Time-of-Flight-Secondary Ion Mass Spectrometry and Principal Component Analysis: Determination of Structures of Lamellar Surfaces

Lau, Yiu-Ting R.; Weng, Lu‐Tao; Ng, Kai Mo; Chan, Chi‐Ming

doi:10.1021/ac902280h

Cited by 18 publications

(16 citation statements)

References 33 publications

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“…x(t, m) ex P(t, m) = P(t) L P(klt)P(mlk) k ( 2) PLCA estimates the unknown parameters P(klt) and P(mlk) from the input signal x(t, m). P(klt) corresponds to c(klt) in (1), and we call P(klt) the probabilistic activation.…”

Section: Problem Stat Ementmentioning

confidence: 99%

“…The system does not know what kind of chemical compounds can be measured in advance, and so the task of the system is a blind source separa tion (BSS) problem. There are many researches that employ BSS for mass spectra separation, such as principal component analy sis (peA) [ 2] and independent component analysis (leA) [ 3,4]. Because peA and leA impose the orthogonality and the indepen dence respectively without constraints of non-negativity, and so these methods are not fit to mass spectrometry domain.…”

Section: Introductionmentioning

confidence: 99%

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ICA-based acceleration of probabilistic latent component analysis for mass spectrometry-based explosives detection

Kawaguchi

Togami

Nagano

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We propose a new method to separate mass spectra into compo nents of each chemical compound for explosives detection. The conventional method based on probabilistic latent component anal ysis (PLeA) is effective because the method can solve the prob lems of non-negativity and non-orthogonality by using sparsity of the domain of explosives detection. However, the convergence of the method is slow. and the calculation time is long. In order to solve this problem. the proposed method makes use of independent component analysis (leA) in the initialization process. Experimen tal results indicate that the convergence of the proposed method is accelerated, and total calculation time is decreased.Index Terms-Mass spectrometry. Blind source separation. Probabilistic latent component analysis (PLeA), Independent com ponent analysis (leA), Sparsity INTRODUC TIONThe threat of improvised explosive devices has become a serious problem for all countries because the procedures and recipes for making them are freely available on the Internet. To prevent ter rorist attacks. we have developed a walkthrough portal explosives detector that consists of a high-throughput vapor sampling portal. a high-sensitivity atmospheric pressure chemical ionization source, and a high-selectivity linear ion trap mass spectrometer [ 1]. The mass spectrometer measures the intensity corresponded to the num ber of ions for each mass-to-charge ratio (mlz). The mlz series of intensities are called the mass spectrum. The detector observes the time series of the mass spectra continuously. and it detects charac teristic patterns of explosives traces from the mass spectra data.In mass spectra of the explosives detection system, explosives compounds, other chemical compounds, and the chemical back ground are mixed with each other. Thus. it is necessary to separate the mass spectra into the different compounds. The system does not know what kind of chemical compounds can be measured in advance, and so the task of the system is a blind source separa tion (BSS) problem. There are many researches that employ BSS for mass spectra separation, such as principal component analy sis (peA) [ 2] and independent component analysis (leA) [ 3,4]. Because peA and leA impose the orthogonality and the indepen dence respectively without constraints of non-negativity, and so these methods are not fit to mass spectrometry domain. Thus these methods suffer from performance degradation. Recently, there have been several researches that apply non-negative matrix factorization (NMF) [ 5, 6] and probabilistic latent component analysis (PLeA) 978-1-4799-0356-6/13/$31.00 ©2013 IEEE 2795[ 7] to the area of mass spectrometry. These approaches have the desirable feature that the estimated components are guaranteed to be non-negative, and the approaches have the advantage that distortion is not caused by negative values. Furthermore. the conventional method based on PLeA [ 7] can solve the uncertainty problem of the number of compounds by using statistical knowledge as sparsity priors. ...

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Section: Problem Stat Ementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ICA-based acceleration of probabilistic latent component analysis for mass spectrometry-based explosives detection

Kawaguchi

Togami

Nagano

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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show abstract

“…All these studies have shown that ToF‐SIMS is a very suitable technique for investigating the surface chemical compositions of aerosol particles because it possesses several advantages: (a) Both inorganic and organic components can be analyzed simultaneously; (b) single particle analysis is possible using ToF‐SIMS imaging when the particles are bigger than a few hundred nanometers; and (c) not only the surface composition of aerosol particles but also the distribution of chemical composition along the depth of aerosol particles can be obtained using ToF‐SIMS. As ToF‐SIMS spectra contain massive amount of data, multivariate method such as principal component analysis (PCA) has been often used to facilitate data interpretation …”

Section: Introductionmentioning

confidence: 99%

Effects of pretreatment temperature on the analysis of size‐fractionated aerosol particles using ToF‐SIMS

Xie

Weng

et al. 2020

Surface & Interface Analysis

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Size‐fractionated aerosol particles were collected with a MOUDI 10‐stage cascade impactor from an urban roadside place in a downtown area of Hong Kong. Fine aerosol particulate samples from stage 6 (aerodynamic particle diameter between 0.56 and 1 μm) and stage 9 (aerodynamic particle diameter between 0.10 and 0.18 μm) were pretreated at a chosen temperature, including −100°C, −50°C, 25°C, and 60°C, in a load lock chamber and then analyzed using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) at the same temperature (−100°C). Principal component analysis (PCA) was applied to further analyze ToF‐SIMS spectra of aerosol particles with different pretreatment temperatures from two selected stages. ToF‐SIMS results showed that the intensities of aliphatic hydrocarbon ions such as C4H7+ and C4H9+ and amine ions such as C2H8N+ and C4H12N+ decreased with an increase of the pretreatment temperature under ultrahigh vacuum conditions. We have shown that analyses of this type of aerosol particles using ToF‐SIMS should not be conducted at ambient temperature but at low temperature (eg, −50°C). In addition, we also developed a procedure that can be used to analyze aerosol particle samples under ultrahigh vacuum environment.

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“…It is necessary to separate the spectral components of each compound from the mass spectra. There are many researches that employ conventional factor analyses for basis decomposition such as Principal Component Analysis (PCA) [2] and Independent Component Analysis (ICA) [3,4]. The components estimated by PCA may contain negative values because PCA uses no constraints of non-negativity.…”

Section: Introductionmentioning

confidence: 99%

Mass spectra separation for explosives detection by using probabilistic latent component analysis

Kawaguchi

Togami

Nagano

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a new method to separate mass spectra into components of each chemical compound for explosives detection. In mass spectra, all components have no negative values. However, conventional factor analyses for basis decomposition use no constraints of nonnegativity, and we can not apply these methods to mass spectra. The proposed method is based on probabilistic latent component analysis (PLCA). The constraints of non-negativity always hold in PLCA, so that the method is effective for mass spectra. In addition, PLCA is defined in a statistical framework, thus PLCA makes it possible to utilize additional a priori information. Therefore, we introduce sparseness assumptions in the domain of mass spectrometry to PLCA in order to estimate the components more accurately. Experimental results indicate that the proposed method outperforms existing methods.

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Time-of-Flight-Secondary Ion Mass Spectrometry and Principal Component Analysis: Determination of Structures of Lamellar Surfaces

Cited by 18 publications

References 33 publications

ICA-based acceleration of probabilistic latent component analysis for mass spectrometry-based explosives detection

ICA-based acceleration of probabilistic latent component analysis for mass spectrometry-based explosives detection

Effects of pretreatment temperature on the analysis of size‐fractionated aerosol particles using ToF‐SIMS

Mass spectra separation for explosives detection by using probabilistic latent component analysis

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