Peiris Kamaranga H S, Bean Scott R, Wu Xiaorong, Sexton-Bowser Sarah A, Tesso Tesfaye
Grain Quality and Structure Research Unit, Center for Grain and Animal Health Research, USDA-ARS, Manhattan, KS 66502, USA.
Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA.
Foods. 2023 Aug 18;12(16):3101. doi: 10.3390/foods12163101.
Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.
近红外(NIR)光谱法被广泛用于评估谷物的品质特性。为了评估完整高粱籽粒的蛋白质含量,以一台成熟的台式仪器(Perten DA - 7250)作为基线,开发了平行近红外校准方法,以测试一款自适应手持式仪器(VIAVI MicroNIR OnSite - W)的效能。使用这两台仪器同时从59个籽粒样品中采集光谱。用33个测试样品对交叉验证校准模型进行验证。选定的DA - 7250校准模型的决定系数(R) = 0.98,交叉验证均方根误差(RMSECV) = 0.41%,预测测试集蛋白质含量时的R = 0.94,预测均方根误差(RMSEP) = 0.52%,性能与偏差比(RPD)为4.13。选定的MicroNIR模型的R = 0.95,RMSECV = 0.62%,预测测试集蛋白质含量时的R = 0.87,RMSEP = 0.76%,RPD为2.74。相比之下,DA - 7250的性能优于MicroNIR,不过,MicroNIR的性能对于筛选完整高粱籽粒蛋白质水平来说也是可以接受的。因此,MicroNIR仪器可作为一种潜在工具,用于在台式仪器不适用的情况下筛选高粱样品,比如在田间筛选样品,或者作为比台式仪器成本更低的选择。