Establishing chemical profiling for ecstasy tablets based on trace element levels and support vector machine

State-of-the-Art Analytical Approaches for Illicit Drug Profiling in Forensic Investigations

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.

Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma - A data mining approach

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.

Interpol review of controlled substances 2016-2019

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.

Air quality warning system based on a localized PM2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection

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 PM_2.5 soft sensor and support vector machine (SVM) classifier is reported. The soft sensor for the estimation of PM_2.5 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 PM_2.5, Pearson correlation coefficient, Kendall's tau coefficient, and Spearman coefficient are computed. To achieve high performance for the PM_2.5 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.