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

• Main Authors: Wen-bo Zou, Xiao-meng Chong, Yan Wang, Chang-qin Hu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-05-01
• Series: Frontiers in Chemistry
• Subjects: near-infrared spectroscopy; universal model; sample selection; spectral library; quantitative analysis
• Online Access: https://www.frontiersin.org/article/10.3389/fchem.2018.00184/full

The accuracy of quantitative models for near-infrared (NIR) spectroscopy is dependent upon calibration samples with concentration variations. Conventional sample-collection methods have shortcomings (especially time-consumption), which creates a “bottleneck” in the application of NIR models for Process Analytical Technology (PAT) control. We undertook a study to solve the problem of sample collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms (ODFs) were used as examples. The aim of this study was to find an approach to construct NIR quantitative models rapidly using a NIR spectral library based on the idea of a universal model. The NIR spectral library of amoxicillin and potassium clavulanate ODFs was defined and comprised the 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 calibration sets of samples 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 these samples was 1.0–1.5%. We concluded that sample selection was not a problem when constructing NIR quantitative models using a spectral library compared with conventional methods of determining universal models. Sample spectra with a suitable concentration range in NIR models were collected rapidly. In addition, the models constructed through this method were targeted readily.