Surface granularity as a discriminating feature of illicit tablets

Lopatka, Martin

doi:10.1016/j.forsciint.2011.03.008

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…) may be partly due to spatial concentration inhomogeneities within the tablets. These spatial differences or granularity patterns are clearly visible in the hyperspectral images and may be an interesting additional feature useful for the comparison of tablet batches .…”

Section: Discussionmentioning

confidence: 96%

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Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

Edelman

Lopatka

Aalders

2013

Journal of Forensic Sciences

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The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured and illicit tablets, created with different amounts of known colorants were analyzed. We derived reflectance spectra from hyperspectral images of these tablets, and successfully determined the most likely colorant used in the production of all self-manufactured tablets and four of five illicit tablets studied. Upon classification, the concentration of the colorant was estimated using a photon propagation model and a single reference measurement of a tablet of known concentration. The estimated concentrations showed a high correlation with the actual values (R(2) = 0.9374). The achieved color information, combined with other physical and chemical characteristics, can provide a powerful tool for the comparison of tablet seizures, which may reveal their origin.

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Section: Discussionmentioning

confidence: 96%

“…Contrary to spectroscopy, it facilitates a high throughput, as the spectral properties of many tablets can be analyzed simultaneously. Additionally, color inhomogeneities and granularity profiles are recorded, which can be used as a discriminating feature of illicit tablets .…”

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confidence: 99%

Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

Edelman

Lopatka

Aalders

2013

Journal of Forensic Sciences

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“…This helps to point out and select sub-problems which deserve greater attention through a tighter and more resourceintensive follow-up, based on more detailed features (modus operandi level profiling) or on a restricted number of cases (series level profiling). For instance, the use of physical profiling or one chemical profiling technique to compare illicit drug seizures could be used first (surface level profiling) to obtain quick links between cases as demonstrated in [16,35,36]. The use of the whole set of chemical profiling techniques could then be applied when sub-problems are identified to obtain more detailed features or when a link is identified using circumstantial information and a confirmation of this link is required using chemical profiling (i.e.…”

Section: 1detection and Deciding Which Traces To Profilementioning

confidence: 99%

Forensic intelligence framework. Part II: Study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

Baechler¹,

Morelato²,

Ribaux³

et al. 2015

Forensic Science International

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The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

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“…In AM (or 3D-printing, a promising technology that enables the fabricating process to create intricate geometries based on layer-by-layer addition of materials), surface topography measurements which can be acquired during AM processes are very useful for both in situ process monitoring and post-process quality inspection [3]. In forensic science, surface topography data provide crucial information for the identification of forensic evidence such as ballistic tool marks [4], firearm identification [5], fingerprints [6], and illicit tablets identification [7]. For example, Imani et al [3] quantified the pores in the laser powder bed fusion AM process and investigated the cause of porosity using optical images obtained by an X-ray computed tomography system.…”

Section: Introductionmentioning

confidence: 99%

Measurement Studies Utilizing Similarity Evaluation between 3D Surface Topography Measurements

Liu,

Li,

Qin

et al. 2024

Mathematics

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In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive similarity-based framework tailored for conducting measurement studies on 3D topography data, aiming to precisely quantify distinct sources of variation through the astute application of similarity evaluation techniques. In the proposed framework, we investigate the mean and variance of the similarity between 3D surface topography measurements to reveal the uniformity of the surface topography measurements and statistical reproducibility of the similarity evaluation procedure, respectively. The efficacy of our framework is vividly demonstrated through its application to measurements derived from additive-fabricated specimens. We considered four metal specimens with 20 segmented windows in total. The topography measurements were obtained by three operators using two scanning systems. We find that the repeatability variation of the topography measurements and the reproducibility variation in the measurements induced by operators are relatively smaller compared with the variation in the measurements induced by optical scanners. We also notice that the variation in the surface geometry of different surfaces is much larger in magnitude compared with the repeatability variation in the topography measurements. Our findings are consistent with the physical intuition and previous research. The ensuing experimental studies yield compelling evidence, affirming that our devised methods are adept at providing profound insights into the multifaceted sources of variation inherent in processes utilizing 3D surface topography data. This innovative framework not only showcases its applicability but also underlines its potential to significantly contribute to the field of quality assurance. By offering a systematic approach to measuring and comprehending variation in 3D topography data, it stands poised to become an indispensable tool in diverse quality assurance contexts.

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Surface granularity as a discriminating feature of illicit tablets

Cited by 12 publications

References 10 publications

Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

Forensic intelligence framework. Part II: Study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

Measurement Studies Utilizing Similarity Evaluation between 3D Surface Topography Measurements

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