Literature DB >> 15732941

Multivariate statistical analysis of concatenated time-of-flight secondary ion mass spectrometry spectral images. Complete description of the sample with one analysis.

V S Smentkowski1, M R Keenan, J A Tony Ohlhausen, P G Kotula.   

Abstract

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS data set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately.

Entities:  

Year:  2005        PMID: 15732941     DOI: 10.1021/ac048468y

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  2 in total

1.  Multivariate analysis strategies for processing ToF-SIMS images of biomaterials.

Authors:  Bonnie J Tyler; Gaurav Rayal; David G Castner
Journal:  Biomaterials       Date:  2007-02-09       Impact factor: 12.479

Review 2.  Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry.

Authors:  Nico Verbeeck; Richard M Caprioli; Raf Van de Plas
Journal:  Mass Spectrom Rev       Date:  2019-10-11       Impact factor: 10.946

  2 in total

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