Classification of Red Oak (Quercus Rubra) and White Oak (Quercus Alba) Wood Using a near Infrared Spectrometer and Soft Independent Modelling of Class Analogies

Abstract

b appalachian Hardwood center, West Virginia university, Morgantown, WV 26506-6125. uSa In this study, a method suitable for online rapid classification and separation of red oak and white oak wood species has been developed with the use of near infrared (NIR) spectroscopy and soft independent modeling of class analogies (SIMCA). The spectra of 10 wood specimens of each species were collected over wavelength window of 800-200 nm. The raw spectra and spectra pre-treated by first derivative and standard normal variate (SNV) transformation were used to develop calibration models using the wavelength ranges 800-200 nm, 1100-2200 nm and 1400-1900 nm by the SIMCA method. Principal component analysis (PCA) models were made for each class from the calibration set consisting of 100 specimens of red oak and white oak, respectively, and specimens not present in the calibration set (Testing set) consisting of 100 specimens were tested for classification according to the SIMCA method at the % and 2% significance level. Type I error (rejection of true member) associated with the models developed ranged from 2% to 20% and from 1% to %, respectively, for % and 2% significance level, while type II error (acceptance of a false member) was 0% for all models developed. There was no significant improvement in models developed with spectra pre-treated with first derivative or standard normal variate transformation over models developed with raw spectra. The full NIR spectral region of 800-200 nm provided the most useful information for distinguishing between the two species. The results of this study showed that NIR spectroscopy coupled with the SIMCA method of multivariate data analysis could be used to reliably separate and sort wood of red oak and white oak.

Wood and charcoal identification of five species from the miscellaneous group known in Brazil as “Angelim” by Near-IR and wood anatomy

“…In other studies, SIMCA classification was efficient to distinguish thermally modified wood of spruce, beech and ash (Bächle et al 2012) and to separate red oak and white oak wood (Adedipe et al 2008). …”

“…Some studies for wood species identification are based on analysis of increment cores, chips or powder (Adedipe et al 2008, Russ et al 2009, Casale et al 2010, Sandak et al 2011. For carbonized material, near infrared spectrometry has been used to distinguish species (Davrieux et al 2010, Nisgoski et al 2015b) and carbonization processes (Monteiro et al 2010).…”

Nondestructive Determination of the Compressive Strength of Wood Using Near-Infrared Spectroscopy

“…For example, basic density (Santos et al 2012), moisture content (Eom et al 2013), drying stress level (Ken et al 2013), wood color (Yang et al 2012), surface roughness (Zhang et al 2015), lignin (Rambo et al 2016), cellulose (Li et al 2015), hemicellulose (Li et al 2015), and extractives (He et al 2013) have been determined using NIR spectroscopy. In addition, NIR spectroscopy has been used to classify wood species and their origins (Adepipe et al 2008;Castillo et al 2008).…”

Population variability based on the morphometry and chemical composition of the acorn in Holm oak (Quercus ilex subsp. ballota [Desf.] Samp.)

“…It is a non-destructive, simple, reliable, and accurate technique, allowing a complete chemical analysis in a single experiment, including fiber, starch, protein, and fatty acids (Jiang et al 2007;Kim et al 2007;Niewitetzki et al 2010;Stuth et al 2003;Takahashi et al 2010). NIR spectroscopy is a rapid and cost-effective method for forestry-breeding programs and can be successfully applied to the selection of 'high yield' trees (Schimleck et al 2000), differentiation of species (Adedipe et al 2008), and oil content (Sousa-Correia et al 2007). …”