The team, publishing in the journal Pharmacia, collected fresh Sargassum polycystum from the east coast of Pangandaran at coordinates 7°42’22.5″S, 108°39’50.3″E, and had the material authenticated as Sargassaceae by the Herbarium Jatinangor at Universitas Padjadjaran. Forty kilograms of fresh algae were cleaned, dried at 40 °C for 24 hours, ground, and sieved, yielding just 2.6 kilograms of dried powder, a dry-material recovery of 6.50 percent. Crucially, a single common batch of this powder fed both extraction experiments, ensuring that any differences between the two methods reflected processing rather than raw-material variability.

Sargassum polycystum is a chemically rich target. Like other brown algae, it contains phlorotannins and other phenolic compounds capable of donating electrons or hydrogen atoms to neutralize free radicals, along with flavonoids, pigments, polysaccharides, and sterol-related metabolites. Brown algal cell walls, however, are formidable barriers: alginates, sulfated polysaccharides, cellulose, and proteins restrict solvent penetration and can slow the release of intracellular constituents. This structural complexity means that a high extraction yield, which simply measures total soluble mass, may largely reflect dissolved polysaccharides, salts, and pigments rather than the phenolic and flavonoid constituents that drive antioxidant quality.

To disentangle these effects, the researchers ran two independent three-factor, three-level Box–Behnken designs, each with 15 extraction runs including three independently prepared center points to estimate pure experimental error. Reflux extraction was tested across temperatures of 50 to 70 °C, durations of 1 to 3 hours, and solid-to-solvent ratios of 1:5 to 1:15, with particle size fixed at 60 mesh and 96 percent ethanol as the solvent. Maceration, the gentle room-temperature alternative, was tested across 24 to 72 hours, particle sizes of 40 to 80 mesh, and the same ratio range at 25 ± 2 °C. Four responses were tracked for every run: extraction yield, total phenolic content (TPC), total flavonoid content (TFC), and radical-scavenging potency expressed as the DPPH IC50, the concentration needed to quench half of the DPPH radicals in the assay.

The observed ranges told the first part of the story. Reflux produced yields of 5.46 to 15.38 percent, with the maximum at 60 °C, 2 hours, and a 1:10 ratio, while maceration spanned 2.01 to 15.30 percent, peaking after 72 hours at 60 mesh and 1:15. But the quality responses inverted the picture. Maceration achieved the highest phenolic content of the entire study, 62.98 mg gallic acid equivalents per gram of extract, and by far the strongest DPPH activity, with an IC50 of just 36.19 µg/mL, both at 24 hours, 80 mesh, and 1:10. Reflux peaked at 53.05 mg GAE/g and an IC50 of 98.66 µg/mL, notably at its mildest condition of 50 °C and 1 hour. In both designs, the recipe for maximum yield was a different recipe from the recipe for maximum antioxidant quality.

The statistical models behind these observations were robust. All eight response models, four per method, were significant, with p-values ranging from 0.001 to 0.006, and lack-of-fit tests were non-significant throughout, indicating the second-order models adequately represented the design space. R² values ranged from 81.78 to 99.16 percent. The dominant factors were revealing: extraction temperature carried the largest F-values among the reflux factors, while extraction time dominated every maceration response. The researchers also flagged an important caveat, noting that predicted R² values for the bioactive responses were substantially lower than adjusted R² values, particularly for reflux, signaling limited out-of-sample precision for TPC, TFC, and IC50 even when the models fit the observed data well.

Desirability-based multi-response optimization then translated the models into recommended operating points. For reflux, the optimum landed at 50.404 °C, 1.0404 hours, and a ratio of approximately 1:9.55, with a composite desirability of 0.663. Validation runs using three independently prepared extractions delivered a 10.32 percent yield, 39.68 mg GAE/g TPC, 33.24 mg QE/g TFC, and an IC50 of 109.25 µg/mL, all within the corresponding 95 percent confidence and prediction intervals. For maceration, the optimum was 24 hours, 80 mesh, and roughly 1:5.30, with a higher composite desirability of 0.704. Validation produced a 5.54 percent yield, 59.39 mg GAE/g TPC, 38.03 mg QE/g TFC, and an IC50 of 55.66 µg/mL, again within intervals, though the maceration IC50 prediction interval was wide, reflecting genuine uncertainty in that estimate.

The most consequential insight emerged when the team normalized results to the starting biomass rather than to the extract itself. Because the optimized maceration extract was more concentrated but obtained at lower yield, reflux actually recovered more total antioxidant-related material per unit of seaweed: an estimated 409.50 mg of gallic acid equivalents and 343.04 mg of quercetin equivalents per 100 grams of dry algae, versus 329.02 and 210.69 mg respectively for maceration. In other words, the choice between methods is not a simple ranking but a decision about the goal. If the priority is a concentrated, potent extract per gram, maceration wins. If the priority is stripping the maximum phenolic and flavonoid payload from a given harvest of biomass, reflux wins, provided temperature and duration are kept mild to protect thermolabile constituents.

The mechanistic explanation fits the physics of each process. Reflux accelerates mass transfer through controlled heating and continuous solvent condensation, but heat is a double-edged sword, since elevated temperatures and prolonged exposure can degrade phenolic and flavonoid structures. That is why reflux quality peaked at its gentlest setting. Maceration relies on passive diffusion, and grinding the seaweed to 80-mesh powder shortened diffusion pathways and increased contact area, allowing phenolics to be recovered within 24 hours. Extending maceration to 72 hours pulled more total soluble material into solution, inflating yield, but the additional mass was evidently not proportional phenolic content, and prolonged contact risks oxidation or degradation of compounds already released.

The authors are careful about the limits of their claims. Qualitative phytochemical screening of representative extracts confirmed reagent-reactive phenolics, flavonoids, tannins, saponins, and steroidal or triterpenoid constituents, with alkaloid reactions depending on the reagent used, but these colorimetric and precipitation tests are group-level indicators, not compound identifications. The Folin–Ciocalteu and aluminum chloride assays quantify equivalent classes rather than specific phlorotannins, and DPPH measures chemical radical scavenging under defined solvent conditions, not activity in living cells. The team recommends chromatographic profiling by HPLC-DAD or LC-MS to link process conditions to individual constituents, additional assays such as ABTS, FRAP, or ORAC to broaden the antioxidant picture, and quantification of residual solvent in future standardization work.

Even with those caveats, the study offers a practical blueprint for a growing industry. Indonesia’s extensive tropical coastline holds substantial Sargassum resources, and brown algae are increasingly pursued for antioxidant, anti-inflammatory, antidiabetic, and neuroprotective applications. The finding that reflux and maceration should be treated as complementary rather than interchangeable options, each with its own validated optimum, gives process developers a defensible starting point. The researchers suggest that future work should explore assisted extraction technologies and green-process metrics, including solvent consumption and energy demand, to push these conventional methods toward more sustainable production. For now, the message from Pangandaran is clear: in seaweed chemistry, what you extract and how much you extract are two different questions, and answering only one of them can leave the real prize in the pot.

Subject of Research: Multi-response optimization of reflux and maceration extraction of bioactive compounds from the brown alga Sargassum polycystum

Article Title: Parallel multi-response optimization of reflux and maceration extraction of Sargassum polycystum from the east coast of Pangandaran using Box–Behnken design

Article References: Susanti, S., Fisabilah, A., Kamil, I., Adlina, S., Rizkuloh, L. R., Mardianingrum, R., & Singgih, M. (2026). Parallel multi-response optimization of reflux and maceration extraction of Sargassum polycystum from the east coast of Pangandaran using Box–Behnken design. Pharmacia, 73, Article e209685. https://doi.org/10.3897/pharmacia.73.e209685

Image Credits: AI Generated

DOI: 10.3897/pharmacia.73.e209685

Keywords: Sargassum polycystum, brown algae, extraction optimization, response surface methodology, Box–Behnken design, antioxidant activity, total phenolic content, total flavonoid content, DPPH assay, reflux extraction, maceration, Pangandaran

The scale of the diversity the team documented is remarkable. Fruit weight among the fifteen accessions spanned from just 7.06 grams to 36.72 grams, a more than fivefold difference, while fruit width ranged from 20.67 to 36.54 millimeters. Such wide morphological variation within a modest collection of local germplasm underscores how much untapped diversity persists in traditional orchards and backyard plantings, even as commercial agriculture narrows the genetic base of many fruit crops. For breeders searching for new combinations of size, shape, and adaptation, these numbers represent a catalog of raw material.

The biochemical picture was equally striking. DPPH radical scavenging activity, a standard laboratory proxy for antioxidant capacity, reached a maximum of 83.20 percent in the collection. Total phenolic content varied dramatically, from 11.15 to 210.52 milligrams per gram of fresh weight, and vitamin C concentrations ranged from 5.29 to 82.03 parts per million. Phenolic compounds, which include flavonoids, anthocyanins, and phenolic acids, are central to the antioxidant behavior of stone fruits, and the nearly twentyfold spread in phenolic content suggests that some of these local plums could be genuinely valuable sources of nutritionally interesting chemistry.

Why does this matter beyond the orchard? Antioxidant activity in fruit is associated in the scientific literature with the neutralization of reactive oxygen species, and diets rich in antioxidant-containing fruits are widely studied for their potential health contributions. If breeders can reliably identify which fruit traits and biochemical components drive antioxidant capacity, they can select for enhanced nutritional quality alongside the agronomic traits that growers care about, such as yield, disease resistance, and fruit size. That is precisely the question the Turkish team posed: can the relationship between measurable fruit characteristics and antioxidant activity be captured computationally, in a way that makes selection faster and more targeted?

To explore that question, the researchers built machine learning models that attempted to predict antioxidant capacity from the morphological and biochemical parameters they had measured. They evaluated several algorithms, including Random Forest, which constructs ensembles of decision trees from bootstrapped samples of the data, and support vector regression with a radial basis function kernel, which maps inputs into high-dimensional feature spaces to capture nonlinear relationships. Crucially, they used leave-one-cultivar-out cross-validation, a demanding evaluation scheme in which the model is trained on all varieties except one and then asked to predict the held-out cultivar. This approach tests whether a model has learned generalizable biology or merely memorized the quirks of specific varieties.

The results were sobering, and the authors deserve credit for reporting them transparently. Random Forest delivered the best performance among the models tested, with an R² of negative 0.251 and a root mean square error of 33.91, while SVR-RBF achieved a lower mean absolute error of 25.24 compared with Random Forest’s 28.99. But a negative R² means the model’s predictions were worse than simply guessing the mean value of the training data for every unseen cultivar. In other words, none of the algorithms could meaningfully generalize to plum varieties it had never encountered. The team explicitly frames the machine learning component as exploratory rather than as evidence of robust prediction or causal relationships among the measured traits.

Feature-importance analysis did point to total phenolic content and vitamin C as the variables contributing most to model predictions, which aligns with biochemical expectations, since both classes of compounds are known to participate in radical scavenging reactions. However, the authors caution that these findings should be interpreted carefully given the limited number of independent cultivars in the dataset. Their SHAP-based interpretation, a technique that decomposes individual predictions into contributions from each feature, did not provide sufficient evidence to support positive or synergistic effects of total phenolic content and vitamin C on antioxidant activity. With only fifteen cultivars, statistical power is inherently constrained, and the models may be capturing noise as readily as signal.

This tension between an ambitious analytical toolkit and a small sample size is a familiar one in agricultural data science, and the study offers a useful lesson for the field. Machine learning excels when trained on large, diverse datasets, but germplasm characterization often proceeds accession by accession, with each variety requiring a full season of fruit collection and laboratory analysis. The negative cross-validation results here do not invalidate the approach; they quantify how far the field still is from models that can predict antioxidant performance in novel varieties. Larger multi-region collections, multi-year datasets, and standardized biochemical protocols would all strengthen future attempts to build genuinely predictive models of fruit quality.

Meanwhile, the core value of the study lies in what it says about the plums themselves. The substantial phenotypic and biochemical variation documented across the fifteen Black Sea varieties confirms that locally adapted European plum germplasm constitutes a meaningful reservoir of functional diversity. Some accessions combine desirable fruit characteristics with high phenolic content or elevated vitamin C, making them candidates for direct use in breeding programs aimed at enhancing nutritional quality. Others may carry adaptation to the cool, humid conditions of the Black Sea coast, traits that could become increasingly relevant as climate patterns shift and stone fruit production faces new environmental pressures.

The authors argue that incorporating these local varieties into conservation and breeding strategies could help develop plum cultivars that combine desirable agronomic traits with enhanced antioxidant potential and nutritional quality. That recommendation carries broader urgency. Landrace and local varieties worldwide are being lost as orchards are replanted with a handful of commercial cultivars, and each lost variety takes with it alleles that might someday underpin the next generation of resilient, nutritious fruit. Documenting collections like this one, even with exploratory modeling and honest caveats, is a first step toward ensuring that the genetic heritage embedded in traditional plum trees is characterized before it disappears, and that breeders can draw on it when the need arises.

Subject of Research: Morphological and biochemical characterization of local European plum germplasm from the Black Sea region using machine learning

Article Title: Integrating machine learning with fruit trait and biochemical profiling of local European plum (Prunus domestica L.) germplasm from the Black Sea region

Article References: Aydın, E., Cengiz, M. A., Macit, İ., & Demirsoy, H. (2026). Integrating machine learning with fruit trait and biochemical profiling of local European plum (Prunus domestica L.) germplasm from the Black Sea region. BMC Plant Biology. https://doi.org/10.1186/s12870-026-10152-1

Image Credits: AI Generated

DOI: 10.1186/s12870-026-10152-1

Keywords: Prunus domestica, European plum, genetic resources, antioxidant activity, total phenolic content, vitamin C, machine learning, Random Forest, SHAP analysis, cross-validation, Black Sea region, plant breeding

The rationale behind the study rests on the well-documented bioactive profiles of its two plant ingredients. Beetroot, a root vegetable of the Chenopodiaceae family, ranks among the top ten antioxidant vegetables thanks to its rich stores of betalains, phenolic acids, saponins, catechins, and flavonoids. It is also unusually high in dietary nitrates, which the human body converts into nitric oxide, a molecule that improves blood flow and cardiovascular function, a property that has earned beetroot a place in athletes’ performance regimens. Orange juice, meanwhile, contributes more than 100 tentatively identified carotenoids across citrus fruits, alongside generous vitamin C, flavonoids, and limonoids with anti-inflammatory and immune-supporting properties. Combining the two in a fermented dairy matrix, the researchers reasoned, could yield a beverage whose bioactive content far exceeds that of plain yoghurt, while the dairy proteins themselves may act as natural carriers that enhance the bioavailability of plant polyphenols through hydrophobic and electrostatic binding.

The experimental design was straightforward but rigorous in its analytical detail. Fresh beetroot roots and sweet oranges at commercial maturity were purchased from a certified market in Dhaka and transported under refrigeration. The beetroot was peeled, cubed, and blanched in boiling water for two minutes to inactivate the enzymes peroxidase and polyphenol oxidase, a step that stabilizes color and carotenoids, before being homogenized into a smooth pulp. Orange juice was extracted mechanically, filtered through muslin cloth, and stored in amber bottles at 4°C to prevent light-induced degradation of vitamin C and carotenoids. Full-fat cow’s milk was heat-treated at 85°C to 95°C for five minutes to denature whey proteins and improve water-holding capacity, then cooled and inoculated with a commercial starter culture of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, fermented to pH 4.5, and blended with the plant ingredients.

Three formulations were prepared, each containing a constant 30 milliliters of orange juice but varying amounts of beetroot pulp. Sample A combined 240 grams of yoghurt with 30 grams of beetroot pulp, Sample B paired 225 grams of yoghurt with a hefty 45 grams of pulp, and Sample C mixed 255 grams of yoghurt with just 15 grams. After homogenization, the drinks were bottled in sterilized 250-milliliter glass containers and refrigerated. This gradient approach allowed the team to probe how increasing beetroot concentration would shift the balance between nutritional enhancement, antioxidant capacity, and the sensory qualities that ultimately determine whether consumers will actually buy such a product.

The proximate analysis revealed meaningful differences tied directly to formulation. Moisture ranged from 81.72 percent in Sample C to 84.27 percent in Sample B, while Sample C, with its higher proportion of yoghurt, delivered the most nutrient-dense profile: 3.75 percent protein, 3.67 percent fat, and 10.33 percent carbohydrate. Sample B, diluted by its large beetroot fraction, had the lowest protein at 3.07 percent and fat at 3.15 percent. The pattern makes intuitive sense, since yoghurt contributes milk solids, proteins, and minerals, whereas beetroot pulp adds water, fiber, and plant solids that shift the moisture-to-total-solids ratio. These water-binding interactions among milk proteins, plant fibers, and polysaccharides matter greatly in fermented dairy, influencing both consistency and stability over time.

The bioactive results told an equally clear story. Sample B, with the most beetroot, achieved the highest DPPH radical-scavenging activity at 56.1 percent inhibition, the strongest total phenolic content at 51.4 milligrams of gallic acid equivalents per 100 grams, and the highest flavonoid content at 14 milligrams of catechin equivalents per 100 grams. Its IC50 value of 429.4 parts per million was the lowest, meaning less extract was needed to neutralize half of the free radicals in the assay. Sample C, with the least beetroot, lagged behind on every bioactive measure, failing to reach 50 percent inhibition even at the highest concentration tested. Beta-carotene-equivalent carotenoid content varied only narrowly, from 4.5 to 4.9 milligrams per 100 grams, across all three drinks. The researchers validated their spectrophotometric carotenoid method carefully, achieving 96.4 percent recovery, a calibration linearity above 0.99, and intra-day and inter-day precision below 5 percent relative standard deviation, though they caution that betalains and citrus pigments may overlap spectrally and recommend chromatographic confirmation in future work.

Sensory evaluation, conducted with a 30-member consumer panel using a nine-point hedonic scale, revealed the study’s central tension: more beetroot means more antioxidants, but also less palatability. Sample A, the moderate formulation, won decisively, scoring 7.63 for appearance, 8.00 for aroma, 8.07 for taste, and 7.90 for texture, all statistically superior to the other two formulations at p < 0.001. Sample C scored lowest on every attribute, a somewhat puzzling result given its lowest beetroot content, though the authors note that overall formulation balance and interactions among ingredients, rather than beetroot intensity alone, likely shaped the panel’s verdicts. The citrus component may play a helpful role here, since the natural sugars, acids, and volatile aromatics of orange juice can mask the earthy geosmin-derived notes that make beetroot polarizing at high concentrations.

On the safety and stability front, the news was reassuring. The pH of all three formulations declined gradually over 35 days of refrigerated storage, from around 4.5 initially to as low as 4.0 in Sample C by day 35, a normal post-acidification pattern driven by the residual metabolic activity of lactic acid bacteria. More importantly, coliform bacteria and Escherichia coli were never detected in any sample at any time point, with counts remaining below the detection limit of one colony-forming unit per milliliter throughout the entire storage period. That performance comfortably meets regulatory limits set by agencies such as the FDA and EFSA, which cap coliforms and E. coli in refrigerated foods at fewer than 10 colony-forming units per gram, and it points to good hygienic processing, proper fermentation control, and effective cold-chain preservation.

The authors are candid about the limitations that temper these findings. The study relied on a single production run, lacked an unfortified control, and measured only selected microbial indicators, leaving total viable counts and starter-culture dynamics uncharacterized. Nutritional analysis stopped at macronutrients, without profiling vitamins, minerals, or specific phytochemicals. Because technical replicates rather than independent batches formed the basis of the physicochemical data, the results should be read as preliminary formulation-level observations rather than definitive product claims. Still, the practical takeaway is clear and actionable: a moderate beetroot-and-orange fortification, as in Sample A, best preserves the taste and texture consumers expect, while a heavier beetroot load, as in Sample B, maximizes antioxidant and phenolic content. Future work with replicated production trials, chromatographic carotenoid profiling, and larger consumer panels will determine whether these vibrant pink-and-orange functional drinks can graduate from the laboratory bench to the supermarket chiller, but the evidence so far suggests that the humble drinking yoghurt has considerable untapped potential as a delivery vehicle for plant-derived bioactive compounds.

Subject of Research: Functional drinking yoghurt fortified with beetroot pulp and orange juice

Article Title: Physicochemical, Bioactive, Sensory, and Microbial Evaluation of Functional Drinking Yoghurt Fortified With Beetroot and Orange Juice

Article References: Khan, M. M. I., Ali, M. N., Tabassum, A., Bhattacharjee, D., Alam, S., & Islam, M. S. (2026). Physicochemical, Bioactive, Sensory, and Microbial Evaluation of Functional Drinking Yoghurt Fortified With Beetroot and Orange Juice. Food Science &amp; Nutrition, 14(10), Article e72452. https://doi.org/10.1002/fsn3.72452

Image Credits: AI Generated

DOI: 10.1002/fsn3.72452

Keywords: drinking yoghurt, beetroot, orange juice, functional foods, antioxidants, betalains, carotenoids, DPPH, total phenolic content, probiotics, food safety, sensory evaluation