Abstract
This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.
Highlights
Soybeans (Glycine max (L.) Merr.) are one of the most valuable legume crops worldwide, with global production reaching 361 million metric tons in 2019 [1]
High protein content combined with an excellent profile of essential amino acids that are highly digestible makes the soybean a valuable source of protein for livestock, with as much as 98% of soybean meal going into livestock feed [4]
The objective of this study was to investigate the feasibility of using real-time, field-deployable, handheld NIR spectrometers to develop partial least squares regression (PLSR) models to rapidly quantify crude protein, essential amino acids, total fat content, major fatty acid composition, and moisture content in ground soybeans and to identify high-oleic soybeans from their conventional counterparts
Summary
Soybeans (Glycine max (L.) Merr.) are one of the most valuable legume crops worldwide, with global production reaching 361 million metric tons in 2019 [1]. The soybean’s economic viability comes from both its high protein (~38%) and oil (~18%) content [2]. Of the U.S oilseed production; soybeans are the largest source of animal protein feed globally [3]. High protein content combined with an excellent profile of essential amino acids that are highly digestible makes the soybean a valuable source of protein for livestock, with as much as 98% of soybean meal (the residue left after oil extraction) going into livestock feed [4]. Many genetic breeding efforts are targeted at silencing the production of polyunsaturated fatty acids while increasing the production of oleic acid [6,7,8]
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