Quantitative analysis of ToF‐SIMS data of a two organic compound mixture using an autoencoder and simple artificial neural networks

Rationale Matrix effects cause a nonlinear relationship between ion intensities and concentrations in mass spectrometry, including time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Here, two artificial neural network (ANN)‐based methods, autoencoder‐based and simple ANN methods, were employ...

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Veröffentlicht in:Rapid communications in mass spectrometry Jg. 37; H. 4; S. e9445 - n/a
Hauptverfasser: Aoyagi, Satoka, Matsuda, Kazuhiro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Wiley Subscription Services, Inc 28.02.2023
Schlagworte:
Artificial neural networks
Butylated Hydroxytoluene
Correlation coefficients
Datasets
Depth profiling
Ions
Linearity
Mass spectrometry
Mixtures
Multilayers
Negative ions
Neural networks
Neural Networks, Computer
Organic Chemicals
Organic compounds
Phenylalanine
Principal components analysis
Qualitative analysis
Quantitative analysis
Scientific imaging
Secondary ion mass spectrometry
Spectrometry, Mass, Secondary Ion - methods
Spectroscopy
ISSN:0951-4198, 1097-0231, 1097-0231
Online-Zugang:Volltext
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Zusammenfassung:Rationale Matrix effects cause a nonlinear relationship between ion intensities and concentrations in mass spectrometry, including time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Here, two artificial neural network (ANN)‐based methods, autoencoder‐based and simple ANN methods, were employed for the quantitative and qualitative analyses of a two organic compound mixture via ToF‐SIMS. Methods The multilayer model sample contained a mixture of Irganox 1010 and Fmoc‐pentafluoro‐L‐phenylalanine (Fmoc‐PFLPA). The sample's positive and negative ion depth profiles were collected through ToF‐SIMS. ToF‐SIMS‐derived cross‐sectional image datasets were analyzed using three unsupervised methods, namely principal component analysis (PCA), multivariate curve resolution (MCR), and use of a sparse autoencoder (SAE). The supervised simple ANN method was optimized based on the spectra and validated by predicting the test dataset ratios of Irganox 1010. Results The results obtained using the SAE demonstrated linear calibration curves and appropriate material distribution images. The Irganox 1010 and Fmoc‐PFLPA positive and negative ion datasets exhibited >0.97 correlation coefficients. The PCA and MCR results demonstrated lower linearity than that of SAE. Moreover, SAE weights indicated the ions important for each organic compound. The simple ANN method accurately predicted the ratios in the test dataset and indicated the important ions. Conclusions Both the supervised and unsupervised methods based on ANN, which were employed in regulating nonlinear relationships, were effective in the quantitative and qualitative analyses of the ToF‐SIMS data of the two organic compound mixtures. Regarding qualitative analysis, both ANN‐based methods indicated specific ions from the molecules in the sample.
Bibliographie:ObjectType-Article-1
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ISSN:0951-4198
1097-0231
1097-0231
DOI:10.1002/rcm.9445