The research team, led by Omar Arroyo-Xochihua, focused on RAW 264.7 macrophages, a murine cell line that has become the workhorse of inflammation research worldwide. These cells were stimulated with lipopolysaccharide, or LPS, a molecule found in the outer membrane of Gram-negative bacteria that reliably triggers a full-blown inflammatory response. Once the macrophages were activated in this way, the scientists treated them with an ethanol-water extract of E. menziesii and with four fractions separated by polarity: hexane, dichloromethane, ethyl acetate, and a residual aqueous fraction. The goal was to see whether any of these preparations could rein in the cellular storm that LPS ignites.

Before any biological testing could begin, the team needed to know what was actually inside the seaweed. Using proton nuclear magnetic resonance spectroscopy, or 1H-NMR, they generated chemical fingerprints of the crude extract and each fraction. The spectra revealed a rich assortment of proton signals spanning alkyl, hydroxyl, amino, alkene, and aromatic regions. Three regions stood out. Signals between 1.10 and 1.25 parts per million corresponded to the methyl groups of fucoidan, a sulfated polysaccharide abundant in brown seaweed cell walls. Signals between 5.0 and 5.8 parts per million marked the anomeric protons of fucoidan’s sugar rings. And a dense cluster between 3 and 5 parts per million pointed to mannitol, a sugar alcohol that brown algae use as a carbon transport molecule and osmolyte.

Safety came first. Using the MTT assay, which measures mitochondrial activity as a proxy for cell health, the researchers exposed macrophages to concentrations ranging from 12.5 to 1000 micrograms per milliliter over 24 and 48 hours. The extract and all fractions kept cell viability at or above 90 percent across the tested range, and the calculated half-maximal inhibitory concentration exceeded 1000 micrograms per milliliter at both time points. Only the highest concentration of the crude extract showed any hint of toxicity at 48 hours, and it was excluded from further experiments. Hydrogen peroxide, used as a positive cytotoxicity control, killed cells as expected, confirming the assay was working. The result establishes that the seaweed preparations are safe for cellular experimentation at the doses that produced biological effects.

With safety established, the team turned to a scratch assay, a simple but informative test of cell migration. A sterile pipette tip drags a wound-like gap through a confluent monolayer of macrophages, and researchers then measure how quickly cells migrate in to close the gap. LPS alone impaired migration, leaving the scratched area largely open at 48 hours. Treatment with the crude ethanol-water extract significantly reversed this effect, reducing the free scratched area by up to 40 percent at 24 hours compared with the LPS-stimulated control. The aqueous fraction performed even better, showing significant scratch closure at both 24 and 48 hours, with reductions approaching 10 percent of the original wound area remaining at the later time point. Curcumin and indomethacin, included as natural and pharmacological controls, also promoted closure, but the seaweed treatments held their own against these benchmarks.

The most technically ambitious part of the study came next. Rather than guessing which compounds were responsible, the researchers applied multivariate data analysis, specifically Orthogonal Partial Least-Squares modeling, to correlate the NMR spectra with the scratch closure measurements. The resulting statistical model was strikingly robust, with explained variance values above 0.95 and predictive Q-squared values of 0.97 and 0.93 at 24 and 48 hours respectively, and cross-validation confirmed the model was not overfitted. The contribution plots revealed that proton signals in the 0.6 to 3 parts per million region were the main contributors to the anti-migratory rescue effect. This region encompasses the methyl groups of fucoidan and the alcohol protons of mannitol, providing chemometric evidence that these two abundant seaweed metabolites are the likely active principles.

Phagocytosis, the process by which macrophages engulf debris and pathogens, told a more nuanced story. Measured with a neutral red uptake assay, phagocytic activity surged in LPS-stimulated cells, as expected. The crude extract brought this activity back down below the levels seen in unstimulated controls, suggesting a normalizing rather than suppressive effect. Interestingly, the dichloromethane, ethyl acetate, and aqueous fractions each increased phagocytic activity above unstimulated baseline levels, hinting that different components of the seaweed may push macrophage behavior in different directions depending on the chemical context. The hexane fraction showed no significant effect either way.

The clearest anti-inflammatory signal emerged from the cytokine measurements. Using reverse transcription quantitative PCR and enzyme-linked immunosorbent assays, the team quantified both messenger RNA and secreted protein levels of interleukin-1 beta and interleukin-6, two pro-inflammatory cytokines central to the M1 macrophage phenotype that LPS induces. The crude extract cut IL-1 beta mRNA expression to less than half of the LPS-stimulated level and kept IL-6 fold change below 3, with corresponding reductions at the protein level that matched the effect of indomethacin. The aqueous fraction also significantly reduced both cytokines, though its protein-level effects were less complete. Notably, neither curcumin nor indomethacin achieved significant reductions in cytokine gene expression in this system, making the seaweed’s performance at the transcript level particularly noteworthy.

The mechanistic story fits with a growing body of literature on sulfated polysaccharides. Fucoidan’s negatively charged sulfate groups are thought to interact electrostatically with inflammatory mediators, and studies of fucoidan from related brown seaweeds have shown that it can block the NF-kappa B signaling pathway, a master transcriptional switch for inflammatory genes. Mannitol, meanwhile, has been shown to reduce markers of M1-like macrophage and microglial activation in animal models of neuroinflammation, possibly through osmotic effects that alter immune cell behavior. The authors caution that the compounds in a crude extract may act synergistically or even antagonistically, and that a single murine cell line in a monolayer cannot capture the full complexity of inflammation in a living organism. Future work, they suggest, should test human cell lines, explore nitric oxide and other inflammatory mediators, and pursue full metabolomic profiling.

Still, the implications are considerable. Non-steroidal anti-inflammatory drugs remain among the most widely consumed medications on Earth, and their long-term use is linked to gastrointestinal damage, ulcers, and elevated cardiovascular risk. Marine organisms remain a strikingly underexplored source of alternatives; between 1981 and 2019, more than fifty natural products and derivatives for treating inflammation were approved, yet marine species account for only a small fraction of the research effort. Egregia menziesii, with its limited range from Alaska to Baja California and its documented safety across multiple cell types and whole-organism models, now joins the shortlist of seaweeds with credible immunomodulatory credentials. If the fucoidan and mannitol hypothesis holds up in more complex systems, the feather boa kelp swaying in the Pacific surf could one day contribute to medicines that quiet inflammation without the collateral damage of current drugs.

Subject of Research: Anti-inflammatory activity and chemical profiling of Egregia menziesii extracts in LPS-activated macrophages

Article Title: Chemical profiling of Egregia menziesii and anti-inflammatory potential in LPS-activated RAW 264.7 macrophages

Article References: Arroyo-Xochihua, O., Sánchez-Medina, A., López-Franco, Ó., Olivares-Bañuelos, T. N., & Zepeda, R. C. (2026). Chemical profiling of Egregia menziesii and anti-inflammatory potential in LPS-activated RAW 264.7 macrophages. Molecular Biology Reports, 53(1), Article 1529. https://doi.org/10.1007/s11033-026-12685-y

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12685-y

Keywords: Egregia menziesii, brown seaweed, fucoidan, mannitol, macrophages, inflammation, cytokines, 1H-NMR, chemometrics, RAW 264.7, sulfated polysaccharides, natural products

Lipophilicity, conventionally expressed as the logarithm of the partition coefficient between n-octanol and water, log P o/w, is one of the most influential parameters in early drug discovery. It governs how readily a molecule crosses cell membranes, binds to plasma proteins, and distributes through tissues, thereby shaping absorption, distribution, metabolism, excretion, and toxicity. According to Lipinski’s rule of five, orally administered small-molecule candidates should ideally fall between 0 and 5 on this scale. Values above that window are frequently associated with poor aqueous solubility, excessive plasma protein binding, tissue accumulation, receptor promiscuity, and elevated in vivo toxicity, while insufficient lipophilicity undermines the same pharmacokinetic machinery from the opposite direction.

Measuring that property experimentally has traditionally relied on the OECD shake flask method, a liquid-liquid extraction procedure that is slow, material-hungry, and unforgiving of impure samples. Reversed-phase HPLC has emerged as a faster and more economical alternative, requiring only tiny amounts of compound and tolerating impurities. Because different stationary phases, from conventional C18 silica to polymer-based supports, present distinct hydrophobic surfaces and residual silanol populations, running a candidate across several columns yields a richer picture of lipophilic behavior than any single system can provide.

In the new work, the researchers dissolved each of the thirty-one cis- and trans-styrylbenzoxazolone derivatives and injected them onto three different C18 columns operated with either methanol-water or acetonitrile-water mobile phases, generating five distinct chromatographic systems. Isocratic elution ran from 95 percent down to 60 percent organic modifier at 0.75 ml/min and 35 °C, with a diode array detector recording at 254, 274, 292, and 305 nm. Because direct measurement in pure water would demand prohibitively long run times, the team measured retention factors at each eluent composition and extrapolated the linear or quadratic relationship between log k and the organic fraction back to zero organic modifier, yielding the chromatographic hydrophobicity index log kw.

Choosing between the linear Snyder-Soczewinski model and its quadratic extension is not trivial, since the quadratic coefficients proved vanishingly small across all systems. The authors therefore applied Mandel’s test, the IUPAC-recommended Fisher F-test comparison of residual variances, to decide which regression form best described each analyte. After extending the mobile phase interval with additional 65 and 75 percent compositions, the test confirmed linear behavior for only a handful of compound-system pairs, including S55-Z in system A, S67-Z in system B, S44-E in system C, and S48-E in system D. Notably, no derivative required a linear model on system E, whose endcapped stationary phase suppresses unwanted secondary interactions and treats all the stilbenes uniformly.

The solvent itself left a clear imprint on the results. Methanol, a protonic solvent capable of hydrogen bonding, strengthens interactions between analyte and the hydrophobic C18 chains, prolonging retention, whereas aprotic, low-polarity acetonitrile competes more effectively in the mobile phase and elutes compounds faster. Accordingly, log kw values were consistently higher in the three methanol-water systems than in their acetonitrile counterparts. System C, built on a polymer-based matrix with wide 300 Å pores, showed the weakest retention, since large pores reduce diffusional obstruction and ease desorption, and the polymeric backbone changes the interaction mechanism altogether. System D, also polymeric and wide-pored, similarly produced the lowest retention values among the acetonitrile systems.

Molecular geometry mattered as well. In reversed-phase HPLC, retention mirrors the way a molecule threads itself between nonpolar stationary phase chains, a process favored by planar, elongated structures and nonpolar fragments. The trans, or E, isomers, being more extended than their cis counterparts, were expected to retain more strongly, and indeed every E-isomer outperformed its Z-partner on system A. Only scattered inversions appeared on the other systems, and two isomer pairs, S61-Z/S62-E on system D and S77-Z/S78-E on system E, converged to identical extrapolated log kw values despite different retention profiles across the mobile phase compositions.

On the computational side, the team used SwissADME to gather predictions from five algorithms: XLOGP3, an atomistic method with corrective factors and a knowledge-based library; WLOGP, a purely atomistic fragmental approach; MLOGP, a topological model built on thirteen molecular descriptors; SILICOS-IT, a hybrid of twenty-seven fragments and seven topological descriptors; and iLOGP, a physics-based method deriving free solvation energies in octanol and water through a Generalized-Born and surface area model. Their arithmetic mean forms the consensus clog P. The consensus values ranged narrowly from 3.10 to 3.24, placing every derivative comfortably within the Lipinski window. Compounds bearing three methoxy groups on ring A were the most polar, while those with a single methoxy group were the most lipophilic, and most E-isomers edged out their Z-analogues, with the largest isomer gap appearing at position 7 of the styryl residue.

Correlating experiment with theory revealed a decisive pattern. The methanol-water systems A, C, and E produced statistically significant agreement between clog P and chromatographic lipophilicity at p < .05, while the acetonitrile systems ranged from moderate (system B, p ≈ .05) to poor (system D, p > .05). The strongest correspondences appeared on the silica-based columns with methanol eluents, marking those configurations as the most reliable predictors of lipophilicity for this compound family, and singling out the polymer-based, wide-pore system D as the least suitable for these analytes.

Chemometrics then consolidated the picture. Hierarchical clustering analysis, visualized as a dendrogram built from a bottom-up merging algorithm and validated with the Sneath-Sokal dissimilarity index, split the compounds into two main clusters that loosely tracked the position of the styryl fragment on ring B. All 3,5-dimethoxy derivatives fell into cluster I, whereas the 3,4-dimethoxy analogues, with one exception, grouped into cluster II, which also harbored most of the Z-isomers and the lower-lipophilicity members of the set. Principal component analysis, using the Kaiser eigenvalue criterion and a statistically validated loading threshold based on p-values rather than arbitrary cut-offs, extracted two components explaining 41.98 and 23.69 percent of total variance. The first component behaved as a silica factor dominated by systems A, B, and E, while the second, shaped by the wide-pored polymeric systems C and D, acted as a polymeric factor. The score biplot confirmed the clustering and flagged S44-E and S74-E as the most strongly retained outliers across all five systems. Together, the chromatographic, computational, and chemometric results deliver a robust framework for steering future ADME-oriented optimization of these anticancer candidates.

Subject of Research: Lipophilicity characterization of novel combretastatin A-4 stilbene derivatives by reversed-phase HPLC and computational prediction

Article Title: Investigation of lipophilicity of new stilbene derivatives with chromatographic and computational approaches

Article References: Georgieva, S., Voyslavov, T., Chanev, C., & Petrov, O. (2026). Investigation of lipophilicity of new stilbene derivatives with chromatographic and computational approaches. Pharmacia, 73, Article e200443. https://doi.org/10.3897/pharmacia.73.e200443

Image Credits: AI Generated

DOI: 10.3897/pharmacia.73.e200443

Keywords: lipophilicity, combretastatin A-4, stilbene derivatives, reversed-phase HPLC, log P, benzoxazolone, chemometrics, principal component analysis, hierarchical clustering, ADME, drug discovery, SwissADME

In a study published in Food Chemistry: X, researchers Mei-Ling Yang, Wang Hui and Li-Xia Zhu systematically mined nearly three decades of wine aroma literature, building what they describe as a harmonised database of volatile compounds drawn from 103 peer-reviewed studies spanning 22 countries and 38 grape cultivars. From this trove, they distilled a panel of 31 candidate compounds that appear to distinguish grape varieties — and then tested whether those markers could actually tell two commercial Chinese red wines apart.

The approach is deceptively simple but technically ambitious. Rather than generating new experimental data under a single controlled protocol, the team harvested quantitative aroma measurements that individual laboratories had already published, then confronted the messy reality that such data were never designed to be combined. Extraction methods, instrumental platforms, quantification strategies and reporting formats all differ wildly between studies, making direct comparison notoriously unreliable.

To tame this heterogeneity, the researchers standardised compound identities using CAS numbers and PubChem records, converted concentrations to micrograms per litre where conversion bases were explicit, and retained detailed analytical metadata for every record. Odour thresholds — critical for calculating aroma activity values — were sourced with their original matrices and units rather than averaged, preserving traceability in a field where threshold values can vary by orders of magnitude depending on ethanol content and sensory methodology.

With the database assembled, the team deployed a three-pronged chemometric screening strategy. Unsupervised principal component analysis first mapped natural clustering among six major varieties. Supervised partial least squares discriminant analysis then identified compounds driving separation, with variable importance in projection scores above 1 flagging key discriminators. Finally, a random forest classifier — tuned via five-fold cross-validation and achieving 87 percent mean accuracy — ranked compounds by permutation importance. Only compounds surviving all three filters were retained, yielding the final panel of 31.

The intersection approach is what gives the result its weight. Ethyl octanoate, a fermentation-derived ester imparting pear and floral notes, topped the PLS-DA ranking with a VIP score of 1.46. Two-phenylethanol, a rose-scented higher alcohol produced via the Ehrlich pathway from phenylalanine, scored 1.40. One-hexanol, a C6 alcohol born from the lipoxygenase pathway of grape membrane lipids, carried a VIP of 1.31 — a marker the authors link to differences in grape maturity and pre-fermentation maceration.

As a proof of concept, the researchers analysed 24 commercial dry red wines — 12 Cabernet Sauvignon and 12 Marselan, all from Chinese producers and vintages 2020 to 2022 — using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry. A trained sensory panel of ten assessors evaluated the wines on a 0–5 intensity scale across six attributes, from fruity and floral to animal-like and green.

The results were striking. In the PLS-DA score plot built from the experimental measurements, the two varieties separated cleanly along the first principal component with no overlap, yielding internal fit statistics of R²Y(cum) = 0.971 and Q²(cum) = 0.947, and a ROC area under the curve of 0.840. Cabernet Sauvignon wines scored higher on green and herbaceous attributes — consistent with their blackcurrant and bell-pepper character — while Marselan showed stronger floral and red-fruit intensities, matching its reputation for violet and cherry notes.

The authors are careful to frame these as candidate indicators rather than universal fingerprints. Wine aroma is shaped by vintage, region, winemaking practice, yeast strain and analytical platform, and the team acknowledges that their marker panel is conditional on threshold availability — only 191 of the 792 catalogued compounds had usable odour thresholds, meaning the other 601 were excluded from activity-based screening regardless of their sensory relevance.

What makes the study genuinely provocative is its implication for the broader data crisis in food science. The authors argue that the fragmentation of aroma literature — inconsistent units, undefined missing values, incompatible reporting — is the single biggest obstacle preventing decades of accumulated knowledge from becoming reusable digital infrastructure. Statistical normalisation, they note, rescales numbers but does not remove systematic differences between analytical platforms.

Their prescription is a cultural shift: unified compound identification systems, complete provenance metadata, explicit definitions of what a non-detect means, and open-access databases governed by FAIR principles. Only then, they argue, can machine learning and artificial intelligence move from pattern-fitting exercises to genuine sensory prediction — the kind that could authenticate a bottle’s varietal claim, flag fraud, or even help winemakers dial in fermentation conditions before the first grape is crushed.

For an industry increasingly targeted by counterfeiters and increasingly dependent on data-driven quality control, the message is clear: the future of wine authentication may not lie in new instruments, but in teaching algorithms to read what science has already written.

Subject of Research: Wine Aroma Data Mining

Article Title: Quantitative mining of wine aroma literature data for specification reuse

Article References: Yang, M.-L., Hui, W., & Zhu, L.-X. (2026). Quantitative mining of wine aroma literature data for specification reuse. Food Chemistry: X, Article 104611. https://doi.org/10.1016/j.fochx.2026.104611

Image Credits: AI Generated

DOI: 10.1016/j.fochx.2026.104611

Keywords: wine aroma, chemometrics, machine learning, GC-MS, data harmonisation, varietal markers, food chemistry, sensory evaluation, bibliometrics, artificial intelligence, wine authentication, volatile compounds

The choice of drying temperature proved to be a decisive variable rather than a trivial detail of sample preparation. Oven drying at 40 °C produced the largest gain in essential oil recovery, raising the yield from roughly 0.105 percent of the fresh residue mass to about 0.276 percent, a 2.63-fold increase. Room-temperature drying, while gentler on the volatile profile, still improved recovery relative to fresh material. The mechanism behind this gain is likely tied to the reduction of water content in the plant tissue: as moisture evaporates, the cellular structures that trap essential oils become more permeable, allowing volatile compounds to be released more efficiently during hydrodistillation. At the same time, moderate heat can promote the formation of new volatile constituents through oxidative and thermally driven reactions, which helps explain why the oil composition shifted so noticeably with oven drying.

Among the compositional changes, the emergence of 1,8-cineole stands out. This oxygenated monoterpene, also known as eucalyptol, was entirely absent from the essential oil of fresh residues yet became abundant after drying. Oxygenated monoterpenes such as 1,8-cineole are generally regarded as important contributors to the biological activity and aroma of essential oils, and their increased relative abundance after drying coincided with the marked enhancement of antibacterial activity observed in the assays. Conversely, the hydrocarbon monoterpenes, including the major constituents limonene and β-pinene, declined in relative abundance as drying progressed. This inverse relationship between hydrocarbon and oxygenated terpenes is a recurring pattern in drying studies of aromatic plants, reflecting both the selective loss of low-molecular-weight hydrocarbons and the oxidative conversion of some terpenes into their oxygenated derivatives.

The chemometric toolkit applied in the study deserves attention because it illustrates how modern analytical chemistry can extract order from complex volatile datasets. Partial least squares discriminant analysis, or PLS-DA, was able to clearly separate the essential oil samples according to drying treatment and duration, while hierarchical cluster analysis and heatmap visualization provided complementary views of the same patterns. These multivariate approaches are increasingly standard in natural products research because the chemical composition of an essential oil involves dozens of compounds whose individual abundance changes may be subtle but whose combined signature is highly informative. By applying such methods, the researchers could demonstrate that the differences among treatments were systematic and reproducible rather than random analytical noise.

The antimicrobial results are striking when placed against the broader context of essential oil research. Fresh residue oils showed minimum inhibitory concentration values of at least 2000 μg mL⁻¹ against the β-lactamase-producing Staphylococcus aureus strain, a level generally considered weak activity. After room-temperature drying for six to eight days, the MIC dropped to 7.5 μg mL⁻¹, representing an improvement of more than two orders of magnitude. The test organism is clinically relevant because β-lactamase-producing strains of S. aureus undermine a class of antibiotics that accounts for the majority of clinical antibiotic use, and the enzymatic hydrolysis these strains perform markedly reduces therapeutic options. Essential oils are thought to act mainly by disrupting bacterial membranes and increasing permeability, mechanisms that are less susceptible to the specific resistance enzymes that defeat β-lactam drugs, which is part of why plant-derived volatiles attract interest as potential adjuncts to conventional therapy.

The timing of the activity peak is also informative. The strongest antibacterial effect appeared after six to eight days of room-temperature drying, not immediately after one or two days, suggesting that the chemical transformations responsible for enhanced activity accumulate progressively as the plant material loses moisture and its volatile profile evolves. This finding implies that producers seeking to maximize the antimicrobial value of guavira residue oils would need to allow sufficient drying time rather than distill the material as soon as it is superficially dry. It also suggests that the relationship between drying duration and biological activity is not linear, and that intermediate time points may represent a balance between the loss of native hydrocarbon terpenes and the accumulation of oxygenated derivatives.

From a sustainability standpoint, the study connects several threads of the Cerrado bioeconomy. Guavira is an emblematic native fruit whose commercial value chains support local food systems, traditional knowledge, and regional tourism, yet the peels and seeds generated by pulp processing remain largely underutilized. Converting these by-products into a stable, high-value raw material for essential oil production adds a new revenue stream to existing fruit value chains without requiring additional land or cultivation. The improved storage stability of dried residues further supports decentralized collection, allowing residues to be aggregated from multiple small processing operations before distillation. Such a model could reduce waste, lower the environmental footprint of fruit processing, and strengthen the sociobiodiversity economy of the Cerrado region.

The potential applications extend across several industries. In cosmetics and dermatology, the antimicrobial and multifunctional properties of terpenic constituents can contribute to microbiological control of the skin and improve the stability of formulations, potentially reducing reliance on synthetic preservatives. In food systems, essential oils with strong antibacterial activity are explored as natural alternatives for controlling spoilage and pathogenic organisms. In pharmaceuticals, the search for novel agents against resistant pathogens has intensified as antimicrobial resistance is now recognized as one of the greatest threats to global public health. An essential oil whose activity can be enhanced through a simple, low-cost post-harvest treatment fits well within this landscape of applied natural products research.

It is worth noting the broader methodological lesson embedded in the work. Many studies of essential oils treat the plant material as a fixed input, distilling it fresh without considering how pre-processing conditions shape the outcome. The guavira residue study demonstrates that drying is not merely a preservation step but an active variable that modulates yield, composition, and biological activity. For any aromatic plant whose residues are being evaluated for essential oil recovery, the drying protocol deserves the same experimental scrutiny as the extraction method itself. The integration of chemical profiling with chemometric analysis and biological testing, as performed here, provides a template for how such evaluations can be conducted rigorously and interpreted meaningfully.

Finally, the study highlights the value of looking at residues rather than the fruit itself. The essential oils recovered from peels and seeds would otherwise be lost when the by-products are discarded, and the bioactive compounds they contain represent a resource already produced by the plant at no additional cultivation cost. By identifying drying conditions that simultaneously improve oil recovery, enhance antibacterial activity, and stabilize the raw material for storage, the research offers a concrete pathway for transforming an agro-industrial waste problem into a source of pharmaceutical, cosmetic, and food ingredients, consistent with the growing emphasis on circular economy approaches within the bioactive natural products field.

Subject of Research: Drying modulates volatile composition and enhances antimicrobial activity of essential oils from Campomanesia adamantium fruit-processing residues: a chemometric study

Article Title: Drying modulates volatile composition and enhances antimicrobial activity of essential oils from Campomanesia adamantium fruit-processing residues: a chemometric study

Article References: Bittencourt, F. O., dos Santos, É. L., Santana, A. C., Micheletti, A. C., Figueiredo, P. D. O., & Yoshida, N. C. (2026). Drying modulates volatile composition and enhances antimicrobial activity of essential oils from Campomanesia adamantium fruit-processing residues: a chemometric study. Frontiers in Natural Products, 5, Article 1887930. https://doi.org/10.3389/fntpr.2026.1887930

Image Credits: AI Generated

DOI: 10.3389/fntpr.2026.1887930

Keywords: essential oils, Campomanesia adamantium, guavira, Cerrado, antimicrobial resistance, Staphylococcus aureus, GC-MS, chemometrics, drying, food waste valorization, monoterpenes, 1,8-cineole

The research team, led by Liu Jing and colleagues, worked with commercially sterilized Dezhou braised chicken produced by Dezhou Xiangsheng Food Co., Ltd. in Shandong Province. Seven independent production batches, all made from the same standardized 817 broiler chickens at 35 days of age and the same proprietary seasoning formula, were stored at 25 ± 1 °C in the dark. Samples were collected monthly at seven storage durations spanning 0 to 6 months, then analyzed in a unified campaign using sensory evaluation, physicochemical characterization, E-tongue, E-nose, and GC–IMS. This stepwise framework—from macro-scale sensory attributes down to micro-scale chemical compositions—was designed to pinpoint the volatile markers most closely linked to sensory deterioration.

The sensory results were unambiguous. Although every attribute declined during storage, odor deteriorated most dramatically, its score falling from 8.35 to 6.43 over six months and approaching the statistical rejection threshold of 6.0. That 1.92-point reduction was significantly greater than the declines in taste (1.16 points), texture (1.02 points), appearance (0.65 points), and color (0.48 points). Spearman correlation analysis confirmed that odor exhibited the strongest relationship with overall acceptability (r = 0.964, P < 0.001), followed by taste, texture, appearance, and color. In other words, the nose, not the eye or the palate, is the first arbiter of whether a braised chicken still deserves its place on the shelf.

Physicochemical measurements provided the chemical basis for this olfactory collapse. Total volatile basic nitrogen, an index of protein decomposition, rose from 10.45 to 18.48 mg/100 g, though it remained below the consumer-rejectable threshold of 25–28 mg/100 g for poultry meat. Thiobarbituric acid reactive substances, a measure of lipid oxidation, climbed from 0.53 to 2.56 mg malondialdehyde equivalents per kilogram, signaling advanced oxidative degradation and the generation of aldehydes and ketones responsible for greasy, oxidized off-flavors. Meanwhile, pH stayed stable between 6.56 and 6.67, and total viable counts remained below 2.0 log CFU/g throughout storage, indicating that the flavor spoilage was driven primarily by chemical reactions rather than by extensive microbial acidification in this commercially sterilized, vacuum-packaged product.

The E-tongue analysis, using the TS-5000Z system with six taste-specific sensors, objectively quantified the taste shift. Umami declined continuously from 11.57 to 11.09, richness fell from 1.42 to 1.04, and sweetness decreased steadily, while saltiness rose from 2.33 to 3.26 and bitterness and astringency increased during later storage. Principal component analysis showed a clear temporal gradient along the PC1 axis, with umami, richness, and sweetness identified as the core contributors to the initial flavor whose continuous decrease drove flavor evolution. The researchers attribute the umami reduction to the degradation of Maillard reaction products and lipid oxidation, while contrast effects between flavor components can make saltiness more prominent when the overall flavor balance is disrupted.

The E-nose, a PEN3 system with ten metal oxide sensors, revealed the odor counterpart of this taste drift. The W5S and W1W sensors, sensitive to nitrogen oxides and sulfur-containing compounds, showed gradually increasing responses that peaked at six months, suggesting the accumulation of nitrogen- and sulfur-containing volatiles. Conversely, the W1S and W2S sensors, responsive to short-chain alkanes, alcohols, aldehydes, and ketones typically abundant in fresh meat, declined over time. The W2W sensor, which detects aromatic and sulfur compounds, decreased initially before rising significantly in the final two months, consistent with the late-stage buildup of sulfur-containing volatiles associated with off-odors. Principal component analysis separated later-storage samples from earlier ones along PC1, confirming a progressive, time-dependent shift in odor characteristics.

GC–IMS then delivered the molecular detail. The technique detected 75 volatile signals, 67 of which were successfully identified, corresponding to 61 distinct compounds across nine chemical classes: 11 alcohols, 14 aldehydes, 9 ketones, 5 esters, 3 acids, 2 terpenes, 5 organosulfur compounds, 4 heterocyclic compounds, and 8 others. Fingerprint analysis revealed two opposing trends. Area

The molecular story told by the GC–IMS fingerprints mirrors the sensory and instrumental decline in a way that gives the industry a practical diagnostic toolkit. Among the 61 identified compounds, aldehydes deserve particular attention because they sit at the intersection of lipid chemistry and human perception. Saturated aldehydes such as hexanal, heptanal, and octanal are classical secondary oxidation products of omega-6 fatty acids, and their odor thresholds are low enough that even modest increases can shift a product from pleasantly meaty to noticeably rancid. The study’s finding that aldehydes constituted the largest identified class, with fourteen members, is consistent with the TBARS trajectory: as malondialdehyde equivalents more than quadrupled over six months, the pool of carbonyl fragments available for volatile release expanded accordingly. Because GC–IMS requires minimal sample pretreatment and generates visual fingerprints that can be compared across batches, the technique offers a realistic route for routine quality monitoring in production settings where full GC–MS workflows would be impractical.

The organosulfur compounds identified by GC–IMS provide a second, complementary marker family. Sulfur-containing volatiles in braised chicken originate largely from the degradation of sulfur amino acids during thermal processing and from subsequent microbial and oxidative transformations during storage. The E-nose sensors W1W and W2W, which respond strongly to these species, tracked their accumulation with the late-stage surge observed in the final two months of storage. This timing matters for shelf-life management: it suggests that the transition from acceptable to rejectable quality is not linear but accelerates toward the end of storage, meaning that sampling frequency in the final weeks of a product’s labeled shelf-life may be more informative than early monitoring alone. The correlation analysis linking these volatile markers to sensory scores effectively converts analytical chemistry into a predictive tool, allowing manufacturers to flag batches before panelists would formally reject them.

The taste-side findings also carry practical weight. The continuous decline in umami and richness scores reflects the erosion of the savory foundation that braised chicken is prized for, and the researchers’ attribution to Maillard reaction product degradation points to a chemical mechanism rather than simple dilution. Maillard products, formed during the initial stewing, are not permanently stable; they can undergo further reactions during ambient storage that alter or diminish their taste activity. Meanwhile, the rise in perceived saltiness despite unchanged formulation illustrates how flavor perception is relational rather than absolute. When umami and sweetness fade, the same sodium concentration registers as more prominent, a contrast effect well documented in sensory science. For product developers, this implies that reformulation to compensate for storage-induced taste drift is possible but must account for the changing balance rather than any single attribute.

Methodologically, the study demonstrates the value of data fusion across techniques that operate at different scales of resolution. Sensory panels capture holistic human experience but are slow and variable; E-tongue and E-nose provide rapid, objective screening suitable for large sample sets; GC–IMS supplies compound-level specificity with high sensitivity. By layering these approaches and connecting them through correlation and chemometric analysis, the researchers built a chain of evidence from consumer-relevant attributes down to individual molecules. The Spearman correlation coefficient of 0.964 between odor and overall acceptability, the highest among all attributes, justified the decision to prioritize odor-linked volatiles in the marker screening, a sensory-guided strategy that prevented the analysis from being diluted by compounds with no perceptual relevance.

The choice of commercially sterilized, vacuum-packaged product as the model system also shapes the interpretation. With total viable counts remaining below 2.0 log CFU/g throughout, microbial metabolism played a negligible role in the observed deterioration, and the flavor changes can be attributed almost entirely to chemical processes: lipid oxidation, protein degradation, and the slow transformation of thermally generated aroma compounds. This is a meaningful distinction for the meat industry, because ambient-stable sterilized products face different spoilage dynamics than chilled fresh meats, where microbial metabolism dominates. The findings therefore speak directly to the growing segment of shelf-stable ready-to-eat products, for which oxidative stability during long ambient storage is the central quality challenge.

Looking forward, the identified volatile markers and their temporal trajectories could support shelf-life prediction models that integrate chemical thresholds with sensory rejection points. The approach that TVB-N remained below regulatory limits while sensory quality had already declined substantially underscores a broader lesson: microbiological and chemical safety indices alone cannot capture flavor quality, and products can be perfectly safe yet organoleptically past their prime. Multidimensional analytical frameworks of the kind employed here offer a path toward quality standards that reflect what consumers actually experience, bridging the gap between regulatory compliance and market acceptance in the ready-to-eat meat sector.

Subject of Research: Flavor evolution and volatile marker identification in braised chicken during six-month storage

Article Title: Insights into the flavor evolution of braised chicken during six-month storage via sensory evaluation, E -tongue, E -nose, and GC–IMS

Article References: Jing, L., Huanjia, L., Caishuan, Y., Zhiwei, S., Yujin, L., Bingui, W., & Yujie, G. (2026). Insights into the flavor evolution of braised chicken during six-month storage via sensory evaluation, E-tongue, E-nose, and GC–IMS. Food Chemistry: X, Article 104631. https://doi.org/10.1016/j.fochx.2026.104631

Image Credits: AI Generated

DOI: 10.1016/j.fochx.2026.104631

Keywords: braised chicken, flavor deterioration, GC–IMS, electronic nose, electronic tongue, lipid oxidation, protein degradation, volatile compounds, sensory evaluation, shelf-life, ready-to-eat meat, chemometrics

The technique exploits a fundamental property of molecules. When monochromatic laser light strikes a sample, most photons scatter elastically, keeping their energy unchanged. But a small fraction exchange energy with molecular vibrations, emerging shifted in wavelength in ways that are unique to the chemical bonds they encountered. This inelastic scattering produces the Raman spectrum: a fingerprint of peaks corresponding to proteins, lipids, nucleic acids, and carbohydrates in the sample. Because sex hormones such as testosterone and estrogen reshape metabolism — altering lipid content, protein profiles, immune function, and stress responses — the biochemical state of a male and a female animal differs measurably, and those differences leave detectable traces in the vibrational spectra of blood-derived fluids. Crucially, the method is label-free, requires minimal sample preparation, and does not destroy the specimen.

The research team, led by Md Salahuddin Majumder with Ernest Smith and Lenka Halámková, worked with ten captive white-tailed deer, five of each sex, aged one to six years, under an approved animal care protocol. Blood was collected aseptically, processed into serum and plasma, and stored at −80 °C before analysis. Using a ThermoFisher DXR3 Raman microscope with a 785-nanometer excitation laser set at 30 milliwatts, they acquired spectra from dried droplets on foil-covered slides, sampling three non-overlapping spots per droplet with three replicate scans each. The careful sampling scheme yielded 36 serum spectra and 36 plasma spectra per animal — 360 spectra for each biofluid, and a total of 720 spectra feeding into the classification models. Spectral resolution was set at 2 cm⁻¹ across a range from 399 to 3,299 cm⁻¹.

Raw Raman spectra from biological fluids are notoriously messy, dominated by fluorescence background and inter-sample variability, so preprocessing mattered. The team applied automatic weighted least squares baseline correction, 1-norm area normalization, and mean-centering before importing the data into MATLAB with the PLS Toolbox. PLS-DA, a supervised multivariate method, then compressed the thousands of correlated wavenumber variables into a small set of latent variables chosen to maximize separation between the male and female classes. Model quality was assessed through classification error rates, Matthews Correlation Coefficient (MCC), prediction R², and root mean square error, with receiver operating characteristic (ROC) analysis providing a threshold-independent measure of discrimination.

Because only ten animals were available, validation design was central to the study’s credibility. The researchers systematically tested four calibration-to-validation splits — 1:4, 2:3, 3:2, and 4:1 — always partitioning by whole animal so that no individual contributed spectra to both training and testing sets. The serum-based models delivered striking results. With severely limited training data (just two deer), accuracy dropped to 79.2 percent. But once calibration was more balanced, performance soared: 99.1 percent for the 2:3 split, 99.3 percent for the 3:2 split, and 98.6 percent for the 4:1 split, with MCC values approaching 1.00 and ROC areas under the curve of 0.998 to 1.00. The full model, trained on all 360 serum spectra, reached 99.4 percent accuracy with an error rate of just 0.56 percent.

Plasma told a more complicated story. Although the full plasma model achieved 99.2 percent internal accuracy, external validation was erratic: 85.8, 82.4, and 86.1 percent in the first three splits, and a startling collapse to 57.0 percent in the 4:1 configuration — barely better than a coin flip. The authors attribute this not necessarily to a worse model but to the very small validation set of only two animals, combined with the greater biochemical variability of plasma. Plasma retains fibrinogen, clotting factors, and anticoagulant effects that serum lacks, introducing spectral noise that makes sex-specific signatures harder to isolate. Serum preparation removes these unstable components, producing cleaner, more reproducible profiles — and the data bore this out, with serum consistently outperforming plasma across every metric.

The spectral differences underpinning the classification were subtle but biochemically coherent. Male deer showed enhanced lipid-associated signals at approximately 1,315–1,330 cm⁻¹ (CH₂ twisting) and 1,445 cm⁻¹ (CH₂/CH₃ bending), while female deer displayed stronger protein-related peaks in the amide III region (1,220–1,280 cm⁻¹) and the amide I/II region (1,540–1,630 cm⁻¹). Notably, these regions overlap with known Raman bands of sex hormones — testosterone signals near 786, 856, 1,490, and 1,636 cm⁻¹, and female hormones in the 682–902 and 1,291–1,625 cm⁻¹ ranges — suggesting the hormonal differences between sexes directly shape the spectral fingerprints. Intriguingly, male samples clustered more tightly in the latent space while female samples dispersed more widely, a pattern the authors link to hormonal fluctuations and reproductive variability, and one consistent with prior Raman studies of saliva and bloodstains.

To confirm the finding was not an artifact of one algorithm, the team repeated the comparison using a second approach: principal component analysis followed by linear discriminant analysis (PCA-LDA), a standard two-step method in vibrational spectroscopy. The results were reassuringly convergent. For serum, PLS-DA achieved 99.1 percent external accuracy while PCA-LDA reached 92.6 percent; for plasma, the two methods landed within a fraction of a percent of each other (82.4 versus 82.9 percent). The method-independent success of serum classification strengthens the argument that sex-specific spectral signatures are genuine biological signals rather than statistical artifacts of a particular model.

The implications reach well beyond deer. In poaching cases, wildlife trafficking investigations, and enforcement of sex-specific hunting regulations, sex can determine whether a crime has occurred at all. For conservation biologists, sex ratios are core demographic parameters guiding population models, breeding programs, and protection laws. Current genetic methods — PCR amplification of sex chromosome-linked genes such as SRY or Amelogenin — are accurate but demand specialized laboratories and intact DNA, which degrades quickly in non-invasive or archived samples. Raman spectroscopy, by contrast, tolerates degraded material, requires no reagents, and is increasingly available in portable formats. The authors envision investigators analyzing dried blood spots in the field, with portable instruments delivering near-immediate results — a capability that could prove decisive for species regulated under CITES, where sex-specific rules apply.

Caveats remain, and the researchers are candid about them. Ten animals cannot represent the full physiological diversity of a species whose hormonal cycles, age structure, and seasonal reproduction all modulate blood chemistry, and future validation across larger cohorts, populations, and reproductive states is essential before courtroom deployment. Seasonal sampling of females, in particular, may influence spectral dispersion and classification accuracy. Still, as a proof of concept, the study marks a genuine milestone: the first demonstration that blood-derived fluids alone, interrogated only by light and statistics, can reveal the sex of one of North America’s most ecologically and economically important cervids. If portable Raman systems and automated analysis pipelines mature as expected, the humble drop of blood may become a forensic witness in its own right.

Subject of Research: Sex determination of white-tailed deer from blood-derived biofluids using Raman spectroscopy and PLS-DA chemometric classification

Article Title: Sex determination of white-tailed-deer (Odocoileus virginianus) from plasma and serum samples by using Raman spectroscopy and PLS-DA method: a forensic perspective

Article References: Majumder, M. S., Smith, E., & Halámková, L. (2026). Sex determination of white-tailed-deer (Odocoileus virginianus) from plasma and serum samples by using Raman spectroscopy and PLS-DA method: a forensic perspective. Frontiers in Analytical Science, 5, Article 1727520. https://doi.org/10.3389/frans.2025.1727520

Image Credits: AI Generated

DOI: 10.3389/frans.2025.1727520

Keywords: Raman spectroscopy, PLS-DA, white-tailed deer, wildlife forensics, sex determination, serum, plasma, chemometrics, machine learning, vibrational spectroscopy, conservation, Odocoileus virginianus

The study, led by Giulia Gorla of the University of the Basque Country together with Jon Ander Iturrioz, Kim Esbensen and Jose Manuel Amigo, takes aim at what the authors call the persistent misconception that data quantity alone ensures representativity. Their central message is blunt: advanced sensor technology and powerful data modelling cannot compensate for systematic measurement bias introduced at the moment of sampling. Data quality, they insist, originates upstream, before analysis, and the mathematical machinery of chemometrics can only extract information that is physically present in the measurements.

To make this case, the authors turn to the Theory of Sampling, a rigorous framework developed originally for mining and bulk materials by Pierre Gy and later expanded by Esbensen and colleagues. The theory classifies materials by their dimensionality, from zero-dimensional discrete objects like tablets to three-dimensional volumes such as reactors and silos, and identifies the specific errors that arise in each case. Constitutional heterogeneity reflects differences in composition between particles, distribution heterogeneity arises from segregation and clustering, and temporal heterogeneity captures fluctuations over time. In dynamic systems, flow-induced heterogeneity adds further complications through velocity gradients and varying residence times in vessels. Unless a sampling strategy explicitly counteracts these heterogeneities, the collected data will reflect the sampling geometry rather than the true process.

The authors demonstrate this with three striking datasets. The first involved a natural sodalite rock from Brazil examined with a portable Raman spectrometer. Fifteen spectra collected from different points on the rock’s two faces revealed dramatic differences: some spots showed pure sodalite signatures, while others carried clear evidence of calcite, a common inclusion. The lesson is that a single surface measurement can completely miss the three-dimensional character of a heterogeneous material, a situation directly analogous to industrial probes that interrogate only a thin layer of a powder bed or slurry.

The second dataset used short-wave infrared hyperspectral imaging of ground coffee deliberately adulterated with chicory or malt. When the components were left unmixed, principal component analysis readily detected both the adulterant and an added salt marker. Once the samples were homogenized, discrimination became far harder, partly because salt has no distinct near-infrared signal of its own and mainly registers through its water of hydration. Even more revealing, the calculated homogeneity curves depended strongly on which region of the image was analysed: the same mixed sample appeared markedly more inhomogeneous when a smaller region of interest was chosen. Field of view, the authors emphasize, is not a technical afterthought but a decisive factor in whether a measurement claims to represent the bulk.

The third dataset came from beer. Drawing on 377 near-infrared spectra from seven batches of Belgian Pale Ale spanning mashing, boiling, fermentation and carbonation, the team built multivariate statistical process control models. Even a sampling frequency of once per day was enough to track the general arc of fermentation, with the first principal component capturing time evolution. But two abnormal batches drifted differently, and detecting such anomalies early would demand higher temporal resolution. The work also exposed an uncomfortable truth: in the early brewing stages the variability was so large that no clear trends could be distinguished among batches, and the researchers could not tell whether this stemmed from sampling, composition, or genuine chemical information. Poorly planned sampling in timing, location or sensor choice distorts model performance and can fabricate misleading cause-and-effect relationships when transport lags delay the recorded signal.

Perhaps the most provocative part of the paper is its critique of fashionable chemometric remedies. The authors tested SMOTE, a widely used synthetic data augmentation technique, on the beer monitoring models. Synthetic spectra generated by interpolation between existing observations did inflate the training set, yet the geometric structure of the monitoring space remained essentially unchanged. The out-of-control samples still fell outside the same limits. The explanation is geometric: projection-based models are built from the covariance structure of real data, and interpolating between points adds no new directions of variance. Regions of the process that were never measured remain unobserved, and heterogeneity that never reached the sensor cannot be reconstructed algorithmically. Worse, artificial densification can give a false sense of coverage and inflate validation statistics without ever testing the model’s ability to extrapolate to genuinely new conditions.

The review is equally skeptical of Interactive Optimization Technologies, promoted recently as a lean alternative to traditional partial least squares calibration. By using spectra of pure raw materials to track blend homogeneity in pharmaceutical manufacturing, these methods slash the calibration burden, a real advantage when active ingredients are scarce or expensive. But the authors note that this is essentially a return to multiple linear regression on pure component spectra, an approach abandoned decades ago because real industrial and natural samples are never pure components. The verdict: useful in the pharmacopeia-driven world of pharmaceutical manufacturing, but no universal panacea.

On the quantitative side, the authors integrate sampling into the framework of measurement uncertainty, distinguishing the total sampling error, the total analytical error, and a newly proposed total data modelling error that captures preprocessing artefacts, overfitting and model misspecification. The hierarchy is stark: sampling error often dominates the analytical error by factors of five to ten, and in highly heterogeneous materials by up to twenty-five. The greatest gains in reliability therefore come from better sampling, not better algorithms. Practical tools exist to quantify this: replication experiments that duplicate the entire lot-to-aliquot pathway, heterogeneity tests, variograms and homogeneity curves, all codified in standards such as the Danish DS 3077.

The authors close with concrete recommendations. Sensor type, position, field of view and penetration depth should be characterized against the physics of the process, not chosen by convenience. Sampling frequency should match process dynamics, since oversampling merely adds redundancy while undersampling misses critical events. Sampling design should be embedded in Quality by Design workflows and evaluated during risk analysis, as regulators are increasingly expected to demand. Emerging technologies, from adaptive sampling that lets exploratory models decide where to measure next, to digital twins that simulate processes virtually, can help, but only when built on informative primary sensors that comply with the Theory of Sampling. In regulated sectors like pharmaceuticals, where multivariate models define the design space under ICH guidelines, sampling representativity is not best practice but a prerequisite. The paper’s conclusion is a warning worth repeating across every industry drowning in sensor data: analysis can refine what was measured, but it can never recover information that was never observed.

Subject of Research: The role of the Theory of Sampling in ensuring representative data acquisition for reliable Process Analytical Technology and chemometric process monitoring

Article Title: Getting “sampling” right for reliable process monitoring: main challenges and solutions

Article References: Gorla, G., Iturrioz, J. A., Esbensen, K. H., & Amigo, J. M. (2026). Getting “sampling” right for reliable process monitoring: main challenges and solutions. Frontiers in Analytical Science, 6, Article 1839552. https://doi.org/10.3389/frans.2026.1839552

Image Credits: AI Generated

DOI: 10.3389/frans.2026.1839552

Keywords: Process Analytical Technology, Theory of Sampling, chemometrics, representative sampling, near-infrared spectroscopy, Raman spectroscopy, hyperspectral imaging, data augmentation, measurement uncertainty, heterogeneity, process monitoring, Quality by Design

The researchers, led by M. A. Nazarkovsky of the Pontifical Catholic University of Rio de Janeiro, synthesized a series of nanocomposites containing 3, 6, and 9 millimoles of manganese-doped ZnO per gram of silica. The recipe was deliberately low-tech: zinc and manganese acetates were dissolved together with urea, spread onto fumed silica, dried, and calcined at 600 degrees Celsius. The result was a family of pale pink powders whose color deepened as the loading of the doped semiconductor increased. X-ray diffraction confirmed that the zinc oxide adopted its usual hexagonal wurtzite structure, with crystallite sizes refined between 27.7 and 31.0 nanometers for the two highest loadings. At the lowest loading, the crystallites were simply too few and too small to produce detectable diffraction peaks — a detection threshold effect rather than a sign of amorphous disorder.

Raman spectroscopy supplied the chemical fingerprints of manganese substitution. Alongside the standard ZnO vibrational modes, the spectra showed a distinct local mode at 502 per centimeter that appears neither in pure zinc oxide nor in any common manganese oxide. That peak, the authors argue, is a hallmark of manganese ions sitting on zinc sites in the wurtzite lattice, breaking the crystal symmetry and turning a normally silent vibration Raman-active. The main longitudinal optical mode also shifted compared with undoped ZnO, consistent with lattice distortion from oxygen vacancies and interstitial zinc — the very defects that give the material its visible-light appetite.

The textural story was equally detailed. Nitrogen adsorption measurements revealed predominantly mesoporous solids with specific surface areas ranging from 159 square meters per gram at the lowest loading down to 111 at the highest, reflecting progressive particle aggregation and pore blocking as the oxide content grew. Atomic force microscopy added a nanoscale twist: the roughest and most morphologically complex surface belonged to the intermediate, 6 millimole sample, whose fractal dimension reached 2.21, while the highest-loading powder paradoxically smoothed out as particles consolidated into dense agglomerates. Scanning electron microscopy, processed with an AI-assisted segmentation model called Tiny SAM, quantified this transition — the particle size distribution of the 6 millimole sample was nearly monodisperse, while the 9 millimole sample was strongly polydisperse, with nearly three times as many particles exceeding 100 nanometers.

Optical measurements explained why these powders work under sunlight. UV-visible spectroscopy showed a broad absorption band near 500 nanometers that intensified steadily with Mn–ZnO loading, arising from spin-forbidden d–d transitions of Mn2+ ions and defect states inside the band gap. Photoluminescence spectra, excited at 473 nanometers, emitted broad green-yellow light associated with oxygen-vacancy recombination centers — and that emission was quenched by roughly 38 percent as loading increased, a hint that charge carriers were being separated and consumed non-radiatively rather than recombining into wasted photons. The authors are careful to note that the band gap itself shifted slightly upward, while visible absorption rose; the two effects stem from different electronic origins and are not contradictory.

The truly novel contribution lies in how the team turned color into a quality-control tool. Standardized photographs taken with a digital SLR camera were chopped into 64 fragments each, and descriptors such as RGB channels, hue, saturation and lightness were extracted. A support vector machine trained on just two of these — saturation and hue — classified the three compositions with 100 percent accuracy on both training and validation sets, using only 12 support vectors. Even more striking, image fragments were converted into 2048-dimensional feature vectors by the Inception v3 convolutional neural network; an SVM built on those embeddings reached 99.3 percent training and 100 percent validation accuracy. In parallel, infrared spectra processed through principal component analysis and NIPALS regression predicted the silica content of the powders with Q2 values above 0.9997, with surface-sensitive ATR spectra outperforming conventional transmission FTIR by 36 percent in prediction error.

Why does all this analytical machinery matter? Because photocatalysts are notoriously difficult to check on a production line. Traditional characterization — diffraction, electron microscopy, gas sorption — is slow, destructive or expensive. The new workflow suggests that a factory could photograph a batch of pink powder, run the image through a trained classifier, and know within seconds whether the active phase hit its target concentration. The same logic extends to infrared spectroscopy coupled with chemometrics, giving manufacturers two independent, nondestructive windows onto composition. The authors frame this as a transferable framework for any doped metal oxide supported on a substrate, not just the manganese–zinc oxide system studied here.

Then came the environmental test. The model pollutant was lopinavir, the HIV protease inhibitor that saw heavy, largely ineffective use during the early COVID-19 pandemic and now turns up in surface waters at concentrations spanning from tens of nanograms to as much as 130 micrograms per liter near some wastewater plants. Photolysis alone removed less than 20 percent of the drug in an hour, but the best composite, 9-Mn–ZnO/SiO2, eliminated 96 percent of lopinavir within 60 minutes under a 550-nanometer lamp, with a pseudo-first-order rate constant of 0.0465 per minute — more than an order of magnitude faster than photolysis alone. Radical scavenger experiments implicated hydroxyl radicals and superoxide anions as the dominant reactive species, generated through a Haber-Weiss-type cascade, while direct hole oxidation played a negligible role. The catalyst retained its performance across four reuse cycles with no measurable deactivation.

Real-world waters told a more sobering but instructive story. Nitrate ions, dissolved organic matter and river water all slowed degradation by competing for photons and scavenging radicals, though at environmentally typical organic matter concentrations of around 10 milligrams per liter the catalyst still performed nearly as well as in distilled water. Even in treated wastewater, substantial removal persisted. The team also noted a satisfying convergence between their AI-assisted microscopy and the photocatalytic trends: the highest activity of the 9 millimole sample was attributed not to its reduced surface area but to the greater absolute mass of photoactive phase and evidence of improved charge-carrier separation. The optimum, in other words, depends on what you are optimizing — texture favors the intermediate loading, while photocatalysis favors the densest one. Together, the study makes the case that the future of photocatalyst manufacturing may be judged, quite literally, by the color of the powder.

Subject of Research: Manganese-doped ZnO/silica nanocomposites for machine-learning-assisted quality control and visible-light photocatalytic removal of pharmaceuticals from water

Article Title: Comprehensive study on the Mn–ZnO/SiO2 nanocomposites: from the digital colorimetry to the visible-light photocatalysis

Article References: Nazarkovsky, M. A., Galaburda, M. V., Sokołowski, A., Czech, B., Dionisio, E. R. C., Chaves, J. F., Artiushenko, O., Dugato, D. A., Caraballo-Vivas, R. J., de Paula, N. F. L., Archanjo, B. S., Pimenta, A. M. G., Costa, R. G., Chacón-Rosales, G., Diaz, E. C. M., Navarro, R. C. S., & Marinkovic, B. A. (2026). Comprehensive study on the Mn–ZnO/SiO2 nanocomposites: from the digital colorimetry to the visible-light photocatalysis. Journal of Materials Science. https://doi.org/10.1007/s10853-026-13831-4

Image Credits: AI Generated

DOI: 10.1007/s10853-026-13831-4

Keywords: photocatalysis, Mn-doped ZnO, nanocomposites, digital colorimetry, machine learning, Inception v3, chemometrics, lopinavir, water treatment, silica support, oxygen vacancies, visible light

Polygonatum sibiricum, known in Chinese as huangjing, is an edible botanical material with a long history in traditional food culture. Like many botanical products, it is not simply sold as harvested. Producers apply traditional food-processing methods that can involve repeated steaming and drying cycles, soaking in honey, or treatment with wine. Each of these treatments is believed to alter the chemical composition of the plant material, changing its flavor, its spectral fingerprint, and potentially its functional properties. That variability is precisely what makes authentication hard: a dried rhizome that has been steamed nine times and sun-dried nine times looks, to the naked eye, broadly similar to one that has merely been dried, yet the market treats the two as distinct products with distinct values.

The core analytical tool in the new study is FTIR spectroscopy, a technique that shines broadband infrared light through or onto a sample and records which frequencies are absorbed. Because chemical bonds absorb infrared radiation at characteristic frequencies, the resulting spectrum acts as a molecular fingerprint. O–H stretches associated with hydroxyl groups, C–H vibrations from carbon-hydrogen bonds, and C–O and C–O–C vibrations typical of ethers and carbohydrates all leave signatures in the mid-infrared region. For carbohydrate-rich plant materials such as Polygonatum rhizomes, these bands carry a great deal of information, since processing methods such as steaming are known to transform sugars and polysaccharides. Crucially, FTIR requires minimal sample preparation, no reagents, and only minutes of measurement time, which makes it attractive for routine quality control compared with slower chromatographic methods.

A raw spectrum, however, is far too complex for a human inspector to decode into a processing category. The bands overlap, baselines drift, and the differences between processing types may be subtle shifts in relative intensity rather than the appearance of new peaks. This is where machine learning enters. The researchers treated each spectrum as a high-dimensional data vector and trained classification algorithms to recognize the patterns associated with each labeled processing type. But they went a step further than many previous studies by insisting on interpretability: rather than accepting a black-box verdict, they wanted the model to show which spectral features drove its decisions.

A central methodological concern in the study is information leakage, a subtle but serious pitfall in spectroscopy-based machine learning. When spectra are preprocessed, features selected, and models evaluated using randomly split measurements, spectra recorded from the same physical sample can end up on both sides of the train-test divide. The model then partly memorizes sample-specific quirks rather than learning genuine chemical differences, inflating reported accuracy. The team avoided this by performing model training, feature selection, and evaluation at the physical-sample level, ensuring that all measurements from a given product were kept together during splitting. This design choice makes the reported performance a more honest estimate of how the system would behave on genuinely new products.

Before classification, the researchers confronted the dimensionality problem. An FTIR spectrum contains thousands of data points, many of them redundant or noisy, and feeding all of them into a classifier invites overfitting. The team compared three dimensionality-reduction strategies: principal component analysis, the classic statistical technique that compresses data into orthogonal axes of maximum variance; the successive projections algorithm, a variable-selection method that seeks subsets of columns with minimal collinearity; and simulated annealing, an optimization procedure inspired by the metallurgical process of slowly cooling material to reach a low-energy state. Simulated annealing explores the space of possible feature subsets by accepting not only improvements but occasionally worse solutions, allowing it to escape local optima that greedy search methods would get stuck in.

The standout configuration paired simulated annealing feature selection with a Random Forest classifier, an ensemble method that builds many decision trees on random subsets of the data and averages their votes. The resulting SA–Random Forest model showed stable discrimination performance across the four labeled processing categories. Stability, in this context, matters as much as raw accuracy: a model that performs brilliantly on one data split but collapses on another is of little use in a regulatory or commercial setting. By anchoring both feature selection and evaluation to physical samples, the authors could demonstrate that the model’s discrimination reflected reproducible chemical differences rather than statistical artifacts.

The interpretability layer came from SHapley Additive exPlanations, or SHAP, a framework borrowed from cooperative game theory that assigns each input feature a contribution value for every individual prediction. Applied to the spectral data, SHAP analysis linked the model’s outputs to specific interpretable spectral features, identifying variables related to O–H, C–H, and C–O/C–O–C vibrations as the key discriminators. These assignments make chemical sense. Repeated steaming is known to promote transformations in carbohydrate-rich components, honey processing introduces sugars with their own hydroxyl-rich signatures, and wine processing can alter the profile of soluble carbohydrates and other constituents. The fact that the model latched onto hydroxyl and glycosidic-bond regions suggests it was reading genuine processing chemistry rather than exploiting confounders such as particle size or instrument drift.

The broader significance of the work lies in what it says about the future of food and botanical authentication. FTIR-based chemical fingerprinting coupled with machine learning has already been applied to saffron adulteration, edible oil verification, sea cucumber origin tracing, and the geographic identification of medicinal herbs, and the new study extends this toolkit to processing-type discrimination, a dimension of authenticity that is often overlooked. Because the approach works at the laboratory level with rapid measurements and no chemical reagents, it could in principle be adapted to screening incoming batches, auditing supplier claims, or supporting market surveillance of traditionally processed botanical foods. The authors are careful to frame the results as demonstrating potential at the lab level rather than a deployable field method, an appropriate caution given that the study examined commercially available products whose labels cannot be independently verified.

There are also instructive lessons here for anyone building spectroscopy-machine learning systems. The comparison of dimensionality-reduction methods shows that feature selection strategy can matter as much as classifier choice, and the emphasis on sample-level data handling sets a standard that many published chemometric studies still fail to meet. The pairing of a powerful ensemble classifier with SHAP explanations demonstrates that accuracy and transparency need not be traded off against each other. For Polygonatum sibiricum specifically, the study establishes that the chemical consequences of traditional processing are large enough to be captured in an infrared fingerprint and robust enough to survive honest validation. As demand for traditionally processed botanical foods grows across Asia and beyond, tools like this one may become the difference between a market built on trust and one built on verifiable chemistry. The work was supported by Yunnan Fundamental Research Projects, and the authors report no competing financial interests.

Subject of Research: FTIR spectroscopy combined with interpretable machine learning to classify processed Polygonatum sibiricum products

Article Title: Interpretable FTIR spectroscopy and machine learning for discriminating commercially available Polygonatum sibiricum products labeled as different processing types

Article References: Guo, Z., Jiang, Y., Cheng, Y., Liu, G., Xie, J., & Ou, Q. (2026). Interpretable FTIR spectroscopy and machine learning for discriminating commercially available Polygonatum sibiricum products labeled as different processing types. Food Science and Biotechnology. https://doi.org/10.1007/s10068-026-02326-2

Image Credits: AI Generated

DOI: 10.1007/s10068-026-02326-2

Keywords: Polygonatum sibiricum, FTIR spectroscopy, machine learning, Random Forest, simulated annealing, SHAP, food authentication, chemical fingerprinting, traditional food processing, chemometrics, infrared spectroscopy, food analysis

Researchers led by Omid Farhangi and Ahmad Banakar of Tarbiat Modares University, together with colleagues in Iran, set out to replace the slow, destructive laboratory tests currently used to detect PPD with a fully non-destructive optical method. Conventional approaches rely on high-performance liquid chromatography or gas chromatography–mass spectrometry, which deliver excellent accuracy but demand lengthy sample preparation, hazardous solvents, expensive instruments, and trained specialists. For market surveillance, customs checkpoints, and factory quality control, that combination is impractical. The team’s alternative was to shine ultraviolet, visible, and near-infrared light onto henna powder, record the reflected spectrum, and let algorithms translate the optical fingerprint into an exact PPD concentration.

The experimental design was deliberately rigorous. Pure henna leaves were collected from the major cultivation regions of Kerman and Sistan-Baluchestan in Iran, ground under controlled laboratory conditions, and supplemented with market samples from Yazd province. Laboratory-grade PPD was then mixed into the powder at seven levels ranging from 0 to 12 percent by weight, a range chosen to mirror the contamination levels actually reported in adulterated commercial henna. Eleven independently prepared samples were made at each concentration, yielding 77 physical samples in total. From each one, five reflectance spectra were recorded at different spots and averaged before any data splitting, a crucial step that prevented replicate measurements from leaking information between training and test sets.

The spectroscopy itself relied on an Ocean Optics USB2000 spectrometer covering 179 to 872 nanometers, illuminated by a UV-B lamp for the ultraviolet region and a halogen source for the visible and near-infrared. Light was delivered and collected through a bifurcated fiber probe, with samples held at a fixed thickness inside a black box to exclude ambient light. The raw spectra revealed a striking feature: a sharp, strong reflectance peak at 380 to 390 nanometers that grew steadily in intensity as PPD concentration increased. This peak arises from the conjugated pi-electron system of PPD’s aromatic amine structure, which interacts strongly with ultraviolet radiation, whereas lawsone’s quinonoid structure responds far more weakly in this window.

Principal component analysis confirmed what the spectra suggested. In the UV region, samples clustered cleanly by PPD concentration along the first principal component, demonstrating that the optical features were chemically specific and systematically tied to contamination. In the visible and near-infrared region, by contrast, no meaningful clustering appeared; the samples lined up along a single trend driven by overall reflectance offset, a physical scattering effect rather than a chemical signature. This distinction proved predictive of the modeling results: the UV region would reward nonlinear machine learning, while the visible region would struggle across the board.

The team compared five regression algorithms: partial least squares regression, decision tree, random forest, support vector regression, and an artificial neural network. On full-spectrum UV data, the decision tree with no preprocessing achieved a test coefficient of determination of 0.9376 with a residual prediction deviation of 4.14, edging out PLSR’s 0.9339. Random forest and support vector regression also performed respectably. In the visible region, performance dropped sharply, with the best model, PLSR with detrending, reaching only 0.7583 on the test set, still above the conventional threshold of 2.0 RPD that indicates a very good model but far below UV-region accuracy.

The study’s most innovative stage involved six metaheuristic optimization algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and learning automata, each tasked with selecting the 15 most informative wavelengths from hundreds of spectral variables. Learning automata emerged as the clear winner in both spectral regions, achieving the lowest error and highest correlation in the UV range. Its advantage stems from an adaptive probability-update mechanism that rewards useful wavelengths and punishes uninformative ones, allowing it to escape local optima that trapped faster competitors. The genetic algorithm, despite the shortest runtime of nine seconds, performed worst in the UV region, highlighting the trade-off between computational speed and convergence quality.

Feature selection delivered a dramatic payoff. After reducing the data to just 15 wavelengths, more than 90 percent of the original spectral variables discarded, random forest with Gaussian filter preprocessing leapt from a test R-squared of 0.9186 to 0.9779, with an outstanding RPD of 6.99, the best result of the entire study. The artificial neural network followed closely with an R-squared of 0.9476 and RPD of 4.51. The improvement shows that ensemble methods, often destabilized by noisy and collinear spectral variables, can unlock their full potential when fed only the most diagnostic features. Interestingly, the learning automata selected wavelengths above 400 nanometers in addition to the characteristic UV peak, indicating that scattering-related changes in the visible range carry complementary information about PPD content.

The practical implications extend well beyond henna. Because the entire analysis depends on only 15 key wavelengths, manufacturers could build dedicated, low-cost multispectral sensors that operate at those specific optical points, deploying them on production lines, at customs checkpoints, and in retail settings for real-time screening. Such devices would align with the broader trend toward miniaturized analytical systems in the food and cosmetic industries. The authors caution, however, that their dataset was generated under controlled laboratory conditions, and real commercial samples may contain interfering compounds, variable particle sizes, and moisture that could affect spectral responses. Validation against chromatographic reference methods on diverse commercial products, calibration transfer protocols for portable instruments, and cross-dataset testing of the selected wavelengths remain necessary next steps.

The work also opens the door to broader applications of the same framework. Future research could extend the approach to simultaneous detection of multiple adulterants, including other aromatic amines, heavy metals, and plant-based fillers, or employ deep learning architectures such as one-dimensional convolutional neural networks and data fusion strategies combining UV-Vis-NIR spectroscopy with Raman techniques. For now, the study stands as a compelling demonstration that an ancient cosmetic product can be protected by thoroughly modern tools: a beam of light, a fiber-optic probe, and algorithms smart enough to read the chemical secrets hidden in a reflection. In a market where a single contaminated tattoo can cause lifelong injury, that combination may prove invaluable.

Subject of Research: Non-destructive detection of para-phenylenediamine adulteration in henna using UV–Vis–NIR spectroscopy and machine learning

Article Title: Non-destructive quantification of para-phenylenediamine in henna using UV–Vis–NIR spectroscopy and machine learning

Article References: Farhangi, O., Banakar, A., Ebadi, M.-T., & Mahdavian, A. (2026). Non-destructive quantification of para-phenylenediamine in henna using UV–Vis–NIR spectroscopy and machine learning. Smart Agricultural Technology, 15, Article 102621. https://doi.org/10.1016/j.atech.2026.102621

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102621

Keywords: henna, para-phenylenediamine, UV-Vis-NIR spectroscopy, machine learning, food adulteration, cosmetic safety, chemometrics, wavelength selection, learning automata, random forest, artificial neural network, quality control