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

Overview of attention for article published in Frontiers in Chemistry, May 2018
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Title
Compilation of a Near-Infrared Library for Construction of Quantitative Models of Oral Dosage Forms for Amoxicillin and Potassium Clavulanate
Published in
Frontiers in Chemistry, May 2018
DOI 10.3389/fchem.2018.00184
Pubmed ID
Authors

Wen-bo Zou, Xiao-meng Chong, Yan Wang, Chang-qin Hu

Abstract

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.

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Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 40%
Student > Ph. D. Student 1 20%
Lecturer > Senior Lecturer 1 20%
Student > Bachelor 1 20%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 40%
Chemistry 2 40%
Medicine and Dentistry 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2018.
All research outputs
#20,504,518
of 23,070,218 outputs
Outputs from Frontiers in Chemistry
#2,946
of 6,028 outputs
Outputs of similar age
#289,969
of 330,346 outputs
Outputs of similar age from Frontiers in Chemistry
#90
of 163 outputs
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