Pravin Ojha

The study uses multivariate and neural network (ANN) to characterize proximate composition and mineral content of 46 Nepalese cereal and legume germplasms. Kruskal Wallis showed differences (p < 0.05) among items. Principal Component Analysis (PCA) of the composition matrix demonstrated 80.77% of variance in three components (PC1 = 60.13%, PC2 = 12.25%, PC3 = 8.39%); PC1 distinguished mineral-rich legumes from carbohydrate-dominant cereals, while PC2 differentiated calcium- and phosphorus-rich millets. Ward's cluster analysis (HCA) identified four clusters: legumes, rice and sorghum, millets and buckwheat, and other cereals. ANN classifier, optimized over 25 hyperparameter combinations (size ∈ {2, 3, 5, 7, 10} × decay ∈ {0.0001–0.5}), achieved 74.5% LOOCV accuracy (κ = 0.714; macro-F1 = 0.851) under 10-fold cross-validation. The random forest classifier outperformed all others (100.0 %). Olden's analysis identified Iron, Sodium, and Phosphorus as principal discriminating variables. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) confirmed the four-cluster structure, with mean AUC of 0.990. Thus, this study provides a foundation for Nepalese food database and dietary plannin

The study aims to use multivariate and artificial neural network (ANN) to characterize the proximate composition and mineral content of 46 Nepalese cereal and legume germplasms. ANOVA showed significant differences (p < 0.05) among the food items for all parameters. Principal Component Analysis (PCA) of the scaled composition matrix demonstrated 90.5% of the total variance in three components; PC1 distinguished mineral-rich legumes from carbohydrate-dominant cereals, while PC2 further differentiated calcium- and phosphorus-rich millets. Ward's hierarchical cluster analysis (HCA) identified four nutritionally distinct clusters namely legumes, rice and sorghum, minor millets and buckwheat, and other cereals. ANN classifier, optimized through a grid search over 25 hyperparameter combinations (size ∈ {2, 3, 5, 7, 10} × decay ∈ {0.0001–0.5}), achieved a classification accuracy of 97.0% (kappa = 0.964) under repeated 10-fold cross-validation. The random forest classifier outperformed all others (100.0 %). Olden's connection weight analysis identified potassium, protein, and calcium as the three principal discriminating variables. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction independently confirmed the four-cluster structure, with a mean ROC-AUC of 1.000 for all. Thus, this study provides a validated analytical and methodological foundation for Nepalese food database and evidence-based dietary planning.

Type 2 diabetes mellitus (T2DM) represents a significant global health burden. The natural alkaloid, 1-Deoxynojirimycin (DNJ), abundant in mulberry (Morus alba L.), offers a promising bioactive approach to its early management. This review comprehensively summarises the multifaceted roles of DNJ in modulating the core pathophysiological dysfunctions of T2DM, including impaired glucose and lipid metabolism, insulin resistance (IR), and dysbiosis of gut microbiota. Specifically, DNJ exerts its therapeutic effects by regulating various pathways involved in glucose and lipid metabolism (e.g., phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) and AMP-activated protein kinase (AMPK) pathways), enhancing insulin sensitivity, modulating the gut microbiota, and upregulating transporter proteins. We highlight emerging methodologies, such as network pharmacology, which underscore the pivotal role of the PI3K/AKT and AMPK signalling pathways as primary targets of DNJ in T2DM management. Although this review elucidates multifaceted mechanisms of DNJ in T2DM management, it also identifies critical research gaps, particularly concerning its effects on pancreatic cells, obesity-related T2DM, and mitochondrial energy metabolism. Further investigation in these areas is crucial for fully understanding DNJ’s preventive and therapeutic potential and for the development of related functional foods.

The study characterized physicochemical and functional quality diversity of 201 wheat (Triticum aestivum L.) genotypes from Nepal’s national wheat improvement programme using chemometric multivariate methods. A total of 603 observations were analyzed for 14 quality parameters, including proximate composition (moisture, crude protein, crude fat, crude fibre, total ash, carbohydrate), mineral content (iron, phosphorus, calcium), and functional properties (dry gluten, swelling capacity, gelation temperature, water absorption capacity, oil absorption capacity). Principal component analysis (PCA), k-means clustering, and partial least squares discriminant analysis (PLS-DA) with variable importance in projection (VIP) scoring were conducted in R. All variables differed significantly across four quality clusters (Kruskal-Wallis χ²(3) = 38.7–372.4; p < 0.001). Three principal components explained 74.47% of variance: PC1 (38.43%) represented protein-gluten quality; PC2 (22.40%) balanced mineral composition versus fat-starch interaction; PC3 (13.64%) reflected dietary fibre and mineral retention. K-means clustering identified four clusters: Cluster 1 (high-phosphorus mineral-dense), Cluster 2 (high-protein-gluten), Cluster 3 (iron-rich functional), and Cluster 4 (high-fat-fibre balanced). PLS-DA highlighted dry gluten (VIP=1.369), crude protein (VIP=1.300), and oil absorption capacity (VIP=1.272) as key discriminators. Nepal’s wheat germplasm exhibits four distinct quality clusters with implications for bread-making, mineral biofortification, and functional food development.

The research aimed to cluster the nutrient profile of six root and tuber crops including chayote root. Potato cv. Janakdev and Khumal Upahar (from Lalitpur) and cv. MS-42-3 (from Nuwakot); sweet potato cv. Suntale 1 and landraces Bensishar White and Kimichaur Seto (from Lalitpur); landrace of elephant foot yam and taro (from Pokhara); landrace of chayote root (from Dhankuta) and cassava (from Udaypur) were collected and Principal Component Analysis (PCA) was done based on their nutrient compositions. The evaluated nutrients included moisture, protein, fat, ash, crude fiber, carbohydrates, iron, calcium, phosphorus, sodium, potassium, zinc, vitamin C, and reducing sugar. PCA revealed that the first four principal components accounted for 85.7% of the total variation. Ash, calcium, and reducing sugar positively correlated with PC1 and PC2, while vitamin C, zinc, and protein were negatively associated with these components. There was a negative correlation of sodium concentration with potassium, phosphorus, and iron concentrations. Additionally, there was a negative correlation between carbohydrates and fat with the moisture content of the potatoes and tubers. The collected potatoes and tubers were classified into four clusters based on similarity and distance levels. The potatoes and sweet potatoes were placed in two distinct clusters. Elephant foot yam and chayote root were categorized in one cluster, distinguishing them from cassava. These findings highlighted the diverse nutritional profiles of the examined root and tuber crops, offering valuable insights for further research. Furthermore, the results can pave the way for commercial exploration of these potatoes and tubers based on their nutritional profiles.

The purpose of the study was to determine how pretreatment techniques, such as blanching in hot water or alkaline solution, affected the bioactive ingredients, and anti-diabetic properties of bitter gourd. The bitter gourd was blanched in hot water and an alkaline solution (a mixture of saturated magnesium oxide and 0.1% sodium carbonate) at 98 °C for three minutes. Both pretreated bitter gourds were submerged in a 5% salt solution for 1 hour. Untreated fresh bitter gourd was used as the control. Ascorbic acid levels (mg/100g) and antioxidant activity (DPPH inhibition %) were dramatically (p<0.05) decreased in pretreated bitter gourd. When bitter gourd was blanched in an alkaline solution, more chlorophyll was retained, and when it was blanched in a hot water solution, the carotenoid concentration rose dramatically (p<0.05). The extract prepared from raw bitter gourd was found to have better inhibitory properties (p<0.05) against alpha-amylase and alpha-glucosidase, though all showed inhibition properties below 50%, which was low. The research concluded that raw bitter gourd is superior, however the extract percent needs to be increased to determine the IC50 value for the enzyme inhibition properties. Further research can be carried out by increasing the concentration of the extract for enzyme inhibition properties and by carrying out the trial in animals.