Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis

Assi, Sulaf, Arafat, Basel, Lawson-Wood, Kathryn and Robertson, Ian (2021) Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis. Applied Spectroscopy, 75 (4). pp. 434-444. ISSN 1943-3530

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Counterfeit medicines represent a global public health threat warranting the development of accurate, rapid, and nondestructive methods for their identification. Portable near-infrared (NIR) spectroscopy offers this advantage. This work sheds light on the potential of combining NIR spectroscopy with principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) for authenticating branded and generic antibiotics. A total of 23 antibiotics were measured “nondestructively” using a portable NIR spectrometer. The antibiotics corresponded to six different active pharmaceutical ingredients being: amoxicillin trihydrate and clavulanic acid, azithromycin dihydrate, ciprofloxacin hydrochloride, doxycycline hydrochloride, and ofloxacin. NIR spectra were exported into Matlab R2018b where data analysis was applied. The results showed that the NIR spectra of the medicines showed characteristic features that corresponded to the main excipient(s). When combined with PCA, NIR spectroscopy could distinguish between branded and generic medicines and could classify medicines according to their manufacturing sources. The PCA scores showed the distinct clusters corresponding to each group of antibiotics, whereas the loadings indicated which spectral features were significant. SIMCA provided more accurate classification over PCA for all antibiotics except ciprofloxacin which products shared many overlapping excipients. In summary, the findings of the study demonstrated the feasibility of portable NIR as an initial method for screening antibiotics.

Item Type: Journal Article
Keywords: Counterfeit medicines, antibiotics, near-infrared spectroscopy, principal component analysis, soft independent modelling of class analogy
Faculty: Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 25 Nov 2020 11:33
Last Modified: 10 Feb 2022 12:26

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