Qualitative Analysis and the Answer Box: A Perspective on Portable Raman Spectroscopy

Carron, Keith T.; Cox, Richard F.

doi:10.1021/ac901951b

Cited by 70 publications

(64 citation statements)

References 29 publications

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“…Some approaches restrict the task to identification of a specific component, [14,15] while others attempt to cluster spectra into logical groups. [19,22] Most methods employ some combination of spectrum preprocessing steps to reduce the influence of noise and fluorescence, [23,24] and some also project spectra into a lower-dimensional feature space, typically using principal components analysis (PCA). [22,25] These applications focus on single-phase identification, although some workers have proposed algorithms for determining the relative contributions of phase in simple mixtures.…”

Section: Introductionmentioning

confidence: 99%

Machine learning tools formineral recognition and classification from Raman spectroscopy

Carey

Boucher

Mahadevan

et al. 2015

J Raman Spectroscopy

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Tools for mineral identification based on Raman spectroscopy fall into two groups: those that are largely based on fits to diagnostic peaks associated with specific phases, and those that use the entire spectral range for multivariate analyses. In this project, we apply machine learning techniques to improve mineral identification using the latter group. We test the effects of common spectrum preprocessing steps, such as intensity normalization, smoothing, and squashing, and found that the last is superior. Next, we demonstrate that full-spectrum matching algorithms exhibit excellent performance in classification tasks, without requiring time-intensive dimensionality reduction or model training. This class of algorithms supports both vector and trajectory input formats, exploiting all available spectral information. By combining these insights, we find that optimal mineral spectrum matching performance can be achieved using careful preprocessing and a weighted-neighbors classifier based on a vector similarity metric. IntroductionUse of Raman spectroscopy in the geosciences is growing rapidly, as evidenced by burgeoning publications in the fields of geology, cultural anthropology, environmental science, and, in particular, planetary science. Raman spectrometers have been proposed for exploration of a diverse range of extraterrestrial targets including asteroids, [1] Europa, [2,3] Mars, [4][5][6] the Moon, [7] and Venus. [8] A Raman laser spectrometer (RLS) is part of the science instrument payload of the European Space Agency 2018 ExoMars mission; the RLS instrument will target mineralogical and astrobiological investigations on the surface and subsurface of Mars. [9,10] Raman will also be used on the upcoming NASA Mars 2020 mission as part of the SuperCam and SHERLOC instruments. [11] What all these applications have in common is their dependence on software and mineralogical databases for phase identification and quantification of relative abundances of mineral components. Because of the structural diversity and chemical complexity of naturally occurring minerals, optimal applications for these purposes require an infusion of work into development of appropriate software and mineral databases.In practice, users commonly depend on a combination of matching software distributed by spectrometer manufacturers and one-on-one comparisons with database spectra for their identifications, but these types of identification have two critical limitations. First, identifications are only as good as the databases used to match them. No database can be entirely comprehensive, so it cannot be unequivocally claimed that any spectrum is a 'perfect match' to exactly one other phase.The RRUFF database was founded in 2006 at Arizona State University by Robert Downs to remedy this situation by providing coverage of all known mineral species. [12] It has quickly become the preeminent resource available for Raman spectra of minerals. RRUFF currently contains over 20 378 spectra acquired at several different laser wavelengths from orien...

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Section: Introductionmentioning

confidence: 99%

Machine learning tools formineral recognition and classification from Raman spectroscopy

Carey

Boucher

Mahadevan

et al. 2015

J Raman Spectroscopy

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“…It is lower in portable instruments: This parameter is often set around 8–12 cm −1 , whereas on benchtop instruments, it can be significantly lower, in the order of few cm −1 . The value of 8–12 cm −1 is normally good enough to discriminate between different compounds, considered that the Raman band width of many solid and liquid substances is around 5–10 cm −1 and supposed that efficient chemometric algorithms for spectral recognition are used . In general, the resolution depends on several parameters, such as the slit width, the focal length of the spectrograph, the groove density of the gratings, the detector (and pixel) size, and the excitation wavelength.…”

Section: Portable Vs Benchtop Instrumentsmentioning

confidence: 99%

“…However, once this problem was technologically solved and the laser emission was locked to a single mode, diode lasers became ideal sources for Raman spectroscopy: Nowadays, they are compact devices, available at a relatively low price and in a wide range of emission wavelengths from the visible to the near infrared. Similarly, the introduction in the market of digital cameras contributed to drive down the price of CCD detectors and to reduce their size …”

Section: Introductionmentioning

confidence: 99%

SERS detection of food contaminants by means of portable Raman instruments

Pilot

2018

J Raman Spectroscopy

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Surface‐enhanced Raman scattering (SERS) has been emerging as a powerful tool for the detection of a variety of analytes due to its very high sensitivity and fingerprinting recognition capabilities. Technological progresses in the equipment for Raman analysis are contributing to its transition from a technically demanding research method to a more widely available analytical technique. In particular, the commercialization of portable or handheld instruments has opened up the possibility of performing in situ analysis. In this review, a selection of the SERS substrates that are expected to be more suitable for use in combination with portable instruments is presented: Substrates are compared, for example, in terms of performance, reproducibility, ease of fabrication, and surface area. Moreover, this paper provides a survey of the current diffusion of portable Raman instruments in the SERS detection of food contaminants: The investigation of several analytes is summarized (mainly toxins, virus, bacteria, pesticides, forbidden food dyes, and preservatives), reporting on the limits of detection and on the eventual coupling with concentration or separation techniques. A brief perspective on possible future developments of the SERS detection with portable instruments is also provided.

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“…Both Raman and NIR are commonly available in portable/handheld platforms from a variety of commercial vendors [17]. One of the greatest benefits of using these instruments is their ability to perform rapid interrogation of the sample under study, often in its original packaging, without any additional sample preparation [16].…”

Section: Introductionmentioning

confidence: 99%

Expanding the analytical toolbox for identity testing of pharmaceutical ingredients: Spectroscopic screening of dextrose using portable Raman and near infrared spectrometers

Srivastava

Wolfgang

Rodriguez

2016

Analytica Chimica Acta

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In the pharmaceutical industry, dextrose is used as an active ingredient in parenteral solutions and as an inactive ingredient (excipient) in tablets and capsules. In order to address the need for more sophisticated analytical techniques, we report our efforts to develop enhanced identification methods to screen pharmaceutical ingredients at risk for adulteration or substitution using field-deployable spectroscopic screening. In this paper, we report our results for a study designed to evaluate the performance of field-deployable Raman and near infrared (NIR) methods to identify dextrose samples. We report a comparison of the sensitivity of the spectroscopic screening methods against current compendial identification tests that rely largely on a colorimetric assay. Our findings indicate that NIR and Raman spectroscopy are both able to distinguish dextrose by hydration state and from other sugar substitutes with 100% accuracy for all methods tested including spectral correlation based library methods, principal component analysis and classification methods.

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Qualitative Analysis and the Answer Box: A Perspective on Portable Raman Spectroscopy

Abstract: Miniaturization of Raman instruments has created a new genre of devices for qualitative analysis of materials. These new devices are introducing Raman spectroscopy to a diverse range of applications.

Cited by 70 publications

References 29 publications

Machine learning tools formineral recognition and classification from Raman spectroscopy

Machine learning tools formineral recognition and classification from Raman spectroscopy

SERS detection of food contaminants by means of portable Raman instruments

Expanding the analytical toolbox for identity testing of pharmaceutical ingredients: Spectroscopic screening of dextrose using portable Raman and near infrared spectrometers

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