Comparative Study of NIR-based Prediction Methods for Biomass Weight Loss Profiles

Abstract

: Biomass has become a major feedstock for bioenergy and other bio-based products because of its renewability and environmental benefits. Various researches have been done in the prediction of crucial characteristics of biomass, including the active utilization of spectroscopy data. Near infrared (NIR) spectroscopy has been widely used because of its attractive features: it's non-destructive and cost-effective producing fast and reliable analysis results. This work developed the multivariate statistical scheme for predicting weight loss profiles based on the utilization of NIR spectra data measured for six lignocellulosic biomass types. Wavelet analysis was used as a compression tool to suppress irrelevant noise and to select features or wavelengths that better explain NIR data. The developed scheme was demonstrated using real NIR data sets, in which different prediction models were evaluated in terms of prediction performance. In addition, the benefits of using right pretreatment of NIR spectra were also given. In our case, it turned out that compression of high-dimensional NIR spectra by wavelet and then PLS modeling yielded more reliable prediction results without handling full set of noisy data. This work showed that the developed scheme can be easily applied for rapid analysis of biomass.

Rapid Characterization and Prediction of Biomass Properties via Statistical Techniques

“…For each of three wood species (redoak, hickory, and yellow poplar) and three others (switch grass, corn stover, and bagasse) three different samples were collected. NIR data for these biomass species were obtained with an advanced spectral devices (ASD) field spectrometer (wavelength range from 1,000 nm to 2,500 nm, Boulder, USA) [10]. The NIR data was reduced by averaging the 1 nm interval spectra to one with 4 nm intervals.…”