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

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.

Promoting early exposure monitoring for respirable crystalline silica: Taking the laboratory to the mine site

“…In preparation for the possible presence of analytical confounders, a more comprehensive use of the infrared spectrum has been preliminarily investigated. [24] This investigation found that the partial least square regression approach may reduce the error associated with the estimation of RCS using the DoF-FTIR technique.…”

Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression

“…The backward Monte Carlo unimportant variable elimination identified an optimal subset of wavenumbers exclusively from the α-quartz doublet region (Figure 6, blue features), closely duplicating results from our previous study. 24 Unlike the case for non-coal samples, kaolinite was readily apparent in the IR spectra of coal dust captured on the filter (Figures 3 and 6a). Thus we anticipated that utilizing only features from the quartz doublet region would lead to somewhat reduced accuracy for RCS prediction, i.e., wavenumber selection provided a strong first step in model optimization but left the decision of whether to incorporate kaolinite confounders to the analyst.…”

“…This has already been shown to be true for RCS determined in non-coal mine samples. 24 Furthermore, the PLS protocol does not require the analyst to directly interact with the FT-IR spectra by performing band integrations, but considers the individual spectral channels (wavenumbers) automatically when performing a calibration. Therefore, the (blind) prediction of field samples is readily accomplished by transforming the regression coefficients ( q ) into the more familiar form as: $$\mathit{b}=\left[\mathrm{bold-italicW}\right]{\left({\left[\mathit{P}\right]}^T\left[\mathit{W}\right]\right)}^{-1}\mathit{q}$$ with the following RCS prediction equation as $${\mathrm{bold-italicy}}_T={\left[\mathrm{bold-italicX}\right]}_T\mathit{b}+\mathit{g}$$ where [ W ] are the PLS loading weights that are discussed in detail elsewhere.…”

Atmospheric particulate matter characterization by Fourier Transform Infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

“…The essential principle of wrapper methods is to apply variable filters successively or iteratively to sample data until only a 10 desirable subset of quintessential variables remain for PLS modeling (Leardi, 2000;Leardi and Nørgaard, 2004;Weakley et al, 2014 As their name implies, embedded methods nest variable selection directly into the main body of the regression algorithm.…”