Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets

doi:10.3832/ifor1495-008

Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets

Alessandro Bellino⁽¹⁾, Claudio Colombo⁽²⁾, Paola Iovieno⁽³⁾, Anna Alfani⁽¹⁾, Giuseppe Palumbo⁽²⁾, Daniela Baldantoni⁽¹⁾

iForest - Biogeosciences and Forestry, Volume 9, Issue 1, Pages 101-108 (2015)
doi: https://doi.org/10.3832/ifor1495-008
Published: Jul 15, 2015 - Copyright © 2015 SISEF

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Chemometric analysis applied to diffuse reflectance spectroscopy is increasingly proposed as an effective and accurate methodology to predict soil physical, chemical and biological properties. Its effectiveness, however, largely varies in relation to the calibration techniques and the specific soil properties. In addition, the calibration of UV-Vis-NIR spectra usually requires large datasets, and the identification of techniques suitable to deal with small sample sizes and high dimensionality problems is a primary challenge. In order to investigate the predictability of many soil chemical and biological properties from a small dataset and to identify the most suitable techniques to deal with this type of problems, we analysed 20 top soil samples of three different forests (Fagus sylvatica, Quercus cerris and Quercus ilex) in southern Apennines (Italy). Diffuse reflectance spectra were recorded in the UV-Vis-NIR range (200-2500 nm) and 22 chemical and biological properties were analysed. Three different calibration techniques were tested, namely the Partial Least Square Regression (PLSR), the combinations wavelet transformation/Elastic net and wavelet transformation/Supervised Principal Component (SPC) regression/ Least Absolute Shrinkage and Selection Operator (LASSO), a kind of preconditioned LASSO. Calibration techniques were applied to both raw spectra and spectra subjected to wavelet shrinkage filtering, in order to evaluate the influence on predictions of spectra denoising. Overall, SPC/LASSO outperformed the other techniques with both raw and denoised spectra. Elastic net produced heterogeneous results, but outperformed SPC/LASSO for total organic carbon, whereas PLSR produced the worst results. Spectra denoising improved the prediction accuracy of many parameters, but worsen the predictions in some cases. Our approach highlighted that: (i) SPC/LASSO (and Elastic net in the case of total organic carbon) is especially suitable to calibrate spectra in the case of small, high dimensional datasets; and (ii) spectra denoising could be an effective technique to improve calibration results.

Keywords

Elastic Net, PLSR, SPC/LASSO, Wavelets, Diffuse Reflectance Spectroscopy, Sample Size

Authors’ address

(1)

Dipartimento di Chimica e Biologia, Università degli Studi di Salerno, v. Giovanni Paolo II 132, I-84084 Fisciano, Salerno (Italy)

(2)

Dipartimento di Agricoltura Ambiente Alimenti, Università degli Studi del Molise, v. De Sanctis, I-86100 ampobasso (Italy)

(3)

Consiglio per la Ricerca e la Sperimentazione in Agricoltura (CRA), Centro di ricerca per l’Orticoltura, v. Cavalleggeri 25, I-84098 Pontecagnano, Salerno (Italy)

Corresponding author

Daniela Baldantoni
dbaldantoni@unisa.it

Citation

Bellino A, Colombo C, Iovieno P, Alfani A, Palumbo G, Baldantoni D (2015). Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets. iForest 9: 101-108. - doi: 10.3832/ifor1495-008

Academic Editor

Arthur Gessler

Paper history

Received: Nov 06, 2014
Accepted: Mar 10, 2015

First online: Jul 15, 2015
Publication Date: Feb 21, 2016
Publication Time: 4.23 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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