Temporal Dynamics of Postharvest Quality in Carrot Genotypes: A Multidimensional Analysis of Physicochemical, Biofunctional, Spectral, and Sensory Attributes Artículo académico uri icon

Abstracto

  • Postharvest quality of carrot (Daucus carota L.) is determined by the interaction between genotype and storage environment, yet systematic comparative evidence across pigmented genotypes with contrasting biochemical profiles remains scarce. This study evaluated the postharvest behavior of five carrot genotypes (6KUR, 14BER, yellow, white, and purple) under refrigeration (4 °C) and room temperature (15 °C) over 30 days, integrating physicochemical, spectral, and consumer-based assessment. Variables included color, fresh weight loss, respiration rate, firmness, titratable acidity, total soluble solids, and β-carotene quantification by spectrophotometry. Non-destructive monitoring was performed using Vis/NIR reflectance (350–1900 nm) with spectral indices sensitive to anthocyanin and carotenoid content (CRI1, CRI2, mARI) and tissue structural integrity (NDVI), and consumer perception (~60 participants per evaluation) was characterized through natural language processing of open-ended responses. Refrigeration significantly reduced β-carotene degradation (~15–20% loss vs. 50–60% at room temperature) and better preserved overall quality across genotypes. Purple carrots demonstrated superior postharvest stability across multiple traits, whereas white carrots showed the greatest susceptibility to quality loss. Spectral indices exhibited genotype-dependent temporal variation, particularly in pigmented roots, supporting their potential for non-destructive pigment monitoring during storage. Consumer descriptors reflected a progressive decline in desirable sensory attributes under both conditions. These findings support the integration of physicochemical, spectral, and sensory approaches for comprehensive postharvest characterization of genotypically diverse carrot germplasm, and identify priority genotypes and trait combinations for future predictive modeling studies

fecha de publicación

  • mayo 25, 2026

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  • 12.0

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  • 6.0

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  • mayo 25, 2026