Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging

Over half the world population relies on rice for energy, but being a carbohydrate-based crop, it offers limited nutritional benefits. To achieve nutritional security targets in Asia, we must understand the genetic variation in multi-nutritional properties with therapeutic properties and deploy this knowledge to future rice breeding. High throughput, VideometerLAB spectral imaging data has been effective in estimating total anthocyanin content, particularly bound anthocyanin content, using the high prediction power of partial least square (PLS) regression models. Multi-pronged nutritional properties of phenolic compounds and minerals, together with videometerLAB features, were utilized to develop models to classify a collection of black rice varieties into three distinct nutritional quality ideotypes. These derived models for black rice diversity panels were created utilizing videometerLAB data (L, A, B parameters), selected phenolic types (total phenolics, total anthocyanins, and bound flavonoids), and minerals (Molybdenum and Phosphorous). Random forest and artificial neural network models depicted the multi-nutritional features of black rice with 85.35 and 99.9% accuracy, respectively. These prediction algorithms would help rice breeders strategically breed nutritionally valuable genotypes based on simple, high-through-put videometerLAB readings and a small number of nutritional assays

Enriched nutraceuticals in gluten-free whole grain rice cookies with alternative sweeteners

Cookies are a popular snack worldwide, but the presence of gluten in most wheat-based cookies poses problems for people with gluten intolerance. Furthermore, gluten-free products are often deficient in nutraceuticals. This study investigated the potential of two traditional Indian rice landraces, Kalanamak and Chak-hao, as alternative cereals for producing whole grain gluten-free cookies with enriched bioactive compounds. The study also evaluated the influence of whole grain rice flours (WGRFs) and different sweeteners on the physical and biochemical properties of the cookies. The substitution of refined wheat flour with WGRFs significantly affected the physical and chemical properties of the cookies. WGRF cookies were generally crispier and had a lower spread ratio resulting in higher sensory evaluation scores. The added health benefits of WGRF derived cookies are likely due to the inherently higher levels of bioactive compounds such as quercetin equivalents with higher hydrogen peroxide scavenging (HPS) capacity and antioxidant activity derived from 2,2-diphenyl-1-picrylhydrazyl (DPPH) in Chak-hao rice and jaggery. This work shows that WGRFs from Kalanamak and Chak-hao could be viable alternatives to refined wheat flour for producing gluten-free cookies with enhanced nutraceutical benefits

Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice

Enhancing the dietary properties of rice is crucial to contribute to alleviating hidden hunger and non-communicable diseases in rice-consuming countries. Germination is a bioprocessing approach to increase the bioavailability of nutrients in rice. However, there is a scarce information on how germination impacts the overall nutritional profile of pigmented rice sprouts (PRS). Herein, we demonstrated that germination resulted to increase levels of certain dietary compounds, such as free phenolics and micronutrients (Ca, Na, Fe, Zn, riboflavin, and biotin). Metabolomic analysis revealed the preferential accumulation of dipeptides, GABA, and flavonoids in the germination process. Genome-wide association studies of the PRS suggested the activation of specific genes such as CHS1 and UGT genes responsible for increasing certain flavonoid compounds. Haplotype analyses showed a significant difference (P < 0.05) between alleles associated with these genes. Genetic markers associated with these flavonoids were incorporated into the random forest model, improving the accuracy of prediction of multi-nutritional properties from 89.7% to 97.7%. Deploying this knowledge to breed rice with multi-nutritional properties will be timely to address double burden nutritional challenge