Machine learning-assisted aroma profile prediction in tomato puree based on flavoromics

Flavor serves as a key quality indicator in tomato puree (TP) processing; however, conventional methods often fall short in providing rapid and accurate assessments. To address this limitation, this study integrated flavoromics with machine learning to characterize sensory transitions and volatile changes during thermal processing and to construct a predictive model for sensory quality. Through HS-SPME-GC-MS analysis, a total of 71 volatile compounds were identified. Corresponding sensory analysis revealed a progressive shift from "Freshness," "Fruity," and "Floral" to "Cooked" and "Sourness" as heat intensity increased. Among the five models evaluated, the multilayer perceptron (MLP) demonstrated superior performance (R² > 0.99), effectively capturing nonlinear relationships between volatiles and sensory responses. Variable importance analysis identified ten key volatiles for each sensory descriptor. Moreover, external validation and aroma recombination confirmed the model's robustness and generalization capacity. These findings offer a practical framework for flavor quality prediction and real-time control in TP production.