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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 #444994

Research Project: PCHI: Rapid and in situ Screening for Key Quality Traits in Pulse Crops

Location: Small Grain and Food Crops Quality Research

Project Number: 3060-21650-002-045-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2023
End Date: Dec 31, 2024

The goal of this work is to develop a Near-Infrared (NIR) methodology to quantify protein and essential amino acids, starch, and oligosaccharides, and for the screening of phytates, an anti-nutritional factor in pulses that adversely affects the absorption of minerals in the gastrointestinal tract. This goal will be achieved through the following objectives: (1) Develop a sensor and analytical methods that accurately quantify pulse traits of interest; (2) Implement, train, and test spectroscopic chemometric algorithms; (3) Verify the performance of the sensor using independent samples; and (4) Develop an interface to display the results.

A handheld, near-infrared spectroscopic sensor will be used to measure key quality traits of dried pulse ingredients. In Year 1 we will target dry pea samples (n> 150) encompassing different dry pea cultivars and breeding lines to develop a robust model that will capture the broad phenotypic variations under different growing conditions, e.g., soil, weather, and organic management conditions. Spectra of pulses will be analyzed by a handheld NIR (NeoSpectra Micro, Si-Ware Ltd., Cairo, Egypt) equipped with an InGaAs detector operating in the 1300-2500 nm region. Fifteen scans will be co-added for each sample to reduce noise, and each sample will be measured in duplicate to evaluate the reproducibility of the measurements. Spectra will be analyzed using pattern recognition for developing predictive regression models. Different spectral data transformations (i.e., normalization, multiplicative scatter correction, signal normal variate and derivatives) will be evaluated. Independent blind samples will be included to test the ability of the models to predict the levels of the quality traits. The predicted level of each sample will be compared to its reference values to determine accuracy and robustness of the algorithm. In Years 2-3 we will continue to optimize and expand studies on dry pea, while extending scope to include other pulse ingredients, such as lentils, chickpeas, and beans.