Visual biomarkers for predicting fruit ripeness and sensory quality: A critical review

Fruit ripening is a complex physiological process that determines fruit quality and influences consumer acceptance. Among the various indicators of ripeness, visual appearance plays a decisive role, as it is the first attribute perceived and often defines expectations regarding flavor, freshness, and overall quality. However, conventional maturity assessment frequently relies on analytical procedures that require destructive sampling, are time-consuming, or have limited applicability for real-time and large-scale monitoring. In this context, this review critically examines the potential of visual biomarkers to connect fruit ripening physiology, non-destructive sensing, predictive modeling, and sensory quality. A bibliometric mapping identifies major research trends, while the critical synthesis distinguishes instrumental quality prediction, associations with sensory attributes, and direct prediction or validation of consumer-relevant outcomes. The review integrates the biochemical foundations of ripening with practical approaches, examining how changes in pigments, sugars, texture, and other ripening-related attributes are translated into measurable visual and spectral features. Imaging and sensing technologies, including RGB, near-infrared, hyperspectral, and thermal approaches, are discussed alongside image processing, chemometrics, and machine learning. Although these technologies can achieve strong performance in maturity classification and physicochemical quality prediction, high instrumental predictive accuracy does not necessarily establish sensory relevance. Sensory evaluation can address this gap either by providing prediction targets or by serving as an independent reference for validating whether instrumentally detected changes correspond to perceived quality. Overall, standardized protocols, external validation, model transferability, and sensory integration from the experimental design stage are needed to advance robust and consumer-relevant fruit ripeness prediction.