Predicting the chemical composition of juvenile and mature woods in Scots pine (Pinus sylvestris L.) using FTIR spectroscopy

Abstract

The chemical composition of wood is one of the key features that determine wood quality. The focus of this study was on identifying differences between juvenile and mature woods in Scots pine (Pinus sylvestris L.) and developing models for predicting the chemical composition of these two wood types. Chemical traits, determined by traditional wet chemistry techniques, included the proportion of lignin, polysaccharides and extractives. Partial least squares regression of Fourier transform infrared (FTIR) spectra was used for model building. The model performance was primarily evaluated by root mean squared error of predictions (RMSEP). High predictive power was attained for the content of lignin (RMSEP of 0.476 and 0.495 for juvenile and mature woods, respectively) and extractives (0.302 and 0.471), good predictive power for cellulose (0.715 and 0.696) and hemicelluloses in juvenile wood (0.719) and low predictive power for hemicelluloses in mature wood (0.823). A distinct band was observed at 1693 cm −1 , and its intensity was strongly associated with the content of extractives (r = 0.968 and 0.861 in juvenile and mature woods, respectively). FTIR has proved suitable for the rapid, non-destructive, costefficient assessment of the chemical composition of juvenile and mature woods in Scots pine. The band at 1693 cm −1 is to be further investigated to unravel its link with individual extractive components.

Genetic improvement of the chemical composition of Scots pine (Pinus sylvestrisL.) juvenile wood for bioenergy production

“…For extractives (trait EXT ): The model for EXT reached the highest predictive power ( RMSEP = 0.30) of all chemical compositional traits included in this study. This high power was likely driven by a band position near 1,693 cm −1 , whose absorbance intensity exhibited the strongest association with this composite trait, with correlation coefficients ranging from 0.91 to 0.97 depending on the normalization method applied (Funda et al., 2020). Furthermore, only less than 5% of the response variation remained unexplained by the model.…”

“…The first breeding step for increasing CEL in juvenile wood within the studied material therefore seems to be to decrease EXT and then focus on improving the CEL : LIG ratio. Considerably less extractives were observed among the same trees in mature wood (Funda et al., 2020), and thus, mature wood's correlation matrix might exhibit a different pattern, reflecting more the results reported in studies which did not include extractives in their analyses. Sykes et al.…”

“…The chemical composition of all 1,245 juvenile wood samples was predicted using partial least squares regression (PLSR) models developed by Funda, Fundova, Gorzsás, Fries, and Wu (2020) in SAS 9.4 (SAS Institute Inc.) based on spectral and chemical compositional data obtained for the selected 70 samples. Standardized FTIR spectra within 771–1,869 cm −1 served as predictor variables (in total 570 variables; each representing absorbance intensity at a given wavenumber) while the nine chemical compositional traits LIG , CEL , HEM , GLU , MAN , XYL , GAL , ARA , and EXT , expressed as percentages of the total dried wood content, served as response variables.…”

“…The first breeding step for increasing CEL in juvenile wood within the studied material therefore seems to be to decrease EXT and then focus on improving the CEL:LIG ratio. Considerably less extractives were observed among the same trees in mature wood (Funda et al, 2020), and thus, mature wood's correlation matrix might exhibit a different pattern, reflecting more the results reported in studies which did not include extractives in their analyses. Sykes et al (2006) and Porth et al (2013) obtained negative correlations between CEL and LIG in their studies of loblolly pine and black cottonwood, respectively, but these variables could not be linked to EXT because EXT were not determined as a separate trait.…”

Determination of hemicellulose, cellulose, holocellulose and lignin content using FTIR inCalycophyllum spruceanum(Benth.) K.Schum. andGuazuma crinitaLam.

“…On the contrary to conventional techniques, FTIR-ATR spectroscopy is a practical and economical analytic method to determine wood chemical composition [15]. This method has been successfully used to identify cellulose, hemicellulose, lignin, monosaccharide, extractive compounds and proteins in forest species [15][16][17][18]. The complex data generated by FTIR-ATR (vibration of chemical bind and functional groups) is processed by multivariate analysis [19] such as partial least squared (PLS).…”