A sparse regularized model for Raman spectral analysis

Di, Wu; Yaghoobi, Mehrdad; Kelly, Shaun I.; Davies, Mike E.; Clewes, Rhea J.

doi:10.1109/sspd.2014.6943306

Cited by 10 publications

(15 citation statements)

References 11 publications

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“…In this setting, sparse representations are used to decompose the spectral mixture to elementary spectra, alongside noise † . 10 Promising results are derived by assuming such a model for spectral mixtures and using standard sparse approximation algorithms. Most sparse approximation algorithms are iterative (semi-) optimisation algorithms, which need to run for a particular number of iterations or achieve a satisfactory convergence criteria.…”

Section: -7mentioning

confidence: 99%

“…Most sparse approximation algorithms are iterative (semi-) optimisation algorithms, which need to run for a particular number of iterations or achieve a satisfactory convergence criteria. 10 As a result, the computational cost of most algorithms are high. If the task is to reduce the computational cost, with the aim of running in a (close to) real-time application on a computationally/memory limited platform, we have to carefully choose the sparsity algorithm.…”

Section: -7mentioning

confidence: 99%

“…We do not investigate this scenario here, while the proposed approach can be modified to handle such a scenario. 10 The mixing model g includes a large class of nonlinear functions to describe the actual spectral mixing process, visible light hyperspectral unmixing analysis provides examples of this. 15,16 The nature of Raman spectral nonlinearities is different to those in other spectral modalities, and it is mainly caused by interaction between different chemicals in the mixture.…”

Section: Signal Modelmentioning

confidence: 99%

“…The regularisation is usually enforced by penalising a metric of the coefficient vector α or enforcing a low-dimensional structure like sparsity. The authors proposed a sparsity based Raman spectral decomposition in 10 and explained how it can help finding spectral decompositions in a small scale setting. Such an algorithm is computationally expensive, which prohibits its application in a computationally limited embedded system.…”

Section: Signal Modelmentioning

confidence: 99%

“…Otherwise, we have to decide on the number of components in the mixture, based on the magnitude of the sorted elements of α. 10 We can then have a set J of non-zero elements.…”

Section: Sparsity Based Fingerprinting and Quantificationmentioning

confidence: 99%

See 4 more Smart Citations

Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

Yaghoobi

Clewes

et al. 2016

Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII

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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 mixtures is crucial for defence and security applications as hazardous materials may be present as mixtures due to the presence of degradation, interferents or precursors. A novel method for spectral unmixing is proposed here. Most modern decomposition techniques are based on the sparse decomposition of mixture and the application of extra constraints to preserve the sum of concentrations. These methods have often been proposed for passive spectroscopy, where spectral baseline correction is not required. Most successful methods are computationally expensive, e.g. convex optimisation and Bayesian approaches.We present a novel low complexity sparsity based method to decompose the spectra using a reference library of spectra. It can be implemented on a hand-held spectrometer in near to real-time. The algorithm is based on iteratively subtracting the contribution of selected spectra and updating the contribution of each spectrum. The core algorithm is called fast non-negative orthogonal matching pursuit, which has been proposed by the authors in the context of nonnegative sparse representations. The iteration terminates when the maximum number of expected chemicals has been found or the residual spectrum has a negligible energy, i.e. in the order of the noise level. A backtracking step removes the least contributing spectrum from the list of detected chemicals and reports it as an alternative component. This feature is particularly useful in detection of chemicals with small contributions, which are normally not detected. The proposed algorithm is easily reconfigurable to include new library entries and optional preferential threat searches in the presence of predetermined threat indicators.Under Ministry of Defence funding, we have demonstrated the algorithm for fingerprinting and rough quantification of the concentration of chemical mixtures using a set of reference spectral mixtures. In our experiments, the algorithm successfully managed to detect the chemicals with concentrations below 10 percent. The running time of the algorithm is in the order of one second, using a single core of a desktop computer.

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Section: -7mentioning

confidence: 99%

Section: -7mentioning

confidence: 99%

Section: Signal Modelmentioning

confidence: 99%

Section: Signal Modelmentioning

confidence: 99%

“…Otherwise, we have to decide on the number of components in the mixture, based on the magnitude of the sorted elements of α. 10 We can then have a set J of non-zero elements.…”

Section: Sparsity Based Fingerprinting and Quantificationmentioning

confidence: 99%

See 3 more Smart Citations

Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

Yaghoobi

Clewes

et al. 2016

Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII

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Mixture analysis using non‐negative elastic net for Raman spectroscopy

Zeng

Hou²,

Ni³

et al. 2020

Journal of Chemometrics

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Raman spectrum is wealth of structural information, which can be used as molecular fingerprint to identify compounds. There are relations between the intensities of Raman peaks and the concentrations of compound, and they can be used to estimate relative concentrations of the compounds in mixture. However, it is still challenging to interpret the Raman spectrum of mixture. The non-negative lasso (NN-LASSO) has been used to solve this problem, but it failed to identify highly correlated compounds. The quadratic term of nonnegative elastic net (NN-EN) can ensure the stability of the fitted model. Therefore, a novel mixture analysis method was developed based on NN-EN in this study. It has been applied to analyze the simulated, liquid, powder, tablet, and quantitative mixture datasets. Results showed that NN-EN can identify the compounds in mixture with high accuracy and estimate their relative concentrations with small deviation. Furthermore, NN-EN was more stable than NN-LASSO when the spectra of some compounds are highly correlated. It is a promising approach for analyzing Raman spectra of mixtures.

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Reduction of flexible container induced error for the composition analysis of in-flexible-container liquid based on differential optical-path spectrum

Hou

Zhang

et al. 2018

Infrared Physics & Technology

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A sparse regularized model for Raman spectral analysis

Cited by 10 publications

References 11 publications

Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification

Mixture analysis using non‐negative elastic net for Raman spectroscopy

Reduction of flexible container induced error for the composition analysis of in-flexible-container liquid based on differential optical-path spectrum

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