A team of researchers led by Stephen Olaide Aremu and corresponding author Caleb Zando, working with colleagues at the Federal University of Lafia and partner institutions, purchased edible tubers of both species from multiple vendors at the Lafia Modern Market to ensure representative sampling. The varietal identities were confirmed with the university’s Department of Agronomy before the tubers were peeled, sliced, and carefully oven-dried at temperatures gradually raised from 60 to 110 degrees Celsius over eighteen hours to avoid hardening. The dried samples were ground into fine powder and subjected to a battery of standardized analytical methods drawn from the Association of Official Analytical Chemists, including micro-Kjeldahl nitrogen determination, permanganate titration for oxalates, ferric chloride titration for phytates, and alkaline titration for cyanogenic glycosides.

The proximate analysis delivered a clear first verdict: the potato wins on protein. Solanum tuberosum registered 14.27 percent crude protein against 11.76 percent for Dioscorea rotundata, a figure the authors note is substantially higher than most previously published values for both crops. Potato proteins, dominated by the glycoprotein patatin and various protease inhibitors, have been reported in earlier literature to rival or exceed those of legumes, and the Lafia samples appear to bear that out. The yam, however, fought back on two fronts: it contained more crude fiber, at 9.99 percent versus 8.53 percent, and more ash, at 6.29 percent versus 5.31 percent, indicating superior mineral density and dietary fiber content.

Fat was the lowest-ranking component in both tubers, at 2.99 percent for yam and 3.22 percent for potato, consistent with the general reputation of root and tuber crops as low-fat foods, though the authors note that frying can change that picture dramatically. Carbohydrate dominated everything else, with the two crops nearly tied at 63.60 percent for yam and 63.74 percent for potato. Both samples showed remarkably low moisture contents of around 5.3 to 5.5 percent, far below the 54 to 75 percent range typical of fresh tubers, a consequence of the dehydration protocol. Low moisture translates to high dry matter, which the researchers point out is a hallmark of good eating and textural quality, and it also means the flours resist microbial spoilage and store well, an important consideration for food industries.

The phytochemical results are where the story gets genuinely nuanced, because the compounds measured are double agents: some harm nutrition, others protect health. Yam contained more than twice the oxalate of potato, 6.52 percent against 2.73 percent, a difference with real consequences, since oxalates bind minerals such as calcium and iron and, at high intakes, can contribute to kidney stone formation. Yam also carried more phytate, at 333.63 milligrams per 100 grams versus 300.10 for potato. Phytate is a notorious chelator of minerals, yet the same molecule exhibits antioxidant and anticancer potential and even binds toxic heavy metals like cadmium and lead, illustrating how context determines whether a compound is villain or guardian.

Potato, meanwhile, led on several other fronts. It contained more alkaloids, 9.47 percent versus 7.37 percent, a notable finding given that alkaloid derivatives display analgesic, antitussive, and antibacterial pharmacological activities, though potato alkaloids are also the class of compounds behind greening-related toxicity when tubers are mishandled. Potato also showed higher tannins, at 3.04 milligrams per 100 grams against 2.14, and a strikingly higher cyanide content of 1.52 milligrams per 100 grams compared with just 0.42 in yam. The authors are careful to note that these cyanide levels remain well below acute toxicity thresholds, but they emphasize that neither tuber should ever be eaten raw and that adequate cooking is critical for degrading residual cyanogenic glycosides. Yam, for its part, dominated the antioxidant ledger, with flavonoids at 9.13 percent against potato’s 3.25 percent, suggesting stronger protection against oxidative stress and inflammation.

The amino acid analysis added another layer of sophistication. Glutamic acid was the most abundant amino acid in both tubers, at 12.65 grams per 100 grams of protein in potato and 9.01 in yam, followed by aspartic acid, which reached 9.87 grams in potato versus 7.60 in yam. Potato outperformed yam on most essential amino acids, including leucine, lysine, isoleucine, tryptophan, valine, methionine, and threonine, while yam held the edge in phenylalanine, proline, arginine, glycine, cystine, and alanine. Arginine is particularly interesting, as the authors note its value as a supplement during pregnancy, trauma, and illness such as cancer. Tryptophan, the sole precursor of the neurotransmitter serotonin, which regulates appetite, sleep, and mood, was also higher in potato, as was the case for the branched-chain amino acids that support muscle repair and blood sugar control.

When the researchers computed composite quality metrics, potato again came out ahead on protein. Its predicted protein efficiency ratio, calculated from leucine and tyrosine content using a classic regression equation, was 2.09 versus 1.92 for yam, and its leucine-to-isoleucine ratio of 3.08 dwarfed yam’s 1.79. Potato also scored higher on total amino acids, total non-essential amino acids, total acidic and basic amino acids, and amino acid scores for most essential residues, leading the authors to conclude that potato delivers the higher overall protein quality. Yam, by contrast, led on total essential amino acid fractions when histidine was included and on sulfur-containing amino acid parameters, underscoring just how complementary the two crops are rather than how one simply beats the other.

The authors are refreshingly candid about the limitations of their work. All measurements were performed only in duplicate, which they acknowledge was insufficient for meaningful inferential statistical comparison, meaning the compositional differences reported here cannot yet be formally tested for significance. They commit to larger replicate numbers in future studies to strengthen reproducibility. This honesty matters in a field where single-sample analyses are sometimes overinterpreted, and it frames the study as a strong, variety-specific baseline for Lafia-grown tubers rather than a definitive word on all Nigerian yams and potatoes.

The bigger picture is one of agricultural and nutritional strategy. Nigeria is the world’s leading yam producer, contributing 65.5 percent of global output, while it ranks fourth in sub-Saharan Africa for potato production with more than 1.28 million metric tonnes annually. As climate pressure and population growth strain food systems, understanding exactly what these staples deliver, and what processing can unlock, becomes a matter of public health. The researchers conclude that all antinutrient levels observed fall within ranges that standard techniques such as boiling, fermentation, soaking, and roasting can effectively minimize, and that cooking yams in particular reduces phytate while water-soluble tannins largely leach away. Their bottom line is pragmatic and encouraging: potato offers the better protein package, yam the better fiber, minerals, and antioxidants, and eaten together, properly cooked, the two tubers form a genuinely balanced dietary partnership for the hundreds of millions who depend on them.

Subject of Research: Comparative nutritional and antinutritional composition of white yam and Irish potato tubers cultivated in Lafia, Nigeria

Article Title: Comparative study of nutritive and antinutritive values of Dioscorea rotundata and Solanum tuberosum cultivated in Lafia, Nigeria

Article References: Aremu, S. O., Zando, C., Aremu, M. O., Ezekiel, E., Aliyu, Z. E., Omosebi, M. O., & Aremu, D. O. (2026). Comparative study of nutritive and antinutritive values of Dioscorea rotundata and Solanum tuberosum cultivated in Lafia, Nigeria. Discover Chemistry, 3(1), Article 473. https://doi.org/10.1007/s44371-026-00917-w

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00917-w

Keywords: Dioscorea rotundata, Solanum tuberosum, white yam, Irish potato, proximate analysis, antinutritional factors, amino acid profile, phytochemicals, food security, Nigeria, protein quality, oxalate

The research team, led by Sunyong Park and colleagues including Sunhwa Ryu, assembled a literature-derived dataset of 552 thermally treated biomass samples, spanning torrefaction and pyrolysis experiments drawn from dozens of published studies. Each record contained ultimate analysis, proximate analysis, higher heating value, and the process conditions—treatment temperature and residence time—that produced the fuel. Rather than feeding these numbers raw into an algorithm, the researchers invested heavily in preprocessing. Because both ultimate and proximate analyses are compositional data whose variables must sum to 100 percent, they applied closure normalization separately to each block and computed centered log-ratio transforms, a technique from compositional statistics that suppresses the spurious correlations that fixed-sum data otherwise generate.

Feature engineering went further. The team built process-aware descriptors—squared temperatures, logarithmic residence times, temperature–time interaction terms, and a combined severity index—to capture the nonlinear way thermal treatment intensity drives devolatilization, oxygen removal, and carbon enrichment. Composition-aware descriptors such as H/C and O/C ratios encoded the degree of carbonization, while proximate-derived quantities like the volatile-matter-to-fixed-carbon ratio captured the balance between volatile release and char formation. Critically, the authors also built in safeguards against information leakage and circular reasoning: auxiliary features were constructed only from training data, and a Dulong-equation-derived heating value surrogate was deliberately excluded from the final heating value models to ensure the algorithm was not simply copying a textbook formula hidden inside its own inputs.

After outlier removal using the interquartile range rule—which cut 234 records, or 42.4 percent of the dataset, leaving 318 cleaned samples—the team trained and compared linear, bagging, boosting, and tree-ensemble models, including Ridge regression, partial least squares, Random Forest, Extra Trees, XGBoost, and CatBoost. Each target variable got its own single-output model, selected by validation performance and tuned with grid search. To avoid the common trap of reporting a flattering single data split, the researchers re-evaluated every selected model across five repeated random train-validation-test partitions and reported the mean and standard deviation of test-set performance.

The results reveal a striking asymmetry between the two prediction directions. Predicting proximate properties and heating value from elemental composition—the ultimate-to-proximate direction—worked well. CatBoost models achieved repeated test-set R-squared values of 0.756 for volatile matter and 0.751 for higher heating value, with XGBoost reaching 0.702 for fixed carbon and Extra Trees 0.607 for ash. In plain terms, knowing what elements a treated biomass contains, along with how hot and how long it was treated, is enough to estimate its volatile content and energy density with useful accuracy. The heating value model predicted values with a mean absolute error of roughly 0.75 megajoules per kilogram—impressive for a screening tool that never lights a flame.

Going the other way proved harder. When the models tried to infer elemental composition from proximate data, performance dropped and became far more sensitive to how the data were partitioned. Carbon, hydrogen, oxygen, and heating value all landed in a moderate range, with repeated R-squared values between roughly 0.41 and 0.57, and large standard deviations betraying instability across splits. The reason is mathematical as much as chemical: proximate analysis compresses the fuel into three broad fractions, and many different elemental combinations can produce nearly identical volatile, fixed-carbon, and ash profiles. The inverse problem is simply underdetermined. Sulphur fared worst of all, with a negative mean R-squared of −0.085, meaning the model performed worse than naively guessing the dataset average. The authors are refreshingly blunt about this: sulphur, present at very low and heavily skewed concentrations, is flagged as unreliable and excluded from quantitative decision-making, and nitrogen is marked as a low-confidence screening estimate only.

The team also stress-tested their own pipeline. A filtering sensitivity analysis compared no outlier removal, gentle filtering, and the strict final setting, and found the effects were target-dependent rather than uniformly beneficial. Volatile matter and heating value barely changed across scenarios, but ash prediction improved substantially with stricter filtering—its R-squared rising from 0.44 to 0.64—because extreme ash values from heterogeneous feedstocks were dragging the model off course. Meanwhile, several inverse-direction targets actually got worse under strict filtering, suggesting that aggressive pruning had removed compositional variability the models needed. This nuanced outcome matters: it demonstrates the reported performance was not an artifact of quietly discarding nearly half the data, while honestly acknowledging that the final models cover a narrower slice of the biomass universe than the raw literature does.

The benchmark against human-devised formulas may be the study’s most compelling result. On the same held-out test set, the Park et al. empirical equations for predicting proximate properties from elements managed R-squared values of 0.72 for volatile matter and 0.40 for ash, while the machine-learning models reached 0.89 and 0.64 respectively. The gap widened dramatically for heating value: the century-old Dulong equation produced a negative R-squared of −1.01 on this thermally treated dataset, whereas the Dulong-free machine-learning model scored 0.87. In the inverse direction, the Shen et al. correlations from 2010 essentially failed on modern torrefied biomass, with carbon prediction collapsing to an R-squared of −0.54, while the machine-learning framework achieved 0.62. The lesson is that fixed-form equations derived decades ago on different feedstocks simply do not transfer to the complex, treatment-dependent fuels now entering the market, whereas flexible nonlinear models trained on relevant data do.

Interpreting the models with SHAP analysis added physical plausibility to the statistics. For heating value prediction, carbon- and oxygen-related descriptors—including closure-aware carbon terms and O/C variables—dominated the feature importance, exactly matching the established chemistry that carbon enrichment and deoxygenation raise energy density during torrefaction. Severity-weighted terms and temperature–residence-time interactions also contributed, indicating the models had genuinely learned treatment-intensity effects rather than memorizing feedstock labels. For ash, the influential features were hydrogen, nitrogen, and sulphur descriptors, which the authors interpret not as causal drivers of ash but as statistical fingerprints of feedstock type and inorganic-rich sample groups—a candid reminder that SHAP values describe model behavior, not experimental mechanism.

To make the framework usable beyond the modeling team, the researchers wrapped it in a lightweight Streamlit graphical interface that accepts either ultimate or proximate inputs plus process conditions and returns cross-predictions with heating value estimates, input validation, compositional closure checks, and warnings when users venture outside the training domain. Reliability flags are baked in: sulphur outputs are explicitly marked unusable for quantitative decisions. The authors are careful to position the tool as a screening aid, not a laboratory replacement—regulatory evaluation of low-concentration elements still demands real analysis, and the literature-derived dataset carries inherent heterogeneity in feedstock, reactor design, and analytical protocol. But for traders, plant operators, and researchers needing a fast preliminary read on whether a biomass batch is worth pursuing, the message is clear: a well-engineered machine-learning model, honest about its own uncertainty, can now deliver in seconds what once required a destructive, instrument-intensive afternoon—and it can do it more accurately than the empirical formulas the field has relied on for decades.

Subject of Research: Machine-learning cross-prediction of ultimate and proximate properties of thermally treated biomass for cleaner fuel assessment

Article Title: Machine-Learning-Based Cross-Prediction of Ultimate and Proximate Properties for Cleaner Biomass Fuel Assessment

Article References: Park, S., Lee, J., Yang, J., Kim, S., Hwang, K., & Ryu, S. (2026). Machine-Learning-Based Cross-Prediction of Ultimate and Proximate Properties for Cleaner Biomass Fuel Assessment. Cleaner Engineering and Technology, Article 101333. https://doi.org/10.1016/j.clet.2026.101333

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101333

Keywords: biomass, machine learning, torrefaction, proximate analysis, ultimate analysis, higher heating value, fuel quality, compositional data, SHAP, XGBoost, CatBoost, cleaner energy

The research, conducted by Francis Gbefo and Phanuel Yao Klogo at the University of Ghana, is notable for its unusually rigorous design. Rather than comparing rice varieties grown on different farms under different conditions, the team grew all four genotypes side by side in the same field at the Soil and Irrigation Research Center in Kpong, receiving identical fertilizer and water. This head-to-head setup matters enormously in nutritional studies, because soil chemistry, rainfall, and postharvest handling can each shift a grain’s mineral content dramatically. By holding the environment constant, the researchers could isolate differences attributable to the plants themselves, or at least to the combination of genetics and processing.

The four contenders represented two distinct strands of Ghanaian rice agriculture. CRI-Amankwatia and CRI-Dartey are improved aromatic varieties released by the Crops Research Institute in Fumesua, Kumasi, products of a national breeding effort aimed at competing with imported rice on quality and aroma. Ex-Baika, an aromatic landrace, and Ex-Viono, a brown rice, are traditional varieties maintained by farmers in the Coastal Savannah, widely cultivated but nutritionally under-characterized in the scientific literature. The experiment used a randomized complete block design with three field replicates per genotype, plots measuring four by three meters, and a standardized fertilization regime of 90 kilograms of nitrogen per hectare split between transplanting and panicle initiation, alongside uniform phosphorus and potassium applications.

At harvest, the grains were dried to a narrow moisture window of roughly 11.5 to 12.5 percent before milling, ensuring that every sample entered analysis under comparable conditions. Each field replicate was milled separately with a Satake rice mill, and milling recoveries ranged from 70 percent for CRI-Amankwatia down to 65 percent for Ex-Viono. Here the study made a deliberate and consequential choice: while Ex-Baika was milled to polished white rice like the improved varieties, Ex-Viono was processed as brown rice, its bran layer retained in keeping with its traditional form. That decision would prove central to interpreting the results, because rice minerals are concentrated precisely in those outer layers that polishing removes.

The laboratory work followed standardized Association of Official Analytical Chemists methods. Proximate composition, meaning moisture, crude fat, crude fiber, ash, crude protein, and carbohydrate, was determined gravimetrically and by Kjeldahl digestion, with protein calculated using the conventional nitrogen-to-protein conversion factor of 6.25. Minerals were quantified by atomic absorption spectrophotometry after acid digestion, with calibration curves showing coefficients of determination of at least 0.999. Every measurement was performed in technical triplicate on each of three biological replicates, and the data passed formal tests of normality and variance homogeneity before analysis of variance. The authors candidly note one limitation: certified reference materials and recovery studies were not available, so independent verification of the mineral measurements was not possible.

The proximate results revealed a grain dominated by carbohydrate, as expected, but with meaningful genotype-specific differences. Protein ranged from 7.41 percent in CRI-Dartey to 8.37 percent in Ex-Viono, a spread that places the Ghanaian varieties at or above levels reported for newly released rice in Ethiopia and Benin. Crude fiber varied from 0.61 to 0.78 percent, again peaking in Ex-Viono, while fat spanned 0.63 to 0.71 percent and ash, the total mineral residue after incineration at 600 degrees Celsius, climbed from 0.43 percent in CRI-Amankwatia to 0.74 percent in Ex-Viono. The researchers are careful to stress that these compositional differences describe the grain’s chemistry; the study did not measure fiber functionality, glycemic response, or any physiological effect, so no health claims can be drawn directly from the numbers.

The mineral data told the more striking story. Potassium was the most abundant element in every genotype, ranging from 112.27 milligrams per 100 grams in CRI-Dartey to 125.16 in Ex-Viono, followed by magnesium at 37.38 to 41.22 milligrams per 100 grams. Calcium and sodium were lower but still significantly different among genotypes, with Ex-Viono recording 4.51 and 13.08 milligrams per 100 grams respectively. Among the micronutrients, manganese was the lone exception to the pattern, statistically indistinguishable across all four varieties at roughly 2.3 to 2.4 milligrams per 100 grams. Iron and zinc, however, differed sharply: Ex-Viono delivered 2.77 milligrams of iron and 2.16 milligrams of zinc per 100 grams, roughly double the iron and nearly double the zinc found in the two improved varieties, with Ex-Baika occupying an intermediate position. Given that iron and zinc deficiencies affect hundreds of millions of people across sub-Saharan Africa, a staple grain that accumulates twice the micronutrient load is not a trivial finding.

Yet the authors urge caution in attributing Ex-Viono’s mineral advantage to genetics alone. Because Ex-Viono was analyzed as brown rice while the other three genotypes were polished, the mineral-rich bran and germ tissues remained in the sample, and decades of research show that elements such as iron, zinc, and magnesium are unevenly distributed within the grain, concentrated in the outer layers that milling strips away. The study therefore cannot disentangle the effect of processing from the effect of genotype, a confound the researchers acknowledge explicitly. Environmental factors, including soil pH, nutrient availability, and redox conditions in the calcic vertisol at Kpong, further complicate any purely genetic interpretation. What the data do establish is a valuable baseline: under identical field conditions and documented processing, these landraces accumulate substantially more of several essential minerals than the improved cultivars currently promoted for their agronomic and aromatic qualities.

The broader context gives the findings their urgency. Ghanaian consumers have long preferred imported rice, citing perceived superiority in appearance, cleanliness, and cooking quality, and market dominance by foreign brands reinforces that preference. National programs, from the National Rice Development Strategy to Planting for Food and Jobs, along with partners such as the Japan International Cooperation Agency and the Coalition for African Rice Development, have pushed to expand domestic production and improve grain quality. Demont and Ndour, whose work on African rice value chains the authors cite, have argued that upgrading the quality of domestic genotypes is essential for market competitiveness. Nutritional characterization of the kind performed here supplies a missing piece of that puzzle, identifying genetic resources that breeders could draw upon to combine high yield and consumer appeal with superior mineral density.

The study’s authors frame their landraces as candidates for future breeding programs, and the genetic literature supports that optimism: genome-wide association studies have identified loci governing mineral accumulation in rice grain, and comparative work across Asia and Africa has repeatedly documented meaningful diversity between landraces and hybrid varieties. The researchers themselves flag the next steps. Their results come from a single site and one season’s conditions, so multi-location trials with greater replication are needed to account for genotype-by-environment interactions before breeding decisions are made. They also recommend integrating nutritional quality with agronomic traits such as yield, disease resistance, adaptability, and consumer preference, since a nutritionally superior variety that farmers will not grow or consumers will not buy helps no one. For now, the message is clear and quietly provocative: the old varieties Ghana’s farmers never abandoned may hold exactly the nutritional assets the country’s rice sector needs to compete, provided science and market strategy can bring them together.

Subject of Research: Comparative nutritional and mineral profiling of rice landraces and improved genotypes grown in Ghana

Article Title: Comparative nutritional profiling of selected rice landraces and improved genotypes grown in Ghana

Article References: Gbefo, F., & Klogo, P. Y. (2026). Comparative nutritional profiling of selected rice landraces and improved genotypes grown in Ghana. Heliyon, 12(15), Article e45552. https://doi.org/10.1016/j.heliyon.2026.e45552

Image Credits: AI Generated

DOI: Not provided

Keywords: rice, landraces, Ghana, mineral composition, iron, zinc, proximate analysis, food security, crop breeding, nutrition, agriculture, Heliyon