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Open AccessArticle
Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes
by
Bryan Eng
Bryan Eng and
Richard N. Dalby
Richard N. Dalby
Dr. Richard N Dalby received his BPharm from the University of Nottingham and his PhD from the of In [...]
Dr. Richard N Dalby received his BPharm from the University of Nottingham and his PhD from the University of Kentucky. In addition to serving as a professor in the Department of Pharmaceutical Sciences (PSC), University of Maryland School of Pharmacy, he is a fellow of the American Association of Pharmaceutical Scientists (AAPS) and a member of the Royal Pharmaceutical Society of Great Britain. His research interests include the formulation and evaluation of pressurized metered dose inhalers, dry powder, nebulizers, and nasal spray products; the development and evaluation of existing and proposed test methods for inhalation products; laboratory testing and patient evaluation of novel pulmonary and nasal delivery devices; and the design of patient education aids. He has more than 25 years of experience as an independent consultant working with both national and international companies and has served as an expert witness and advisor for pharmaceutical companies engaged in intellectual property and other disputes associated with inhaled and nasal medications and devices.
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School of Pharmacy, University of Maryland, Baltimore, MD 21201, USA
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Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5676; https://doi.org/10.3390/s24175676 (registering DOI)
Submission received: 5 August 2024
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Revised: 21 August 2024
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Accepted: 27 August 2024
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Published: 31 August 2024
Abstract
This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose’s capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette.
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MDPI and ACS Style
Eng, B.; Dalby, R.N.
Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes. Sensors 2024, 24, 5676.
https://doi.org/10.3390/s24175676
AMA Style
Eng B, Dalby RN.
Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes. Sensors. 2024; 24(17):5676.
https://doi.org/10.3390/s24175676
Chicago/Turabian Style
Eng, Bryan, and Richard N. Dalby.
2024. "Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes" Sensors 24, no. 17: 5676.
https://doi.org/10.3390/s24175676
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