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

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

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

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

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

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

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

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

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

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

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

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

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103899

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

The choice of these two botanicals was anything but arbitrary. Triphala, whose name literally means three fruits, is a classical formulation combining the dried fruits of Amalaki (Emblica officinalis), Bibhitaki (Terminalia bellirica) and Haritaki (Terminalia chebula). It has long been prized in Ayurveda as a rejuvenating Rasayana, credited with benefits ranging from digestive support to eye health, and modern phytochemistry has attributed much of its activity to a rich arsenal of polyphenols, tannins, gallic acid and vitamin C. Punarnava, meanwhile, derives its name from the Sanskrit for renewing or reviving, and the creeping herb Boerhavia diffusa has been used traditionally to treat inflammation, liver disorders and urinary complaints. Despite this long history of parallel use, the two remedies had rarely been evaluated head to head under identical analytical conditions, a gap the Indian team set out to close.

A key methodological innovation of the study lies in how the plant material was extracted. Rather than relying on conventional maceration or Soxhlet techniques, which can be slow and thermally punishing, the researchers employed microwave-assisted extraction, or MAE, using a hydroalcoholic solvent mixture of 70 percent ethanol and 30 percent water. The extraction was performed at 200 watts and 50 degrees Celsius for just 18 minutes. This approach exploits the ability of microwave energy to heat solvent and plant cell moisture from within, rupturing cell walls rapidly and liberating bioactive compounds efficiently while minimizing degradation of heat-sensitive molecules such as phenolics and tannins. The ethanol water blend was chosen deliberately: water extracts highly polar constituents, ethanol captures moderately polar phenolics, and the combination maximizes the diversity of compounds recovered from both formulations.

With extracts in hand, the team assembled an unusually comprehensive battery of antioxidant tests, seven in total, each probing a different chemical facet of radical-scavenging capacity. The DPPH assay measures the ability of antioxidants to neutralize a stable synthetic nitrogen radical, providing a rapid readout of hydrogen-donating capacity. The ABTS assay extends this logic to both hydrophilic and lipophilic antioxidants by generating a green chromophore that decolorizes when reduced. The FRAP and CUPRAC assays assess reducing power, quantifying the capacity to convert ferric ions to ferrous ions and cupric ions to cuprous ions, respectively, which serves as a proxy for total antioxidant potential. Complementing these four, the researchers measured scavenging of nitric oxide, hydroxyl radical and superoxide radical, three reactive species with direct biological relevance, since these molecules are generated in living tissues during inflammation and contribute to oxidative damage of lipids, proteins and DNA.

Across the DPPH, ABTS, FRAP and CUPRAC assays, Triphala consistently outperformed Punarnava, achieving lower IC50 or EC50 values, meaning smaller concentrations of extract were needed to produce a half-maximal effect. This concentration-dependent superiority is chemically plausible: the three fruits of Triphala are famously dense in gallic acid, ellagic acid, chebulinic acid and ascorbic acid, compounds whose structures are optimized for electron donation and radical stabilization. The result positions Triphala as a broad-spectrum antioxidant capable of intercepting multiple classes of reactive oxygen and nitrogen species, the molecular vandals implicated in oxidative stress, a state now associated with aging, cardiovascular disease, neurodegeneration, diabetes and chronic inflammatory conditions.

The anti-inflammatory half of the investigation used two complementary assays that model different mechanisms of tissue protection. The protein denaturation assay examines whether an extract can prevent the structural unfolding of proteins, such as bovine serum albumin, under stress, since protein denaturation is thought to trigger autoimmune responses and inflammation in vivo, a mechanism implicated in rheumatic diseases. The proteinase inhibition assay, by contrast, tests the capacity to block proteolytic enzymes such as trypsin, which are released during inflammatory episodes and contribute to tissue destruction. Non-steroidal anti-inflammatory drugs, the clinical standard, are known to act partly through these mechanisms, making the assays a meaningful benchmark for botanical candidates.

Here the rankings flipped. Punarnava exhibited stronger anti-inflammatory activity than Triphala in both the protein denaturation and proteinase inhibition assays, again in a concentration-dependent fashion. The researchers interpret this as evidence that Boerhavia diffusa contains constituents particularly adept at stabilizing protein structure and restraining proteolytic cascades. The plant is known to harbor alkaloids such as punarnavine, along with flavonoids, lignans and ecdysteroids, any of which could underlie this protective behavior, although the present study did not attempt compound-level attribution. What matters from a pharmacological standpoint is the pattern: the two botanicals are not redundant but complementary, each excelling where the other is merely competent.

That complementarity is the study’s most intriguing implication. Oxidative stress and inflammation are deeply intertwined pathologies: reactive oxygen species activate inflammatory signaling pathways, including those involving tumor necrosis factor-alpha, and inflammatory cells in turn generate more free radicals, creating a self-amplifying loop. A therapeutic strategy that pairs a potent antioxidant with a potent anti-inflammatory agent could theoretically interrupt this loop at both ends. The authors suggest that Triphala and Punarnava, used together, could serve as natural sources for the development of new complementary therapeutic modalities for managing diseases and disorders in which both oxidative damage and inflammation play driving roles. Such conditions include, plausibly, metabolic syndrome, arthritis and ocular disorders, where both mechanisms converge.

Important caveats temper the enthusiasm, and the authors are careful to acknowledge them implicitly by framing the work as in vitro. Test-tube assays demonstrate chemical activity but say nothing about whether the active compounds survive digestion, reach target tissues in sufficient concentrations, or exert comparable effects in the complexity of a living organism. Bioavailability, metabolism, dosing and safety in humans all remain open questions that will require animal studies and, ultimately, controlled clinical trials. Nor did the study characterize the extracts’ full chemical fingerprints or isolate the specific molecules responsible for each activity. Nevertheless, by applying standardized, quantitative, concentration-dependent benchmarks to two venerable botanicals prepared with a modern extraction technology, the research provides a reproducible analytical foundation for that next phase. It transforms what has often been traditional assertion into testable hypothesis, and in doing so hands formulation scientists a provocative pairing: Triphala to quench the radicals, Punarnava to calm the inflammatory response, each doing what it demonstrably does best.

Subject of Research: Comparative in vitro evaluation of antioxidant and anti-inflammatory activities of Triphala and Punarnava extracts

Article Title: In vitro assessment of antioxidant and anti-inflammatory activities of Triphala and Punarnava: a comparative study

Article References: Makhijani, S., & Khobragade, D. (2026). In vitro assessment of antioxidant and anti-inflammatory activities of Triphala and Punarnava: a comparative study. BMC Complementary Medicine and Therapies. https://doi.org/10.1186/s12906-026-05580-3

Image Credits: AI Generated

DOI: 10.1186/s12906-026-05580-3

Keywords: Triphala, Punarnava, Boerhavia diffusa, antioxidant activity, anti-inflammatory activity, microwave-assisted extraction, DPPH, ABTS, FRAP, CUPRAC, Ayurveda, in vitro study

Natural deep eutectic solvents, or NaDES, have emerged in recent years as one of the most promising tools in green chemistry. These liquids are formed when certain natural compounds, such as sugars, alcohols, and organic acids, are mixed together in specific ratios, causing them to melt into a stable liquid through an extensive network of hydrogen bonds. Unlike many conventional organic solvents, NaDES are typically non-toxic, biodegradable, inexpensive, and tunable in polarity, which makes them attractive replacements for the volatile organic solvents that have long dominated phytochemical extraction. The United Nations has explicitly called for a transition away from traditional organic solvents as part of its 2030 Agenda for Sustainable Development, and NaDES chemistry is one of the leading answers to that call.

The research team selected two contrasting NaDES for their investigation. The first was a hydrophilic mixture of glycerol and choline chloride, abbreviated GCC, prepared by heating the two components in a one-to-three molar ratio at 70 degrees Celsius until a clear viscous liquid formed. The second was a hydrophobic blend of camphor and thymol, abbreviated CT, which liquefied within ten minutes at 35 to 40 degrees Celsius in a one-to-one ratio. Because deep eutectic solvents are inherently viscous and struggle to penetrate plant tissue on their own, the researchers diluted the GCC system with water and the CT system with acetonitrile, lowering viscosity and improving the solvents’ ability to reach the phytochemicals locked inside the leaf matrix.

To squeeze the maximum performance out of each solvent system, the team turned to microwave-assisted extraction, a technique that heats the solvent and plant material from the inside out. Microwaves interact with polar molecules by inducing dipole rotation, generating heat directly within the extraction medium. This localized heating builds internal pressure inside plant cells, rupturing cell walls and releasing intracellular compounds far faster than conventional stirring or reflux methods. The approach conserves energy, slashes extraction times to mere seconds, and reduces the risk of degrading heat-sensitive molecules because the total exposure window is so short. In this study, powdered leaves were combined with 30 milliliters of the solvent-diluent mixture in a conical flask and irradiated in a domestic microwave oven at controlled power and duration settings.

Optimizing the process required a statistical framework capable of mapping how multiple variables interact simultaneously. The researchers employed response surface methodology using a Box-Behnken design, which generated 17 experimental runs per solvent system with five replicates at central points. Three factors were varied across three levels each: the concentration of the NaDES in its diluent, the microwave power ranging from 220 to 440 watts, and the irradiation time ranging from 20 to 40 seconds. Five responses were measured for every run: total phenolic content expressed as gallic acid equivalents, total flavonoid content expressed as rutin equivalents, DPPH radical scavenging activity expressed as ascorbic acid equivalents, ABTS radical cation scavenging activity expressed as Trolox equivalents, and metal iron chelating activity expressed as EDTA equivalents, all normalized to grams of dry leaf powder.

The results exposed a striking polarity-driven divide between the two solvent systems. The hydrophilic GCC system proved dramatically superior for phenolic recovery, achieving a predicted optimal total phenolic content of 137.49 milligrams of gallic acid equivalents per gram of dry weight, nearly three times the 48.35 milligrams obtained with the hydrophobic CT system. The GCC extracts also dominated in ABTS radical scavenging, reaching 11.57 milligrams of Trolox equivalents per gram, while the CT extracts managed only 0.17 milligrams. This makes chemical sense: the ABTS assay operates through single-electron transfer and preferentially detects highly polar antioxidant molecules, which dissolve readily in the hydrogen-bonding, water-compatible environment that glycerol and choline chloride provide.

The story reversed for flavonoids. The hydrophobic camphor-thymol system extracted 61.72 milligrams of rutin equivalents per gram, roughly five times the 12.17 milligrams achieved by GCC. Many flavonoids possess substantial hydrophobic aromatic ring systems, and the non-polar character of camphor and thymol enables favorable hydrophobic and pi-pi interactions with these structures. The CT system also edged out GCC slightly in DPPH radical scavenging, at 28.38 versus 27.08 milligrams of ascorbic acid equivalents per gram, and in metal chelating activity, at 23.39 versus 21.38 milligrams of EDTA equivalents per gram, suggesting that a meaningful fraction of the leaf’s lipophilic antioxidants and metal-binding ligands are better captured in the hydrophobic medium. Both systems produced comparable DPPH responses overall, indicating that radical-scavenging compounds were extracted efficiently across most processing conditions.

The statistical models underlying these findings proved highly reliable, with coefficients of determination exceeding 0.91 for every response and non-significant lack-of-fit tests confirming that the quadratic equations adequately described the experimental data. Solvent concentration emerged as the most influential variable, followed by microwave power and extraction time. For the GCC system, moderate DES concentration around 75 percent paired with 220 watts and 40 seconds produced optimal results with a desirability value of 1.0, and experimental validation showed relative standard deviations between 0.15 and 5.10 percent. For the CT system, the optimum landed at 60 percent DES concentration in acetonitrile, 220 watts, and 30 seconds, with validation deviations between 0.16 and 6.25 percent. The researchers explain that too much water in the GCC mixture dilutes its solubilizing power for moderately polar polyphenols, while too little water leaves the solvent too viscous to penetrate the biomass, so an intermediate composition balances fluidity and polarity. Similarly, acetonitrile lowers the viscosity of the hydrophobic CT solvent while adding just enough polarity to assist extraction.

Beyond simple solubility, the study highlights a deeper mechanism by which NaDES enhance recovery: direct attack on the plant cell wall. The chloride ions of the GCC mixture can form hydrogen bonds with the hydroxyl groups of cellulose, disrupting the wall’s architecture, while camphor and thymol interact with cell wall components in their own way. Combined with the internal pressure buildup from microwave dielectric heating, this dual assault on cellular structure accelerates the desorption and release of target compounds from the plant matrix. The authors conclude that microwave-assisted extraction coupled with carefully chosen NaDES offers a sustainable, rapid, and selective strategy for recovering antioxidant phytochemicals from Eucalyptus citriodora leaves, and that the choice between hydrophilic and hydrophobic solvents should be guided by the specific class of biomolecules a manufacturer or researcher wishes to target, whether for nutraceutical, pharmaceutical, or food applications.

Subject of Research: Microwave-assisted extraction of phenolic and antioxidant compounds from Eucalyptus citriodora leaves using hydrophilic and hydrophobic natural deep eutectic solvents

Article Title: Microwave-assisted extraction of phenolic and antioxidant compounds from Eucalyptus citriodora leaves using hydrophilic and hydrophobic natural deep eutectic solvents

Article References: Asif, M., & Ahmed, D. (2026). Microwave-assisted extraction of phenolic and antioxidant compounds from Eucalyptus citriodora leaves using hydrophilic and hydrophobic natural deep eutectic solvents. Discover Green Chemistry, 1(1), Article 21. https://doi.org/10.1007/s44509-026-00025-z

Image Credits: AI Generated

DOI: 10.1007/s44509-026-00025-z

Keywords: Eucalyptus citriodora, natural deep eutectic solvents, microwave-assisted extraction, green chemistry, polyphenols, flavonoids, antioxidant activity, response surface methodology, glycerol-choline chloride, camphor-thymol, DPPH, ABTS