Secondary metabolites are important components in terms of nutrition and health. Carotenoids and tocopherols, two groups of the fat-soluble components, are also included in this category. There is an increasing interest in the detection of secondary metabolites with near-infrared spectroscopy. However, the number of scientific studies for the detection of these components, especially for tocopherols in corn flour or oil samples by near-infrared reflectance spectroscopy is rather limited. This study was carried out to determine the amount of carotenoids and tocopherols in flour and oil samples of 250 different maize genotypes by near-infrared reflectance spectroscopy using the partial least squares regression modeling method. Liquid chromatography mass spectrophotometry was used as a reference method in order to determine the contents of five carotenoids and four tocopherol subcomponents. The estimation models were created by using the spectral data collected from ground samples, and oil samples extracted from the same flour; along with the results of the reference analysis. The reliability of these models was tested by external validation (n?=?50). The prediction models generated by the spectra taken from corn flour yielded more successful results than the models created with the spectra taken from the oil samples. Among the models compared, the one developed with the spectra taken from flour samples for lutein was the most successful. It is seen that the estimation models generated from flour samples can be used for screening purposes, though different approaches are needed to increase the success of models.