A Machine Learning Techniques to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser

Book chapter


Alsallal, Muna, Sharif, M., Al-Ghzawi, Baydaa and al Mutoki, Sabah Mohammed Mlkat 2019. A Machine Learning Techniques to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser. in: Miraz, Mahdi H., Excell, Peter S., Jones, Andrew, Soomro, Safeeullah and Ali, Maaruf (ed.) Proceedings 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE) IEEE. pp. 118-122
AuthorsAlsallal, Muna, Sharif, M., Al-Ghzawi, Baydaa and al Mutoki, Sabah Mohammed Mlkat
EditorsMiraz, Mahdi H., Excell, Peter S., Jones, Andrew, Soomro, Safeeullah and Ali, Maaruf
Abstract

Since so many sub-standard and fake medicines
are being openly sold, the counterfeit medicines have become
widespread. The forgers succeeded in imitating the genuine
medicines and make them look like genuine ones. This paper
has proposed an approach that based on analysing the
Tenormin
®
50mg medicine by using non-
destructive
X-Ray
Fluorescence Technique. This technique has been proposed
over other heavy chemical analyzing methods to detect
counterfeit Tenormin® due to its s
peed and reliability. There
are 10 samples of Tenormin tablets from different
manufactures were tested. All samples contained the active
element Atenolol 50 mg and other inactive elements. Moreover
two supervised machine learning techniques; RBF Support
Vector Machine (RBF
-SVM) and K
-Nearest Neighbor (KNN)
are employed. These two supervised machine learning
algorithms were proposed as a step to design an automated
approach in order to determine fake from genuine Tenormin
®
without a need for trained chemists. The results revealed that
X-Ray Fluorescence Technique has discriminated three
elemental composition samples which differ from other 7
samples. The results also revealed the SVM proposed
approach outperforms the KNN based approach with an
overall accur
acy of 93%.

Book titleProceedings 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Page range118-122
Year2019
PublisherIEEE
Publication dates
Print07 Mar 2019
Publication process dates
Deposited10 Aug 2018
Submitted17 Jun 2018
EventIEEE International Conference on Computing, Electronics & Communications Engineering 2018 (iCCECE '18)
ISBN978-1-5386-4904-6
978-1-5386-4903-9
978-1-5386-4905-3
Digital Object Identifier (DOI)doi:10.1109/iCCECOME.2018.8659110
Web address (URL)https://doi.org/10.1109/iCCECOME.2018.8659110
Additional information

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LicenseAll rights reserved (under embargo)
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