Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration

Abstract

Determination of quality parameters such as lignin and extractive content of wood samples by wet chemistry analyses takes a long time. Near-infrared (NIR) spectroscopy coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the NIR spectra, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus brutia Ten. is the most growing pine species in Turkey. Its rotation period is around 80 years; the forest products industry has widely accepted the use of Pinus brutia Ten. because of its ability to grow on a wide range of sites and its suitability to produce desirable products. Pinus brutia Ten. is widely used in construction, window door panel, floor covering, etc. Determination of lignin and extractive content of wood provides information to tree breeders on when to cut and how much chemicals are needed for the pulping and bleaching process. In this study, 58 samples of Pinus brutia Ten. trees were collected in Isparta region of Turkey, and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near-infrared spectrometer between 1,000 and 2,500 nm in diffuse reflectance mode, and multivariate calibration models were built with genetic inverse least squares method for both lignin and extractive content using the concentration information obtained from wet standard reference method. Overall, standard error of calibration (SEC) and standard error of prediction (SEP) ranged between 0.35% (w/w) and 2.40% (w/w).

Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

“…Among the many available modeling methods, a chemometric approach of partial least squares (PLS) is extensively used for NIR application in wood (Alves et al 2012;Hou and Li 2010;Hein et al 2010;Ü ner et al 2011). The PLS method is typically used to identify and quantify chemical components in wood based on NIR spectra and provides better predictive diagnostics than other methods (e.g., principal components regression) in wood chemistry (Rodrigues et al 2006;Via et al 2014).…”

An Estimation of the Rotation Age Using Autoregressive Price Model and Trunk Analysis Data: Results for Pinus Brutia Ten

“…Hence, it must be attempted to develop plantations of fast-growing species or wood farming in various parts of the country (Nouri, 1999;Arian et al, 2017). In the present study, Pinus brutia was selected because of its good physical and mechanical properties in commercial pulp production (Üner et al, 2011;Fakhryan Roghani, 2015). The aim of this research is to determine whether this species is economically justifiable or not, and to calculate the maximum net present value in an infinite series or estimate the optimal rotation age.…”

Lignification Markers of the Tracheid Walls of Scots Pine (Pinus sylvestris (L.)) in Various Forms of Dead Bark

“…Lignin appears in all of the layers of the cell wall, i.e., the primary and secondary cell wall, and in the middle lamella (Boerjan et al 2003;Üner et al 2011;Antonova et al 2014;Kim and Daniel 2014;Tsuyama and Takabe 2014). The lignification process of plant cells is closely connected with the synthesis of lignin, and its accumulation in the plant cell walls ranges from 16% to 30% according to Pereira et al (2003), from 25% to 30% of dry mass according to Miidla (1989), or from 18% to 36% according to Sarkanen and Ludwig (1971).…”