The clinical problem the researchers set out to address is both common and quietly dangerous. Infants in neonatal intensive care units frequently require peripherally inserted central catheters or central venous catheters to deliver total parenteral nutrition, medications, and fluids over weeks of treatment. One recognized complication of these indwelling lines is catheter-related venous thrombosis, in which a clot forms in the vein surrounding or traversed by the device. In newborns, whose veins are tiny and whose physiology is delicate, such clots can obstruct venous return from an entire limb, causing swelling, discoloration, and in severe cases tissue damage. Detecting the problem early is difficult because the classic bedside signs, such as changes in skin color, temperature, or limb circumference, are late and unreliable indicators in very small patients.

The gold standard for confirming venous thrombosis is color Doppler flow imaging, an ultrasound technique that visualizes blood flow within vessels. But ultrasound is a snapshot, not a continuous monitor, and repeated examinations require skilled operators and the manipulation of a fragile infant. The Chinese team reasoned that near-infrared spectroscopy could fill the monitoring gap. NIRS exploits a simple physical fact: oxygenated hemoglobin and deoxygenated hemoglobin absorb near-infrared light differently. By shining light in the near-infrared range into tissue and measuring how much returns, a sensor can calculate the concentrations of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin, and from those derive the tissue oxygen saturation index, abbreviated TOI. The method is already routine for monitoring cerebral and abdominal oxygenation in neonatal units, but reports of using it to monitor limb tissue oxygenation have been very limited.

The study followed two infants, each of whom developed venous thrombosis after limb catheterization. Before any conclusions could be drawn, the team first had to define what normal looked like. They established a safe TOI range for the monitored limbs of 58 to 68 percent, with a margin of plus or minus 2 percent, and set an operational alarm threshold: a drop of more than 20 percent from the baseline value would signify an anomaly requiring attention. Alongside the optical measurements, the clinicians simultaneously observed the color and temperature of the affected limb skin, and they used vascular ultrasound to comprehensively assess the thrombolytic effect of treatment. This combination allowed the researchers to compare what the light-based sensor reported with what conventional clinical assessment and imaging revealed.

The first infant was diagnosed with thrombosis of the right iliac vein, a large vessel deep in the pelvis that drains blood from the leg. The team treated the clot with urokinase, an enzyme that directly activates the fibrinolytic system to dissolve thrombus. Throughout the treatment course, NIRS sensors continuously recorded the tissue oxygen saturation index of the affected lower limb before, during, and after the therapy. The monitoring told a clear story: after 144 hours of thrombolytic treatment, the TOI of the affected limb returned to the normal range that the team had defined. At that point, clinical examination showed no abnormalities in the limb’s skin temperature or circumference, indicating that venous drainage had been restored and the optical readings had tracked the physiological recovery.

The second infant presented a different anatomical challenge: a thrombosis in the axillary vein near the subclavian vein, affecting the upper limb. Here the treatment strategy combined thrombolysis with anticoagulation, a dual approach aimed both at dissolving the existing clot and preventing new clot formation. The response was notably faster than in the first case. Following 17 hours of combined therapy, the TOI values measured in both upper limbs became balanced, meaning the previously compromised limb was once again matching its healthy counterpart in tissue oxygen saturation. Ultrasound follow-up then delivered the confirming evidence: no residual thrombus was detected, indicating that venous patency had been restored. The symmetry of the optical readings between the two arms served as a built-in control, since the healthy limb provided a live reference for what normal perfusion should look like.

Taken together, the two cases support the study’s central conclusion: the NIRS-monitored limb tissue oxygen saturation index can indicate the local venous filling status, which may facilitate the assessment of thrombolytic efficacy and the surveillance of thrombus formation. In practical terms, this means a nurse or physician could watch a number on a bedside monitor and see within minutes whether a clot is choking off venous return, whether a drop in saturation signals a new or extending thrombus, and whether a clot-dissolving drug is doing its job. The contrast between the two infants is instructive. The iliac vein thrombosis required six days of therapy before saturation normalized, while the axillary vein case resolved in under a day, and in both instances the optical trend mirrored the clinical and ultrasonographic outcome.

The technical elegance of the approach lies in what it measures. Unlike pulse oximetry, which reads arterial oxygen saturation at a fingertip or earlobe, NIRS captures venous-weighted tissue saturation, reflecting the balance between oxygen delivery and oxygen extraction in the microcirculation. When a vein is obstructed, blood backs up in the limb, capillary pressure rises, and the tissue becomes congested with deoxygenated blood, driving the TOI downward. As the clot dissolves and venous outflow is re-established, the stagnation clears and the saturation recovers. This makes the measurement particularly sensitive to the venous side of the circulation, which is precisely where catheter-related thrombosis does its damage. The technology also quantifies tissue hemoglobin concentrations, offering a window into local blood volume that complements the saturation reading.

As with any preliminary exploration, the caveats are substantial. The findings rest on only two infants, and the safe TOI range of 58 to 68 percent was established within this small cohort rather than validated across a population of newborns of different sizes, gestational ages, and clinical conditions. The researchers themselves describe the work as preliminary, and the published version is an early-release, peer-reviewed accepted manuscript subject to further edits. Larger prospective studies will be needed to confirm reference values, define alarm thresholds that balance sensitivity against false alarms, and determine whether continuous optical monitoring genuinely improves outcomes compared with standard surveillance. Questions also remain about sensor placement, motion artifact in active infants, and how conditions such as anemia or shock, which independently alter tissue saturation, might confound the thrombosis signal.

Even so, the implications for neonatal intensive care are compelling. Infants with central catheters are among the most vulnerable patients in medicine, and a noninvasive, continuous monitor that flags venous obstruction before a limb becomes visibly swollen could change the tempo of intervention. Rather than waiting for clinical signs or scheduling serial ultrasound examinations, clinicians could receive an early optical alert, confirm with targeted imaging, and begin thrombolysis with a real-time readout of its effect. The work from Hangzhou adds a new application to a technology that neonatologists already trust for brain and abdominal monitoring, and it points toward a future where the humble bedside light sensor becomes part of the standard safety net around every central line in the neonatal unit.

Subject of Research: Near-infrared spectroscopy monitoring of limb tissue oxygen saturation in neonatal catheter-related venous thrombosis

Article Title: Preliminary exploration of tissue oxygen saturation index using near-infrared spectroscopy in two infants of neonatal catheter-associated venous thrombosis

Article References: Zhu, H., Ling, Y., Dong, X., Luo, F., & Zhu, J. (2026). Preliminary exploration of tissue oxygen saturation index using near-infrared spectroscopy in two infants of neonatal catheter-associated venous thrombosis. BMC Pediatrics, 26(1), Article 908. https://doi.org/10.1186/s12887-026-07711-8

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07711-8

Keywords: near-infrared spectroscopy, tissue oxygen saturation index, neonatal thrombosis, catheter-related venous thrombosis, urokinase thrombolysis, neonatal intensive care, central venous catheter, vascular ultrasound, newborn health, hemodynamics, case report, Preliminary

MAJIS is the visible and near-infrared imaging spectrometer at the heart of JUICE’s payload, designed to map the surfaces of Ganymede, Callisto and Europa and to probe the atmosphere of Jupiter across a spectral range from 0.5 to 5.56 micrometers. The instrument splits light into two channels, a VISNIR channel covering 0.495 to 2.35 micrometers and an infrared channel spanning 2.28 to 5.56 micrometers, each recording up to 640 spectral samples. During a planned 3.3-year tour of the Jupiter system followed by nearly a year in orbit around Ganymede, MAJIS will search for water ice, minerals, organic signatures and transient atmospheric phenomena. But before any of that could happen, the team needed to know whether the instrument’s delicate calibration had survived launch.

The answer came from an extraordinary opportunity: the Lunar-Earth Gravitational Assist, or LEGA, a double flyby executed on 19 and 20 August 2024 that brought JUICE within 750 kilometers of the Moon and 6,100 kilometers of Earth. Earth’s atmosphere turned out to be a gift for calibration. It is laced with narrow absorption bands from oxygen, water vapor, carbon dioxide, methane, nitrous oxide and carbon monoxide distributed across nearly the entire wavelength range of both MAJIS channels. By comparing MAJIS spectra of Earth with high-precision radiative transfer simulations from the 4A/OP model, the team could measure the exact position of each spectral channel, its width and its distortions across the field of view with nanometer precision.

The results revealed that launch had shifted the instrument’s spectral registration slightly, by about 3.8 nanometers in the VISNIR channel and 5.4 nanometers in the infrared channel, a finding first spotted using the instrument’s internal calibration unit, which carries a tungsten halogen lamp and a black body source. Crucially, the Earth observations confirmed that these small shifts apply uniformly across each channel’s full wavelength range, validating the updated calibration to within a single nanometer. For wavelengths beyond 3.5 micrometers, where the ground calibration setup had been limited, the flyby data provided the first reliable measurements, revealing residuals of only about 1.5 nanometers on average against a nominal spectral sampling of 6.5 nanometers. Independent checks against the PRISMA hyperspectral Earth-observation satellite and the IASI infrared sounder on the MetOp satellites produced residuals never exceeding 1.5 nanometers, a striking agreement between instruments calibrated entirely independently.

The radiometric story, meaning how accurately MAJIS measures the brightness of light, proved more intricate. Observations with the internal calibration unit before and after launch showed that the spatial distribution of the calibration signal had changed, with more light falling on the right side of the field of view and less on the left. The most likely explanation is that the geometric relationship between the internal light sources and the scattering screen shifted during launch, rather than any deformation of the main optics, since observations of the Moon and Earth otherwise matched expectations. More surprisingly, the team discovered a contamination of the left portion of the infrared detector by aliphatic compounds, identifiable by their characteristic carbon-hydrogen absorption signature near 3.4 micrometers, which reduced the response there by up to 37 percent. Because the contamination has remained stable since the commissioning phase in mid-2023, the team was able to compensate for it fully in the updated calibration, verified by the consistency of lunar spectra across the entire field of view.

To validate the absolute radiometric calibration, the team turned to the Moon, a target whose reflectance does not change over time. MAJIS lunar reflectance spectra fell comfortably within the wide range of measurements from previous missions, including the multiband imager and spectral profiler on Selene, the Moon Mineralogy Mapper and SIR-2 on Chandrayaan-1, and Earth-based telescopic systems. An even cleaner comparison came from JANUS, JUICE’s own high-resolution camera, whose line of sight is aligned with MAJIS to within 0.3 degrees. For the same lunar surface elements observed under essentially identical geometry, JANUS measured radiances about 15 percent higher than MAJIS, a difference well within the expected uncertainty between two independently calibrated instruments.

Earth observations added further confidence, though with caveats. Comparing MAJIS with the German ENMAP hyperspectral mission over the Pacific Ocean, with observations separated by only 1.3 hours and 400 kilometers, the radiances for cloud-free ocean areas matched remarkably well, and the shapes of the water vapor absorption bands at 1.5 and 2 micrometers aligned almost perfectly after straylight correction. A comparison with TROPOMI on Sentinel-5P, which observed the same regions about three hours later, found MAJIS radiances lower by roughly 10 percent in one visible band and higher by about 20 percent in two shortwave infrared bands, both within the range expected between independently calibrated instruments given the drifting cloud cover and differing viewing angles. In the thermal infrared, where IASI comparisons are most reliable, the match was described as outstanding, and lunar brightness temperatures derived from MAJIS agreed within 10 kelvin with measurements from a thermal mapper on NASA’s Lunar Reconnaissance Orbiter, implying the infrared calibration is accurate to a few percent at the longest wavelengths.

One subtle effect deserves attention for future science. MAJIS’s VISNIR channel suffers from straylight, scattered light that contaminates measurements at wavelengths shorter than 1.3 micrometers, originating from the region beyond 1.5 micrometers. For the Moon, whose reflectance rises steeply with wavelength, this contamination can reach 40 percent at 0.65 micrometers, but for blue targets like Earth, Jupiter and the icy moons, whose deep water and methane absorption bands suppress the straylight source region, the contribution drops to around 15 percent or less. The team showed that the straylight is relatively featureless and can be reliably corrected, and they plan to shift the readout window slightly during Jupiter operations to monitor it directly, at a cost of only 2.5 percent of the field of view. Intriguingly, the high signal-to-noise lunar spectra, exceeding 200 even at full resolution, hint at a weak hydroxyl absorption near 2.9 micrometers at high solar incidence, consistent with known variability of lunar water and hydroxyl signatures, though the team urges caution because the feature lies close to the detector’s filter boundary.

Perhaps the most exciting conclusion concerns what all this means for Jupiter. Extrapolating from the operating conditions at 1 astronomical unit to those at 5, where JUICE will operate, the team confirms that MAJIS will deliver high-quality data throughout its science phase. During the Earth flyby, the instrument achieved signal-to-noise ratios of up to 400 in single spectra, and higher with stacking, with very high detector operability even at integration times as short as 11 milliseconds. At Jupiter, colder detector temperatures and longer integrations should push the signal-to-noise ratio for observations of Jupiter’s hot spots in the 4.5 to 5.5 micrometer methane window to as much as 1,000. With two more Earth flybys scheduled for September 2026 and January 2029, and continued monitoring of the internal calibration unit throughout the cruise, the MAJIS team will have every opportunity to refine the calibration further before the instrument turns its gaze on Ganymede, Europa, Callisto and the giant planet itself in 2031. The August 2024 flyby, it turns out, was not just a gravitational slingshot but a resounding vote of confidence in one of ESA’s most capable planetary instruments.

Subject of Research: Post-launch spectral and radiometric calibration of the MAJIS VIS-NIR imaging spectrometer aboard the JUICE mission using Earth and Moon flyby observations

Article Title: Post launch spectral and radiometric performances of MAJIS, the VIS-NIR imaging spectrometer of JUICE

Article References: Langevin, Y., Rodriguez, S., Guerlet, S., Poulet, F., Piccioni, G., Agostini, L., Armante, R., D'Aversa, E., Filacchione, G., Fletcher, L., Oliva, F., Royer, C., Seignovert, B., Stephan, K., Tosi, F., & Trent, T. (2026). Post launch spectral and radiometric performances of MAJIS, the VIS-NIR imaging spectrometer of JUICE. Annales Geophysicae, 44(2), 825-853. https://doi.org/10.5194/angeo-44-825-2026

Image Credits: AI Generated

DOI: 10.5194/angeo-44-825-2026

Keywords: JUICE, MAJIS, imaging spectrometer, spectral calibration, radiometric calibration, Jupiter Icy Moons Explorer, Earth flyby, Moon flyby, Ganymede, near-infrared spectroscopy, ESA, internal calibration unit

One of the most powerful tools for stabilizing and boosting yields in any crop is hybrid breeding, which exploits heterosis, the phenomenon by which the offspring of two genetically distinct parents outperform both. In crops like maize and rye, hybrid cultivars have delivered dramatic gains in both yield and yield stability. Faba bean, unfortunately, has never been able to join the hybrid club. Although cytoplasmic male sterility systems, the biological machinery that makes large-scale hybrid seed production possible in other crops, have been identified in faba bean since the 1960s, they have proven too unreliable for commercial use. Breeders have instead turned to synthetic cultivars, which combine at least two inbred parental lines and propagate them together under open pollination. The resulting mixtures capture some of heterosis’s benefits, but at a cost: the best yields typically appear only in the second or third synthetic generation, requiring up to two additional generations of costly field propagation before seed reaches farmers.

A research team working within the BreedPath project, led by scientists at Justus Liebig University Giessen in collaboration with the breeding company NPZ Innovation GmbH, has now tested a strikingly different approach to this problem, one that begins with a camera and a phenomenon first described in 1881. That year, the German botanist Wilhelm Focke noticed that fruits arising from crosses between radish species could be distinguished from those of the parents by their color, an influence of pollen on fruit and seed characteristics that he termed xenien. Today, xenia effects, the measurable impact of paternal pollen on seed traits such as weight, shape, and chemical composition, are documented in crops including maize and pea. In faba bean, earlier studies had reported increases in seed weight and cotyledon cell number in hybrid seeds. The German team reasoned that if hybrid faba bean seeds carry even subtle physical or chemical signatures of their hybridity, hyperspectral imaging might be able to detect them, allowing breeders to sort hybrid seeds from self-pollinated ones without a single DNA test.

The technical setup was ambitious. The researchers assembled eighteen biparental synthetic Syn-1 combinations of spring faba bean, each produced by open-pollinating two nearly homozygous parental lines under isolation tents stocked with bumblebees to guarantee outcrossing. Each Syn-1 lot therefore contained a mixture of F1 hybrid seeds from cross-pollinations and homozygous inbred seeds from self-pollinations. Seeds from each combination, along with pure seeds of the parental lines, were placed into 24-well tissue culture trays and imaged with two hyperspectral line-scanning sensors covering an extraordinary range from 400 to 2500 nanometers, spanning the visible, near-infrared, and short-wave infrared regions. A 1000-watt halogen spotlight provided illumination, and every image was calibrated against a Zenith Polymer diffuser of known reflectance, with dark-current correction applied pixel by pixel. Segmentation algorithms then isolated each individual seed from the tray images, extracting mean reflectance, reflectance variability, and seed size for every single seed in the experiment.

To know the truth about each seed’s genetic status, the team grew all the imaged seeds and genotyped them. Using SNP chip data from the parental lines, they designed Kompetitive Allele Specific PCR markers that could classify each offspring as homozygous, corresponding to one of the two parents, or heterozygous, meaning a true F1 hybrid. This ground-truthing revealed that the proportion of hybrid seeds in the Syn-1 lots ranged from 13.5 to 26.9 percent, with a mean of 17.9 percent, reflecting the modest natural outcrossing rates of the material. With spectral data in hand and genetic labels attached, the researchers deployed five machine learning algorithms: k-nearest neighbors, naive Bayes, C5.0 decision trees, random forest, and support vector machines. Spectral curves were denoised with Savitzky-Golay filtering, hyperparameters were tuned by Bayesian optimization, and performance was assessed through fivefold cross-validation repeated ten times, with accuracy as the target metric for parental classification and the F1 score, a balance of precision and recall, for hybrid identification.

The first question was whether the algorithm could tell the two parental lines of each cross apart from their spectra alone. In the initial global scenario, with 96 seeds per combination, the answer was a qualified yes. The single best combination, a random forest classifier fed with spectral data plus seed weight, size, and cross-identity information, achieved a mean accuracy of 90.5 percent across the eighteen crosses, with individual runs reaching 95.1 percent. Notably, the weight and size data contributed little; the spectral signatures alone carried most of the discriminating power. When the team then resampled two selected combinations with 300 additional seeds each, performance climbed further still, reaching a mean accuracy of 95.6 percent for one combination and an impressive 98.9 percent for the other, where random forest correctly assigned nearly every seed to its parental line. These figures place faba bean alongside wheat, maize, okra, and loofah, where spectral variety identification has previously achieved accuracies of up to 98 percent.

Identifying the hybrid seeds themselves proved far harder. When all seeds in a Syn-1 lot, hybrids and inbreds alike, were thrown into the classification task, the best F1 score in the global scenario was a meager 19.7 percent. Principal component analysis offered an explanation: the spectra of F1 seeds generally fell within the clusters formed by their parental lines, a pattern consistent with strong maternal effects on seed spectral properties, which encompass seed color, known to be maternally inherited in faba bean. In other words, the hybrid seeds looked, spectrally, like their mothers. Yet the picture was not entirely hopeless. When the eighteen crosses were ranked by the spectral distance between their parental components, the more divergent crosses yielded consistently better predictions, echoing earlier findings that xenia effects are more pronounced between genetically distant parents. And in the enlarged second-stage experiments, F1 scores rose to around 38 to 40 percent, above the 33 percent expected from random assignment among three seed classes.

The most intriguing result emerged when the researchers exploited the maternal clustering directly. For one Syn-1 combination whose parental components could be cleanly separated in a principal component analysis, the team assigned each hybrid seed to the parental cluster it resembled, on the hypothesis that maternal effects pulled hybrids toward their seed parent. Analyzing the two parental components separately, they found that for one parent the decision tree algorithm reached an F1 score of 62.2 percent with 64.8 percent accuracy, and for the other, naive Bayes achieved an F1 score of 60.4 percent with 59.0 percent accuracy, both well above the 50 percent random-assignment threshold. A wavelength-specific strategy, restricting the analysis to spectral regions where hybrid seeds deviated from the midpoint between their parents, also nudged performance upward. The authors suggest that future gains could come from hybrid convolutional neural networks that combine spectral reflectance with structural and textural seed features, an approach that has already outperformed single-feature models in wheat variety identification.

The practical implications, while modest on the surface, could be meaningful for breeders. With outcrossing rates in the studied material between roughly 14 and 20 percent, an F1 score of 39 percent would translate into a sorted seed lot containing around 39 percent hybrid seed, up from under 20 percent naturally. Given that heterosis for midparent yield in faba bean has been estimated at 33 to 51 percent, even a partially enriched synthetic cultivar could be expected to yield more than a conventional one. Better still, if hybrid-enriched Syn-1 seed could match the performance that normally requires propagation through Syn-2 or Syn-3 generations, breeders could save an entire generation of expensive seed multiplication. The same spectral pipeline might also serve as a screening tool to identify parental lines with naturally high outcrossing rates, or to replace costly DNA marker checks for confirming the hybridity of offspring from controlled crosses. The method has not yet produced a pure hybrid cultivar, and the authors caution that their results, obtained exclusively from breeding material, still need validation across the remarkable diversity of faba bean germplasm, which ranges from small-seed Minor types to large-seed Major types in colors spanning violet, red, green, beige, brown, black, and spotted. But the study demonstrates, for the first time in a legume, that a camera, a lamp, and a machine learning model can peer into a pile of seeds and begin to pick out the hybrids hiding among them, a small optical trick that could help bring the yield benefits of hybrid breeding to one of the world’s most sustainable protein crops.

Subject of Research: Using hyperspectral imaging and machine learning to differentiate parental and F1 hybrid seeds in faba bean synthetic cultivars

Article Title: Progeny Differentiation in Faba Bean Using Hyperspectral Images and Machine Learning

Article References: Schlichtermann, R.-H., Warnemünde, S., Tietgen, H., Welna, G., Stahl, A., Wittkop, B., & Snowdon, R. J. (2026). Progeny Differentiation in Faba Bean Using Hyperspectral Images and Machine Learning. Plant Direct, 10(10), Article e70193. https://doi.org/10.1002/pld3.70193

Image Credits: AI Generated

DOI: 10.1002/pld3.70193

Keywords: faba bean, hyperspectral imaging, machine learning, xenia effect, hybrid breeding, synthetic cultivars, heterosis, seed sorting, near-infrared spectroscopy, plant breeding, Vicia faba, phenomics

The study, published in Food Chemistry: X, tackled a problem that has long frustrated the grain industry. The official national standard procedures for measuring gluten properties, defined in Chinese standards GB/T 5506.2-2024 and GB/T 5506.4-2008, involve mixing flour with salt solution, kneading a dough, washing away the starch through a fine sieve, centrifuging the sticky residue, and drying it to a crisp. The process is tedious, demands skilled operators, destroys the sample, and consumes substantial reagents. That may be tolerable for a quality control bench, but it is hopeless for the modern demands of real-time process adjustment in mills, online grading of incoming grain, and the high-throughput genotype screening that accelerates breeding programs. Breeders evaluating hundreds of experimental wheat lines simply cannot wash dough fast enough.

Near-infrared spectroscopy has long promised a way out. Because the overtones and combination vibrations of hydrogen-bearing bonds in the 930 to 1630 nanometer range carry fingerprints of the organic molecules in a sample, a handheld DLP NIRscan Nano spectrometer can press against a flour surface and capture 389 spectral dimensions in moments. The research team, led by Rui Zhang and colleagues, applied the instrument to whole wheat flour milled from 100 verified wheat varieties grown in Beijing’s Shunyi district, with each variety’s identity confirmed by SSR molecular markers at the Beijing Wheat Seed Testing Center. From each sample they collected ten spectral readings and averaged them, calibrating against a white reference roughly every twenty scans to keep the instrument honest.

The targets of prediction were five gluten-related indicators. Wet gluten content, its moisture-corrected variant on a 14 percent moisture basis, and dry gluten content describe the quantity of gluten proteins, chiefly glutenin and gliadin. Wet gluten remaining on the sieve after centrifugation and the gluten index, the proportion of wet gluten that stays on the sieve, describe the strength and elasticity of the gluten network. In the sample set, wet gluten content hovered around 35 percent with a narrow range, while the gluten index spanned nearly the full 0 to 1 scale, from 0.01 to 1.00, offering the model a wide and challenging spread of quality to learn from. Correlation analysis revealed that the two strength indicators moved together almost perfectly, with a correlation coefficient of 0.955, while the content indicators formed their own tightly linked cluster.

The first innovation came before any modeling at all: how to split 100 samples into training and test sets. The classic Kennard-Stone algorithm and its extension, SPXY, both rely on Euclidean distances that implicitly assume the response variables are independent. But gluten indicators are anything but independent, as the strong correlations show. The team therefore proposed a covariance-guided stratified sampling strategy, or CGSS, which computes the covariance matrix of the response matrix, extracts its dominant eigenvector, and projects each sample’s centered response vector onto that principal axis of joint variation. Samples are then partitioned so that both training and test sets cover this composite quality axis evenly. On every evaluation metric, from normalized mean difference to range coverage, CGSS produced training and test sets that matched each other more closely than either classical method, achieving full spectral coverage of the test set by the training set and visibly more uniform scatter in the quality indicator space.

The second and more surprising innovation addressed the target variables themselves. Rather than feeding raw gluten measurements into a regression model, the researchers built what they call a bilinear surface function model. Each target value is normalized, then multiplied by a weight function controlled by a single hyperparameter alpha, which was tuned to 1.8425 through partial least squares regression with ten-fold cross-validation. The transformed variable encodes prior knowledge about how the gluten indicators relate to one another, effectively letting the model exploit the correlation structure among the five responses instead of treating each as an isolated number. A regularized inverse regression then reconstructs the actual predicted values. The effect was dramatic. With raw spectra and ordinary partial least squares regression, the test coefficient of determination for the gluten index was a dismal 0.17. After the surface transformation, it leapt to 0.86, and wet gluten remaining on the sieve climbed from 0.05 to 0.73.

Spectral preprocessing told an instructive cautionary tale. Multiplicative scatter correction, standard normal variate, first derivative, and detrending were each tested, and most either failed to help or actively hurt. First-derivative processing, in particular, degraded the surface-model results, dropping most test R-squared values below 0.5, apparently because differentiation amplifies noise and discards key spectral information. Detrending, by contrast, removed baseline drift and pushed the dry gluten prediction to 0.89. The lesson, consistent with a growing body of critique in the chemometrics literature, is that aggressive preprocessing is not automatically beneficial; sometimes the raw spectrum already contains what the model needs, and correction steps can distort or amplify noise at the expense of the analyte signal.

The full pipeline then coupled three wavelength selection methods, the successive projections algorithm, competitive adaptive reweighted sampling, and principal component analysis, with five machine learning regressors: quantile gradient boosting, elastic net, ridge, LASSO, and partial least squares. LASSO emerged as the clear winner in every configuration, a result the authors attribute to its sparsity advantage in high-dimensional spectral data, where it shrinks irrelevant coefficients to exactly zero and avoids overfitting the limited sample set. The best overall combination was CARS wavelength selection plus the bilinear surface model plus LASSO. On the independent test set, the coefficients of determination reached 0.98 for wet gluten remaining on the sieve, 0.93 for the gluten index, 0.89 for dry gluten content, 0.84 for wet gluten content, and 0.83 for the moisture-corrected variant. The corresponding RPD values, a robustness metric where anything above 2.0 is considered suitable for analytical purposes, were 6.73, 3.77, 3.08, 2.47, and 2.40, placing every indicator comfortably in the high-reliability zone.

What makes this work resonate beyond the laboratory is the combination of hardware and mathematics. The spectrometer is a handheld consumer-grade device, not a benchtop instrument, which means the entire analytical chain could in principle run at a grain elevator, in a breeding nursery, or on a mill floor. And because a single unified model predicts all five gluten indicators simultaneously, it avoids the fragmented approach of maintaining separate calibrations for each trait, which earlier studies required. The authors are careful to note that further validation is needed to assess performance in practical, real-world deployment, and the sample set of 100 varieties from one district and one season is modest by industrial standards. Still, the framework offers something the wet-chemistry standards never could: a nondestructive, reagent-free, seconds-per-sample measurement of both how much gluten a whole wheat flour contains and how strong that gluten will be, which is precisely the information breeders and millers need to sort the bread wheats from the cookie wheats at scale.

Subject of Research: Rapid nondestructive determination of gluten content and strength in whole wheat flour using near-infrared spectroscopy with a bilinear surface model and machine learning

Article Title: Bilinear surface model-enhanced determination of gluten quality in whole wheat flour by NIR spectroscopy

Article References: Zhang, R., Qu, P., Sun, H., Feng, G., Liu, L., Pan, D., & Luo, B. (2026). Bilinear surface model-enhanced determination of gluten quality in whole wheat flour by NIR spectroscopy. Food Chemistry: X, Article 104541. https://doi.org/10.1016/j.fochx.2026.104541

Image Credits: AI Generated

DOI: 10.1016/j.fochx.2026.104541

Keywords: near-infrared spectroscopy, whole wheat flour, gluten quality, gluten index, wet gluten content, LASSO regression, chemometrics, machine learning, sample partitioning, bilinear surface model, wheat breeding, food quality detection

The review begins with a problem that has quietly plagued the field for years: the assays themselves are chemically artificial. DPPH, the most widely used decolorization test, relies on a stable purple radical that is reduced to a yellow product, with the drop in absorbance at 517 nanometers serving as the readout. It is cheap and fast, but the radical is poorly soluble in water, forcing the use of methanol or ethanol mixtures that distort the thermodynamic behavior of hydrophilic antioxidants. Worse, the radical site sits buried behind bulky phenyl rings, so large antioxidants may simply fail to reach it, producing false negatives or artificially slowed kinetics that bear little resemblance to real radical clearance in living tissue.

ABTS improves on solubility, dissolving in both aqueous and organic media and thus accommodating hydrophilic and lipophilic antioxidants alike. But the blue-green radical cation must be pre-generated with potassium persulfate, a step that takes twelve to sixteen hours of stabilization, and the probe is entirely non-physiological: its reduction does not reproduce the reactivity, localization, or lifetime of biologically relevant species such as hydroxyl, superoxide, or nitric-oxide-derived radicals. Steric hindrance around the nitrogen-centered radical also restricts its reaction with polymeric phenols. FRAP, which measures the reduction of ferric iron complexed with tripyridyltriazine at low pH, is rapid and inexpensive but is not a radical assay at all. It gauges reducing capacity rather than radical scavenging, underestimating antioxidants that act through metal chelation while potentially overestimating polyphenols that undergo secondary autoxidation or release free ferrous iron that accelerates Fenton chemistry.

Even the hydrogen-atom-transfer assays, which are mechanistically closer to how chain-breaking antioxidants actually halt lipid peroxidation, have stumbled. The ORAC assay, long considered a gold standard, tracks the protection of a fluorescent probe from peroxyl radicals and integrates the area under the fluorescence decay curve. But it is exquisitely sensitive to temperature fluctuations across microplate readers, and natural product matrices full of endogenous pigments and fluorescent compounds interfere with the probe’s emission. These problems contributed to the United States Department of Agriculture officially discontinuing its public validation databases for ORAC values in foods. The authors’ comparison table makes the trade-offs explicit: every one of the four principal acellular assays carries known interferents, and correlations with cell-based or in vivo markers are not consistently reported across independent studies for any of them.

The review then climbs the biological ladder. Cell-based antioxidant activity assays preload living cells, such as Caco-2 or HepG2 lines, with a cell-permeable probe like DCFH-DA, which cellular esterases convert into a trapped, non-fluorescent molecule. When an oxidative stressor is applied alongside a candidate antioxidant, reduced fluorescence signals scavenging of intracellular reactive oxygen species. These assays capture cellular uptake, metabolism, and bioavailability, but they demand sterile culture facilities, expensive imaging instruments, and produce results that vary with cell type, insult, and probe, which can itself be pro-oxidant. At the top sit in vivo models in rodents, zebrafish, and nematodes, which capture absorption, distribution, metabolism, and excretion, along with the endogenous antioxidant enzyme network of superoxide dismutase, catalase, and glutathione peroxidase. They are indispensable but ethically constrained, slow, costly, and variable, and extrapolating from animals to humans remains an open challenge.

The alternative the authors champion is to stop running the wet chemistry altogether. Near-infrared, mid-infrared, and Raman spectroscopy can capture a vibrational fingerprint of an intact sample in seconds, and a multivariate calibration model, most often partial least-squares regression, maps the spectral absorption bands directly to reference antioxidant values. Once validated, the model predicts antioxidant capacity from the optical spectrum alone, consuming zero reagents and leaving the sample untouched. The evidence base is substantial: near-infrared models with variable-selection algorithms predicted phenolic content and antioxidant capacity of peanut seeds with calibration coefficients of determination up to 0.95, similar architectures worked for black goji berries without any extraction step, and mid-infrared models predicted FRAP values of propolis extracts at comparable accuracy. The authors are careful to note that these headline values are calibration statistics, which tend to be inflated relative to cross-validated or external-test figures, and that a model built on one matrix, such as peanut seed, generally cannot be transferred to a structurally different matrix without recalibration. Cross-matrix generalizability remains an emerging frontier.

Machine learning is extending this pipeline in two directions. Quantitative structure-activity relationship models map computed molecular descriptors to measured antioxidant activity, and the review highlights several rigorous demonstrations. Jung and colleagues trained five algorithms on more than 1,900 compounds using extended-connectivity fingerprints, with Random Forest and Support Vector Machines achieving classification accuracy above 0.90 and generalizing to the external BATMAN natural-product database. Ghironi and colleagues compared eleven regression models on 1,911 small molecules from the AODB database, finding that ensemble tree methods, particularly Extra Trees and a consensus model combining Extra Trees, Gradient Boosting, and XGBoost, substantially outperformed linear approaches, and that the consensus model correctly predicted the antioxidant potency of urolithin A, a compound absent from training, in close agreement with experiment. Mateus and Abreu showed the approach can be democratized, building a four-descriptor model with entirely open-source tools, though the authors caution that with only 70 compounds and over 12,000 candidate descriptors, overfitting risk cannot be fully excluded.

The second direction is image-based prediction, which replaces the spectrophotometer with a smartphone camera. In one platform, a drop of sample reacts with DPPH on a moving-drop device, and the magenta-to-yellow color ratio is converted into IC50 and Trolox-equivalent values statistically indistinguishable from the reference method. Paper-based tests now allow complex food emulsions to be applied directly to DPPH-spotted strips with no extraction step at all. Machine learning classifiers operating across RGB, HSV, and CIELAB color spaces adapt to varying lighting, cameras, and users, overcoming the fragility of purely optical approaches. The most striking demonstration is a smartphone-integrated system for point-of-care antioxidant testing in human saliva: a convolutional neural network reached about 78 percent classification accuracy, a stacking ensemble of four CNNs with a Support Vector Machine meta-classifier pushed it to 92 percent, and adding a YOLOv4-tiny object detection step to localize the reaction vial raised accuracy to nearly 98 percent, all running in real time on an Android device without cloud processing, using a single image captured two minutes into the reaction.

The review is refreshingly honest about what these tools cannot do. Every chemometric and QSAR model is trained against the same wet-chemical reference values whose non-physiological limitations were catalogued at the outset, so AI currently accelerates and de-reagents the execution of these assays without, by itself, closing the biorelevance gap. Models trained on narrow compound sets fail to generalize without recalibration, and high-performing black-box models rarely reveal which features drove a prediction, a barrier to regulatory acceptance. The authors point to explainable AI frameworks such as SHAP, which assign each spectral band or molecular descriptor a quantitative contribution to a prediction, as a way to verify that models rely on mechanistically plausible features like phenolic hydroxyl count and conjugation rather than spurious correlations. They also sketch a future of multimodal sensor fusion combining orthogonal spectra and image data, federated learning networks that let laboratories train shared models without exchanging proprietary data, standardized open repositories of curated antioxidant datasets, and even blockchain-enabled provenance architectures that would give regulators tamper-evident assurance that a reported calibration curve corresponds to an untampered, timestamped dataset.

The paradigm shift the authors describe is ultimately about throughput and trust. A destructive, multi-hour wet-chemistry assay becomes a sub-minute, reagent-free spectral measurement; a benchtop spectrophotometer becomes a phone in a field worker’s hand; a black-box score becomes an auditable, interpretable prediction. None of this erases the need for cell-based and in vivo confirmation of biological efficacy, and the authors are explicit that triangulating across assay categories remains essential. But for the pharmaceutical, nutraceutical, and food industries screening thousands of candidate compounds and complex extracts, the message is clear: the next generation of antioxidant analysis will be learned from data rather than measured in a cuvette, and the laboratories that build validated, interpretable, transferable models first will define how antioxidant science is done for years to come.

Subject of Research: Limitations of traditional antioxidant assays and the integration of AI and non-destructive spectroscopic techniques for antioxidant activity prediction

Article Title: Overcoming the limitations of traditional antioxidant assays: the role of AI and non-destructive techniques

Article References: Overcoming the limitations of traditional antioxidant assays: the role of AI and non-destructive techniques. (n.d.). https://doi.org/10.1016/j.rechem.2026.103899

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103899

Keywords: antioxidants, DPPH, ABTS, FRAP, ORAC, machine learning, QSAR, near-infrared spectroscopy, chemometrics, smartphone colorimetry, deep learning, oxidative stress

Preterm infants face a unique physiological predicament. Born before their organs have completed development, they must maintain blood pressure, thermoregulation, respiration and digestion simultaneously, often while receiving intensive respiratory and circulatory support. The kidneys and the gastrointestinal tract are both highly oxygen-dependent organs, and both are vulnerable to injury when oxygen delivery falls short of demand. Necrotizing enterocolitis, a devastating bowel disease, and acute kidney injury are among the most feared complications of prematurity, and both have been linked in previous research to periods of compromised perfusion and tissue hypoxia.

The research team used near-infrared spectroscopy, a bedside optical technique that shines near-infrared light through tissue and measures how much is absorbed by oxygenated and deoxygenated hemoglobin, to track regional oxygen saturation continuously. Probes placed over the flank captured renal oxygenation, while probes over the abdomen monitored intestinal oxygenation. Because the technique is entirely non-invasive and painless, it can be applied repeatedly or continuously in the smallest and sickest infants without adding to their physiological burden, a major advantage over blood sampling or imaging approaches that offer only snapshots.

What makes the study particularly valuable is its paired design. Rather than examining kidney or gut oxygenation in isolation, the investigators measured both regions simultaneously in the same infants, allowing direct comparison of how the two vascular beds behave under identical systemic conditions. This matters because the splanchnic and renal circulations respond differently to stress. The gut is exquisitely sensitive to feeding, expanding its metabolic and oxygen demands with every milk bolus, while the kidney’s oxygen consumption is dominated by active sodium reabsorption and is influenced by medications, oxygen therapy and hemodynamic swings.

The study evaluated how feeding timing interacted with these oxygenation patterns. Feeding practices in neonatal intensive care units vary widely: some units begin minimal enteral feeds within hours of birth, others delay until the infant is clinically stable, and the pace of advancing feeds is a subject of ongoing debate. The reasoning behind cautious feeding is that a gut with marginal oxygen reserve may be poorly equipped to handle the postprandial surge in demand, potentially increasing the risk of feed intolerance or necrotizing enterocolitis. Yet prolonged delays in feeding carry their own costs, including gut atrophy, prolonged dependence on intravenous nutrition and increased risk of infection and liver injury.

By correlating continuous renal and intestinal oxygen saturation measurements with feeding episodes, the researchers were able to characterize how preterm infants’ regional oxygenation responds around feeds and whether the magnitude or pattern of those responses differs between the kidney and the gut. The paired measurements revealed that intestinal oxygenation behaves dynamically in relation to feeding, whereas renal oxygenation follows a more independent course shaped largely by systemic hemodynamics and clinical interventions. This dissociation underscores that a single global measure of oxygenation, such as arterial oxygen saturation, cannot capture the redistribution of blood flow that occurs between organ beds in the transitional period after preterm birth.

The clinical implications are potentially significant. If intestinal oxygenation measurements can identify infants whose guts are experiencing relative hypoxia before clinical signs such as feeding intolerance, abdominal distension or bloody stools appear, monitoring could shift from reactive to preventive. Neonatologists could, in principle, titrate the initiation and advancement of feeds against a real-time physiological signal, feeding more aggressively when intestinal oxygenation is robust and holding back when it signals distress. Similarly, renal oxygenation trends could serve as an early warning for acute kidney injury, which currently is often detected only after serum creatinine rises, a lagging marker that reflects injury after it has already occurred.

Like all physiological monitoring studies in this population, the work comes with methodological caveats. Near-infrared spectroscopy measures a regional venous-weighted saturation rather than direct tissue oxygen tension, and probe positioning, skin thickness and local edema can influence readings. The kidney’s retroperitoneal location and the interposition of bowel gas make renal signals technically more challenging to acquire than cerebral signals, and interpreting absolute values requires normative data across gestational ages and postnatal days. The authors’ paired comparison design helps address some of these concerns by using each infant as their own physiological control, isolating differences between organ beds from confounding by systemic factors.

Broader context strengthens the relevance of the findings. Research over the past two decades has established that low regional oxygenation in the first days of life is associated with adverse outcomes, including necrotizing enterocolitis, and that postnatal adaptation involves complex redistribution of cardiac output away from some vascular beds. Yet most monitoring studies have focused on the brain, leaving the abdomen comparatively underexplored despite the gut’s central role in neonatal morbidity. By bringing the kidney and the intestine into a single analytical frame and tying both to feeding, a modifiable care practice, this study adds a practical dimension to a growing physiological literature.

For now, near-infrared spectroscopy of the abdomen and kidney remains a research and select-care tool rather than routine standard practice, and no oxygenation threshold has been validated as a trigger for feeding decisions. But the trajectory of the field is clear: as continuous multi-organ monitoring becomes more feasible, the goal of individualized, physiologically guided nutrition in preterm infants moves closer to reality. The study’s paired demonstration that kidney and gut oxygenation diverge in the same infant at the same moment is a reminder that preterm physiology cannot be read from any single monitor, and that the organs we cannot see are often the ones most in need of a window.

Subject of Research: Simultaneous monitoring of renal and intestinal oxygenation in preterm neonates in relation to feeding timing

Article Title: Evaluation of kidney oxygenation compared to intestinal oxygenation and feeding timing in preterm neonates

Article References: Condit, P. E., Lindstrom, R., Gunderson, S. N., & Harer, M. W. (2026). Evaluation of kidney oxygenation compared to intestinal oxygenation and feeding timing in preterm neonates. Journal of Perinatology. https://doi.org/10.1038/s41372-026-02915-3

Image Credits: AI Generated

DOI: 10.1038/s41372-026-02915-3

Keywords: preterm neonates, near-infrared spectroscopy, kidney oxygenation, intestinal oxygenation, feeding timing, necrotizing enterocolitis, acute kidney injury, neonatal intensive care, regional oxygen saturation, enteral feeding, neonatal hemodynamics, Journal of Perinatology