The physical basis of the technique rests on two fundamental processes. Molecular vibrations, the bending and stretching of chemical bonds when they absorb radiation matching their vibrational frequency, produce characteristic absorption bands that dominate the shortwave-infrared and mid-infrared regions. Electronic transitions between atomic orbital energy levels generate absorptions concentrated in the visible range. The key soil chromophores are well understood: organic matter darkens reflectance progressively as carbon content rises, iron-bearing minerals such as hematite and goethite leave distinctive fingerprints through charge transfers and crystal-field transitions, and clay minerals produce O-H stretching and Si-O-Al bending signatures at defined wavelengths. Because these features overlap and interact, no single wavelength maps cleanly onto a single soil property; instead, chemometric and machine-learning models must untangle the composite signal.

The review, led by Tushar Kumar of Maharana Pratap University of Agriculture and Technology in Udaipur, traces the field from the X-ray and microscopical studies of soil colloids in the 1930s through Condit’s foundational 1970 spectral library of American soils to the digital revolution of the 1980s and 1990s, when chart recorders gave way to instruments capturing thousands of data points per spectrum. Today the instrumental ecosystem spans laboratory benchtop analysers, portable handheld sensors, drone-mounted hyperspectral cameras, and satellite-borne spectrometers. Vis-NIR instruments operating from 350 to 2500 nanometres have been miniaturized into smartphone-compatible units, while the most sensitive mid-infrared systems still require liquid nitrogen cooling, confining them largely to well-resourced laboratories.

On the central question of which spectral region performs better, the evidence is nuanced. A comprehensive synthesis by Soriano-Disla and colleagues concluded that mid-infrared spectroscopy outperforms Vis-NIR for pH, soil organic carbon, cation exchange capacity, particle-size fractions, phosphorus, and potassium, because MIR wavelengths probe directly the functional groups that govern these properties. Yet biological properties such as microbial biomass carbon are more reliably predicted in the visible-near infrared, whose chromophores respond to the organic matrix in which biological activity is embedded. Practical considerations sharpen the trade-off: portable NIR units are dramatically cheaper and more field-deployable, making them the default for large surveys, while MIR’s theoretical advantage justifies its cost only for properties where the accuracy gain is significant.

Raw spectra, the review stresses, are rarely fit for modelling. Variable sample packing, particle-size differences, moisture, and instrument drift superimpose additive and multiplicative distortions on the true signal. Pre-processing is therefore integral, not optional. Standard steps include trimming noisy spectral extremes, converting reflectance to absorbance via the Beer-Lambert relationship, smoothing with Savitzky-Golay filters that preserve peak shape better than simple averaging, and scatter correction through the standard normal variate transformation, which centres each spectrum to zero mean and unit variance. Derivative transformations sharpen overlapping bands. Crucially, the review cites evidence that no universally optimal pre-processing sequence exists; the best combination depends on soil type, instrument, property, and model, and reported optima shift with spectral resolution rather than converging on a single setting.

Among prediction models, partial least squares regression remains the dependable baseline. Unlike principal component regression, which selects components by spectral variance rather than predictive relevance, PLSR extracts latent variables that maximize covariance with the target property, making it robust against the severe collinearity of spectral data. Where the spectral-property relationship is genuinely non-linear, alternatives earn their keep. Support vector regression achieved R² of 0.912 for total nitrogen in one study, and ensemble tree methods such as random forests outperformed PLSR for available sulphur. Deep-learning architectures including convolutional neural networks have produced strong results at global scale, but the review counsels caution: CNNs are data-hungry, overfit readily on the small local datasets typical of a single regional survey, and their reported gains over well-tuned classical baselines are frequently modest and inconsistent. The most defensible use of deep learning in data-scarce settings is transfer learning, pre-training on large external libraries and fine-tuning locally, an approach whose advantage for heterogeneous soils remains an open empirical question.

The property-by-property verdict falls into three reliability tiers. Clay and texture, soil organic carbon, moisture, cation exchange capacity, calcium, magnesium, and total nitrogen sit in a robust tier, predicted well wherever calibration is representative, with texture predictions across studies reaching R² values of 0.67 to 0.87 and organic carbon reaching R² of 0.89 in some Vis-NIR calibrations. A conditional tier, including pH, potassium, sorbed phosphorus, bulk density, and aggregate properties, is predictable only through proxies such as clay mineralogy and organic matter, so performance travels poorly between soils. A weak tier remains unreliable regardless of model: electrical conductivity in non-saline soils, extractable plant-available phosphorus, and DTPA-extractable micronutrients, whose trace concentrations are swamped by the spectra of dominant constituents. Potassium illustrates the contradictions sharply, with one study reporting R² of 0.41 and another 0.84, the difference hinging on whether soils contain abundant, spectrally active potassium-bearing clays such as illite.

The review is unsparing about the field’s untested assumptions. Laboratory reference measurements are treated as error-free ground truth despite carrying their own uncertainties; the chromophore framework, which assumes spectral features arise from a stable, approximately additive set of constituents, has never been subjected to systematic perturbation experiments; and model transferability is assumed whenever a global library calibrates predictions for a new region, even though Indian studies consistently report weaker performance than global benchmarks promise. Five frontier questions are identified, chief among them whether spectroscopy can deliver transferable predictions across the extreme variability of landscapes like Udaipur in Rajasthan, where soils range from red gneissic types at 1300 metres to deep black Vertisols on the plains.

For India, whose Soil Health Card programme has issued 24.74 crore cards through 8,272 laboratories, the realistic near-term role for spectroscopy is complementary rather than substitutive: rapid spectral screening for well-supported properties, reserving wet chemistry for extractable phosphorus, potassium in low-illite soils, and micronutrients. Global-to-local transfer learning offers a tested shortcut, with one study reporting lower error in 91 percent of comparisons after transfer, but local reference samples and independent validation remain indispensable. The review also flags data governance concerns, noting that digital soil mapping ties fertility records to identifiable landholdings and calling for consent, de-identification, and access controls as spectral monitoring scales.

The ultimate message is that diffuse reflectance spectroscopy has matured from a mineral identification tool into a credible multi-property platform underpinning digital soil mapping and precision agriculture, yet the field’s next advance will come not from another incremental model comparison but from rigorously testing its own foundations. Whether the technology can finally deliver affordable, trustworthy soil surveillance to the farmers who depend on it will be decided by the answers.

Subject of Research: Advances in diffuse reflectance spectroscopy for rapid soil fertility assessment

Article Title: A critical review of advances in diffuse reflectance spectroscopy for rapid soil fertility assessment

Article References: Kumar, T., Yadav, K. K., Meena, R. H., Lakhawat, S. S., & Jat, G. (2026). A critical review of advances in diffuse reflectance spectroscopy for rapid soil fertility assessment. Discover Soil, 3(1), Article 160. https://doi.org/10.1007/s44378-026-00314-w

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00314-w

Keywords: diffuse reflectance spectroscopy, Vis-NIR, MIR, soil fertility, soil organic carbon, PLSR, machine learning, digital soil mapping, spectral pre-processing, soil testing, transfer learning, India

Researchers at Chungnam National University in Daejeon, South Korea, working with a colleague at Gadjah Mada University in Indonesia, set out to test whether a short-wave infrared (SWIR) hyperspectral imaging system could reliably predict moisture content in pork meat emulsions while simultaneously revealing how uniformly the batter had been mixed. Their system swept across 1,000 to 1,700 nanometers — a range where water, fat, and protein all leave distinctive spectroscopic fingerprints — and, combined with machine learning models, produced moisture maps of the emulsion in roughly 20 seconds per sample. For an industry racing to build smart factories, the implications are considerable.

The experimental design was deliberately systematic. The team prepared pork ham and back fat batters at three lean meat-to-fat ratios: Group A at 6:2, Group B at 5:3, and Group C at 4:4. Each formulation was mixed in a silent cutter operating at 1,440 rpm for seven different durations: 0, 1, 2, 5, 7, 10, and 15 minutes, with processing temperatures held at approximately 10 to 15 degrees Celsius. Over three batches, the researchers generated 84 samples, each imaged with a line-scan SWIR camera capable of capturing 275 spectral bands across 894 to 2,505 nanometers. Only the cleanest 120 bands, spanning 1,000 to 1,700 nanometers, entered the analysis. Reference moisture values came from the classic, laborious route: oven-drying one-gram subsamples at 105 degrees Celsius for 24 hours, repeated in triplicate for every sample.

The chemical results told a clear story before any photons were analyzed. Group A, richest in lean meat, held the most water at 63.4 percent, followed by Group B at 57.2 percent and Group C at 52.4 percent — differences that were highly statistically significant. This aligns with established meat science: lean muscle proteins form networks that bind water effectively, while increasing fat dilutes that capacity. Mixing time mattered too. Freshly assembled batter at zero minutes averaged 61.8 percent moisture, but after just one minute of cutting the value dropped and then stabilized at roughly 56 to 58 percent through the full 15 minutes. Two-way analysis of variance confirmed both factors as significant main effects, with no meaningful interaction between them — formulation and mixing time independently shape the water content of the final batter.

The spectral data told a parallel story in reflected light. Batters with more moisture absorbed more strongly, since water exhibits intense O–H absorption features in the near infrared; fattier batters reflected more light because fat particles scatter it. Group A showed the lowest overall reflectance, Group C the highest, with the most informative differences appearing near 1,100 nanometers, across 1,250 to 1,350 nanometers, and from 1,600 to 1,700 nanometers — regions corresponding to water O–H overtones and fat C–H overtones. Even more striking was what happened over time: at the start of mixing the spectra varied widely from sample to sample, but as mixing progressed the curves converged into stable, nearly uniform profiles, a direct optical signature of the batter homogenizing.

Turning spectra into numbers required serious modeling. The team extracted 840 mean spectra and trained four different regression approaches — partial least squares regression (PLSR), random forest (RF), Elastic Net, and a weighted ensemble of all three. To avoid the pitfall of a single lucky or unlucky train-test split, they used Monte Carlo random sampling, repeating the random 60/40 calibration-validation partition 100 times for each model and reporting averaged performance metrics. Ten-fold cross-validation guided model selection, with final models judged on the lowest root mean square error of cross-validation. Seven spectral preprocessing techniques, from standard normal variate to Savitzky–Golay derivatives, were tested against raw spectra as well.

The winner was, somewhat counterintuitively, the simplest treatment. Elastic Net regression applied to raw, unpreprocessed spectra delivered the best predictive performance, achieving a prediction coefficient of determination of 0.76 with a root mean square error of prediction of 2.54 percent. The ensemble model, which weighted each base model by the inverse of its cross-validation error squared, performed nearly as well and proved the most stable overall. Random forest posted the highest calibration fit at 0.87 to 0.89, a hallmark of mild overfitting, while PLSR and Elastic Net landed in a comparable band across validation and prediction sets. The researchers also noted that raw spectra often beat preprocessed ones in their experiments — a finding consistent with earlier literature showing that preprocessing is not automatically beneficial when baseline drift and scattering are minimal during acquisition.

Just as revealing was where each model looked. PLSR and Elastic Net concentrated their attention on the lower SWIR wavelengths, around 1,000 to 1,224 and 1,200 to 1,400 nanometers, where water’s O–H second overtone and combination bands dominate — exactly the regions expected to encode moisture. Elastic Net also flagged bands from 1,288 to 1,341 nanometers, hinting at additional fat-related C–H information. Random forest, by contrast, emphasized mid-to-high wavelengths between 1,359 and 1,641 nanometers, corresponding to the first overtone of hydrocarbon C–H bonds and to protein N–H and lipid C–H vibrations, reflecting its nonlinear capacity to capture composite spectral changes. The ensemble blended both perspectives, highlighting water bands near 1,200 and 1,400 nanometers alongside fat and protein regions extending to 1,700 nanometers.

Perhaps the most visually compelling result came from the chemical imaging. Applying the winning Elastic Net model pixel by pixel, the team generated false-color maps of moisture distribution across each batter block. At zero to two minutes of mixing, all groups showed scattered hotspots of locally high moisture exceeding 70 percent — evidence of poorly dispersed muscle proteins and fat particles. By 5 to 15 minutes the maps became visibly homogeneous, mirroring the spectroscopic convergence and the physical reality of proteins encapsulating fat globules into a stable emulsion. Fattier formulations, especially Group C, showed progressively declining moisture across the mixing timeline. The authors caution that the study used a modest sample set under controlled laboratory conditions, and that industrial deployment will demand validation on larger datasets in real processing environments, along with model simplification for low-latency, real-time operation. Still, the demonstration that a 20-second camera scan can replace a 24-hour oven test — while also showing whether a batch is evenly mixed — marks a meaningful step toward moisture monitoring that food processors can actually run on the factory floor.

The SWIR region occupies a particularly informative slice of the electromagnetic spectrum for food analysis. While visible and near-infrared instruments have long been used to assess meat quality, wavelengths beyond 1,000 nanometers probe stronger overtone and combination vibrations of the O–H, C–H, and N–H bonds that define water, lipid, and protein chemistry. This gives SWIR systems inherently richer contrast among the major constituents of a meat batter, though at the cost of weaker detector sensitivity and higher instrument expense, which has historically limited their adoption on processing lines.

The imaging approach also differs fundamentally from the point-sensor methods used in earlier emulsion studies. Fiber-optic probes and benchtop spectrometers average the signal over a small spot, so a single reading can mask pockets of unmixed fat or free water within a batch. By scanning an entire 18 by 10 centimeter block pixel by pixel, the hyperspectral camera turns heterogeneity itself into a measurable quantity, which is why the moisture maps could reveal localized hotspots above 70 percent that a spot measurement would likely have smoothed away.

The choice of reference chemistry matters as well. Oven drying at 105 degrees Celsius for 24 hours remains the AOAC gold standard, but it is destructive, slow, and impractical for in-line control. A prediction error of 2.54 percent moisture, while larger than the 0.291 percent reported in an earlier multispectral study of sausage emulsions, was achieved across a broader range of formulations and mixing times, and with the added benefit of spatial visualization rather than a single averaged value.

The finding that raw spectra outperformed preprocessed ones carries practical weight for industry. Preprocessing pipelines add computational overhead and can amplify noise when scattering conditions are already stable, so a model that tolerates uncorrected spectra simplifies deployment in smart-factory environments where low-latency predictions are essential.

Looking ahead, the authors’ emphasis on validation under real processing conditions points to the next hurdles: variable raw material lots, fluctuating line temperatures, and the calibration transfer needed to move a laboratory model onto multiple production lines without retraining from scratch.

Subject of Research: Non-destructive prediction of moisture content and mixing uniformity in meat emulsions using SWIR hyperspectral imaging and chemometric modeling

Article Title: Monitoring of moisture content in meat emulsion using an SWIR hyperspectral imaging system

Article References: Kim, J., Rho, T.-G., Park, E.-S., Kwon, O.-T., Lee, S.-J., Faqeerzada, M. A., Joshi, R., Amanah, H. Z., & Cho, B.-K. (2026). Monitoring of moisture content in meat emulsion using an SWIR hyperspectral imaging system. Food Science of Animal Resources, 46(1), Article 100. https://doi.org/10.1007/s44463-026-00101-9

Image Credits: AI Generated

DOI: 10.1007/s44463-026-00101-9

Keywords: SWIR hyperspectral imaging, meat emulsion, moisture prediction, chemometrics, Elastic Net regression, PLSR, random forest, ensemble modeling, process monitoring, meat batter quality, food analysis, non-destructive testing