Impurity profiling of amphetamine and methamphetamine using Gas Chromatography Mass Spectrometry (GCMS) harmonised methods

Impurity profiling of drug seizures is a scientific approach employed to understand drug trafficking networks thus has becoming increasingly important in criminal investigation. This paper presents the feasibility of using the Collaborative Harmonisation of Methods for the Profiling of AmphetamineType Stimulants (CHAMP) established by the European Commission authority for impurity profiling of amphetamine and methamphetamine samples. Both drugs were analysed using similar extraction procedure and analytical conditions. The impurities were extracted from an alkaline buffer solution (pH8.1) using toluene prior to gas chromatography-mass spectrometry (GC-MS) analyses. The results showed that the reproducibility of the method for detecting amphetamine and methamphetamine ranged between 7.4-8.9 and 6.2-8.4 %RSD, respectively. Identification of impurities was performed by referencing against the available MS databases as well as to previous reported impurity profiling studies. Phenyl-2-propanone (P2P), also known as benzyl-methyl-ketone (BMK), as well as other specific impurities such as 4-methyl-5-phenylpyrimidine, bis-(1-phynelisopropyl) amine, N-formylamphetamine and N,N-di (b-phenylisopropyl) amines were identified in the amphetamine samples, indicating Leuckart’s pathway as the route of synthesis. Because P2P was also detected in the methamphetamine samples, the possible route of synthesis of the methamphetamine samples being Leuckart’s, nitrostyrene synthesis or reductive amination could not be ruled out

Laser-induced breakdown spectroscopy (LIBS) for printing ink analysis coupled with principle component analysis (PCA)

Laser-induced breakdown spectroscopy (LIBS) has been applied to perform elemental analysis of printing ink samples. Samples of black printing inks from three types of printers viz. inkjet, laser-jet, and photocopier (three different brands for each type) and one control sample (blank white A4 paper) were analysed under optimised conditions. Results revealed that the LIBS method when coupled with PCA was able to provide discriminative evidence on elemental differences among all the different printing inks. Considering its time and cost effectiveness as well as requiring only minute amount of sample with no sample pre-treatment steps, the combination of LIBS and PCA may prove useful for forensic questioned document practical caseworks

The application of pattern recognition techniques to data derived from the chemical analysis of common wax based products and ignitable liquids

Strathclyde theses - ask staff. Thesis no. : T13216Pattern recognition is a term that can be used to cover various stages of the investigation of characterising data sets including contributing to problem formulation and data collection through to discrimination, assessment and interpretation of results. Chemometrics techniques and Artificial Neural Networks (ANNs) are pattern recognition techniques commonly used to visualise and gather useful information from multidimensional datasets i.e. datasets with n-samples with m- variables. Of the many chemometric techniques available, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are the most commonly used in the evaluation of dataset(s) generated from the analysis of samples which have relevance to forensic science. By contrast, Artificial Neural Networks (ANNs) and in particular Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) have had limited application in forensic science eventhough these pattern recognition techniques have been known for almost 30 years. This study focuses on the applicability of the Artificial Neural Networks (ANNs) to specific datasets of forensic science interest and compares these with 'conventional' PCA and HCA techniques. Datasets generated from the analysis of wax based products and lighter fuels were used. The wax based product data set contained information obtained from Thin Layer Chromatography (TLC), Microspectrophotometry (MSP), Ultra-Violet and Visible Spectroscopy (UV/Vis) and Gas Chromatography with Flame Ionisation Detector (GC-FID) analysis of a variety of products from multiple sources where discrimination by brand was the objective. The data provided for the lighter fuel samples was obtained from analysis of a number of brands, both unevaporated and evaporated by Gas Chromatography-Mass Spectroscopy (GC-MS) and the objective was to discriminate the samples by brand as well as link degraded samples from the same brand together. The wax based product analysis provided simple, straight forward data whilst the lighter fuel analysis provided a more complicated and challenging dataset to investigate in terms of facilitating sample discrimination and/or linkage. In all cases, the 'conventional' Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) failed to provide any meaningful discrimination of the samples by product type regardless of the nature of the datasets. In contrast, the Artificial Neural Networks (ANNs) techniques provided full discrimination of the samples by product type even when the samples had undergone considerable ageing and weathering. This work has demonstrated the potential use of Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) to datasets of forensic science relevance. The findings of this work provide avenues for further exploration of Artificial Neural Networks (ANNs) in forensic science.Pattern recognition is a term that can be used to cover various stages of the investigation of characterising data sets including contributing to problem formulation and data collection through to discrimination, assessment and interpretation of results. Chemometrics techniques and Artificial Neural Networks (ANNs) are pattern recognition techniques commonly used to visualise and gather useful information from multidimensional datasets i.e. datasets with n-samples with m- variables. Of the many chemometric techniques available, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are the most commonly used in the evaluation of dataset(s) generated from the analysis of samples which have relevance to forensic science. By contrast, Artificial Neural Networks (ANNs) and in particular Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) have had limited application in forensic science eventhough these pattern recognition techniques have been known for almost 30 years. This study focuses on the applicability of the Artificial Neural Networks (ANNs) to specific datasets of forensic science interest and compares these with 'conventional' PCA and HCA techniques. Datasets generated from the analysis of wax based products and lighter fuels were used. The wax based product data set contained information obtained from Thin Layer Chromatography (TLC), Microspectrophotometry (MSP), Ultra-Violet and Visible Spectroscopy (UV/Vis) and Gas Chromatography with Flame Ionisation Detector (GC-FID) analysis of a variety of products from multiple sources where discrimination by brand was the objective. The data provided for the lighter fuel samples was obtained from analysis of a number of brands, both unevaporated and evaporated by Gas Chromatography-Mass Spectroscopy (GC-MS) and the objective was to discriminate the samples by brand as well as link degraded samples from the same brand together. The wax based product analysis provided simple, straight forward data whilst the lighter fuel analysis provided a more complicated and challenging dataset to investigate in terms of facilitating sample discrimination and/or linkage. In all cases, the 'conventional' Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) failed to provide any meaningful discrimination of the samples by product type regardless of the nature of the datasets. In contrast, the Artificial Neural Networks (ANNs) techniques provided full discrimination of the samples by product type even when the samples had undergone considerable ageing and weathering. This work has demonstrated the potential use of Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) to datasets of forensic science relevance. The findings of this work provide avenues for further exploration of Artificial Neural Networks (ANNs) in forensic science

Fourier Transform Infrared (FTIR) Spectroscopy with Chemometric Techniques for the Classification of Ballpoint Pen Inks

FTIR spectroscopic techniques have been shown to possess good abilities to analyse ballpoint pen inks. These in-situ techniques involve directing light onto ballpoint ink samples to generate an FTIR spectrum, providing “molecular fingerprints” of the ink samples thus allowing comparison by direct visual comparison. In this study, ink from blue (n=15) and red (n=15) ballpoint pens of five different brands: Kilometrico®, G-Soft®, Stabilo®, Pilot® and Faber Castell® was analysed using the FTIR technique with the objective of establishing a distinctive differentiation according to the brand. The resulting spectra were first compared and grouped manually. Due to the similarities in terms of colour and shade of the inks, distinctive differentiation could not be achieved by means of direct visual comparison. However, when the same spectral data was analysed by Principal Component Analysis (PCA) software, distinctive grouping of the ballpoint pen inks was achieved. Our results demonstrate that PCA can be used objectively to investigate ballpoint pen inks of similar colour and more importantly of different brands

Combined Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA): an efficient chemometric approach in aged gel inks discrimination

<p>The gel ink pen is the fastest growing pen class available on the modern market. Consequently, its prevalence in forensic casework is expected to increase. This poses a challenge to forensic scientists, since the chemistry of a gel pen ink differs to other commonly encountered inks; thus, discrimination of aged gel pen inks by traditional methods such as Thin Layer Chromatography (TLC) is limited and a new analytical methodology is required for distinguishing different formulations effectively. An objective multivariate statistical methodology (PCA and HCA) incorporating effective data pre-preprocessing (autoscaling) was developed and successfully applied to aged IR Spectroscopic data. Principal Component Analysis (PCA) revealed similar observations to Hierarchical Cluster Analaysis (HCA) and both techniques were more effective in distingushing the ink samples from all others Therefore, utilization of non-destructive analysis coupled with chemometrics techniques for discrimination of aged inks for forensic applications is supported.</p

Potassium triiodide enhanced multi-walled carbon nanotubes supported lipase for expediting a greener forensic visualization of wetted fingerprints

Evidences of crime are often disposed in waterways to destroy all ties to the crime. Nonetheless, these evidences are not inevitably lost as the water insoluble lipid components may remain on the object. Currently, Small Particle Reagent (SPR) is used for visualizing such wet fingerprints and it comprises of several chemicals that are relatively hazardous to the crime investigator and the environment. The adaptation of a greener nano-biotechnological route might be useful, but there is still much to be done to improve this fingerprint visualisation method. Henceforth, this study was carried out to optimise the visualization protocol for CRL-MWCNTs/GA/I3K/SAF on split natural fingerprints immersed in purified tap water for one-and 15 days using response surface methodology (RSM). The addition of I3K as the mordant expedited the overall staining process. This study achieved a better mean fingerprints quality for the one-day immersed samples fared better compared to 15 days, thus suggesting the adequate use of CRL in the formulation. Hence, it was shown that RSM is reliable in predicting the optimum condition that yielded the highest mean fingerprint quality for both time intervals (one and 15 days)

Classification model for detection and discrimination of inedible plastic adulterated palm cooking oil using atr-ftir spectroscopy combined with principal component analysis

Adulteration of edible oil by replacing or admixing cheaper or waste oil is an irresponsible act motivated by profiteering. A more sinister act of dissolving inedible plastic materials in hot oil during frying to enhance the crispiness and prolong the shelf life of deep-fried snacks has been alleged. In this study, a protocol using ATR-FTIR spectroscopy combined with principal component analysis (PCA) for detection of inedible plastic materials in palm cooking oil is presented. To achieve this, palm cooking oil samples purchased from convenience stores were heated and adulterated either with low-density polyethylene (LDPE), high-density polyethylene (HDPE) or polypropylene (PP). The resultant spectra from 4000-600 cm-1 were subjected to direct visual examinations prior to PCA. Detection of plastic materials in the samples from direct visual examinations of the resultant spectra was difficult as all samples revealed similar spectra dominated by major absorption bands at 2922 cm-1, 2853 cm-1, 1740 cm-1, 1465 cm-1, 1377 cm-1 and 721 cm-1, which were typical of triacylglycerols. Despite the similarities, the detection was possible when the resultant spectra were subjected to PCA. The results demonstrated the potential of ATR-FTIR spectroscopy combined with PCA for the detection of inedible plastic adulterated palm cooking oil