Rapid spectroscopic separation of three Canadian softwoods

Near Infrared (NIR) and Fluorescence (FS) spectroscopy were investigated for their ability to rapidly separate three Canadian softwoods: balsam fir, western hemlock, and white spruce. NIR and FS spectral data were used to develop classification models using soft independent modeling of class analogies (SIMCA) method. For each wood species, spectra of 90 wood specimens were collected over a wavelength window of 800–2,500 nm for NIR spectral data and a wavelength range of 380–540 and 380–705 nm for FS spectral data. Raw spectra and first-derivative-transformed spectra were used to develop NIR calibration models to separate the three wood species using the wavelength ranges, 800–2,500, 1,100–2,200, and 1,300–2,000 nm, by the SIMCA method. Similarly, FS raw spectral data were also used to develop FS calibrations using wavelength ranges of 380–540 and 380–705 nm. Principal component analysis models were made for each class from the calibration set consisting of 65 specimens of each of the three wood species. Specimens not present in the calibration set (27 specimens of each wood species) were tested for classification according to the SIMCA method at a 5 and 25% significance level. Type I error associated with the models developed with NIR spectral data ranged from 0 to 19 and 0 to 52% for the 5 and 25% significance levels, respectively, while type II error ranged from 2 to 50 and 0 to 19%, respectively. When tested at a 5% significance level, there was no significant improvement in NIR models developed with first-derivative-transformed spectra over models developed with raw spectra. Type I error associated with the models developed with Fluorescence spectral data ranged from 0 to 4 and 7 to 30% for the 5 and 25% significance levels, respectively, while type II error ranged from 1 to 9 and 0 to 1%, respectively. There were no significant differences in performance of FS models developed with spectra using wavelength ranges of 380–540 and 380–705 nm.