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Updated: Jul 2, 2026

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions
Published on: January 10, 2019
Characterization of estuarine sediments by near infrared diffuse reflectance spectroscopy
Javier Moros1, María C Barciela-Alonso, Paula Pazos-Capeáns
1Department of Analytical Chemistry, University of Valencia, 50 Dr. Moliner Street, 46100 Burjassot, Valencia, Spain.
Abstract:
It has been developed a partial least squares near infrared (PLS-NIR) method for the determination of estuarine sediment physicochemical parameters. The method was based on the chemometric treatment of first order derivative reflectance spectra obtained from samples previously lyophilized and sieved through a lower than 63 microm grid. Spectra were scanned from 833 to 2976 nm, averaging 36 scans per spectrum at a resolution of 8 cm(-1), using chromatographic glass vials of 9.5 mm internal diameter as measurement cells. Models were built using reference data of 31 samples selected through the use of a hierarchical cluster analysis of NIR spectra of sediments obtained from the Ria de Arousa estuary and prediction parameters were established from a validation set of 50 samples of the same area. pH, redox potential (Eh), carbon (C), nitrogen (N) and hydrogen (H) content together with Sn, Pb, Cd, As, Sb and total Cr and also acid soluble, reducible and oxidable Cr fractions were employed as characteristic parameters of the studied sediments. Standard error of prediction values for C and N content were of the order of 4 and 1.3 mg g(-1) for H. Prediction errors for pH and Eh were 0.15 units and 37 mV, respectively, thus indicating the good prediction capabilities of the method. Regarding trace metal concentrations PLS-NIR provided prediction error levels for unknown samples around 20% for Sn, Pb, As and Sb and root mean square errors of prediction around 40% for concentration levels of 400 ng g(-1) Cd and 100 microg g(-1) Cr. For the different extractable fractions of Cr the residual prediction deviation varied from 1.3 to 1.7 but relative errors found for samples of the validation set were only useful for screening purposes.
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