Loading…
A portable electronic nose system for the identification of cannabis-based drugs
We report on a research aimed at evaluating the capacity of a simple, low-cost, portable electronic nose system based on commercially available metal oxide gas sensors to classify different types of drugs. Five drugs, namely cannabis buds, cannabis plants, hashish, snuff tobacco and tobacco leaves w...
Saved in:
Published in: | Sensors and actuators. B, Chemical Chemical, 2011-07, Vol.155 (2), p.456-463 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33 |
---|---|
cites | cdi_FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33 |
container_end_page | 463 |
container_issue | 2 |
container_start_page | 456 |
container_title | Sensors and actuators. B, Chemical |
container_volume | 155 |
creator | Haddi, Z. Amari, A. Alami, H. El Bari, N. Llobet, E. Bouchikhi, B. |
description | We report on a research aimed at evaluating the capacity of a simple, low-cost, portable electronic nose system based on commercially available metal oxide gas sensors to classify different types of drugs. Five drugs, namely cannabis buds, cannabis plants, hashish, snuff tobacco and tobacco leaves were employed. A dedicated real-time data acquisition system based on dynamic headspace sampling, a microcontroller and a laptop computer have been designed and constructed for this application. To demonstrate its discrimination capability, unsupervised and supervised classification models have been built and validated. Principal Component Analysis (PCA) of volatile profiles revealed five distinct groups corresponding to the five different drugs analyzed. This was further confirmed by a multivariate analysis of variance (MANOVA) test. Support Vectors Machines (SVMs) were applied to build a classifier and reached a 98.5% success rate in the recognition of the different drugs analyzed. This work demonstrates for the first time that the electronic nose technology could be successfully applied to the identification of illegal drugs. |
doi_str_mv | 10.1016/j.snb.2010.12.047 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1008835194</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0925400510009779</els_id><sourcerecordid>1008835194</sourcerecordid><originalsourceid>FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33</originalsourceid><addsrcrecordid>eNp9kEFrGzEQhUVpoG6SH9BTdexlnZnVyivTUwhNWwik0OQsJO0olVlLrmZdyL-vjHvuaXjwvQfzCfEBYY2Am5vdmrNf93DK_RqG8Y1YoRlVp2Ac34oVbHvdDQD6nXjPvAOAQW1gJX7cykOpi_MzSZopLLXkFGQuTJJfeaG9jKXK5RfJNFFeUkzBLalkWaIMLmfnE3feMU1yqscXvhIX0c1M1__upXi-__J09617ePz6_e72oQtKD0sXHfpoMPajdyqMkzJRbQwSoY6OTPAUqVdOm1ErH_RWmQnReBUhYAheqUvx6bx7qOX3kXix-8SB5tllKke2CGCM0rgdGopnNNTCXCnaQ017V18bZE_27M42e_Zkz2Jvm73W-XjuRFese6mJ7fPPBmgAxNGAbsTnM0Htyz-JquWQKAeaUm0e7VTSf_b_AuczghY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1008835194</pqid></control><display><type>article</type><title>A portable electronic nose system for the identification of cannabis-based drugs</title><source>ScienceDirect Journals</source><creator>Haddi, Z. ; Amari, A. ; Alami, H. ; El Bari, N. ; Llobet, E. ; Bouchikhi, B.</creator><creatorcontrib>Haddi, Z. ; Amari, A. ; Alami, H. ; El Bari, N. ; Llobet, E. ; Bouchikhi, B.</creatorcontrib><description>We report on a research aimed at evaluating the capacity of a simple, low-cost, portable electronic nose system based on commercially available metal oxide gas sensors to classify different types of drugs. Five drugs, namely cannabis buds, cannabis plants, hashish, snuff tobacco and tobacco leaves were employed. A dedicated real-time data acquisition system based on dynamic headspace sampling, a microcontroller and a laptop computer have been designed and constructed for this application. To demonstrate its discrimination capability, unsupervised and supervised classification models have been built and validated. Principal Component Analysis (PCA) of volatile profiles revealed five distinct groups corresponding to the five different drugs analyzed. This was further confirmed by a multivariate analysis of variance (MANOVA) test. Support Vectors Machines (SVMs) were applied to build a classifier and reached a 98.5% success rate in the recognition of the different drugs analyzed. This work demonstrates for the first time that the electronic nose technology could be successfully applied to the identification of illegal drugs.</description><identifier>ISSN: 0925-4005</identifier><identifier>EISSN: 1873-3077</identifier><identifier>DOI: 10.1016/j.snb.2010.12.047</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Allelochemicals ; Cannabis ; Data analysis ; drugs ; Drugs detection ; electronic nose ; headspace analysis ; leaves ; multivariate analysis ; Pattern recognition techniques ; Portable electronic nose ; principal component analysis ; support vector machines ; tobacco ; volatile compounds</subject><ispartof>Sensors and actuators. B, Chemical, 2011-07, Vol.155 (2), p.456-463</ispartof><rights>2010 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33</citedby><cites>FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Haddi, Z.</creatorcontrib><creatorcontrib>Amari, A.</creatorcontrib><creatorcontrib>Alami, H.</creatorcontrib><creatorcontrib>El Bari, N.</creatorcontrib><creatorcontrib>Llobet, E.</creatorcontrib><creatorcontrib>Bouchikhi, B.</creatorcontrib><title>A portable electronic nose system for the identification of cannabis-based drugs</title><title>Sensors and actuators. B, Chemical</title><description>We report on a research aimed at evaluating the capacity of a simple, low-cost, portable electronic nose system based on commercially available metal oxide gas sensors to classify different types of drugs. Five drugs, namely cannabis buds, cannabis plants, hashish, snuff tobacco and tobacco leaves were employed. A dedicated real-time data acquisition system based on dynamic headspace sampling, a microcontroller and a laptop computer have been designed and constructed for this application. To demonstrate its discrimination capability, unsupervised and supervised classification models have been built and validated. Principal Component Analysis (PCA) of volatile profiles revealed five distinct groups corresponding to the five different drugs analyzed. This was further confirmed by a multivariate analysis of variance (MANOVA) test. Support Vectors Machines (SVMs) were applied to build a classifier and reached a 98.5% success rate in the recognition of the different drugs analyzed. This work demonstrates for the first time that the electronic nose technology could be successfully applied to the identification of illegal drugs.</description><subject>Allelochemicals</subject><subject>Cannabis</subject><subject>Data analysis</subject><subject>drugs</subject><subject>Drugs detection</subject><subject>electronic nose</subject><subject>headspace analysis</subject><subject>leaves</subject><subject>multivariate analysis</subject><subject>Pattern recognition techniques</subject><subject>Portable electronic nose</subject><subject>principal component analysis</subject><subject>support vector machines</subject><subject>tobacco</subject><subject>volatile compounds</subject><issn>0925-4005</issn><issn>1873-3077</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kEFrGzEQhUVpoG6SH9BTdexlnZnVyivTUwhNWwik0OQsJO0olVlLrmZdyL-vjHvuaXjwvQfzCfEBYY2Am5vdmrNf93DK_RqG8Y1YoRlVp2Ac34oVbHvdDQD6nXjPvAOAQW1gJX7cykOpi_MzSZopLLXkFGQuTJJfeaG9jKXK5RfJNFFeUkzBLalkWaIMLmfnE3feMU1yqscXvhIX0c1M1__upXi-__J09617ePz6_e72oQtKD0sXHfpoMPajdyqMkzJRbQwSoY6OTPAUqVdOm1ErH_RWmQnReBUhYAheqUvx6bx7qOX3kXix-8SB5tllKke2CGCM0rgdGopnNNTCXCnaQ017V18bZE_27M42e_Zkz2Jvm73W-XjuRFese6mJ7fPPBmgAxNGAbsTnM0Htyz-JquWQKAeaUm0e7VTSf_b_AuczghY</recordid><startdate>20110720</startdate><enddate>20110720</enddate><creator>Haddi, Z.</creator><creator>Amari, A.</creator><creator>Alami, H.</creator><creator>El Bari, N.</creator><creator>Llobet, E.</creator><creator>Bouchikhi, B.</creator><general>Elsevier B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20110720</creationdate><title>A portable electronic nose system for the identification of cannabis-based drugs</title><author>Haddi, Z. ; Amari, A. ; Alami, H. ; El Bari, N. ; Llobet, E. ; Bouchikhi, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Allelochemicals</topic><topic>Cannabis</topic><topic>Data analysis</topic><topic>drugs</topic><topic>Drugs detection</topic><topic>electronic nose</topic><topic>headspace analysis</topic><topic>leaves</topic><topic>multivariate analysis</topic><topic>Pattern recognition techniques</topic><topic>Portable electronic nose</topic><topic>principal component analysis</topic><topic>support vector machines</topic><topic>tobacco</topic><topic>volatile compounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Haddi, Z.</creatorcontrib><creatorcontrib>Amari, A.</creatorcontrib><creatorcontrib>Alami, H.</creatorcontrib><creatorcontrib>El Bari, N.</creatorcontrib><creatorcontrib>Llobet, E.</creatorcontrib><creatorcontrib>Bouchikhi, B.</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Sensors and actuators. B, Chemical</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Haddi, Z.</au><au>Amari, A.</au><au>Alami, H.</au><au>El Bari, N.</au><au>Llobet, E.</au><au>Bouchikhi, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A portable electronic nose system for the identification of cannabis-based drugs</atitle><jtitle>Sensors and actuators. B, Chemical</jtitle><date>2011-07-20</date><risdate>2011</risdate><volume>155</volume><issue>2</issue><spage>456</spage><epage>463</epage><pages>456-463</pages><issn>0925-4005</issn><eissn>1873-3077</eissn><abstract>We report on a research aimed at evaluating the capacity of a simple, low-cost, portable electronic nose system based on commercially available metal oxide gas sensors to classify different types of drugs. Five drugs, namely cannabis buds, cannabis plants, hashish, snuff tobacco and tobacco leaves were employed. A dedicated real-time data acquisition system based on dynamic headspace sampling, a microcontroller and a laptop computer have been designed and constructed for this application. To demonstrate its discrimination capability, unsupervised and supervised classification models have been built and validated. Principal Component Analysis (PCA) of volatile profiles revealed five distinct groups corresponding to the five different drugs analyzed. This was further confirmed by a multivariate analysis of variance (MANOVA) test. Support Vectors Machines (SVMs) were applied to build a classifier and reached a 98.5% success rate in the recognition of the different drugs analyzed. This work demonstrates for the first time that the electronic nose technology could be successfully applied to the identification of illegal drugs.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.snb.2010.12.047</doi><tpages>8</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0925-4005 |
ispartof | Sensors and actuators. B, Chemical, 2011-07, Vol.155 (2), p.456-463 |
issn | 0925-4005 1873-3077 |
language | eng |
recordid | cdi_proquest_miscellaneous_1008835194 |
source | ScienceDirect Journals |
subjects | Allelochemicals Cannabis Data analysis drugs Drugs detection electronic nose headspace analysis leaves multivariate analysis Pattern recognition techniques Portable electronic nose principal component analysis support vector machines tobacco volatile compounds |
title | A portable electronic nose system for the identification of cannabis-based drugs |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T19%3A40%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20portable%20electronic%20nose%20system%20for%20the%20identification%20of%20cannabis-based%20drugs&rft.jtitle=Sensors%20and%20actuators.%20B,%20Chemical&rft.au=Haddi,%20Z.&rft.date=2011-07-20&rft.volume=155&rft.issue=2&rft.spage=456&rft.epage=463&rft.pages=456-463&rft.issn=0925-4005&rft.eissn=1873-3077&rft_id=info:doi/10.1016/j.snb.2010.12.047&rft_dat=%3Cproquest_cross%3E1008835194%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c354t-fa1bf81f27ba3c7d38f3681ee15fae8cbefe23a58753bc5938d118b3f0c1ccb33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1008835194&rft_id=info:pmid/&rfr_iscdi=true |