Paper Info
Title
Detection Of Counterfeit Medicines Using Hyperspectral Sensing
Abstract
Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated lab and relevant methods. However, such methods require time, sample preparation and technical expertise with lab setup. These methods are not feasible and scalable to be used in the field by the general public. The objective of our research work was to detect counterfeit medicines using simpler and faster method using hyperspectral sensing. In this experiment, a visible - near infrared (350nm - 1050nm) hyperspectral device was used to capture spectral signature of the medicines. We used 24 medicine tablets of different companies. To imitate counterfeit medicines, tablet powders were adulterated by adding different levels of calcium carbonate. Spectral signatures were captured from original stage to all stages of adulterations and analyzed using machine learning (multilayer perceptron classifier). Result shows that we are able to achieve more than 90% classification accuracy. Portable hyperspectral sensing combined with medicines spectral database can be a good field level test method for detection of counterfeit medicines, as it is very fast, easy to use and does not require technical expertise.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176419
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
Keywords
DocType
Volume
Counterfeit medicines, Hyperspectral, Spectral signature, Portable hyperspectral, Spectral database, Multilayer perceptron
Conference
2020
ISSN
Citations 
PageRank 
1557-170X
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Sujit R Shinde110.35
Karan Bhavsar211.02
Sanjay Kimbahune3105.86
Sundeep Khandelwal412.04
Avik Ghose59323.94
Arpan Pal619551.41