Development of near-infrared reflectance spectroscopy calibration for sugar content in ground soybean seed using Perten DA7250 analyzer

There is growing interest in developing calibrations for soybean [Glycine max (L.) Merr.] seed sugars on near-infrared reflectance spectroscopy (NIRS) instruments to increase the efficiency of sugar profiling. In this study, a set of 253 samples from Virginia Tech's soybean germplasm with a wide range of sugar content were used to create prediction models for sucrose, raffinose, and stachyose in ground soybean seed on the Perten DA7250 NIRS instrument. Following acquisition of spectral data, seed sugars were extracted from ground samples and analyzed using high-performance liquid chromatography (HPLC) to obtain reference data. Spectral and HPLC data were modeled using partial least squares regression (PLSR) on CAMO Unscrambler X software and internally cross-validated using the same software. Resulting calibrations showed high quantitative accuracy with the coefficient of determination of calibration (R-C(2)) = .901, the coefficient of determination of cross-validation (R-CV(2)) = .869, root mean squared error of calibration (RMSEC) = .516, and root mean squared error of cross-validation (RMSECV) of .596 for sucrose and R-C(2) = .911, R-CV(2) = .891, RMSEC = .361, and RMSECV of .405 for stachyose. These calibrations appear suitable for use in breeding operations. Meanwhile, performance of the raffinose calibration remained poor with R-C(2) = .568, R-CV(2) = .476, RMSEC = .129, and RMSECV = .143. Alternative methods for more accurate and rapid quantification of raffinose concentration in soybean seed should be investigated.

Automated summary

In this study, prediction models for sucrose and stachyose in soybean seed using near-infrared reflectance spectroscopy showed high quantitative accuracy, while the calibration for raffinose remained poor. Different methods for more accurate and rapid quantification of raffinose concentration in soybean seed should be explored.