Multivariate analysis of ToF-SIMS data for biological applications

Ji Won Park; Hyegeun Min; Young Pil Kim; Hyun Kyong Shon; Jinmo Kim; Dae Won Moon; Tae Geol Lee

doi:10.1002/sia.3049

Multivariate analysis of ToF-SIMS data for biological applications

Ji Won Park
, Hyegeun Min
, Young Pil Kim
, Hyun Kyong Shon
, Jinmo Kim
, Dae Won Moon
, Tae Geol Lee

Department of New Biology

Research output: Contribution to journal › Article › peer-review

38 Scopus citations

Abstract

Applications of time-of-flight secondary ion mass spectrometry (ToF-SIMS) were demonstrated, focusing on multivariate analysis (MVA)suchasprincipal componentanalysis(PCA),principal component regression (PCR), and maximum autocorrelation factors (MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surface characterization of chemical composition, and image-based analysis in disease-related tissues. The use of MVA in a ToF-SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled the straightforward analysis of ToF-SIMS data. Even on biological samples with high complexity, the MVA method effectively contributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA with ToF-SIMS data will be widely used for exploring biological studies in a reliable and simple way.

Original language	English
Pages (from-to)	694-703
Number of pages	10
Journal	Surface and Interface Analysis
Volume	41
Issue number	8
DOIs	https://doi.org/10.1002/sia.3049
State	Published - Aug 2009

Keywords

Image analysis
Multivariate analysis
Principal component analysis
ToF-SIMS

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10.1002/sia.3049

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title = "Multivariate analysis of ToF-SIMS data for biological applications",

abstract = "Applications of time-of-flight secondary ion mass spectrometry (ToF-SIMS) were demonstrated, focusing on multivariate analysis (MVA)suchasprincipal componentanalysis(PCA),principal component regression (PCR), and maximum autocorrelation factors (MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surface characterization of chemical composition, and image-based analysis in disease-related tissues. The use of MVA in a ToF-SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled the straightforward analysis of ToF-SIMS data. Even on biological samples with high complexity, the MVA method effectively contributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA with ToF-SIMS data will be widely used for exploring biological studies in a reliable and simple way.",

keywords = "Image analysis, Multivariate analysis, Principal component analysis, ToF-SIMS",

author = "Park, \{Ji Won\} and Hyegeun Min and Kim, \{Young Pil\} and Shon, \{Hyun Kyong\} and Jinmo Kim and Moon, \{Dae Won\} and Lee, \{Tae Geol\}",

year = "2009",

month = aug,

doi = "10.1002/sia.3049",

language = "English",

volume = "41",

pages = "694--703",

journal = "Surface and Interface Analysis",

issn = "0142-2421",

publisher = "John Wiley and Sons Ltd",

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TY - JOUR

T1 - Multivariate analysis of ToF-SIMS data for biological applications

AU - Park, Ji Won

AU - Min, Hyegeun

AU - Kim, Young Pil

AU - Shon, Hyun Kyong

AU - Kim, Jinmo

AU - Moon, Dae Won

AU - Lee, Tae Geol

PY - 2009/8

Y1 - 2009/8

N2 - Applications of time-of-flight secondary ion mass spectrometry (ToF-SIMS) were demonstrated, focusing on multivariate analysis (MVA)suchasprincipal componentanalysis(PCA),principal component regression (PCR), and maximum autocorrelation factors (MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surface characterization of chemical composition, and image-based analysis in disease-related tissues. The use of MVA in a ToF-SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled the straightforward analysis of ToF-SIMS data. Even on biological samples with high complexity, the MVA method effectively contributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA with ToF-SIMS data will be widely used for exploring biological studies in a reliable and simple way.

AB - Applications of time-of-flight secondary ion mass spectrometry (ToF-SIMS) were demonstrated, focusing on multivariate analysis (MVA)suchasprincipal componentanalysis(PCA),principal component regression (PCR), and maximum autocorrelation factors (MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surface characterization of chemical composition, and image-based analysis in disease-related tissues. The use of MVA in a ToF-SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled the straightforward analysis of ToF-SIMS data. Even on biological samples with high complexity, the MVA method effectively contributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA with ToF-SIMS data will be widely used for exploring biological studies in a reliable and simple way.

KW - Image analysis

KW - Multivariate analysis

KW - Principal component analysis

KW - ToF-SIMS

UR - https://www.scopus.com/pages/publications/68349145044

U2 - 10.1002/sia.3049

DO - 10.1002/sia.3049

M3 - Article

AN - SCOPUS:68349145044

SN - 0142-2421

VL - 41

SP - 694

EP - 703

JO - Surface and Interface Analysis

JF - Surface and Interface Analysis

IS - 8

ER -

Multivariate analysis of ToF-SIMS data for biological applications

Abstract

Keywords

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