Prediction of Lignin and Extractive Content ofPinus nigra Arnold. var. PallasianaTree 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).

Determination of honey adulteration with beet sugar and corn syrup using infrared spectroscopy and genetic‐algorithm‐based multivariate calibration

“…Those variables that were selected by the GA were then put into an evolutionary process where the best subset of the variables was used in the models. The algorithmic details of the GILS algorithm were given in a number of previous studies and will not be repeated here. PLS is a well‐known factor‐based multivariate calibration method originally proposed by Svante Wold and has been used in many applications previously …”

“…[28][29][30][31][32][33][34][35] In this study, FTIR spectroscopy coupled with a three-reflection diamond attenuated total reflectance (ATR) accessory was used to determine honey adulteration based on pure and adulterated honey samples synthetically prepared in the laboratory with three different adulterants (beet sugar, corn syrup and water). A genetic-algorithm-based inverse least squares (GILS) multivariate calibration method [36][37][38] was used to develop calibration models with pure and synthetically adulterated honey samples. In order to study the predictive performance of the GILS method, the partial least squares (PLS) method was also used to develop calibration models with the same data set, and these models were tested with 100 pure honey samples.…”

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

“…Recently, visible and near infrared (NIR) spectroscopy has been recognized as one of the most promising technique for prediction of physical and chemical properties of mass materials, due to its powerful, rapid, nondestructive, simple sample preparation and good reproducibility 8 . NIR has been used in analyzing compositions of wood, corn stover, and rice straw 8 , such as lignin 9 10 , cellulose 11 12 and hemicellulose 8 . Meanwhile, NIR spectroscopy has also been applied for measuring chemical components of bamboo, such as Klason lignin contents 13 14 , neutral detergent fiber (NDF), acid detergent fiber (ADF) 15 and holocellulose, a-cellulose 14 .…”

Determination of Lignin Content in Norway Spruce Wood by Fourier Transformed near Infrared Spectroscopy and Partial Least Squares Regression. Part 1: Wavenumber Selection and Evaluation of the Selected Range

“…research and applications in wood and paper science have been reviewed by Workman 2 and tsuchikawa 3 and a combination of ft-nIr and multivariate statistical methods 4 such as partial least squares regression (plS-r) have been applied to predict the lignin content for more than 15 years. [5][6][7][8] In a study using turkish pine (Pinus brutia tenore), a standard error of prediction for lignin content of about 3% was obtained, 9 for european black pine (Pinus nigra J.f. arnold) up to 2.4% 10 and for tropical pines up to 0.5%, but with an additionally high number of plS vectors 11 and, for loblolly pine (Pinus taeda l.) milled increment cores, a standard error of cross-validation of 0.72% was reported.…”