Box plots: A simple graphical tool for visualizing overfitting in peak fitting as demonstrated with X-ray photoelectron spectroscopy data
Creators
- 1. Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, 84602 (United States)
- 2. Casa Software Ltd., Bay House, Teignmouth (United Kingdom)
- 3. CNRS, Institut des Matériaux Jean Rouxel, IMN, Université de Nantes, F-44000, Nantes (France)
- 4. Commonwealth Scientific and Industrial Research Organization (CSIRO) Manufacturing, Clayton, Victoria, 3168 (Australia)
Description
Highlights: • XPS peak fitting is often performed with too many fit parameters. • An excess of fit parameters leads to local minima in a fit space. • Box plots identify fitting protocols with too many varying parameters. • Box plots are illustrated using four- and ten-component fits to a moderately complex C 1s spectrum. • Box plots, Monte Carlo analysis, and uniqueness plots identify different aspects of poor peak fitting. While peak fitting of spectra/data is frequently performed in science, recent reports suggest that the quality of peak fitting in the scientific literature is often inadequate. Here, we describe a new statistical tool for determining the quality of fitting protocols, illustrating this capability with X-ray photoelectron spectroscopy (XPS) data. This tool, box plots of random starting conditions and their results, helps identify local minima in the multidimensional fit space of the fit parameters. Ideally, there should be a single global minimum for a fitting protocol such that different, reasonable starting conditions lead to the same result. To determine whether a fit space contains multiple local minima, a series of reasonable starting conditions is randomly chosen for the fit. If the boxes in the box plot of the peak areas of these multiple fits are narrow, the different possibilities converge to a single global minimum. Conversely, if the boxes are wide, multiple local minima are present. This method is related to the mathematical concept of 'disproof by contradiction'. Our approach is demonstrated with four- and ten-component fits to a moderately complex C 1s XPS narrow scan. The results from our analysis compare favorably to those of traditional Monte Carlo analyses and uniqueness plots, where box plots are also applied to the Monte Carlo results, and each of these statistical tools performs a different function/probes a fit space/protocol differently.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.elspec.2021.147094Additional details
Identifiers
- DOI
- 10.1016/j.elspec.2021.147094;
- PII
- S0368204821000487;
Publishing Information
- Journal Title
- Journal of Electron Spectroscopy and Related Phenomena
- Journal Volume
- 250
- Journal Page Range
- vp.
- ISSN
- 0368-2048
- CODEN
- JESRAW
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54054290
- Subject category
- S75: CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY;
- Descriptors DEI
- MONTE CARLO METHOD; PEAKS; RANDOMNESS; SPECTRA; X-RAY PHOTOELECTRON SPECTROSCOPY
- Descriptors DEC
- CALCULATION METHODS; ELECTRON SPECTROSCOPY; PHOTOELECTRON SPECTROSCOPY; SPECTROSCOPY
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier B.V. All rights reserved.