A multivariate approach to the analysis of pine needle samples using NIR

Near infrared reflectance (NIR) spectroscopy was used to the determine the concentrations of nitrogen, starch and various carbohydrates in milled Scots pine needle samples. A multivariate calibration using partial least square regression (PLS) was used. To remove variation due to light scattering, which is particularly difficult to handle in diffuse reflectance spectroscopy, multiplicative scatter correction (MSC) was in the case of nitrogen and carbohydrate analyses and the second-order derivation of the spectra for starch was used. The NIR prediction was good for nitrogen and somewhat less for starch. The predictability (Q2) was 0.83 for nitrogen and 0.86 for starch, and the root mean square error of prediction (RMSEP) was 0.06 for nitrogen and 0.91 for starch. The preliminary results of the carbohydrate model indicated that NIR spectroscopy has a potentially useful role in the measurement of the carbohydrate content of the pine needle samples; however, further development of the method is still necessary to reach acceptable accuracy.