substance had a moderate dependence with R(RGB), S(HSV), and b(Lab) and a significant dependence with H(HSV) and a(Lab); the ascorbic acid substance had a moderate dependence with H(HSV), S(HSV), and a(Lab); the titratable organic acids had a moderate dependence with S(HSV) and b(Lab); a significant dependence was found with R(RGB), and a strong dependence was found with V(HSV), L(Lab), a(Lab), X(XYZ), and Y(XYZ); the general dyes had a significant dependence with H(HSV), S(HSV), and a(Lab); the lycopene had a significant dependence with H(HSV), S(HSV),a and a(Lab); the beta-carotene had a significant dependence with R(RGB), G(RGB), B(RGB), V(HSV), L(Lab), X(XYZ), Y(XYZ), and Z(XYZ). The obtained results showed that the chemical composition values of tomatoes could be predicted with polynomial regression models that use color components from the HSV and Lab models: dry matter-H(HSV); ascorbic acid-H(HSV); titratable organic acids-a(Lab); general dyes-H(HSV), S(HSV), and a(Lab); lycopene-H(HSV), S(HSV), and a(Lab). For the determination of beta-carotene, it is necessary to use more than one color component to obtain mathematical models with a higher coefficient of determination. The obtained results are a prerequisite for this approach to be used in the automated systems for evaluating the quality of tomatoes in the harvesting process.

Keywords

• color digital images
• tomato quality assessment
• correlation analysis
• polynomial regression models