In this work, the capabilities of a novel miniaturized and portable MicroNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of cannabinoids in hemp flour. In order to develop a predictive model able to identify and simultaneously to quantify the residual amount of cannabinoids, specimens from flours commercially available on the markets were considered and spiked with increasing amount of Cannabidiol (CBD), Δ9-Tetrahydrocannabinol (THC) and Cannabigerol (CBG). Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) were applied for the simultaneously detection and quantification of cannabinoids. Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify the presence of CBD, THC and CBG in simulated samples containing cannabinoids from 0.001 to 0.1 %ww, with the accuracy and sensitivity of the reference official methods actually proposed. The method was checked against false positive and true positive response and results proved to be those required for confirmatory analyses; permitting to provide a fast and accurate method for the monitoring of cannabinoids in hemp flours.