Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA.
Thermofisher Scientific, 168 3rd Ave, Waltham, MA, 02451, USA.
J Pharm Biomed Anal. 2021 Oct 25;205:114305. doi: 10.1016/j.jpba.2021.114305. Epub 2021 Aug 3.
Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (< 5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.
拉曼化学映射是一种固有较慢的分析工具。准确且稳健的多变量分析算法，需要在方法开发中花费最少的时间和精力，这是理想的。无校准回归和分辨率方法，如经典最小二乘法（CLS）和交替最小二乘法（MCR-ALS）的多变量曲线分辨率，分别有助于减少方法开发所需的资源。然而，传统的 CLS 没有考虑适当的约束条件，这可能导致负和/或大于 100%的拉曼浓度得分，而 MCR-ALS 并不总是像基于回归的算法那样准确。线性迭代优化技术（IOT）是另一种无校准算法，以前已经证明在在线和离线药物混合物组成测定中具有良好的前景，并且具有适当的约束条件。本文旨在评估线性 IOT 算法在药物片剂中活性药物成分（API）、稀释剂和润滑剂的拉曼化学映射中的性能。对原始拉曼映射光谱应用了两种预处理策略。将结果与 CLS（当前参考方法）和 MCR-ALS 进行了比较。特别强调了在低拉曼曝光时间下的映射，以实现可行的总采集时间（<5 小时）。通过计算 IOT/CLS（或 MCR-ALS）的最大预测浓度（或分辨光谱）对应的光谱与纯粉末成分光谱之间的相关因子，评估了 IOT/CLS/MCR-ALS 估计的拉曼浓度预测的质量。可视化了拉曼化学图谱，并比较了平均拉曼浓度得分。结果表明，IOT 在药物片剂的拉曼化学映射中具有实用性。在这项研究中使用的稀释剂（乳糖）和 API（半细 APAP）可以通过 IOT 在相对较短的拉曼曝光时间内可靠地估计。另一方面，正如预期的那样，在所研究的任何情况下都无法检测到润滑剂（硬脂酸镁），无论使用哪种算法。总的来说，对于本配方中使用的 API 和稀释剂以及化学映射条件，与 MCR-ALS 相比，线性 IOT 似乎可以更好地估计纯光谱强度和平均拉曼得分（更接近 CLS）。此外，在线性 IOT 中应用适当的约束条件避免了在基于 CLS 的拉曼化学映射中观察到的负和/或大于 100%的拉曼浓度得分。
