Fine Grinding Is Needed To Maintain The High Accuracy Of Mid-Infrared Diffuse Reflectance Spectroscopy For Soil Property Estimation

ORCID

Wijewardane: https://orcid.org/0000-0001-8962-9451; Sanderman: https://orcid.org/0000-0002-3215-1706

MSU Affiliation

College of Agriculture and Life Sciences; Department of Agricultural and Biological Engineering; James Worth Bagley College of Engineering

Creation Date

2026-06-30

Abstract

In mid-infrared diffuse reflectance (MIR) soil spectroscopy, grinding is one major step that can have pronounced effects on spectra and model calibrations. The reported literature on the effects of fine grinding on spectroscopic model performance have been inconsistent, likely in part because of limitations in sample set and model calibrations in previous studies. This study was focused on answering the question whether fine grinding is necessary for MIR spectroscopy in order to minimize model uncertainty. The main goal of this study was to compare model performance with and without fine grinding for eight soil properties using two different modeling techniques: partial least squares regression (PLS) and artificial neural networks (ANN). Approximately 500 soil samples were extracted from a large MIR spectral library in the United States to obtain spectra at non-fine ground (NG, < 2 mm,) and fine-ground (FG, < 0.18 mm,) states. Performance of calibration models built using subsets of the 500 FG and 500 NG spectra were compared with models built using the entire FG spectral library (n > 40,000). All the model calibrations and validations were repeated 100 times to evaluate the uncertainty of the model performances. The results showed that PLS performed similar to ANN for the smaller dataset, but the best model performance was obtained with the FG full spectral library with ANN models. Predictions on the FG spectra always outperformed predictions on the NG spectra in terms of goodness-of-fit and variance of statistics. Overall, this study confirmed the importance of fine grinding to ensure the best MIR spectroscopic model performance.

Publication Date

11-10-2020

Publication Title

Soil Science Society of America Journal

Publisher

Wiley

First Page

263

Last Page

272

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2020 The Authors

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Digital Object Identifier (DOI)

https://doi.org/10.1002/saj2.20194