Identification of aroma compounds responsible for apple flavor via palatability score prediction in tree-based regression models

Abstract

Apple flavor has been linked to the composition of aroma compounds; however, the impact of varying concentrations of aroma compounds remains poorly understood. While sensory evaluation is commonly used to analyze food palatability, assessing the effect of concentration of all aroma compounds poses challenges. This study screened 174 apple genotypes for significant aroma compounds using regression models, Gas chromatography–mass spectroscopy analysis, and sensory palatability scores. Two models viz. Random Forest and XGBoost were employed to predict palatability scores, surpassing the limitations of correlation analysis. Shapley's additive explanatory value analysis identified 5-hexenol, ethyl 2-methyl butyrate, 2-methylbutyl acetate, (E,E)-α-farnesene, and (Z)-3-hexenol as key aroma compounds. In a sensory evaluation validating the effect of aroma compound concentration, the variation in (E,E)-α-farnesene concentration negatively affected sensory palatability scores. This study demonstrated a novel approach for identifying aroma compounds that impact apple flavor, providing valuable insights to enhance apple quality and palatability.