Dataset on viscosity and starch polymer properties to predict texture through modeling

Abstract

Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture” [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained.

Structure-property machine learning models with predictive capabilities for glycans in food

“…Hardness and stickiness also have a wide range implying the variation in rice samples perceived by consumers. The subset used in this study are provided in the Supplementary Information File, and we refer the reader to the cited publication by Beunafe et al 39 for the complete dataset.…”

“…al. from the International Rice Research Institute, show the promise of predictive methods for understanding how starch structural data and physical and sensory properties are related 39 . The food samples in their dataset are varieties of rice.…”

“…Data samples and features We used the dataset from the study (referenced in Section 2.5) by Beunafe et al at the International Rice Research Institute (IRRI) 39 which includes three types of data: composition, physical, and sensory, as shown in Figure 1. The composition data includes amylose content (AC) and protein content (PC).…”

“…This interaction leads to different values of viscosity depending on the applied temperatures, and this information is important in temperature settings for food processes. The relevant range of viscosities is studied by a standardized analytical test (rapid visco analyser) 25,26,29–31,39 that measures the viscosity at 4 second intervals for a 12 minute period, where the sample solution is treated to a temperature profile of increasing temperature up to a maximum which is held constant and then decreased. Peak viscosity is the viscous effect at the peak temperature, where amylose and amylopectin are fully dispersed in water and maximally disordered.…”