Using NIR /PLS to predict composition and reactivity of herbaceous feedstocks

Obtaining accurate chemical composition and reactivity information for biomass feedstocks in a time efficient manner is necessary for the commercialization of biofuels.  We used Near Infrared (NIR) spectroscopy and Projection to Latent Structures (PLS) multivariate analysis to develop calibration models to predict the composition and the release and yield of soluble carbohydrates generated by bench-scale dilute acid pretreatment and enzymatic hydrolysis assay.  These models are useful for rapidly screening sample populations to identify unusual samples. Feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial grasses, and miscanthus. We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the methods.