Wheat flour is widely used on an industrial scale in baked goods, pasta, food
concentrates, and confectionaries. Ash content and moisture can serve as important indicators of
the wheat flour’s quality and use, but the routinely applied assessment methods are laborious.
Partial least squares regression models, obtained using Raman spectra of flour samples and the
results of reference gravimetric analysis, allow for fast and reliable determination of ash and
moisture in wheat flour, with relative standard errors of prediction of the order of 2%. Analogous
calibration models that enable quantification of carbon, oxygen, sulfur, and nitrogen, and hence
protein, in the analyzed flours, with relative standard errors of prediction equal to 0.1, 0.3, 3.3,
and 1.4%, respectively, were built combining the results of elemental analysis and Raman spectra.