Improving physicochemical and nutritional attributes of rice starch through green modification techniques.

Rice, has long been an inseparable part of the human diet all over the world. As one of the most rapidly growing crops, rice has played a key role in securing the food chain of low-income food-deficit countries. Starch is the main component in rice granules which other than its nutritional essence, plays a key role in defining the physicochemical attributes of rice-based products.

Development of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach.

The purpose of this study was to create a tool for predicting the growth of total mesophilic bacteria in spinach using machine learning-based regression models such as support vector regression, decision tree regression, and Gaussian process regression. The performance of these models was compared to traditionally used models (modified Gompertz, Baranyi, and Huang models) using statistical indices like the coefficient of determination (R) and root mean square error (RMSE). The results showed that the machine learning-based regression models provided more accurate predictions with an R of at least 0.

Development and characterization of edible films based on modified corn starch and grape juice.

Chemically modified corn starch with sodium trimetaphosphate (STMP) or citric acid (CA) and grape juice was used to produce edible films. Modification reactions were discussed by results of FT-IR scan, water solubility, swelling power, viscosity and degree of cross-linking properties. Mechanical, barrier, physical (solubility, color, transparency, microstructure) and glass transition temperature properties of films were studied to understand the effects of grape juice and modified starch usage in films.