This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powd. goat milk (target class) against adulteration with powd. cow milk.Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, resp.), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed.Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD).Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent anal. insights for the authentication of pure powd. goat milk, with a significance level of 0.01.This methodol. furnishes distinct spectral signatures associated with the macromol. composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% weight/weight of cow milk added to powd. goat milk.Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food anal., especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.