Advanced algorithms for the identification of mixtures using condensed-phase FT-IR spectroscopy

Arnó, Josep; Andersson, Greger; Levy, Dustin; Tomczyk, C. A.; Zou, Peng; Zuidema, Eric

doi:10.1117/12.883886

Cited by 7 publications

(2 citation statements)

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“…Most of the handheld FT‐IR spectrometers shown in Figure 4 include an unsupervised, automated mixture analysis function based on spectral library searching. The mathematical details of the algorithms are not generally disclosed by the spectrometer vendors, but in some cases the identification performance has been [84, 85], and a number of approaches have been described in the scientific literature [86–91]. These algorithms attempt to identify the mixture components from the library spectra of pure materials, generally up to three components, and provide a confidence or probability score and an estimate of the fractional concentration.…”

Section: Vibrational Spectroscopymentioning

confidence: 99%

“…Most of the handheld FT-IR spectrometers shown in Figure4include an unsupervised, automated mixture analysis function based on spectral library searching. The mathematical details of the algorithms are not generally disclosed by the spectrometer vendors, but in some cases the identification performance has been[84,85], and a number of approaches have been described in the scientific literature[86][87][88][89][90][91]. These algorithms F I G U R E 4 Models and specifications of commercially available handheld FT-IR spectrometers that can be used for in-field identifications of fentanyl derivatives.…”

mentioning

confidence: 99%

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Field‐portable detection of fentanyl and its analogs: A review

Crocombe,

Giuntini,

Schiering

et al. 2023

Journal of Forensic Sciences

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The need to detect fentanyl and its analogs in the field is an important capability to help prevent unintentional exposure or overdose on these substances, which may result in death. Many portable methods historically used in the field by first responders and other field users to detect and identify other chemical substances, such as hazardous materials, have been applied to the detection and identification of these synthetic opioids. This paper describes field portable spectroscopic methods used for the detection and identification of fentanyl and its analogs. The methods described are automated colorimetric tests including lateral flow assays; vibrational spectroscopy (mid‐infrared and Raman); gas chromatography–mass spectrometry; ion mobility spectrometry, and high‐pressure mass spectrometry. In each case the background and key details of these technologies are outlined, followed by a discussion of the application of the technology in the field. Attention is paid to the analysis of complex mixtures and limits of detection, including the required spectral databases and algorithms used to interrogate these types of samples. There is also an emphasis on providing actionable information to the (likely) non‐scientist operators of these instruments in the field.

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Section: Vibrational Spectroscopymentioning

confidence: 99%

mentioning

confidence: 99%

Field‐portable detection of fentanyl and its analogs: A review

Crocombe,

Giuntini,

Schiering

et al. 2023

Journal of Forensic Sciences

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Identification and Confirmation Algorithms for Handheld Spectrometers

Gardner¹,

Green²

2000

Encyclopedia of Analytical Chemistry

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Handheld analytical devices based on a gamut of technologies (e.g. infrared, Raman, X‐ray fluorescence, and mass spectrometries) are now widely available. These tools have seen increasing adoption for field‐based assessment by diverse users including military, emergency response, law enforcement, and regulated manufacturers. Frequently, end users of handheld devices are nonscientists who rely on embedded software and the associated algorithms to convert collected data into actionable information. Algorithms developed to identify or confirm the identity of material under test, therefore, need to process nonideal field data into a reliable answer. This article presents important algorithm concepts for material identification and confirmation. We discuss real‐world data collection challenges and solutions for field‐based measurements, types of algorithms designed to answer specific user questions, approaches for how to display analysis results to nonexperts, how to minimize computation time to satisfy typical users, and finally, performance characterization methods and results.

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