Analytical and Bioanalytical Chemistry

, Volume 406, Issue 19, pp 4715–4724 | Cite as

Quantifying silica in filter-deposited mine dusts using infrared spectra and partial least squares regression

  • Andrew Todd Weakley
  • Arthur L. Miller
  • Peter R. Griffiths
  • Sean J. Bayman
Research Paper

Abstract

The feasibility of measuring airborne crystalline silica (α-quartz) in noncoal mine dusts using a direct-on-filter method of analysis is demonstrated. Respirable α-quartz was quantified by applying a partial least squares (PLS) regression to the infrared transmission spectra of mine-dust samples deposited on porous polymeric filters. This direct-on-filter method deviates from the current regulatory determination of respirable α-quartz by refraining from ashing the sampling filter and redepositing the analyte prior to quantification using either infrared spectrometry for coal mines or x-ray diffraction (XRD) from noncoal mines. Since XRD is not field portable, this study evaluated the efficacy of Fourier transform infrared spectrometry for silica determination in noncoal mine dusts. PLS regressions were performed using select regions of the spectra from nonashed samples with important wavenumbers selected using a novel modification to the Monte Carlo unimportant variable elimination procedure. Wavenumber selection helped to improve PLS prediction, reduce the number of required PLS factors, and identify additional silica bands distinct from those currently used in regulatory enforcement. PLS regression appeared robust against the influence of residual filter and extraneous mineral absorptions while outperforming ordinary least squares calibration. These results support the quantification of respirable silica in noncoal mines using field-portable infrared spectrometers.

Figure

Partial least square's predicted (Yfit) vs. observed (Yobs) reparable silica using infrared absorbance from the α-quartz doublet region of filter-deposited mine dust sample spectra. predictive features selected via backward Monte Carlo unimportant variable elimination (lower right hand corner) are also shown

Keywords

Partial least squares Monte Carlo unimportant variable elimination Silica measurement FT-IR Mine dust 

Supplementary material

216_2014_7856_MOESM1_ESM.pdf (44 kb)
ESM 1 (PDF 163 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew Todd Weakley
    • 1
  • Arthur L. Miller
    • 2
  • Peter R. Griffiths
    • 3
  • Sean J. Bayman
    • 2
  1. 1.Department of Chemical and Materials EngineeringUniversity of IdahoMoscowUSA
  2. 2.National Institute for Occupational Safety and HealthSpokaneUSA
  3. 3.Griffiths Consulting LLCOgdenUSA

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