Quantitative analysis of pyrazinamide polymorphs in ternary mixtures by ATR-FTIR and Raman spectroscopy with multivariate calibration

The polymorphism of drugs exists widely in solid chemical drugs. It will affect the physical and chemical properties of drugs, as well as bioavailability. So it is very necessary to establish an quantitative method to improve the quality control level of polymorphic drugs. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and Raman spectra have been included in many countries’ pharmacopoeia as the drug polymorph analytical technique, and they have many unique advantages. However, for multiple mixed systems, due to the complexity of optical signals, it is difficult to obtain an ideal content prediction model by classical linear regression, so the application of chemometric methods shows advantages. Pyrazinamide is a typical polymorphism drug, three polymorphic forms (α, δ, γ) were obtained. The model prediction ability of two kinds of spectroscopy combined with three kinds of stoichiometric methods was investigated by orthogonal experiment. On this basis, the influence of different combinations of five data preprocessing methods on improving modeling quality was investigated. In this research, Raman spectra combined with partial least squares (PLS), multiplicative scatter correction (MSC), denoise, median and first derivative at the whole spectral range resulted in a better calibration model. It had a RMSEP of 5.3%, 21.6%, and 20.8% for polymorphs α, δ, and γ, respectively. Several methods were used for preprocessing the spectral data could remove unimportant baseline (offset) interference from samples or to correct scattering effects and emphasize spectral the interesting signals. PLS can derive a few components from the independent variable system. Therefore, it may be an effective method to establish a quantitative model for a multi-polymorphism component mixed system.