Modelling starch phase transitions and water uptake of rice kernels during cooking

Whole grain rice: Updated understanding of starch digestibility and the regulation of glucose and lipid metabolism

Nowadays, resulting from disordered glucose and lipid metabolism, metabolic diseases (e.g., hyperglycemia, type 2 diabetes, and obesity) are among the most serious health issues facing humans worldwide. Increasing evidence has confirmed that dietary intervention (with healthy foods) is effective at regulating the metabolic syndrome. Whole grain rice (WGR) rich in dietary fiber and many bioactive compounds (e.g., γ-amino butyric acid, γ-oryzanol, and polyphenols) can not only inhibit starch digestion and prevent rapid increase in the blood glucose level, but also reduce oxidative stress and damage to the liver, thereby regulating glucose and lipid metabolism. The rate of starch digestion is directly related to the blood glucose level in the organism after WGR intake. Therefore, the effects of different factors (e.g., additives, cooking, germination, and physical treatments) on WGR starch digestibility are examined in this review. In addition, the mechanisms from human and animal experiments regarding the correlation between the intake of WGR or its products and the lowered blood glucose and lipid levels and the reduced incidence of diabetes and obesity are discussed. Moreover, information on developing WGR products with the health benefits is provided.

Modeling the gelatinization-melting transition of the starch-water system in pulses (lentil, bean and chickpea)

Cooking-induced conversion of starch, the major carbohydrate in pulses, is crucial for the digestibility of the seed. The gelatinization-melting transition of lentil, bean and chickpea starches was studied using Differential Scanning Calorimetry at different temperatures (T values ranged from 20 to 160 °C) and water contents (X from 0.2 to 3 kg kg^-1 db). Gelatinization and melting endotherms were successfully modeled as two desummed Gaussian functions. This modeling enabled to generate the degree of starch conversion for any T and X conditions, a valuable indicator that could be used in predictive cooking models. As previously reported for melting, the temperature of gelatinization was found to depend on moisture in a way that can be modeled using the Flory-Huggins equation. The results suggest that starch undergoes melting transition irrespective of water content. The similar starch conversion diagram for the three pulses suggest that starches have similar thermal behavior.

Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture

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Multivariate analysis was used to develop twelve cooking and eating quality classes.

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Two-layered random forest model was used to predict rice classification.

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High classification accuracy of cooking and eating quality ideotypes were obtained.

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Mismatches from IRRI-released and consumer-preferred lines was capture by the model.

Acceptance of new rice genotypes demanded by rice value chain depends on premium value of varieties that match consumer demands of regional preferences. High throughput prediction tools are not available to breeders to classify cooking and eating quality (CEQ) ideotypes and to capture texture of varieties. The pasting properties in combination with starch properties were used to develop two layered models in order to classify the rice varieties into twelve distinct CEQ ideotypes with unique sensory profiles. Classification models developed using random forest method depicted the overall accuracy of 96 %. These CEQ models were found to be robust to predict ideotypes in both Indica and Japonica diversity panels grown under dry and wet seasons and across the years. We conducted random forest modeling using 1.8 million high density SNPs and identified top 1000 SNP features which explained CEQ model classification with the accuracy of 0.81. Furthermore these CEQ models were found to be valuable to predict textural preferences of IRRI breeding lines released during 1960–2013 and mega varieties preferred in South and South East Asia.

Modelling Processes and Products in the Cereal Chain

In recent years, modelling techniques have become more frequently adopted in the field of food processing, especially for cereal-based products, which are among the most consumed foods in the world. Predictive models and simulations make it possible to explore new approaches and optimize proceedings, potentially helping companies reduce costs and limit carbon emissions. Nevertheless, as the different phases of the food processing chain are highly specialized, advances in modelling are often unknown outside of a single domain, and models rarely take into account more than one step. This paper introduces the first high-level overview of modelling techniques employed in different parts of the cereal supply chain, from farming to storage, from drying to milling, from processing to consumption. This review, issued from a networking project including researchers from over 30 different countries, aims at presenting the current state of the art in each domain, showing common trends and synergies, to finally suggest promising future venues for research.

Digestibility prediction of cooked plantain flour as a function of water content and temperature

The effect of temperature (T=55-120°C) and water content (X1=1.4-2.0 kg kg(-1) dry basis) on the gelatinization and digestibility of plantain flour (Dominico Harton genotype) were investigated. The degree of plantain starch gelatinization (α) was measured by DSC and modelled as a function of T and X1, using the Weibull model. Rapidly digestible starch (RDS) and resistant starch (RS) fractions were evaluated for different α values. An appropriate dimensionless variable was introduced to the analyzed and modelled RDS and RS as a function of α. Starch gelatinization begins at a temperature above 59.6 ± 0.5°C and α is strongly dependent on T in non-limiting water conditions. The combined effects of T and X1 on the RDS and RS can be explained by α. We demonstrate that various heat treatments and water contents lead to the same α, with the same RDS and RS values.