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 (R²_C) = .901, the coefficient of determination of cross‐validation (R²_CV) = .869, root mean squared error of calibration (RMSEC) = .516, and root mean squared error of cross‐validation (RMSECV) of .596 for sucrose and R²_C = .911, R²_CV = .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 R²_C = .568, R²_CV = .476, RMSEC = .129, and RMSECV = .143. Alternative methods for more accurate and rapid quantification of raffinose concentration in soybean seed should be investigated.

中文翻译：

---

使用Perten DA7250分析仪开发用于大豆粉中糖含量的近红外反射光谱法校准

人们对开发大豆校准品的兴趣越来越高[ Glycine max[L.）Merr。]在近红外反射光谱（NIRS）仪器上添加种子糖，以提高糖谱分析的效率。在这项研究中，使用Perten DA7250 NIRS仪器从弗吉尼亚理工大学大豆种质中提取的253个样品（含糖范围广泛）创建了地面大豆种子中蔗糖，棉子糖和水苏糖的预测模型。采集光谱数据后，从地面样品中提取种子糖，并使用高效液相色谱（HPLC）分析以获得参考数据。光谱和HPLC数据在CAMO Unscrambler X软件上使用偏最小二乘回归（PLSR）进行建模，并使用同一软件进行内部交叉验证。所得的校准结果显示出很高的定量准确度，并且具有确定的校准系数（R ² _C）= .901，交叉验证的确定系数（R ² _CV）= .869，校准的均方根误差（RMSEC）= .516，交叉验证的均方根误差（RMSECV）对于蔗糖为0.596，对于水苏糖，R ² _C = 0.911，R ² _CV = .891，RMSEC = .361，RMSECV为.405。这些校准似乎适用于育种操作。同时，当R ² _C = .568，R ² _CV时，棉子糖校准的性能仍然较差= .476，RMSEC = .129和RMSECV = .143。应该研究更准确，快速定量大豆种子中棉子糖浓度的替代方法。