Laboratory of Analytical Chemistry, CIRM, University of Liège, 1 Avenue de l'Hôpital, 4000 Liège, Belgium.
J Pharm Biomed Anal. 2010 Nov 2;53(3):510-6. doi: 10.1016/j.jpba.2010.06.003. Epub 2010 Jun 10.
The aim of the present study was first to develop a robust near infrared (NIR) calibration model able to determine the acetaminophen content of a low-dose syrup formulation (2%, w/v). Therefore, variability sources such as production campaigns, batches, API concentration, syrup basis, operators and sample temperatures were introduced in the calibration set. A prediction model was then built using partial least square (PLS) regression. First derivative followed by standard normal variate (SNV) were chosen as signal pre-processing. Based on the random subsets cross-validation, 4 PLS factors were selected for the prediction model. The method was then validated for an API concentration ranging from 16 to 24 mg/mL (1.6-2.4%, w/v) using an external validation set. The 0.26 mg/mL RMSEP suggested the global accuracy of the model. The accuracy profile obtained from the validation results, based on tolerance intervals, confirmed the adequate accuracy of the results generated by the method all over the investigated API concentration range. Finally, the NIR model was used to monitor in real time the API concentration while mixing syrups containing various amounts of API, a good agreement was found between the NIR method and the theoretical concentrations.
本研究的目的首先是开发一种稳健的近红外(NIR)校准模型,能够确定低剂量糖浆制剂(2%,w/v)中对乙酰氨基酚的含量。因此,在校准集中引入了生产批次、批次、API 浓度、糖浆基础、操作人员和样品温度等变异性来源。然后使用偏最小二乘(PLS)回归建立预测模型。选择一阶导数和标准正态变量(SNV)作为信号预处理。基于随机子集交叉验证,选择了 4 个 PLS 因子用于预测模型。然后使用外部验证集验证 API 浓度在 16 至 24mg/mL(1.6-2.4%,w/v)范围内的方法。0.26mg/mL RMSEP 表明模型的全局准确性。基于公差区间的验证结果的准确性轮廓证实了该方法在整个研究的 API 浓度范围内生成的结果具有足够的准确性。最后,NIR 模型用于实时监测混合含有不同 API 量的糖浆时的 API 浓度,发现 NIR 方法和理论浓度之间存在良好的一致性。