Predictive modeling of CIELAB color parameters in okra accessions based on phytochemical composition and antioxidant activity: A non-destructive Imagej and RSM approach

Okra (Abelmoschus esculentus), native to Africa, is an economically important crop cultivated globally for its nutritional value, bioactive compounds, and traditional medicinal uses. In fruits and vegetables, color is a key visual attribute that can be correlated with antioxidant activity and the presence of bioactive compounds such as phenolics and flavonoids. Changes in pod color may therefore reflect variations in their phytochemical content and organoleptic quality. The objective of the present study was to investigate the relationship between the bioactive compound (TPC, TFC, and AOA) and color features (L∗, a∗ and b∗) of ten okra accessions using response surface methodology (RSM) and Imagej software, as a nondestructive, fast, and fairly accurate method. Analysis of variance (ANOVA) confirmed the significance of the developed models, with determination coefficients (R²) of 0.89, 0.82, and 0.91 for L∗, a∗, and b∗ responses, respectively. The strong relationship between color features and bioactive compounds suggests that color-based modeling can serve as a simple and cost-effective tool for selecting okra genotypes with enhanced phytochemical content.