<p>A reliable analytical method for detecting adulteration in ginger powder (GP) is essential to ensure product quality and maintain consumer trust. In this study, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was employed to provide a rapid and simple method for detecting GP adulterated with white powder fillers, namely corn starch (CS), potato starch (PS), rice flour (RF), tapioca starch (TS), and wheat flour (WF). Two chemometric approaches were applied for the classification of the mid-infrared spectra (800–650&#xa0;cm⁻¹): class-modelling and discriminant approaches. Data-driven soft independent modelling of class analogy (DD-SIMCA) was utilized to model only the authentic GP class, while partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were employed as binary classifiers using both authentic and adulterated calibration samples. All models demonstrated good classification performance on the prediction set within their respective frameworks. The discriminant models showed high sensitivity and specificity for the detection of adulterated samples, with efficiency values of 0.980 for PLS-DA and 1.000 for SVM. Although DD-SIMCA yielded a lower efficiency value of 0.919, it successfully classified the authentic GP samples, with a sensitivity value of 0.959, and demonstrated its suitability for one-class authentication tasks. Overall, the results highlight the potential of ATR-FTIR spectroscopy combined with appropriate chemometric strategies for the detection of adulteration in ginger powder.</p>