Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods

Gabriel W D Ferreira; Jussara V Roque; Emanuelle M B Soares; Ivo R Silva; Eulene F Silva; Aline A Vasconcelos; Reinaldo F Teófilo

doi:10.1016/j.talanta.2018.05.073

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods

Talanta. 2018 Oct 1:188:168-177. doi: 10.1016/j.talanta.2018.05.073. Epub 2018 May 23.

Authors

Gabriel W D Ferreira¹, Jussara V Roque², Emanuelle M B Soares³, Ivo R Silva³, Eulene F Silva⁴, Aline A Vasconcelos³, Reinaldo F Teófilo⁵

Affiliations

¹ Universidade Federal de Viçosa, Soil Department, 36570-900 Viçosa, MG, Brazil; Universidade Federal de Lavras, Forest Sciences Department, 37200-000 Lavras, MG, Brazil.
² Universidade Federal de Viçosa, Chemistry Department, 36570-900 Viçosa, MG, Brazil.
³ Universidade Federal de Viçosa, Soil Department, 36570-900 Viçosa, MG, Brazil.
⁴ Universidade Federal Rural do Semi-Árido, Environmental and Technological Sciences Department, 59625-900 Mossoró, RN, Brazil.
⁵ Universidade Federal de Viçosa, Chemistry Department, 36570-900 Viçosa, MG, Brazil. Electronic address: rteofilo@ufv.br.

PMID: 30029359
DOI: 10.1016/j.talanta.2018.05.073

Abstract

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (R_p) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process.

Keywords: Discriminant analysis; Eucalyptus harvest residues; Near-infrared spectroscopy; Ordered predictors selection; Partial least squares; Principal component analysis.