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Article type: Research Article
Authors: Peerapattana, Jomjaia | Hattori, Yusukeb | Otsuka, Makotob;
Affiliations: [a] Center for Research and Development of Herbal Health Products, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, Thailand | [b] Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, Shinmachi, Nishi-Tokyo, Japan
Correspondence: [*] Corresponding author: Makoto Otsuka. Tel./Fax: 81424688658; E-mail: motsuka@musashino-u.ac.jp
Abstract: BACKGROUND:In order to manufacture pharmaceutical products, real-time monitoring in the manufacturing process is necessary, but large equipment cost is required to achieve it. OBJECTIVE:The aim of this research is to use ultra-violet-visible spectroscopy along with chemometrics procedure to simultaneously carry out quantitative analysis of indomethacin (IMC) and benzoic acid (BA) in the gel during pharmaceutical manufacturing process. METHODS:The gel preparations contained 0.1–1.5% IMC, 0.015–0.225% BA, 2% carbopol® 941 and 95% ethanol solution. The calibration models were constructed using the partial least square regression (PLS). RESULTS:The relationships of the measured and predicted concentrations for both IMC and BA had linear plots. The developed PLS calibration models were used to monitor the IMC and BA concentrations during mixing of the gels by the planetary centrifugal and conventional mixers, respectively. IMC and BA were gradually dispersed, dissolved and completely homogeneous within 30 min by the centrifugal mixer. In contrast, IMC and BA were slowly dispersed, dissolved and completely homogeneous at more than 60 min by the conventional mixer. CONCLUSIONS:The ultra-violet-visible spectrophotometric method couples with multivariate chemometric techniques for quantitative data analysis were successfully applied for the simultaneous determination of major component IMC and trace component BA in the gel.
Keywords: Ultra-violet-visible spectroscopy, indomethacin, benzoic acid, process monitoring, partial least square regression, process analysis technology
DOI: 10.3233/BME-181034
Journal: Bio-Medical Materials and Engineering, vol. 30, no. 1, pp. 73-84, 2019
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