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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Related Experiment Video

Updated: Feb 3, 2026

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
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Application of chemometric methods to XRF-data - A tutorial review.

Vitaly Panchuk1, Irina Yaroshenko2, Andrey Legin3

  • 1Institute of Chemistry, St. Petersburg State University, St. Petersburg, Russia; Laboratory of Artificial Sensory Systems, ITMO University, St. Petersburg, Russia; Institute for Analytical Instrumentation RAS, St. Petersburg, Russia.

Analytica Chimica Acta
|October 18, 2018
PubMed
Summary
This summary is machine-generated.

Chemometric methods enhance X-ray fluorescence (XRF) spectrometry for more informative element analysis. This review details popular chemometric techniques and their applications in diverse scientific fields.

Keywords:
ChemometricsData processingX-ray fluorescence

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy

Background:

  • X-ray fluorescence (XRF) spectrometry is a key technique for elemental analysis.
  • Applications span forensic science, archeology, and geochemistry.

Purpose of the Study:

  • To review popular chemometric techniques for XRF data processing.
  • To demonstrate how chemometrics can enhance XRF informativeness.

Main Methods:

  • Data visualization
  • Exploration of hidden data relations
  • Sample classification
  • Quantitative treatment of spectral data

Main Results:

  • Chemometric tools enable deeper data insights.
  • Techniques address challenges like noisy and overlapped spectra.
  • Successful applications are illustrated with examples.

Conclusions:

  • Chemometrics significantly boost the utility of XRF analysis.
  • Integration of chemometrics expands XRF applications.