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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Small Grain and Food Crops Quality Research » Research » Research Project #440657

Research Project: PCHI: Quantifying, Predicting, and Parallelizing the Examination of Post-digestive Properties of Common Beans

Location: Small Grain and Food Crops Quality Research

Project Number: 3060-21650-002-019-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 1, 2021
End Date: Dec 31, 2024

(1) Determine the nature and extent of changes in bean quality profiles from samples with contrasting seed coat color patterns, during processing and simulated gastric and small intestinal digestion. (2) Develop predictive models for seed coat patterns and quality profiles (before and after processing and simulated digestion), for use in breeding and product placement. (3) Develop appropriate experimental conditions for a recently constructed, modular (12-channel) simulated digestion instrument for higher-throughput, routine use in pulse breeding for examination of post-digestive quality profiles.

For Objective 1, we will assay seed coat color patterning and at-harvest quality profiles in 24 field-grown recombinant inbred lines (RILs) and eight commercial cultivars grown in four California field sites with contrasting growing season temperature regimes. Patterning will be calculated as percentage of pixels having the non-background coloration, via an ImageJ program. At-harvest quality profiles (total phenolics, protein, and starch) will be quantified using a FOSS DS2500 benchtop near-infrared spectrometer. Twelve RILs and four commercial cultivars from three (out of four) field environments per year that exhibit contrasting patterns and at-harvest quality profiles will be soaked and cooked using standardized cooking methods. We will also process separate aliquots (from the same bulked samples by plot) of four RILs and two commercial cultivars, via industrial canning, to compare digestive outcomes in samples after these two relevant forms of processing. Simulated digestion will use the Human Gastric Simulator (HGS) for the gastric phase and a shaking water bath for the small intestinal (SI) phase. We will quantify the following before and after simulated digestion, via colorimetric assays: total phenolics, antioxidant capacity, starch hydrolysis, and protein hydrolysis/digestibility. Individual phenolics will be quantified before and after simulated digestion via high-performance liquid chromatography-diode array detection. For Objective 2, we will build a predictive model for patterning and at-harvest quality profiles, using the data collected for these traits in Obj. 1 on 24 RILs and eight commercial cultivars. We will first fit a baseline model that includes Genotype and Environment (main and interaction effects), without specifying individual terms (markers or covariates) therein. We will then test models incorporating one or more of several covariates, to improve accuracy and enhance understanding of which covariates are most predictive of seed coat color patterns. For Objective 3, we will utilize established in vitro digestion protocols to determine the appropriate experimental conditions in a 12-channel digestion instrument to best match results from the HGS (Obj. 1). The following conditions will be tested as part of this Objective: (a) sample amounts varying from 5 to 25 g will be tested to develop the minimum sample amount that can be used without excessive dilution of compounds of interest; (b) the pH distribution and addition of gastrointestinal secretions in the individual modules will be varied to achieve a similar pH distribution to that observed in the HGS; (c) the small intestinal (SI) phase will be continued in the 12-channel unit to use fewer lab materials and simplify experiments; and (d) from samples representing Genotype-Environment combinations, extent of complexing and retention (compared to pre-digestion levels) of each of the compounds of interest will be monitored and compared to values obtained from the HGS using analysis of variance.