The experiment used Moneymaker tomato plants grown in pots at 25 ± 2 °C under a 16-hour-light, eight-hour-dark cycle. A characterized ToBRFV isolate was mechanically introduced into young leaves, and the plants were then maintained in an insect-free greenhouse. Spectral measurements began 24 hours after inoculation and continued on each of the next six days. Progressive chlorosis and curling were visually documented from day 3 onward, but the researchers did not include a separate healthy or mock-inoculated group. Accordingly, the seven classes in the analysis represented days after inoculation, not infection versus no infection. This design limits the study’s ability to determine when a plant first becomes spectrally distinguishable from an uninfected control.

For each plant and sampling day, investigators measured three young, actively growing leaves, avoiding major veins. Three positions were assessed on each leaf, and three consecutive scans at each position were averaged, producing one representative spectrum per leaf. This yielded 81 leaf-level spectra per day for both optical modes. All three spectra from a given plant remained within the same training or test partition during data splitting, preventing measurements of the same individual from leaking across partitions. A later external-validation campaign included approximately seven additional plants at each of the seven days, but it was conducted under the same greenhouse and instrumental setup rather than as an independent biological trial involving a different growth batch, season or facility.

Measurements were made with a portable Apogee PS-100 spectrometer operating over a nominal 350–1100 nm range, although the analysis was restricted to 400–1100 nm because detector-edge pixels were noisy. A tungsten–halogen source, fiber-optic probe and approximately 5 mm working distance were used, with leaves supported on a matte, non-reflective surface. Before each session, the instrument received white-reference and dark-reference calibration. Measurements were performed at 20 ± 2 °C with automatic exposure settings; no fixed integration time was recorded. The instrument generated reflectance and absorbance outputs directly, with reflectance expressed in raw digital counts and absorbance in the instrument’s native units rather than calculated from reflectance after acquisition.

Mean spectra revealed time-dependent differences in the visible region, the red edge at about 700–750 nm and the near-infrared region from 750–1100 nm. Absorbance around the chlorophyll-associated feature near 680 nm was generally higher at later sampling days, while the red edge showed changes in slope and position. In the near infrared, absorbance rose at intermediate stages before declining at days 6 and 7, producing a nonlinear pattern across the experimental period. Reflectance also varied nonlinearly, including an anomalously high day-6 signal and a partial decrease or recovery the following day. These observations suggest a multiphase response, but the researchers measured no independent chlorophyll, carotenoid, water-content, anatomical or viral-load variables, so the proposed physiological causes remain plausible interpretations rather than demonstrated mechanisms.

The red edge was identified as a particularly sensitive domain for tracking gradual change, with the near-infrared region providing complementary information associated with leaf structure and water status. In PLS-DA score plots, raw spectra showed partial separation between early and late classes but overlap among intermediate days, together with a curved or horseshoe-like structure in absorbance data. SNV produced a more organized representation, and SNV-treated reflectance formed a smooth temporal trajectory from day 1 through day 7. The most absorbance-model errors occurred between adjacent days, especially days 4–5 and 5–6, consistent with a soft boundary between consecutive stages. Day 6 was the most uncertain absorbance class, whereas day 7 was the most consistently separated. The plots therefore suggest that the spectral response changes progressively rather than as seven discrete states, even where a supervised classifier assigns each observation to a specific day.

Reflectance consistently outperformed absorbance in the internal training and cross-validation assessment. With raw spectra, the reflectance model produced precision of 0.9905, recall of 0.9890, specificity of 0.9980 and an F1-score of 0.9894, compared with 0.9457, 0.9355, 0.9903 and 0.9383 for raw absorbance. SNV raised the absorbance F1-score to 0.9482 but produced perfect precision, recall, specificity and F1 values for reflectance. Savitzky–Golay filtering alone reduced the absorbance F1-score to 0.8967, while leaving the reflectance score unchanged at 0.9894. Combining SNV with Savitzky–Golay filtering reduced absorbance performance to an F1-score of 0.9177 but retained perfect reflectance metrics. The comparison indicates that normalization was more useful than smoothing in this dataset and that additional smoothing after SNV did not provide a measurable gain.

Those perfect reflectance results require careful interpretation. Wavelength selection was performed on the full dataset before the cross-validation and test partitions were evaluated, rather than being repeated independently within each training fold. The model therefore had access to spectral variables selected with information from samples that later appeared in validation sets. The reported F1-score of 1.000 should consequently be treated as an internal upper-bound estimate rather than a fully unbiased measure of performance on new plants. The study also did not provide a tabulated, ranked list of retained wavelengths or their coefficient and variable-importance-in-projection values, so the attribution of discrimination to specific red-edge and near-infrared bands remains qualitative. Visualized external-validation points include predictions on both sides of the classification threshold, reinforcing that perfect internal separation did not translate into perfect performance for the later acquisition campaign.

The experiment offers a technically detailed demonstration that reflectance spectra can encode temporal changes in ToBRFV-inoculated tomato leaves under controlled conditions, but it does not yet establish a field-ready diagnostic test for the virus. The strongest internal results came from SNV-corrected reflectance, while absorbance remained useful but less consistent, particularly around intermediate stages. The absence of same-day healthy controls means the reported classes document progression among inoculated plants rather than the onset of detectable infection. Fully nested validation, independent biological batches, direct reference measurements of plant chemistry and structure, and testing under changing field conditions would be needed before the method could support conclusions about pre-symptomatic detection or practical crop-health assessment. Within the present scope, the results identify a promising spectral-modeling strategy and a clear dependence of classification performance on both optical mode and preprocessing.

Subject of Research: Experimental comparison of reflectance and absorbance spectroscopy for classifying early ToBRFV infection stages in tomato leaves

Article Title: Temporal reflectance and absorbance spectral signatures of ToBRFV-infected tomato leaves: a PLS-DA modeling comparison over a 1–7-day post-inoculation period

Article References: Pourdarbani, R., Sabzi, S., Torshizi, M. V., Pouresmaeil, M., & Saadati, N. (2026). Temporal reflectance and absorbance spectral signatures of ToBRFV-infected tomato leaves: a PLS-DA modeling comparison over a 1–7-day post-inoculation period. Smart Agricultural Technology, 15, Article 102629. https://doi.org/10.1016/j.atech.2026.102629

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102629

Keywords: ToBRFV, tomato leaves, plant virus, visible-near-infrared spectroscopy, reflectance, absorbance, PLS-DA, SNV preprocessing, plant disease detection, greenhouse study, Temporal, spectral

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