TITLE:
Application of NIR Reflectance Spectroscopy on Rapid Determination of Moisture Content of Wood Pellets
AUTHORS:
Jaya Sundaram, Sudhagar Mani, Chari V. K. Kandala, Ronald A. Holser
KEYWORDS:
Wood Pellets, NIR Reflectance Spectroscopy, Moisture Content, Partial Least Square, Relative Percent Deviation
JOURNAL NAME:
American Journal of Analytical Chemistry,
Vol.6 No.12,
November
13,
2015
ABSTRACT: NIR
spectroscopy was used to measure the moisture concentration of wood pellets.
Pellets were conditioned to various moisture levels between 0.63% and 14.16%
(wet basis) and the moisture concentration was verified using a standard oven
method. Samples from various moisture levels were separated into two groups, as
calibration and validation sets. NIR absorption spectral data from 400 nm to
2500 nm with 0.5 nm intervals were collected using pellets within the
calibration and validation sample sets. Spectral wavelength ranges were taken
as independent variables and the MC of the pellets as the dependent variable
for the analysis. Measurements were obtained on 30 replicates within each
moisture level. Partial Least Square (PLS) analysis was performed on both raw
and preprocessed spectral data of calibration set to determine the best
calibration model based on Standard Error of Calibration (SEC) and coefficient
of multiple determinations (R2). The PLS model that yielded the best
fit was used to predict the moisture concentration of validation group pellets.
Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were
calculated to validate goodness of fit of the prediction model. Baseline and
Multiple Scatter Corrected (MSC) reflectance spectra with 1st derivative model gave the highest RPD value of 4.46 and R2 of 0.95.
Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those
had RPD value more than 3.0 with less number of factors. Therefore, this model
was selected as the best model for moisture content prediction of wood pellets.