Flavoromics integrated with machine learning to elucidate key aroma compounds in aroma-directed chili peppers and predict sensory quality.

Improving chili pepper aroma quality is essential for industry transformation and high-value development. However, the complex volatile composition and its quantitative relationship with sensory quality remains unresolved, limiting targeted breeding of aroma-directed varieties. This study employed quantitative descriptive analysis, E-nose, and HS-SPME-GC-MS combined with chemometrics, OAV values, and machine learning to systematically analyze aroma differences between three aroma-directed (MJ7, MJ8, MJ9) and three commercial chili peppers. Aroma-directed varieties significantly outperformed traditional peppers in fruity, floral, and sweet attributes, with MJ7 achieving a floral score of 8.44 and MJ9 a fruity score of 8.16. Among 202 identified volatile components, aroma-directed peppers predominantly contained esters and ketones, while traditional varieties were alkane-rich. OPLS-DA identified characteristic compounds including β-caryophyllene and 2-methylcarbazole, with 30 key aroma compounds identified through OAV-based quantification. An Adaptive Weighted Consensus Regression (AWCR) model established quantitative relationships between key compounds and sensory attributes, showing 33.3 % improved prediction accuracy over single machine learning approaches. Feature importance analysis revealed phenylacetaldehyde as the core compound for fruity aroma and linalool for floral notes, providing precise targets for molecular breeding of aroma-directed chili peppers.