The relationship between surface color and internal fruit quality represents one of the most extensively studied phenomena in postharvest science, and its application to mangoes carries particular significance given the fruit’s dramatic chromatic transformation during ripening. As chlorophyll degrades in the exocarp, underlying carotenoid pigments become visually dominant, shifting the peel from deep green through yellow-green to fully yellow or red-blushed hues depending on cultivar. This visible progression is not merely cosmetic; it is biochemically coupled to the same developmental program that drives starch-to-sugar conversion, organic acid decline, and the synthesis and degradation of ascorbic acid within the flesh. Consequently, external reflectance measurements can serve as a non-destructive proxy for internal compositional attributes that would otherwise require destructive sampling, juice extraction, and laboratory titration or chromatography to quantify.
Total soluble solids, typically expressed in degrees Brix, constitute the standard industry metric for sweetness and ripeness in mango. The measurement integrates the concentration of sugars, primarily sucrose, glucose, and fructose, along with smaller contributions from organic acids, amino acids, and other dissolved compounds. Conventional determination requires homogenizing flesh samples and reading refractometry values, a process that destroys the fruit and provides information only about the sampled tissue. Because mangoes display considerable spatial heterogeneity in soluble solids, with gradients from the stem end to the blossom end and from the peel inward toward the stone, destructive sampling introduces uncertainty about whether a single measurement represents the whole fruit. A color-based predictive model circumvents this limitation by estimating quality from the intact exterior, enabling repeated assessment of the same fruit across time.
Vitamin C presents an even greater analytical challenge than soluble solids. Ascorbic acid is labile, oxidizing readily upon exposure to oxygen, light, heat, and enzymes released during tissue disruption. Accurate quantification demands rapid extraction into stabilizing media such as metaphosphoric acid, followed by titration with 2,6-dichlorophenolindophenol or separation by high-performance liquid chromatography. These procedures are time-consuming, reagent-intensive, and subject to artifacts if samples are not handled immediately. The finding that peel reflectance characteristics can predict flesh ascorbic acid content therefore offers substantial practical value, particularly for breeding programs and quality assurance workflows where hundreds or thousands of fruit must be screened rapidly without access to full analytical laboratories.
The scientific rationale linking external color to internal vitamin C rests on shared biosynthetic and catabolic pathways. In climacteric fruit such as mango, the respiratory burst accompanying ripening accelerates reactive oxygen species production, and ascorbic acid functions as a principal antioxidant defense. As ripening proceeds, the balance between ascorbate synthesis, recycling through the glutathione-ascorbate cycle, and irreversible oxidation shifts, producing characteristic declines or plateaus in vitamin C content that coincide temporally with pigment changes in the peel. Both chlorophyll catabolism and ascorbate turnover are modulated by ethylene signaling, harvest maturity, and postharvest storage conditions, creating the statistical covariance that predictive models exploit. This coupling is cultivar-dependent, however, since varieties differ in their carotenoid profiles, ascorbate retention, and the degree to which peel coloration tracks flesh maturity.
Reflectance color measurement itself relies on well-established colorimetric principles, most commonly the CIELAB system, in which L* describes lightness, a* the green-to-red axis, and b* the blue-to-yellow axis. Portable colorimeters or spectrophotometers illuminate a small area of the peel with a standardized light source and record the spectrum or tristimulus values of reflected light. These coordinates can be used directly as predictor variables or transformed into indices such as hue angle and chroma, which often correlate more intuitively with human perception of ripeness. Compared with hyperspectral imaging or near-infrared spectroscopy, simple reflectance colorimetry requires inexpensive instrumentation, minimal training, and no complex spectral preprocessing, making it attractive for deployment in packinghouses, wholesale markets, and even field conditions in producing regions.
Statistical modeling of the relationship between color coordinates and quality attributes typically employs regression frameworks ranging from simple linear models to machine learning approaches such as support vector regression, random forests, and artificial neural networks. Model performance is conventionally evaluated through the coefficient of determination and the root mean square error of prediction on independent validation sets. A recurring theme in the literature is that prediction accuracy for soluble solids generally exceeds that for vitamin C, reflecting the tighter biochemical linkage between pigment development and sugar accumulation than between pigments and ascorbate dynamics. Preharvest factors, including orchard location, canopy position, irrigation regime, and maturity at harvest, introduce variability that models trained on one population may not generalize to another, underscoring the importance of cultivar-specific and season-specific calibration.
The practical implications of validated color-based prediction extend across the mango supply chain. Growers can time harvests more precisely, reducing the incidence of fruit picked too early, which never develops full flavor, or too late, which deteriorates rapidly in transit. Packinghouse operators could sort fruit into ripeness classes non-destructively, enabling targeted distribution so that riper lots reach nearby markets while greener fruit is reserved for long-distance shipping. Retailers might monitor displayed inventory and adjust pricing or discounting based on predicted remaining shelf life. For consumers, the approach underpins the growing interest in smartphone-based applications that estimate fruit quality from photographs, democratizing access to quality information that was previously confined to laboratory settings.
Food loss and waste provide an additional motivation for this line of research. Mangoes are climacteric and highly perishable, with postharvest losses in some producing regions estimated at a substantial fraction of total production. A significant portion of these losses stems from mismatches between fruit maturity and market timing: fruit that appears acceptable externally may be internally underripe or overripe when it reaches the consumer. Objective, non-destructive quality assessment allows interventions such as modified atmosphere packaging, controlled temperature regimes, or accelerated marketing to be applied selectively to fruit predicted to be at risk, rather than uniformly to entire lots. This targeted approach conserves resources and reduces the environmental footprint associated with wasted production inputs.
From a breeding perspective, rapid phenotyping of vitamin C content addresses a persistent bottleneck in developing nutritionally enhanced cultivars. Biofortification efforts aimed at increasing micronutrient content in staple and horticultural crops require screening large segregating populations across multiple seasons and environments. Destructive vitamin C assays limit throughput and consume valuable fruit that breeders may wish to retain for seed or further evaluation. If reflectance color measurements can reliably predict ascorbic acid concentration, breeders could screen far more individuals at earlier stages, accelerating genetic gain. Similar logic applies to soluble solids, a heritable trait that directly influences consumer acceptance and market price, and for which high-throughput indirect phenotyping has long been sought.
Several methodological considerations temper enthusiasm and define the agenda for future work. Color measurements capture only the superficial few hundred micrometers of the peel, so their predictive power depends entirely on statistical association rather than direct sensing of flesh composition. This association can be disrupted by treatments that decouple peel color from flesh maturity, such as ethylene degreening, hot water treatment, controlled atmosphere storage, or the application of skin coatings. Pathogen damage, sap burn, lenticel discoloration, and sunburn alter surface optics without proportional changes in internal quality, potentially biasing predictions. Robust deployment therefore requires either careful fruit selection and cleaning protocols or models that incorporate additional spectral bands beyond the visible range to distinguish genuine ripeness signals from surface defects.
Instrument standardization presents a further challenge. Different colorimeters vary in illuminant geometry, aperture size, and calibration, and ambient lighting conditions influence measurements taken with consumer devices. Efforts to harmonize protocols, publish open calibration datasets, and report colorimetric conditions alongside model coefficients would facilitate comparison across studies and support the development of transferable models. The growing adoption of standardized reporting in food research journals reflects recognition that reproducibility is essential if color-based prediction is to move from academic demonstration to industrial practice. Cultivar-specific calibration databases, updated across seasons and growing regions, would constitute valuable shared infrastructure for the mango industry.
The broader scientific context situates this work within the field of non-destructive food quality evaluation, which encompasses hyperspectral imaging, near-infrared spectroscopy, Raman spectroscopy, acoustic and vibration methods, computer vision, and electronic noses. Each technique occupies a niche defined by cost, speed, penetration depth, and the specific quality attributes it senses most effectively. Visible reflectance colorimetry sits at the accessible end of this spectrum, trading depth of information for simplicity and affordability. Hybrid systems that combine color coordinates with a small number of near-infrared wavelengths, or that fuse color imaging with mass estimation and shape analysis, represent a promising middle ground that could improve prediction of attributes like vitamin C while retaining practical deployability.
Looking forward, the integration of color-based prediction models with digital supply chain infrastructure offers transformative potential. When paired with lot-level tracking, temperature logging, and ripening models, per-fruit color measurements taken at packing could feed dynamic shelf-life forecasts that inform logistics decisions in near real time. Machine learning models retrained continuously on incoming measurement-outcome pairs could adapt to seasonal drift and regional variation. In producing countries where laboratory capacity is limited, validated color-based methods could extend quality assessment capabilities to cooperatives and smallholder aggregation centers, improving bargaining position and reducing losses at the point closest to production. The convergence of inexpensive optical sensing, robust statistical modeling, and mobile computing thus positions external color as a durable and scalable window into the internal quality of mangoes and, by extension, other climacteric horticultural commodities.
Subject of Research: Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement
Article Title: Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement
Article References: Kusumiyati, K., Sutari, W., Supratman, U., & Munawar, A. A. (2026). Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement. npj Science of Food. https://doi.org/10.1038/s41538-026-01120-y
Image Credits: AI Generated
DOI: 10.1038/s41538-026-01120-y
Keywords: Color-based, prediction, mango, total, soluble, solids, vitamin, reflectance, color, measurement, scientific research
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Alan Morgan. (September 11, 2026). Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement. Scienmag. https://scienmag.com/color-based-prediction-of-mango-total-soluble-solids-and-vitamin-c-using-reflectance-color-measurement/
Alan Morgan. “Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement.” Scienmag, 11 September 2026, https://scienmag.com/color-based-prediction-of-mango-total-soluble-solids-and-vitamin-c-using-reflectance-color-measurement/. Accessed 11 September 2026.
Alan Morgan. “Color-based prediction of mango total soluble solids and vitamin C using reflectance color measurement.” Scienmag. September 11, 2026. https://scienmag.com/color-based-prediction-of-mango-total-soluble-solids-and-vitamin-c-using-reflectance-color-measurement/
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Tags: biochemistry of mango peel color transformationchromatic changes during mango ripeningcolorColor-basedcultivar-specific mango ripening indicatorsmangomango fruit color analysismeasurementnon-destructive mango quality assessmentnon-invasive methods for assessing mango sweetness and vitamin Cpostharvest mango quality monitoringpredicting mango total soluble solids using colorpredictionreflectancereflectance color measurement for mango ripenessrelationship between mango peel color and internal sugar contentScientific Researchsolidssolublespectrophotometric measurement of mango fruittotalvitaminvitamin C estimation in mangoes via reflectance
