Optimization of Edible Coating Formulation Using Response Surface Methodology for Delaying the Ripening and Preserving Tomato (Solanum lycopersicum) Fruits

Abstract

Tomato is a climacteric fruit that has a short shelf life under natural conditions. However, some treatments can be applied in order to extend the shelf life and improve the quality of fruits. The present study aimed at formulating a coating solution based on coffee leaf extract, starch, and Gum Arabic in order to extend the postharvest shelf life of tomato. The response surface methodology (RSM) was used to determine the optimal concentrations of the different abovementioned components in the coating, for better delay the ripening process of tomatoes. The central composite design (CCD) was generated, and the effect of different factors on the shelf life and quality (percent ripening, firmness, physiological loss of mass, pH, chlorophylls a and b contents, lycopene, and β-carotene contents) of tomato fruits were determined 14 days after treatment and stored at room temperature. Chlorophyll a and chlorophyll b contents as well as firmness of treated fruits were higher than those of control fruits. In addition, treated fruits recorded low lycopene and β-carotene contents, physiological loss of mass, pH, and ripening percentage compared to control fruits. Coffee leaf extract had a significant effect ( $p<0.05$ ) on chl a content, pH, and ripening percentage. On the other hand, starch and Gum Arabic did not significantly ( $p>0.05$ ) influence pH, physiological loss of mass, and percentage of ripening. The interaction between coffee leaf extract and starch (X1X2) had the most significant effect ( $p<0.05$ ) on the studied parameters. The results showed that the experimental data could be adequately fitted into a second-order polynomial model with coefficients of determination greater than 80% for all variables studied. The optimal coating formulation consisted of 78.5 g/l coffee leaves extract, 56 g/l starch, and 9.5% Gum Arabic. There was no significant difference between the experimental and predicted data.