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

The heart of the new study is a technique called total infectome metatranscriptomics. Unlike targeted polymerase chain reaction assays, which can only detect organisms a researcher already suspects, RNA-based metatranscriptomic sequencing captures the full transcriptional activity of every microbe in a sample—RNA viruses, actively replicating DNA viruses, bacteria, fungi, and other eukaryotic microorganisms alike. Between 2021 and 2025, the team conducted epidemiological surveys across major grass carp-producing regions of China, including provinces in the Yangtze, Pearl, and Yellow River basins, confirming that OWS has spread well beyond its point of first documentation. From affected and healthy fish, the researchers dissected eight organs each—liver, spleen, kidney, intestine, gill, brain, muscle, and skin—and processed every organ as an independent sequencing library, generating 80 metatranscriptomes. After quality filtering and ribosomal RNA depletion, 8.09 billion high-quality reads remained, averaging 101.2 million per library, providing the depth needed for broad pathogen discovery.

The sequencing results revealed a strikingly complex microbial landscape. Across all libraries, the team identified 107 dominant microbial species: 32 viruses, 65 bacteria, and 10 eukaryotic microorganisms, spanning nine RNA viral supergroups, two DNA viral families, seven bacterial phyla, and seven eukaryotic phyla. Bacteria constituted the largest share of detected organisms at 60.7 percent, followed by RNA viruses at 22.4 percent, eukaryotes at 9.3 percent, and DNA viruses at 7.5 percent. Perhaps most tellingly, 68 of the 107 species—63.6 percent—were putatively novel, indicating that the majority of the grass carp-associated infectome had never been characterized before. Among the known and emerging agents were a grass carp hepacivirus, the first of its kind detected in this host; Chinook salmon nidovirus 1, previously reported only from salmonids; and a divergent aquareovirus the team named Shunde grass carp aquareovirus. Several parasitic eukaryotes from groups including Cnidaria, Euglenozoa, Fornicata, and Platyhelminthes also appeared in the dataset.

Detection alone, however, cannot establish causation—a lesson that has repeatedly frustrated disease investigators in aquaculture. To prioritize candidates, the researchers applied a comparative infectomics framework, quantifying microbial abundance as reads per million non-rRNA reads and retaining 33 taxa above a threshold of RPM ≥ 1. Differential abundance analysis, using a criterion of at least a fourfold change with a false discovery rate below 0.05, showed that overall microbial profiles clearly separated diseased fish from healthy controls. Among the enriched taxa, one organism stood out decisively: Flavobacterium psychrophilum was detected in every diseased individual, across multiple organs, and at its highest abundance in muscle and skin—precisely the tissues where OWS lesions were most severe. By contrast, parasitic eukaryotes such as Ichthyobodonidae, Trypanosomatidae, and Thelohanellus species showed inconsistent, sporadic occurrence, and the RNA viruses enriched in diseased fish phylogenetically clustered with invertebrate-associated lineages whose abundance correlated with parasite loads rather than direct infection of the fish.

With F. psychrophilum prioritized as the leading candidate, the team moved to experimental validation. The bacterium was isolated from lesion-associated muscle tissue of naturally diseased fish, yielding pale-yellow colonies on TYES agar after incubation at 15 degrees Celsius. Sequencing of the 16S rRNA gene placed the representative isolate, designated GC30-154, firmly within the F. psychrophilum clade with maximum bootstrap support. Healthy grass carp were then challenged by intramuscular injection with graded doses ranging from 10^4 to 10^8 colony-forming units. Control fish injected with buffer remained entirely healthy, while infected fish developed clinical signs beginning four days post-injection, with morbidity climbing in a dose-dependent fashion from 25 percent at the lowest dose to 100 percent at the highest. Mortality followed the same pattern, reaching 95 percent by day 18 in the highest-dose group, and the bacterium was successfully re-isolated from the lesions of deceased fish—satisfying key elements of Koch’s postulates.

The pathological picture in experimentally infected fish mirrored natural OWS with remarkable fidelity. Gross signs included focal erythema and swelling at the injection site, reddening around the pectoral-fin base, mild snout reddening, and tail erosion, while histopathology revealed severe muscle fiber degeneration, extensive vacuolation, and disruption of skin architecture—lesions closely resembling those in field cases, and concentrated in external and barrier tissues while liver, spleen, and kidney remained largely intact. Critically, the team then performed post-challenge total infectome analysis to rule out a role for secondary microbes in driving the experimental disease. Only F. psychrophilum appeared at consistently high abundance in infected animals, with the same muscle- and skin-dominant organ distribution seen in naturally diseased fish, while controls showed no signal whatsoever. The convergence of clinical signs, tissue pathology, mortality patterns, and infectome signatures established the bacterium as sufficient—and therefore the primary cause—of OWS.

A second layer of the investigation explained the disease’s peculiar seasonality. F. psychrophilum is classically regarded as a cold-water pathogen of salmonids, causing bacterial cold-water disease and rainbow trout fry syndrome, typically at temperatures below 10 degrees Celsius. Yet in grass carp the story was different. When challenged fish were held at constant temperatures of 10, 15, or 20 degrees Celsius, mortality was highest at 15 degrees—55 percent—with no deaths at the other temperatures during the observation period. More striking still was a temperature-shift experiment designed to mimic the overwintering-to-spring transition. Fish injected at 10 degrees and held there for 15 days showed no abnormalities; only when water temperature was gradually raised to 15 degrees did ulcers appear and mortality surge, reaching 95 percent within 14 days of warming. This thermal profile closely matches the late-overwintering and early-spring window in which natural OWS outbreaks occur, and it suggests the grass carp isolate may represent a host-adapted variant with altered temperature-dependent virulence.

Beyond the headline finding, the study carries broader implications for how infectious disease is investigated in complex systems. Aquatic environments teem with microbial diversity, and intensive aquaculture—shared water systems, high stocking densities, seasonal environmental stress—creates ideal conditions for polymicrobial communities to obscure etiology. The framework demonstrated here links epidemiological surveying, cohort-based comparative infectomics, targeted isolation, experimental infection, re-isolation, and post-challenge infectome validation into a coherent chain of evidence that converts unbiased pathogen discovery into causal inference. The authors emphasize that its success depends on careful attention to cohort representativeness, sampling coverage, and the detectability of pathogen-derived transcriptional signals, and that sampling and validation workflows must be tailored to the ecology of each disease system. Applied to OWS, the approach correctly demoted opportunistic eukaryotes and invertebrate-associated viruses that might otherwise have been mistaken for culprits, while flagging latent pathogen diversity—including novel hepaciviruses and nidoviruses—that could matter under future environmental or co-infection scenarios.

As aquaculture continues to expand and intensify worldwide, the connectivity between farming systems grows apace, raising the risk of pathogen transmission across previously separated host species and the emergence of new disease syndromes. The grass carp OWS resolution offers both a practical answer for producers—pointing toward surveillance and control of F. psychrophilum during spring warming—and a methodological template for wildlife, livestock, and even clinical medicine, where metagenomic detection increasingly outpaces causal interpretation. The study’s raw sequencing data have been deposited in a public aquatic pathogen platform, and all alignments and phylogenetic trees are openly available, reflecting the authors’ intent that the total infectome framework be adopted, adapted, and tested broadly. What began as an attempt to solve one stubborn disease in Chinese carp ponds may ultimately change how scientists everywhere distinguish the true cause of an outbreak from the microbial noise that surrounds it.

Subject of Research: A total infectome framework for resolving complex disease etiology in aquaculture

Article Title: A total infectome framework for resolving complex disease etiology in aquaculture

Article References: Xu, Y., Liu, H., Wu, W., Gu, Y., Zhang, N., Zhang, C., Zhou, R., Zhang, D., Weng, S., Shi, M., He, J., & He, J. (2026). A total infectome framework for resolving complex disease etiology in aquaculture. Advanced Biotechnology, 4(3), Article 31. https://doi.org/10.1007/s44307-026-00125-8

Image Credits: AI Generated

DOI: 10.1007/s44307-026-00125-8

Keywords: total, infectome, framework, resolving, complex, disease, etiology, aquaculture, scientific research