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Ahmed R, Altamimi MJ, Hachem M. State-of-the-Art Analytical Approaches for Illicit Drug Profiling in Forensic Investigations. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27196602. [PMID: 36235138 PMCID: PMC9571531 DOI: 10.3390/molecules27196602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/19/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022]
Abstract
In forensic chemistry, when investigating seized illicit drugs, the profiling or chemical fingerprinting of drugs is considered fundamental. This involves the identification, quantitation and categorization of drug samples into groups, providing investigative leads such as a common or different origin of seized samples. Further goals of drug profiling include the elucidation of synthetic pathways, identification of adulterants and impurities, as well as identification of a drug's geographic origin, specifically for plant-derived exhibits. The aim of this state-of-art-review is to present the traditional and advanced analytical approaches commonly followed by forensic chemists worldwide for illicit drug profiling. We discussed numerous methodologies for the physical and chemical profiling of organic and inorganic impurities found in illicit drug. Applications of powerful spectroscopic and chromatographic tools for illicit drug profiling including isotope-Ratio mass spectrometry (IRMS), gas chromatography-mass spectrometry (GC-MS), gas chromatography-isotope ratio mass spectrometry (GC-IRMS), ultra-high-performance liquid chromatography (UHPLC), thin layer chromatography (TLC), liquid chromatography-mass spectrometry (LC-MS) and inductively coupled plasma-mass spectrometry (ICP-MS) were discussed. Altogether, the techniques covered in this paper to profile seized illicit drugs could aid forensic chemists in selecting and applying a suitable method to extract valuable profiling data.
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Affiliation(s)
- Reem Ahmed
- Department of Chemistry, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Mohamad J. Altamimi
- Dubai Police, General Department of Forensic Science and Criminology, Forensic Chemistry Section, Dubai P.O. Box 1493, United Arab Emirates
| | - Mayssa Hachem
- Department of Chemistry and Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence:
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da Costa NL, de Sá Alves M, de Sá Rodrigues N, Bandeira CM, Oliveira Alves MG, Mendes MA, Cesar Alves LA, Almeida JD, Barbosa R. Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma - A data mining approach. Comput Biol Med 2022; 143:105296. [PMID: 35149458 DOI: 10.1016/j.compbiomed.2022.105296] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/03/2022] [Accepted: 01/20/2022] [Indexed: 12/13/2022]
Abstract
Data mining has proven to be a reliable method to analyze and discover useful knowledge about various diseases, including cancer research. In particular, data mining and machine learning algorithms to study oral squamous cell carcinoma (OSCC), the most common form of oral cancer, is a new area of research. This malignant neoplasm can be studied using saliva samples. Saliva is an important biofluid that must be used to verify potential biomarkers associated with oral cancer. In this study, first, we provide an overview of OSSC diagnoses based on machine learning and salivary metabolites. To our knowledge, this is the first study to apply advanced data mining techniques to diagnose OSCC. Then, we give new results of classification and feature selection algorithms used to identify potential salivary biomarkers of OSCC. To accomplish this task, we used the filter feature selection random forest importance algorithm and a wrapper methodology to evaluate the importance of metabolites obtained from gas chromatography mass-spectrometry (GC-MS) in the context of differentiation of OSCC and the control group. Salivary samples (n = 68) were collected for the control group, and the OSCC group were from patients matched for gender, age, and smoking habit. The classification process occurred based on Random Forest (RF) classification algorithm along with 10-cross validation. The results showed that glucuronic acid, maleic acid, and batyl alcohol can classify the samples with an area under the curve (AUC) of 0.91 versus an AUC of 0.76 using all 51 metabolites analyzed. The methodology used in this study can assist healthcare professionals and be adopted to discover diagnostic biomarkers for other diseases.
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Affiliation(s)
- Nattane Luíza da Costa
- Informatics Nucleo, Goiano Federal Institute of Education, Science and Technology, Campus Urutaí, Urutaí-GO, Brazil.
| | - Mariana de Sá Alves
- Department of Biosciences and Oral Diagnosis, Institute of Science and Technology, São Paulo State University (Unesp), São José dos Campos, Brazil.
| | - Nayara de Sá Rodrigues
- Department of Biosciences and Oral Diagnosis, Institute of Science and Technology, São Paulo State University (Unesp), São José dos Campos, Brazil.
| | - Celso Muller Bandeira
- Department of Biosciences and Oral Diagnosis, Institute of Science and Technology, São Paulo State University (Unesp), São José dos Campos, Brazil.
| | - Mônica Ghislaine Oliveira Alves
- Technology Reaearch Center (NPT), Universidade Mogi das Cruzes, Mogi das Cruzes, Brazil; School of Medicine, Anhembi Morumbi University, São José dos Campos, Brazil.
| | | | - Levy Anderson Cesar Alves
- School of Dentistry, Universidade Paulista, São Paulo, Brazil; School of Dentistry, Universidade Municipal de São Caetano do Sul, São Caetano do Sul, Brazil.
| | - Janete Dias Almeida
- Department of Biosciences and Oral Diagnosis, Institute of Science and Technology, São Paulo State University (Unesp), São José dos Campos, Brazil.
| | - Rommel Barbosa
- Instituto de Informática, Universidade Federal de Goiás, Goiânia-GO, Brazil.
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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Safi MS, Safi SMM. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102338] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Jones NS, Comparin JH. Interpol review of controlled substances 2016-2019. Forensic Sci Int Synerg 2020; 2:608-669. [PMID: 33385148 PMCID: PMC7770462 DOI: 10.1016/j.fsisyn.2020.01.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 01/23/2020] [Indexed: 12/14/2022]
Abstract
This review paper covers the forensic-relevant literature in controlled substances from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.
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Affiliation(s)
- Nicole S. Jones
- RTI International, Applied Justice Research Division, Center for Forensic Sciences, 3040 E. Cornwallis Road, Research Triangle Park, NC, 22709-2194, USA
| | - Jeffrey H. Comparin
- United States Drug Enforcement Administration, Special Testing and Research Laboratory, USA
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Balram D, Lian KY, Sebastian N. Air quality warning system based on a localized PM 2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2019; 182:109386. [PMID: 31255868 DOI: 10.1016/j.ecoenv.2019.109386] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/22/2019] [Accepted: 06/24/2019] [Indexed: 06/09/2023]
Abstract
It is highly significant to develop efficient soft sensors to estimate the concentration of hazardous pollutants in a region to maintain environmental safety. In this paper, an air quality warning system based on a robust PM2.5 soft sensor and support vector machine (SVM) classifier is reported. The soft sensor for the estimation of PM2.5 concentration is proposed using a novel approach of Bayesian regularized neural network (BRNN) via forward feature selection (FFS). Zuoying district of Taiwan is selected as the region of study for implementation of the estimation system because of the high pollution in the region. Descriptive statistics of various pollutants in Zuoying district is computed as part of the study. Moreover, seasonal variation of particulate matter (PM) concentration is analyzed to evaluate the impact of various seasons on the increased levels of PM in the region. To investigate the linear dependence of concentration of different pollutants to the concentration of PM2.5, Pearson correlation coefficient, Kendall's tau coefficient, and Spearman coefficient are computed. To achieve high performance for the PM2.5 estimation, selection of appropriate forward features from the input variables is carried out using FFS technique and Bayesian regularization is incorporated to the neural network system to avoid the overfitting problem. The comparative evaluation of performance of BRNN/FFS estimation system with various other methods shows that our proposed estimation system has the lowest mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the coefficient of determination (R-squared) is around 0.95 for the proposed estimation method, which denotes a good fit. Evaluation of the SVM classifier showed good performance indicating that the proposed air quality warning system is efficient.
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Affiliation(s)
- Deepak Balram
- Department of Electrical Engineering, National Taipei University of Technology, No. 1, Section 3, Zhongxiao East Road, Taipei, 106, Taiwan, Republic of China
| | - Kuang-Yow Lian
- Department of Electrical Engineering, National Taipei University of Technology, No. 1, Section 3, Zhongxiao East Road, Taipei, 106, Taiwan, Republic of China.
| | - Neethu Sebastian
- Institute of Organic and Polymeric Materials, National Taipei University of Technology, No. 1, Section 3, Zhongxiao East Road, Taipei, 106, Taiwan, Republic of China
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Methods of Authentication of Food Grown in Organic and Conventional Systems Using Chemometrics and Data Mining Algorithms: a Review. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-018-01413-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Maione C, Barbosa RM. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Crit Rev Food Sci Nutr 2018; 59:1868-1879. [DOI: 10.1080/10408398.2018.1431763] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Camila Maione
- Instituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brazil
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