Rowlands, S, Assi, S and Al-Jumeily, D (2023) Identification of authentic and counterfeit Viagra tablets using near-infrared spectroscopic methods and machine learning algorithms. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE) . (2023 15th International Conference on Developments in eSystems Engineering (DeSE), Baghdad, Iraq).

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Abstract

Counterfeit medicinal and lifestyles products are a global issue that impacts public health. Counterfeit products are often made in unsafe and unsanitary conditions before their release to the public without testing by regulatory bodies. One product that is particularly susceptible to online counterfeiting is Viagra, which is one of the highest selling medicines worldwide. A total of 57 Viagra tablets were used for the study; this included 27 authentic and 30 counterfeit tablets which were measured using near-infrared spectroscopy (NIRS). Spectra obtained using the NIR spectrometer non-destructively were exported into a multi-paradigm numerical computing environment where machine learning algorithms (MLAs) were applied using Matlab 2007a. Four algorithms were used related to correlation in wavelength space (CWS), K-nearest neighbour (KNN), principal component analysis (PCA) and PCA combined with fuzzy C-mean clustering (PCA/FCM). The algorithms were applied unsupervised to the authentic and counterfeit tables with no prior labelling to any of the tablets. The results showed two clear groups/clusters between the authentic and counterfeit tablets. In particular, PCA and PCA/FCM showed further subgroups among the counterfeit tablets that corresponded to their varying manufacturing sources. In summary, the use of NIRS and MLAs proved an effective method for identifying counterfeit Viagra medicines rapidly and non-destructively.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	R Medicine > RM Therapeutics. Pharmacology
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	IEEE
SWORD Depositor:	A Symplectic
Date Deposited:	20 Feb 2023 10:59
Last Modified:	26 Apr 2023 10:42
DOI or ID number:	10.1109/DeSE58274.2023.10100015
URI:	https://researchonline.ljmu.ac.uk/id/eprint/18938

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