Fatty acid profiling of soybean cotyledons by near-infrared spectroscopy

In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

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.

Use of near-infrared spectroscopy for the rapid evaluation of soybean [Glycine max (L.) Merri.] water soluble protein content

Water soluble protein content (WSPC) is a parameter of great significance to the soybean food industry. So far, genetic studies and breeding practices have been limited by the lack of a rapid technique for the evaluation of WSPC. Near-infrared reflectance spectroscopy (NIRS) is widely applied for rapid quantification of many traits, including moisture, protein and oil content, and dietary fiber. The present study aimed to establish and evaluate a NIRS regression model for the rapid prediction of WSPC in soybean. Results showed that seed coat color had a profound impact on the accuracy of protein content prediction, whereas the seed coat itself deeply influenced protein determination. We established a partial least squares (PLS) regression model with 167 soybean samples whose seed coat had been removed. Based on multiplicative scatter correction and Savitsky-Golay transformation, the highest determination coefficient (R2) was 0.831, and the relative predictive determinant was 2.417. Further analysis showed that seed roundness correlated negatively with WSPC (r=-0.59, P<0.001) and greatly impacted PLS regression model prediction accuracy. The PLS model was suitable only for intact seeds whose coat had been peeled off, but not for broken seeds, soy powder, and green cotyledon soybean seeds. This study highlights the effect the seed coat has on soybean composition determination by NIRS. Moreover, the established PLS model for soybean WSPC determination could facilitate genetic studies and breeding.

NIR transmission spectroscopy for rapid determination of lipid and lyoprotector content in liposomal vaccine adjuvant system CAF01

It is of crucial importance to determine the concentration of the different components in the formulation accurately, during production. In this respect, near-infrared (NIR) spectroscopy represents an intriguing alternative that offers rapid, non-invasive and non-destructive sample analysis. This method, combined with multivariate data analysis was successfully applied to quantify the total concentration of lipids in the liposomal CAF01 adjuvant, composed of the cationic surfactant dimethyldioctadecylammonium bromide (DDA) and the immunomodulator alpha,alpha'-trehalose 6,6'-dibehenate (TDB). The near-infrared (NIR) detection method was compared to a validated high-performance liquid chromatography (HPLC) method and a differential scanning calorimetry (DSC) analysis, and a blinded study with three different sample concentrations was performed, showing that there was no significant difference in the accuracy of the three methods. However, the NIR and DSC methods were more precise than the HPLC method. Also, with the NIR method it was possible to differentiate between various concentrations of trehalose added as cryo-/lyoprotector. These studies therefore suggest that NIR can be used for real-time process control analysis in the production of CAF01 liposomes.