Evaluation of alternative sample preparation methods for development of NIR models to assess chemical properties of wood

Abstract

The aim of this work was to investigate the influence of sample preparation including variation in moisture content and particle size on the accuracy of near infrared (NIR) spectroscopy models developed to predict Klason lignin, total lignin, and holocellulose in wood. Seventy-five samples of sawdust obtained from a eucalyptus plantation were divided into aliquots and submitted to three different treatments: traditional (TRAD), large particle dried at room temperature (LPRT), and large particle oven-dried (LPOD). The influence of sample preparation method on models’ accuracy was compared by statistical analysis. Overall, grinding to a larger particle size and drying at room temperature (treatment LPRT) did not decrease the accuracy of the prediction models when compared to the TRAD sample preparation method. These findings were more evident for Klason lignin and holocellulose. This is relevant because resources used for sample preparation (i.e. grinding and drying) can be minimized, which is expected to reduce the costs associated with analysis of wood properties by NIR.