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Prediction of Caffeine in Tablets Containing Acetylsalicylic Acid, Dipyrone, and Paracetamol by Near-Infrared Spectroscopy, Raman Scattering, and Partial Least Squares Regression
Journal of Applied Spectroscopy ( IF 0.7 ) Pub Date : 2021-09-12 , DOI: 10.1007/s10812-021-01239-8
L. L. M. Guio 1 , L. O. Coutinho 1 , Z. B. Amorim 1 , J. S. Ribeiro 1 , V. Cavalcante 2 , A. Ferreira 3
Affiliation  

Two chemometric models drawing on diffuse reflectance near-infrared spectroscopy and Raman scattering are proposed to predict caffeine content in tablets based on acetylsalicylic acid, dipyrone, and paracetamol contents. However, data mining from these analyses to create models generally requires a prior comparison between spectral data and the results from reference values obtained by analytical methodology. Therefore, the construction of a robust calibration model entails that both analytical methods are simultaneously employed on several samples, which represents a limiting factor for the widespread use of spectroscopy. In this case, grounded tablets of different brands, containing only the active principles acetylsalicylic acid, dipyrone, or paracetamol and their excipients, were doped with controlled amounts of pure caffeine ranging from 0 to 10%(w/w) and used as calibration samples. Thus, caffeine quantification with a reference method was not necessary. The prediction samples had at least one of the aforementioned active ingredients and caffeine in its original formulation. Hence, the %(w/w) values of caffeine used as reference for the prediction steps were calculated from the values described on the drug description leaflet and the tablet final mass. Partial least squares regression was used as a multivariate method to construct the models. The near-infrared and Raman prediction models for caffeine, using four latent variables, presented the respective values of 0.79 and 0.78 of root-mean-square errors of cross validation, 0.74 and 1.00 of root-mean-square errors of prediction, and 0.97 and 0.97 of correlation coefficients.



中文翻译:

通过近红外光谱、拉曼散射和偏最小二乘回归预测含有乙酰水杨酸、苯吡酮和扑热息痛的片剂中的咖啡因

提出了两种利用漫反射近红外光谱和拉曼散射的化学计量模型,以根据乙酰水杨酸、苯吡酮和扑热息痛的含量预测片剂中的咖啡因含量。然而,从这些分析中挖掘数据以创建模型通常需要先将光谱数据与通过分析方法获得的参考值的结果进行比较。因此,构建稳健的校准模型需要同时对多个样品采用两种分析方法,这代表了光谱学广泛使用的限制因素。在这种情况下,不同品牌的接地片剂,仅含有乙酰水杨酸、苯吡酮或扑热息痛及其赋形剂的活性成分,掺入受控量的纯咖啡因,范围从 0 到 10% (w/w),并用作校准样品。因此,没有必要使用参考方法对咖啡因进行定量。预测样品的原始配方中至少含有上述活性成分和咖啡因之一。因此,用作预测步骤参考的咖啡因的 %(w/w) 值是根据药物说明传单上描述的值和片剂最终质量计算的。偏最小二乘回归被用作构建模型的多元方法。咖啡因的近红外和拉曼预测模型使用四个潜在变量,其交叉验证的均方根误差分别为 0.79 和 0.78,预测的均方根误差分别为 0.74 和 1.00,以及 0.97和 0。

更新日期:2021-09-13
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