Microscopy in forensic science

This chapter examines the use of electron microscopy, atomic force microscopy and other analytical techniques in forensic investigation and research. These tools can be used to enhance examination of human remains and trace evidence to improve understanding of cause of death, victim identification or post mortem interval.A police-designed scenario is used to highlight trace evidence such as glass, gun shot residue and paint. The validity of forensic techniques is discussed, with reference to international standards, repeatability, and false convictions. Ballistic evidence is used to highlight the complexities in evidence interpretation, including manufacturing variability, environmental effects and likelihood ratios.The use of scanning electron microscopy (SEM), atomic force microscopy (AFM) and other techniques in the development of forensic research is showcased, with particular examples from the field of fingerprints. Examples include improvements in the development of fingermarks from difficult surfaces, interaction of evidence types, and added intelligence from the crime scene, such as forensic timeline or gender of perpetrator

Quantitative analysis and classification of AFM images of human hair

The surface topography of human hair, as defined by the outer layer of cellular sheets. termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the case of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density. allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.2151132

Calibration and detailed analysis of second-order flow injection analysis data with rank overlap

With the current popularity of second-order (or hyphenated) instruments, there now exists a number of chemometric techniques for the so-called second-order calibration problem, i.e. that of quantifying an analyte of interest in the presence of one (or more) unknown interferent(s). Second-order instruments produce data of varying complexity, one particular phenomenon sometimes encountered being that of rank overlap (or rank deficiency), where the overall rank of the data is not equal to the sum of the ranks of the contributing species. The purpose of the present work is to evaluate the performance of two second-order calibration methods, a least squares-based and an eigenvalue-based solution, in terms of their quantitative ability and stability, as applied to flow injection analysis (FIA) data which exhibits rank overlap. In the presence of high collinearity in the data, the least squares methods is found to give a more stable solution. Two-mode component analysis (TMCA) is used to investigate the reasons for this difference in terms of the chemical properties of the species analysed. The success of second-order calibration of this data is found to depend strongly on the collinearity between the acidic and basic time profiles and the reproducibility of the pH gradient in the FIA channel, both of which are shown to be related to the pK(a) values of the species. (C) 2000 Elsevier Science B.V. All rights reserved.4221213

Analysis of video images from a gas-liquid transfer experiment: a comparison of PCA and PARAFAC for multivariate image analysis

The use of chemical imaging is a developing area which has potential benefits for chemical systems where spatial distribution is important. Examples include processes in which homogeneity is critical, such as polymerizations, pharmaceutical powder blending and surface catalysis, and dynamic processes such as the study of diffusion rates or the transport of environmental pollutants. Whilst single images can be used to determine chemical distribution patterns at a given point in time, dynamic processes can be studied using a sequence of images measured at regular time intervals, i.e. a movie. Multivariate modeling of image data can help to provide insight into the important chemical factors present. However, many issues of how best to apply these models remain unclear, especially when the data arrays involved have four or five different dimensions (height, width, wavelength, time, experiment number, etc.). In this paper we describe the analysis of video images recorded during an experiment to investigate the uptake Of CO2 across a free air-water interface. The use of PCA and PARAFAC for the analysis of both single images and movies is described and some differences and similarities are highlighted. Some other image transformation techniques, such as chemical mapping and histograms, are found to be useful both for pretreatment of the raw data and for dimensionality reduction of the data arrays prior to further modeling. Copyright (C) 2003 John Wiley Sons, Ltd.17740041

Direct sampling tandem mass spectrometry (MS/MS) and multiway calibration for isomer quantitation

Direct sampling tandem mass spectrometry (MS/MS) was used for the quantitation of mixtures of the isomers 2-, 3- and 4-ethyl pyridine. The similarity between the analytes and the second-order nature of MS/MS data require the use of multivariate calibration techniques capable of handling multiway data. Multilinear PLS (N-PLS) was applied here, as well as the alternative technique of unfolding the data and using standard two-way PLS. Particular attention was paid to the optimal type of spectral preprocessing. Due to the presence of heteroscedastic noise the logarithmic transform of the spectra prior to calibration gives the best results. Predictions errors of the order of 10-15% were obtained, which compare well with other results found in the literature.12781054106