Teaching old presumptive tests new digital tricks with computer vision for forensic applications

Bugeja, Nathalie; Oliver, Cameron; McGrath, Nicole; McGuire, Jake; Yan, Chunhui; Carlysle-Davies, Felicity; Reid, Marc

doi:10.1039/d3dd00066d

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…Recent advancements in CV models have showcased improved utility by incorporating real-time quantication capabilities. [16][17][18][19][20] Additionally, advancements in machine learning (ML) have facilitated the development of more exible CV methods, overcoming the limitations associated with task-specic approaches. Convolutional neural networks (CNNs) in particular can achieve near-human classication accuracy when trained with large datasets, enabling comprehensive analysis of diverse objects.…”

Section: Introductionmentioning

confidence: 99%

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

El-khawaldeh,

Guy,

Bork

et al. 2024

Chem. Sci.

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Section: Introductionmentioning

confidence: 99%

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

El-khawaldeh,

Guy,

Bork

et al. 2024

Chem. Sci.

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“…Kineticolor is a computer vision software package under ongoing development in our team, and provides a non-contact bulk method for analysing chemical processes. 19,[28][29][30] The software uses video input and user-selected regions of interest to track averaged and spatially resolved changes in reaction bulk over time. One such metric that features heavily in this work is the spatially resolved ∆E (Figure 3, centre), the Euclidean distance between a reference color and another in the CIE-L*a*b* space.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision Approach Towards Verifying CFD Models of Stirred Tank Reactors

Fyfe,

Barrington,

Gordon

et al. 2024

Preprint

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Mixing is one of the most important non-chemical considerations in the design of scalable processes. While non-invasive imaging approaches to deliver a quantifiable understanding of mixing dynamics are well-known, the use of imaging to verify computational fluid dynamics (CFD) models remains in its infancy. Herein, we use colorimetric reactions, and our kinetic imaging software, Kineticolor, to explore: (i) the correlation of imaging kinetics with pH probe measurements, (ii) feed point sensitivity for Villermaux-Dushman-type competing parallel reactions, and (iii) the use of experimental imaging kinetics data to qualitatively assess CFD models. We report further evidence that the influences of stirring rate, baffle presence, and feed position on mixing in a tank reactor can be informatively captured with a camcorder, and help to experimentally verify CFD models. Overall, this work advances scarce little precedent in demonstrating the use of computer vision to verify CFD models of fluid flow in tank reactors.

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Section: Introductionmentioning

confidence: 99%

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

El-khawaldeh,

Guy,

Bork

et al. 2023

Preprint

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This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color), offering a method for rapid data acquisition and deeper analysis from multiple visual cues. We demonstrate a single platform (consisting of CV, machine learning, real-time monitoring techniques, and flexible hardware) to monitor and control vision-based experimental techniques, including solvent exchange distillation, antisolvent crystallization, evaporative crystallization, cooling crystallization, solid ̶ liquid mixing, and liquid ̶ liquid extraction. Both qualitative (video capturing) and quantitative data (parameters measurement) were obtained which provided a method for data cross-validation. Our CV model's ease of use, generalizability, and non-invasiveness make it an appealing complementary option to in situ and real-time analytical monitoring tools and mathematical modeling. Additionally, our platform is integrated with Mettler-Toledo’s iControl software, which acts as a centralized system for real-time data collection, visualization, and storage. With consistent data representation and infrastructure, we were able to efficiently transfer the technology and reproduce results between different labs. This ability to easily monitor and respond to the dynamic situational changes of the experiments is pivotal to enabling future flexible automation workflows.

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Teaching old presumptive tests new digital tricks with computer vision for forensic applications

Abstract: Presumptive (or 'spot') tests have served forensic scientists, law enforcement, and legal practitioners for over a hundred years. Yet, the intended design of such tests, enabling quick identification of drugs...

Cited by 6 publications

References 41 publications

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

A Computer Vision Approach Towards Verifying CFD Models of Stirred Tank Reactors

Keeping an “eye” on the experiment: computer vision for real-time monitoring and control

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