Developing and Evaluating NIR Calibration Models for Multi-Species Herbaceous Perennials

Abstract

Near-infrared (NIR) calibration models for lignin, glucan, and xylan were developed with 55 samples of individual and mixed biomass species containing wheatgrass (Thinopyrum spp.), switchgrass (Panicum virgatum L.), wild rye (Leymus spp.), big bluestem (Andropogon gerardii), alfalfa (Medicago sativa L.), and sweetclover (Melilotus officinalis L.) planted in five locations across North Dakota. The models were developed using a diode array (DA) 7200 NIR spectrometer (950–1,650 nm) and GRAMS statistical software. Models were validated with 10 samples, and the root mean square errors of prediction (RMSEP) were 0.54, 1.12, and 0.97 for lignin, glucan, and xylan, respectively. Lignin, glucan, and xylan models had R² values of 0.873, 0.843, and 0.902, respectively. The RMSEP and R² values indicate that it is possible to develop acceptable calibration models to predict chemical composition for mixtures of herbaceous perennials. NIR instrument-based component predictions were consistent over a period of hours with coefficients of variation (CV) for replications between 0.8% and 1.8%. The effect of repacking (presentation) was also low, as the CVs for lignin, glucan, and xylan contents of repacked replicate samples were all less than 3.6%. NIR prediction was more precise than wet chemistry analysis as 95% confidence intervals for NIR composition predictions for replicates analyzed over 10 successive days were 50–63% lower than those for standard wet chemistry analyses.