Mirela Praisler

Pattern recognition techniques screening for drugs of abuse with gas chromatography–Fourier transform infrared spectroscopy☆

As many drugs of abuse are relatively volatile substances, gas chromatography-mass spectrometry (GC-MS), and more recently gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR) became the most powerful techniques applied for their identification. We are presenting a combination of pattern recognition techniques discriminating illicit amphetamines according to the substitution pattern associated with the psychotropic activity (stimulants and hallucinogens) for which they are abused, and with the corresponding level of health hazard. As we determined, GC-FTIR provides the best selectivity in identifying the structural features associated with the full constellation of pharmacological effects of amphetamines. The toxicological questions to be answered and the spectroscopic features enabling the screening based on soft independent modeling of class analogy (SIMCA) are discussed. The accuracy, sensitivity and selectivity of the system recommend its use for automating the investigations of illicit drugs for epidemiological, clinical, administrative and forensic purposes. As opposed to the traditional tests screening for drugs of abuse, the system may also be applied as a broad-spectrum screening test. The extent to which the output of a query for amphetamines may be used for assessing the class identity of a negative (i.e. other hallucinogens or stimulants, sympathomimetic amines, narcotics and precursors) was determined by a systematic principal component analysis (PCA). The basic information is summarized in tables according to the category or class of compounds found suitable for screening.

Exploratory analysis for the automated identification of amphetamines from vapour-phase FTIR spectra

An exploratory analysis was performed in order to evaluate the feasibility of building an expert system automating the identification of the basic skeleton of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical, and forensic purposes. Emphasis was placed on the selection of the sample preparation and data processing techniques yielding the best differentiation and recognition of the structural patterns of amphetamines. Discrimination between amphetamine analogues and nonamphetamines has been put in evidence. More accurate discrimination among amphetamine analogues according to substitution patterns was obtained with spectra of amphetamine analogues, rather than with those of their HFB-derivatives. The automated recognition of substitution patterns influencing the intensity of the pharmacological activity seems also feasible, especially in the case of the ring nonsubstituted amphetamines.

Identification of novel illicit amphetamines from vapor-phase FTIR spectra : a chemometrical solution

A computer-aided procedure automating the identification of illicit amphetamine analogs eluting from a gas chromatograph coupled to a Fourier transform infrared spectrometer is presented. The expert system discriminates novel amphetamines from other classes of drugs of abuse normally screened in illicit tablets or powders. The main analytical advantages of the system over the automated procedures dedicated to general unknown analysis are the objectivity and the accuracy in predicting the class identity of the compound (i.e. stimulant, hallucinogen) when the reference spectrum is not present in the spectral library. The expert system uses quantitative thresholds defining the similarity of the unknown to the classes of illicit amphetamines and checks the presence of the molecular skeletons associated with different psychotropic effects of amphetamines. The challenge in building the system was the fuzziness of vapor-phase Fourier transform infrared spectrometer spectra of low-weight molecules such as amphetamines. This paper emphasizes the chemometrical techniques found most appropriate for modeling such spectral behavior. An exploratory (principal component) analysis indicated the sample preparation and the feature weight function yielding the best input for the knowledge base. The class identity of a compound was assigned using Soft Independent Modeling of Class Analogy. A rule-based decision system was implemented to enhance the accuracy in identity assignment. The flow diagram optimizing the knowledge base content of each model is presented. Finally, up to 81.13% (out of 159 tested compounds) were classified with a 5% confidence level. The total correct classification rate was 93.93%, for a yield of 96.30% true positive amphetamines.

Chemometric detection of thermally degraded samples in the analysis of drugs of abuse with gas chromatography-Fourier-transform infrared spectroscopy

We present a chemometric procedure for the identification of the reference standard chromatographic peak in cases where the GC-FTIR analysis of commercial standards results in the appearance of more than one peak in the GC chromatogram. The procedure has been designed for phenethylamines, which represent the class with the largest number of individual molecules on the illicit drug market, and which are abused for their stimulant and/or hallucinogenic effects. The similarity between their vapor-phase FTIR spectra was modeled using principal component analysis (PCA), and class identity was assigned on the basis of soft independent modeling of class analogy (SIMCA). Additional peaks could be assigned to impurities in the standards, but most often they were artifacts formed during the GC-FTIR analysis of thermolabile or chemically unstable compounds. The latter case is illustrated by the identification of the reference standard chromatographic peak and FTIR spectrum of the potent psychotropic amphetamine derivative N-methyl-1-(3,4-methylenedioxyphenyl)-2-butanamine (MBDB), and by the elucidation of the chemical changes that occur in the molecule of MBDB due to thermal degradation.

Principal component analysis coupled with artificial neural networks--a combined technique classifying small molecular structures using a concatenated spectral database.

In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed.

Bacteriological and environmental characterisation of the water quality in the Danube River Basin in the Galati area of Romania

In order to contribute with date to the Danube River Basin which is a prime European waterway, this analysis was carried out on the one hand to investigate the possibilities of sanitary risks that are incurred by the riverside population as they are engaged in professional recreational activities that impose a direct contact between man and water, that is intensely developed along the aquatic system and on the other hand to indicate a clear cut picture of the final level of coliforms and Escherichia coli that is actually present in the Galati industrial segment of the Danube River. Total coliforms, faecal coliforms and E. coli could attain values reaching 1.5×10, 9.5×10 and 6.4×10 CFU/ml, respectively for the aquatic ecosystems analysed. A variation of these parameters with respect to the ecodynamical characteristic of the Danube water quality such as temperature, pH, total dissolved solids, salinity and hydrogen sulphite reveal the preponderant role that abiotic factors play in the dispersion of biocontaminants in a broad basin ecosystem. While the persistence of E. coli during the sampling period from June to September confirm the fact that there is a continuous faecal pollution of this medium. The high presence of organic pollutants in this medium, combined with the presence of coliforms and E. coli, could be related to an accumulation of waste matter all along the ecosystem, also due to the lack of wastewater treatment plants for domestic and industrial discharges, the high impact of human activities across the international river basin and the difficulties encountered in the natural operational processes of self purification.

Optimization of amphetamines multivariate detection by GC-FTIR spectra preprocessing

A chemometric method developed for the automatic detection of illicit amphetamines, which represent the most popular drugs in Europe, is presented. The detection system is based on Principal Component Analysis. The training database includes the GC-FTIR spectra of the main amphetamines, their precursors and derivatives. Although the system does not include any information on the biological activity of modeled compounds, the amphetamine analogues form two distinct clusters, according to their biological activity and toxic side effects (stimulants or hallucinogens). The clustering was optimized by preprocessing the spectra with a selective amplifier. This procedure increases significantly the efficiency of the detection system. The system correctly assigns the class identity of an unknown compound, even if its spectrum is not present in the database.

Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment

An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA) is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC) that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA).

Intelligent screening for designer drugs: A signal analysis

We are presenting an optimized intelligent screening tool built for the automated detection of amphetamines, which represent the main designer drugs found on the black market in Europe. The GC-FTIR spectra of the target compounds were preprocessed with a w2 function that enhances the absorptions identified as having a high modeling and / or discrimination power. The absorptions responsible for the discrimination of amphetamines according to their biological (stimulant and / or hallucinogenic) effect were determined and interpreted. The positive effect of the w2 selective amplifier on the efficiency of the detection system is highlighted by a comparative analysis based on agglomerative clustering.

Pattern recognition techniques applied for the detection of amphetamines based on infrared laser spectroscopy

We are presenting an exploratory analysis assessing the feasibility of detecting amphetamines based on their absorptions in the 1550-1330 cm-1 spectral window, in which the tested External Cavity (EC) Quantum Cascade Laser (QCL) is emitting. The Principal Components Analysis indicates that amphetamines can be detected efficiently, this pattern cognition method generating models distinguishing these illicit compounds according to their biological activity. The predictive power of the first three principal components has been evaluated based on estimated density distributions. The sensitivity and selectivity of the class identity assignment was assessed by using a hierarchical clustering algorithm, i.e. agglomerative clustering. The results indicate that the evaluated EC-QCL is an adequate source for advanced portable sensors.