Pearl millet (PM) is a nutrient-rich, climate-resilient cereal with potential for low-glycemic food applications. This study examined molecular and microstructural determinants of in vitro starch digestibility in 11 PM genotypes. Variation in starch composition (amylose: 18.27-27.68%; amylopectin: 30.62-51.12%) influenced the distribution of digestible fractions. Rapidly digestible starch ranged from 26.32% (HHB-67 Improved) to 44.74% (PC-701), while slowly digestible starch varied from 14.65% (Pusa-1803) to 27.44% (Chanana Bajri) and was negatively correlated with predicted glycemic index (pGI: 54.63-58.87; r =-0.633). Rheological analysis showed an inverse relationship between peak viscosity and pGI (r =-0.828), suggesting restricted starch gelatinization reduces enzymatic accessibility. X-ray diffraction revealed that higher crystallinity (18.44-26.97%) was also associated with lower pGI (r =-0.788). The Principal Component Analysis grouped Chanana Bajri, PC-701 and 86M94 as structurally compact, slowly digestible types, while Dhodsar Local and Pusa-1803 aligned with higher RDS and pGI, confirming microstructure-governed digestibility variation.

The impact of climate change presents an opportunity for orphan crops like millet to contribute to sustainable food systems. A prime example of an orphan crop with special traits and the potential to develop climate-smart agriculture is Barnyard millet (BYM). Alkaloids, steroids, polysaccharides, glycosides, tannins, phenols, dietary lignans and flavonoids are just a few of the antioxidants that are abundant in BYM. BYM’s antioxidant potential, prebiotic status, anti-inflammatory, hypoglycemic, antibacterial and anticancerous properties help it to combat a myriad of diseases. The antinutritional compounds present in BYM such phytic acid, tannins, polyphenols, and amylase inhibitors limit the absorption of minerals because they form complexes with dietary minerals like calcium, zinc, magnesium, and iron and make them inaccessible for absorption. Various processing methods like dehulling, soaking, heating, gamma irradiation, cold plasma processing, fermentation might enhance the nutritional and technological functional qualities of BYM. The bioactives in BYM can improve their bioavailability, in vitro digestibility, efficiency, structural modification and stability by biological processing techniques such as germination and fermentation employing microbial strains. BYM straw’s high cellulose and hemicellulose percentage makes C5 and C6 sugars accessible for bioconversion into bioethanol.

Phosphorus (P) is an essential macronutrient that is critical for plant growth and development. In rice cultivation, limited soil P availability is a major constraint that negatively affects root development, photosynthetic performance, and grain yield, potentially compromising the quality of subsequent generation seeds. To evaluate the impact of phosphorus availability on plant physio-biochemical and agronomic performance and its influence on subsequent seed quality mediated by the (Phosphorous uptake 1) Pup1 QTL, two contrasting rice genotypes were used: Pusa-44 (P-deficiency–sensitive) and its near-isogenic line NIL-23 (P-deficiency–tolerant due to the introgression of Pup1). Plants were grown under varying Pi regimes: [low/deficient (4 ppm), optimum (16 ppm), and excess (32, 48, and 64 ppm)] in PusaRicH hydroponic medium. Physio-biochemical analyses revealed that NIL-23 exhibited a superior root system architecture and higher acid phosphatase activity under P deficiency, indicating enhanced P acquisition efficiency. Correspondingly, NIL-23 displayed improved agronomic performance, with a higher number of tillers and panicles, greater grain yield, and higher filled grain percentage per plant compared with Pusa-44. Seeds from NIL-23 also contained higher reserves of starch, protein, and crude fat under P-deficient conditions, highlighting the regulatory role of Pup1 in conferring P-deficiency tolerance. Pi availability in seed significantly influenced the performance, wherein NIL-23 seeds germinated faster, reached to 50

Fermented soymilk is a widely consumed plant-based beverage, and fermentation enhances its nutritional and functional properties. However, the influence of soybean seed characteristics on the quality and functionality of fermented soymilk remains insufficiently understood. Therefore, this study investigated the effect of three soybean varieties-food-grade (Karune), low-KTI (V23), and black (VL-Bhat-65)- on the nutritional, functional, technological, and sensory properties of soymilk fermented with Lactobacillus acidophilus 1132. All soybean varieties supported bacterial growth and lactic acid production. Fermentation significantly (p < 0.05) improved the nutritional and functional quality of soymilk by reducing raffinose-family oligosaccharides (RFOs) and antinutritional factors (ANFs), while enhancing isoflavone bioconversion and phenolic content. Black soybean soymilk exhibited the highest protein and mineral contents and viscosity; food-grade soymilk showed the greatest antioxidant activity, lowest phytic acid and trypsin inhibitor activity, and highest sensory acceptability, whereas low-KTI soymilk demonstrated the greatest conversion of glucoside to aglycone isoflavones. These findings demonstrate that soybean variety significantly influences the quality and functionality of fermented soymilk and provide a scientific basis for selecting appropriate soybean varieties for the development of nutritionally enhanced fermented plant-based beverages.

Rice (Oryza sativa L.) is the primary carbohydrate source for more than half of the global population, highlighting the urgent need for nutritionally improved, low-glycemic rice varieties. Among dietary carbohydrates, resistant starch (RS) has attracted considerable attention because of its ability to resist digestion in the small intestine, undergo fermentation in the colon, and confer multiple health benefits, including improved glycemic control and gut health. RS pool comprise of both inherent resistant starch (IRS) and processing-induced RS. Among which, IRS fraction comprising of RS I, II and V gets synthesized naturally during grain development and reflects the intrinsic compositional, structural and molecular characteristics of the rice grain. Despite its uniqueness, the genetic, biochemical, and structural mechanisms governing IRS formation remain poorly understood. In rice, IRS generally accounts for only 1–5

Millets have received considerable research attention and consumer preference recently as they are emerging as sustainable sources of nutrition. These gluten-free grains, known for their nutritional richness and resilience, have gained significant interest in recent years due to the bioactive properties of their peptides. These millet-derived bioactive peptides (MBAPs) demonstrate a range of biological activities, including antioxidant, antimicrobial, anti-inflammatory, anti-hypertensive, and anti-diabetic effects, making them promising candidates for various health-enhancing properties. This chapter explores the potential role of MBAPs in functional foods, nutraceuticals, and therapeutic interventions. The extraction and characterization techniques crucial for harnessing these peptides are discussed, along with the challenges posed by their stability, bioavailability, and large-scale production. The techno-functional properties and the prospective applications of MBAPs as functional food ingredients are discussed. Despite the promising potential, the commercialization of MBAPs faces several hurdles, including regulatory challenges, consumer acceptance, and the need for further research to establish their efficacy and safety. Addressing these challenges is essential for fully realizing the benefits of MBAPs in promoting health and wellness. This chapter seeks to present a thorough overview of the existing knowledge on MBAPs, highlighting their uses and the challenges that must be overcome for successful market integration.

Indian dwarf wheat (Triticum sphaerococcum Percival) is a valuable genetic resource for improving nutritional and functional quality in wheat. We evaluated 116 accessions and seven bread wheat checks across four environments for starch composition, glycemic index (GI), and key nutritional traits including crude protein, zinc, and iron. Significant genotypic variation was observed for all traits, with high heritability for GI, amylopectin, and micronutrient content, indicating strong genetic control, while moderate genotype & times; environment interactions reflected environmental influence on starch biosynthesis and digestibility. Total starch (50-85%), amylose (16-35%), and amylopectin (27-66%) were higher in T. sphaerococcum than in modern wheat. Low-GI accessions, including TS31 (43.3), TS70 (44.2), TS73 (46.5), and TS74 (44.9), exhibited stable performance across environments, while elite genotypes such as TS14, TS31, TS57, TS49, and TS65 combined low GI with high zinc (35-71 ppm), iron (33-63 ppm), and protein (11-21%), highlighting their potential for functional wheat breeding. Correlation analyses revealed strong associations among starch components and between micronutrients and crude protein, whereas GI was influenced by amylose content and starch-protein interactions. These results demonstrate that T. sphaerococcum harbors germplasm with both health-promoting and biofortification traits, offering a mechanistic framework for targeted breeding strategies to develop wheat cultivars with reduced glycemic response and enhanced micronutrient density.

Resistant starch (RS) is a key component of dietary fiber that offers significant health benefits, including improved gut health, glycemic control, and reduced risk of chronic diseases. Accurate prediction of RS content in foods is crucial for the development of functional foods aimed at promoting better health outcomes. Traditional methods of measuring RS are labor-intensive, time-consuming, and often require extensive analytical procedures. Machine learning (ML) techniques offer a promising alternative by utilizing large datasets of food composition, processing parameters, and digestion properties to predict RS content efficiently. This study explores the application of machine learning (ML) models to predict RS levels. The study analyzed 20 different varieties of rice (Oryza sativa) and 14 features including nutritional and functional traits to establish a correlation with RS. Analysis revealed peak viscosity (PV), hardness (HN), and gel consistency (GC) as the top three most important features contributing to the best-performing model's predictive power and provided insights into the key factors affecting RS content. Other viscosity-related metrics such as final viscosity (FV) and setback (SB) viscosity contributed moderately to the model, alongside total amylose content (TAC). Starches with higher PV often form stronger gel structures thus increasing GC and HN upon cooling, which can reduce enzyme access and slow digestion rates, subsequently high RS

Drought is a major global limiting factor for rice (Oryza sativa L.) production. Drought conditions reduce the quality and yield of rice. In the current research, we explored the effects of diverse seed priming agents and their combinations on multiple facets of grain quality, bio-molecular mechanisms, and enzyme activities. Combinations of the priming agents like MJ (methyl jasmonate) + Zinc sulphate heptahydrate (MJZ), MJ + Iron sulphate heptahydrate (MJI), and MJ + Zinc sulphate heptahydrate + Iron sulphate heptahydrate (MJIZ) were used for the seed priming. High performance liquid chromatography based abscisic acid content analysis showed 3.16 and 2.56-fold higher amounts in MJ-primed samples in N-22 and PS-5, respectively compared to unprimed controls. In unprimed controls, N-22 had lower amylose content (4.4

Ecosystem services degradation due to unsustainable agricultural practices is a pressing challenge for environmental and agricultural sustainability. Hence, integrating legumes into cereal-based systems with balanced nitrogen application can be a promising strategy to enhance ecosystem functions. Therefore, the current study was conducted to quantify the effect of legume integration, viz., cowpea, Sesbania, and black gram with maize over sole maize with varying nitrogen levels (recommended dose of nitrogen; RDN, 125

Diabetes is a chronic metabolic disorder characterized by persistent hyperglycemia due to insufficient insulin production or impaired insulin response. Recent advances in nanotechnology offer innovative solutions for improving diabetes management, particularly through the use of starch nanoparticles (SNPs). SNPs have emerged as promising carriers for insulin delivery. Thanks to their biocompatibility, biodegradability, and ability to protect insulin from degradation in the gastrointestinal tract. The present review highlights the role of SNPs in enhancing insulin bioavailability, enabling sustained release via controlled delivery, and improving glucose regulation. Additionally, SNPs facilitate enzyme inhibition, particularly alpha-amylase, reducing postprandial glucose spikes and offering better glycemic control. The application of pH-responsive and glucose-sensitive SNP systems further enhances targeted insulin delivery, potentially mimicking the body’s natural glucose regulation mechanisms. These developments position SNPs as a key component in future diabetes therapies, offering less invasive, more efficient, easily metabolized, and patient-friendly treatment options. However, challenges such as scalability, stability, and long-term safety remain, warranting further research to optimize SNP-based therapies for clinical use.

Altering the digestibility of starch to enhance the resistance to digestion, specifically targeting the creation of resistant starch (RS), holds significant importance in the fields of agriculture, food, and nutrition. This modification not only restricts the amplitude of glycemic response but also promotes gut health. Traditionally, quantifying RS has relied on complex, time-consuming, and costly human digestion simulation assays. Acknowledging the association between starch digestibility and various factors, such as microstructure, gelatinization temperature, total starch (TS), total amylose, and amylopectin, this study aimed to establish a fundamental relationship among these explanatory variables and RS through the development of a forecasting model known as the starch quality matrix (SQM). Constructed using Pearson’s correlation, the SQM proved to be significant based on model statistics, and the regression model’s adequacy was confirmed through residual diagnosis. Notably, both TS and total amylose content exhibited a significantly positive impact on RS, with coefficients of 0.030 and 0.024, respectively. Model validation utilized root mean square error and mean absolute error. The correlation between RS and inherent glycemic potential was further verified through in-house developed in-vitro starch hydrolyzation kinetics. This study unveils, for the first time, a perspective on the relationship influencing starch digestibility and introduces the SQM tool. This tool is poised to facilitate future efforts in breeding high-RS rice varieties with a low glycemic index.

Mushrooms are rich in bioactive polysaccharides, particularly α- and β-glucans, renowned for their immunomodulatory properties. The present study optimised the cultivation methods and quantified α-, β-, and total glucan from Hericium erinaceus (Lion's Mane) and Lentinula edodes (Shiitake) mushrooms. Shiitake exhibited an α-glucan content ranging from 0.806% to 5.521%, with the highest at 5.521% (DMRO-356), approximately 8.2 times higher than Lion's Mane (HE-TC), which had 0.673%. For β-glucans, Shiitake strains showed a range of 13.433-26.190%, with the highest value at 26.190% (DMRX-2022) which was 1.5-fold higher than HE-TC at 17.442%. The findings emphasise Indian Shiitake strains' remarkable α and β-glucan content and their potential as immune-boosting supplements. Incorporating these mushrooms into commercial products and health supplements requires more research on their bioavailability and health advantages.

The accurate quantification of glycemic index (GI) remains crucial for diabetes management, yet current methodologies are constrained by resource intensiveness and methodological limitations. In vitro digestion models face challenges in replicating the dynamic conditions of the human gastrointestinal tract, such as enzyme variability and multi-time point analysis, leading to suboptimal predictive accuracy. This review proposes an integrated technological framework combining non-enzymatic electrochemical sensing with artificial intelligence to revolutionize GI assessment. Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. This technological convergence represents a paradigm shift in food science analytics, promising improved throughput and precision in GI assessment. Future developments should focus on system scalability and broader applications across nutritional science, advancing diabetic management and personalized nutrition strategies.

Lipid-induced digestive resistance could be an affordable management strategy to lower the glycemic amplitude of dietary starch. This study evaluated the influence of fatty acid (FA) composition, chain length, and saturation of five cooking fats-ghee (GH), coconut oil (CO), sunflower oil (SO), mustard oil (MO), and til oil (TO)-on the inherent glycemic potential (IGP) of starches from pearl millet (PM) and rice. Starch-lipid (S-L) complexes were analyzed using in vitro starch hydrolysis kinetics. The inclusion of cooking fats in starches resulted in higher resistant starch (RS) content, which was attributed to the formation of stable S-L structures. Fourier transform infrared spectroscopy and x-ray diffraction revealed that GH and MO-induced complexes exhibited longer and shorter starch molecule assemblies in PM and rice, which ultimately limited the IGP to 57.13% and 58.87%, respectively. Subsequently, the in vitro glucose diffusion assay validated the lesser glucose bioavailability from MO-induced starch complexes in the system, revealing the correlation among chain length and degree of saturation of cooking fats in the context of IGP of dietary starches. Henceforth, by understanding these S-L interactions, newer food prototypes could be designed in the near future.

Millets are small-grained, climate-resilient cereals that assume great significance in imparting sustainable food and nutritional security. Besides their agronomic advantages, the millets are highly nutritious and contain a wide range of phytochemicals with human health beneficial properties. There has been a recent surge in the consumption of these heritage grains and food technologists across the globe are exploring several heat-imparted processing methods for developing improved products out of these gluten-free grains. Therefore, this work aims to serve as a resource covering the state-of-the-art known information about the conventional and emerging thermal processing techniques employed in millets. However, identifying a specific conventional or emerging technique for precise improvement in nutritional or overall quality remains a distant goal, as the effects of these techniques depend on factors like dosage, duration, and other process-related variables, along with the dynamics of the food matrix. This review not only analysed the common thermal processing methods used for millets but also their influence on nutritional quality, digestibility, bio accessibility, and bioavailability. Understanding these changes can aid in developing new millet-based food products that meet consumer demands, enhance market potential, and support health claims. Overall, this area of research is still under-explored, and future research on emerging technologies and value-added millet products should take into account the relationship between composition and nutrient bioavailability.

High potential is attributed to the concomitant use of probiotics and prebiotics in a single food product, called “synbiotics”, where the prebiotic component distinctly favours the growth and activity of probiotic microbes. This study implemented a detailed comparison between the prebiotic effect of Fructooligosaccharides (FOSs) and Raffinose family oligosaccharides (RFOs) on the viable count of bacteria, hydrolysis into monosaccharides, the biosynthesis of short-chain fatty acids and sensory attributes of soymilk fermented with 1% (v/v) co-cultures of Lacticaseibacillus rhamnosus JCM1136 and Weissella confusa 30082b. The highest viable count of 1.21 × 109 CFU/mL was observed in soymilk with 3% RFOs added as a prebiotic source compared with MRS broth with 3% RFOs (3.21 × 108) and 3% FOS (6.2 × 107 CFU/mL) when replaced against glucose in MRS broth. Raffinose and stachyose were extensively metabolised (4.75 and 1.28-fold decrease, respectively) in 3% RFOs supplemented with soymilk, and there was an increase in glucose, galactose, fructose (2.36, 1.55, 2.76-fold, respectively) in soymilk supplemented with 3% FOS. Synbiotic soymilk with 3% RFOs showed a 99-fold increase in methyl propionate, while the one supplemented with 3% FOS showed an increase in methyl butyrate. The highest acceptability based on the sensory attributes was for soymilk fermented with 2% RFOs + 2% FOS + 2% table sugar + 1% vanillin (7.87 ± 0.52) with high mouth feel, product consistency, taste, and flavour. This study shows that the simultaneous administration of soy with probiotic bacteria and prebiotic oligosaccharides like FOSs and RFOs enhance the synergistic interaction between them, which upgraded the nutritional and sensory quality of synbiotic soymilk.

The current study aimed to identify suitable legumes for co-culturing with maize and optimize nitrogen dose for sustainable maize production in the semi-arid region. The experiment was conducted in a split-plot design by assigning four maize-legumes integrations, viz., sole maize, maize + cowpea (knockdown at 65 days after sowing, DAS), maize + black gram, and maize + Sesbania (knockdown at 25 DAS) in main plots and three nitrogen levels viz., recommended dose of nitrogen (150 kg N ha−1; RDN), 125

Shiitake mushroom (Lentinula edodes), widely recognized for its medicinal properties, the essential nutraceutical compound in this mushroom is a type of β-glucan called lentinan (LNT), and its triple helical conformation is crucial for its immunomodulatory and other pharmacological activities. In the present study, an attempt was carried out to screen biologically active form of LNT in 4 different metabotypes of L. edodes, along with optimizing efficient extraction for scale-up. The samples were collected from ICAR-NEH, Manipur, India and there genetic identification revealed 99.8–100