Comprehensive profiling of banana ripening (Musa spp.) through multivariate analysis of biochemical attributes

Banana ripening is a complex biological process that determines their final quality and shelf life. However, our comprehensive understanding of how their physicochemical, nutritional, and enzymatic attributes vary throughout this process remains limited. This study used a multivariate approach to characterize and classify the profile of Ecuadorian bananas across nine stages of ripening. Twenty-seven samples (3 replicates per stage T1–T9) were analyzed, determining 29 variables: carbohydrates, proximate composition, minerals, vitamins, color, texture, and enzymatic activity (PPO and POD). The data were evaluated using PERMANOVA, Principal Component Analysis (PCA), Spearman’s correlation, and k-means clustering. PERMANOVA confirmed that the ripening stage explains virtually all multivariate variation (pseudo-F = 2758.3; R 2 = 0.999; p = 0.001). PCA revealed a dominant gradient (Dim1 = 86.2%) that organizes the synchronized transition from a structural block (high in starch, firmness, and minerals) in the green stages (T1–T3) to a solubilization block (high in sugars, PPO/POD activity, and softening) in the overripe stages (T7–T9). Vitamin C showed a maximum peak in the intermediate stage T5. Banana ripening functions as a highly coordinated system along a common physiological gradient. This study proposes functional stages based on multivariate signatures, providing a robust database for optimizing postharvest quality management and developing non-destructive monitoring tools within the framework of digital agriculture.