Abstract
A single-kernel near infrared (SKNIR) instrument was designed and tested for rapid measurement of corn andsoybean attributes. The design was centered on achieving a spectral collection rate of 10 kernels/s, which limited integrationtimes of the spectrometer to 30 ms. A spectrum of an individual kernel was collected as it slid along the length of a glass tubeand was illuminated by multiple lamps. PLS regression models, developed to predict constituents from spectra, resulted inmodels with standard errors of cross validation (SECV) of 0.93% dry basis moisture content (MCdb) for corn, 0.32% MCdbfor soybean moisture content, and 0.99% for soybean protein content. RPD values for these models were 4.4 for corn moisturecontent, 7.3 for soybean moisture content, and 4.9 for soybean protein content. RPD values were defined as the ratio of thestandard deviation of the reference data to the SECV for each model. Multiplicative scatter correction improved predictionsfor soybean moisture and protein content but not for corn moisture content. These results indicate that reasonable predictionscan be made at fast NIR scan rates.
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