To demonstrate the potential of Raman spectroscopy for the qualitative and quantitative analysis of solid dosage pharmacological formulations, different concentrations of Sitagliptin, an Active Pharmaceutical Ingredient (API) currently prescribed as an anti-diabetic drug, are characterised. Increase of the API concentrations induces changes in the Raman spectral features specifically associated with the drug and excipients. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), were used for the qualitative and quantitative analysis of the spectral responses. A PLSR model is constructed which enables the prediction of different concentrations of drug in the complex excipient matrices. During the development of the prediction model, the Root Mean Square Error of Cross Validation (RMSECV) was found to be 0.36 mg and the variability explained by the model, according to the (R²) value, was found to be 0.99. Moreover, the concentration of the API in the unknown sample was determined. This concentration was predicted to be 64.28/180 mg (w/w), compared to the 65/180 mg (w/w). These findings demonstrate Raman spectroscopy coupled to PLSR analysis to be a reliable tool to verify Sitagliptin contents in the pharmaceutical samples based on calibration models prepared under laboratory conditions.

为了证明拉曼光谱法在定性和定量分析固体剂型药理学制剂方面的潜力，表征了不同浓度的西他列汀（目前被指定为抗糖尿病药物的一种活性药物成分（API））。API浓度的增加会引起与药物和赋形剂特别相关的拉曼光谱特征的变化。主成分分析（PCA）和偏最小二乘回归（PLSR）用于光谱响应的定性和定量分析。构建PLSR模型，该模型能够预测复杂赋形剂基质中不同浓度的药物。在建立预测模型的过程中，发现交叉验证的均方根误差（RMSECV）为0。²）值，发现是0.99。此外，确定了未知样品中API的浓度。与65/180 mg（w / w）相比，该浓度预计为64.28 / 180 mg（w / w）。这些发现表明，拉曼光谱法与PLSR分析相结合是一种可靠的工具，可基于在实验室条件下制备的校准模型来验证药物样品中西格列汀的含量。