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Compilation of a Near-Infrared Library for Construction of Quantitative Models of Oral Dosage Forms for Amoxicillin and Potassium Clavulanate

机译:建立用于建立阿莫西林和克拉维酸钾口服剂量形式定量模型的近红外文库

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The accuracy of NIR quantitative models depends on calibration samples with concentration variability. Conventional sample collecting methods have some shortcomings especially the time-consuming which remains a bottleneck in the application of NIR models for Process Analytical Technology (PAT) control. A study was performed to solve the problem of sample selection collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms were used as examples. The aim was to find a normal approach to rapidly construct NIR quantitative models using an NIR spectral library based on the idea of a universal model [2021]. The NIR spectral library of amoxicillin and potassium clavulanate oral dosage forms was defined and consisted of spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (rT) was used to indicate the similarities of the spectra. The samples’ calibration sets were selected from a spectral library according to the median rT of the samples to be analyzed. The rT of the samples selected was close to the median rT. The difference in rT of those samples was 1.0% to 1.5%. We concluded that sample selection is not a problem when constructing NIR quantitative models using a spectral library versus conventional methods of determining universal models. The sample spectra with a suitable concentration range in the NIR models were collected quickly. In addition, the models constructed through this method were more easily targeted.
机译:NIR定量模型的准确性取决于具有浓度变化性的校准样品。常规的样品收集方法有一些缺点,特别是耗时,这仍然是将NIR模型应用于过程分析技术(PAT)控制的瓶颈。为了解决构建近红外定量模型的样本选择问题,进行了研究。以阿莫西林和克拉维酸钾口服剂型为例。目的是找到基于通用模型[2021]的思想,使用近红外光谱库快速构建近红外定量模型的常规方法。定义了阿莫西林和克拉维酸钾口服剂型的NIR光谱库,该光谱库由26家国内制药公司生产的377批次样品的光谱组成,包括片剂,分散片,咀嚼片,口服混悬剂和颗粒剂。相关系数(rT)用于指示光谱的相似性。根据要分析的样品的中值rT从光谱库中选择样品的校准集。所选样品的rT接近中值rT。这些样品的rT差异为1.0%至1.5%。我们得出的结论是,使用光谱库与确定通用模型的常规方法构建NIR定量模型时,样品选择不是问题。快速收集NIR模型中具有合适浓度范围的样品光谱。此外,通过这种方法构建的模型更容易成为目标。

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