Criminals typically adulterate drugs with other substances to increase drug-quality to obtain high profits， which has adverse effects on the society. The purpose of this study is to achieve fast and accurate， qualitative and quantitative analysis of adulterated drugs， and explore the influence of various factors on the results of model classification， e.g.， the modeling method， the spectral band， and dimension reduction. Here， 135 infrared spectrograms of caffeine， glucose， acetaminophen， phenacetin， and starch mixed with methamphetamine hydrochloride of different mass fraction were collected using attenuated total reflection-Fourier transform infrared spectrometer. A classification model was then constructed after data preprocessing. The results demonstrate that the characteristic variable has higher classification accuracy than the original variable. Multilayer perceptron （MLP） and radical basis function （RBF） could be used to classify and identify five additives； however， they were not able to distinguish methamphetamine samples with different mass fractions. A classification model of spectral fingerprint data was constructed using factor analysis and dimension reduction combined with Bayes discriminant analysis （BDA）， and the complete differentiation of five additives and methamphetamine samples with different mass fraction was achieved on 16 and 33 dimensional variables， with accuracy of up to 100%. We achieved fast and accurate qualitative and quantitative analysis of methamphetamine and five added components， which provide scientific data support for the inference of drug source. The results also provide theoretical support and method reference for drug-related cases.