Feasibility of handheld mid-infrared spectroscopy to predict particle size distribution: influence of soil field condition and utilisation of existing spectral libraries
Leslie J. Janik
A C , José M. Soriano-Disla A B and Sean T. Forrester A
+ Author Affiliations
- Author Affiliations
A CSIRO Environmental Contaminant Mitigation and Technologies Program, CSIRO Land and Water, Waite Campus, Waite Road, Urrbrae, 5064, South Australia, Australia.
B Present address: Technology Centre for Energy and Environment (CETENMA), Polígono Industrial Cabezo Beaza, C/ Sofía 6–13, 30353, Cartagena, Spain.
C Corresponding author. Email: les.janik@csiro.au
Soil Research 58(6) 528-539 https://doi.org/10.1071/SR20097
Submitted: 6 April 2020 Accepted: 9 June 2020 Published: 29 July 2020
Journal Compilation © CSIRO 2020 Open Access CC BY NC ND
Abstract
Partial least-squares regression (PLSR), using spectra from a handheld mid-infrared instrument (the ExoScan), was tested for the prediction of particle size distribution. Soils were sampled from agricultural sites in the Eyre Peninsula under field conditions and with varying degrees of soil preparation. Issues relevant to field sampling were identified, such as sample heterogeneity, micro-aggregate size and moisture content. The PLSR models for particle size distribution were derived with the varying degrees of preparation. Cross-validation of clay content in the as-received in situ soils resulted in low accuracy: coefficient of determination (R2) = 0.55 and root mean square error (RMSE) = 7%. This was improved by manual mixing, drying, sieving to < 2 mm and fine grinding, resulting in R2 values of 0.64, 0.75 and 0.81, and RMSE of 6%, 5% and 4% respectively; less improvement resulted for sand, with corresponding R2 values of 0.82, 0.88, 0.91 and 0.89, and RMSE of 10%, 8%, 6% and 7%. Predictions for silt remained poor. Where only archival benchtop calibration models were available, predictions of clay contents for spectra scanned with the handheld ExoScan spectrometer resulted in high error because of spectral intensity mismatch between benchtop and handheld spectra (R2 = 0.72, RMSE = 24.2% and bias = 21%). Pre-processing the benchtop spectra by piecewise direct standardisation resulted in more successful predictions (R2 = 0.73, RMSE = 6.7% and bias = –1.5%), confirming the advantage of piecewise direct standardisation for prediction from archival spectral libraries.
Additional keywords: DRIFT, partial least-squares regression, particle size analysis, piecewise direct standardisation.
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