Model construction for the flavour evolution of dry red wine during storage based on sensory data

The study aimed to provide technical guidance for process optimization, aging management, and market promotion of wines from specific geographical regions by systematically analyzing the flavor quality evolution of dry red wines during aging and constructing a predictive model for flavor evolution. The experiment focused on Cabernet Sauvignon dry red wines from Turpan, with 81 samples collected across 2-15 years of aging. Conventional physicochemical indicators (e.g., alcohol content, total acidity, volatile acidity) were analyzed using national standard method (GB/T 15038-2006), while flavor characteristics (e.g., appearance, aroma, mouthfeel) were quantified by a trained tasting panel. Sensory data underwent cluster analysis and principal component analysis (PCA), followed by weighted calculation of PCA loading contribution rates for sensory traits. A Gaussian Process Regression (GPR) model was developed using nonlinear least squares fitting to construct a mathematical model for wine flavor evolution. Results showed that the tested wines met national physicochemical standards. Cluster analysis of sensory data revealed three distinct groups (2-6 years, 7-9 years, >9 years) with significant intra-group sensory similarity, indicating stage-specific differentiation of flavor traits across aging. PCA demonstrated that the first two principal components (PC1 and PC2) accounted for 92.9% and 2.3% of total variance, respectively. The "overall impression" exhibited the highest weight on PC1, while astringency and acidity showed minimal weights. The flavor evolution model indicated a 95% confidence interval covering 0-12.40 years of aging. Sensory quality initially increased to a peak at 2.95 years, then gradually declined until 7.77 years, after which maximum degradation occurred. Model outputs showed that wines aged 2-4 years achieved high scores in appearance, aroma, and mouthfeel; aged 5-8 years displayed slow degradation in mouthfeel and aftertaste but retained basic quality; wines exceeding 9 years of aging exhibited intensified oxidation and significant sensory decline. The integration of PCA and Gaussian process regression (GPR) provided a novel framework to capture the nonlinear evolution of sensory quality, overcoming the limitations of traditional linear models. The hybrid approach identified critical inflection points and quantified the contributions of core sensory attributes to overall quality. The model’s ability to predict aging-related changes in flavor profiles offers a data-driven tool for optimizing production consistency and storage protocols in wine manufacturing. Notably, sensory variability among same-vintage samples highlighted the need for improved standardization in grape selection, maceration control, and oak aging practices. The study emphasized that expanding the sample size and refining process parameters could enhance the model’s reliability and reduce vintage-to-vintage inconsistencies. The PCA-GPR model demonstrated its scientific and industrial relevance by bridging the gap between sensory science and data-driven modeling. By constructing a region-specific sensory weight system through principal component analysis, the regional adaptability of the evaluation model was significantly improved. Furthermore, GPR through kernel function design, can flexibly capture the nonlinear stage evolution characteristics of flavor quality and wine age, and utilize the covariance matrix to dynamically assess the uncertainty of flavor decay in products of different vintages, providing a probabilistic decision-making basis for storage risk management. Overall, the research provides a foundation for developing region-specific quality assurance systems, enabling wineries to balance aging duration with economic feasibility and improve product value through precise flavor management.