Described in this paper are two data sets of near infrared (NIR) spectra that are now available for general use. One data set consists of NIR spectra of 100 wheat samples with known protein and moisture content. The second set contains NIR spectra of 60 gasoline samples with known octane numbers. Results from a recent wavelength selection study using the two data sets are summarized. Other results based on the new regression approach of cyclic subspace regression (CSR) are briefly described. Included in CSR are principal component regression, partial least squares and least squares. An explicit description of calibration and validation samples used in both investigations is provided.