Prediction of lignin and extractive content of pinus nigra Arnold. var. Pallasiana tree 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 spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus nigra Arnold. Var. pallasiana is the second most growing pine species in Turkey. Even though its rotation period is very high, around 120 years, the forest products industry has widely accepted the use of Pinus nigra because of its ability to grow on a wide range of sites and its suitability to produce desirable products. In this study, 51 samples of Pinus nigra trees were collected and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near infrared spectrometer between 1000 and 2500 nm in diffuse reflectance mode. 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) were ranged between 0.35% (w/w) and 2.4% (w/w).