Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII 2016
DOI: 10.1117/12.2241834
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Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

Abstract: Raman spectroscopy is a well-established spectroscopic method for the detection of condensed phase chemicals. It is based on scattered light from exposure of a target material to a narrowband laser beam. The information generated enables presumptive identification from measuring correlation with library spectra. Whilst this approach is successful in identification of chemical information of samples with one component, it is more difficult to apply to spectral mixtures. The capability of handling spectral mixtu… Show more

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Cited by 9 publications
(11 citation statements)
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“…The Euclidean distance approach does not meet these criteria. The most reliable qualitative or matching algorithms 70 account for every feature in the spectrum, model noise, account for mixtures, 560 and in the case of Raman spectra, also model fluorescence; these are statistical-based or probabilistic approaches. 561,562 The efficacy of these algorithms can be evaluation by examining the false-positive and false-negative rates, and using receiver operator curves (ROC) to model and visualize that data.…”
Section: Applications Development For Portable Spectrometersmentioning
confidence: 99%
“…The Euclidean distance approach does not meet these criteria. The most reliable qualitative or matching algorithms 70 account for every feature in the spectrum, model noise, account for mixtures, 560 and in the case of Raman spectra, also model fluorescence; these are statistical-based or probabilistic approaches. 561,562 The efficacy of these algorithms can be evaluation by examining the false-positive and false-negative rates, and using receiver operator curves (ROC) to model and visualize that data.…”
Section: Applications Development For Portable Spectrometersmentioning
confidence: 99%
“…To decipher the contribution of individual Raman probes, the amide region (1500-1700 cm −1 ) and cell-silent region (2000-2300 cm −1 ) were linearly decomposed by using the reference spectral profiles of each probe (Fig. 5d) [54][55][56] . We applied the unmixing algorithm on single-cell Raman spectra (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…29 The greedy selection methods are among the computationally cheapest methods for the sparsity diets of Raman spectral decomposition, that is, very sparse. 24 The sparse decomposition algorithm is also known as a non-negative orthogonal matching pursuit, which is presented in Figure 1. 30 In simple terms, we select a new column of M, that is, a spectrum in this case, at each step and find the best set of positive weights to match the library spectra to the experimental spectrum.…”
Section: ■ Experimental Sectionmentioning
confidence: 99%
“…The use of a small number of peak positions to represent the overall spectrum of a molecule is known as sparse decomposition 23 and has previously been demonstrated for the analysis of complex mixtures using Raman spectroscopy. 24 Our proposed cocrystal detection method builds on the concept of sparse decomposition and is based on the observation that the spectra of physical mixtures and cocrystals are different from each other (i.e., the spectra of cocrystals often have different peak positions compared to the spectra of the individual components). 25 In this paper, we demonstrate that we can automatically decompose the spectra and compare them to the spectra of the individual components.…”
Section: ■ Introductionmentioning
confidence: 99%
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