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