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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #319167

Title: Application of NIR reflectance spectroscopy on rapid determination of moisture content of wood pellets

Author
item SUNDARAM, JAYA - University Of Georgia
item SUDHAGAR, MANI - University Of Georgia
item Kandala, Chari
item Holser, Ronald

Submitted to: American Journal of Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2015
Publication Date: 11/13/2015
Citation: Sundaram, J., Sudhagar, M., Kandala, C., Holser, R.A. 2015. Application of NIR reflectance spectroscopy on rapid determination of moisture content of wood pellets. American Journal of Analytical Chemistry. 6:923-932.

Interpretive Summary: It is possible to measure the moisture content in different material by shining light of a particular wavelength on a sample and observing the amount of light either absorbed or reflected. The application of this method to biomass is significant to determine when the material is ready to be converted into energy or other useful products. The method was tested on wood chips and able to accurately measure moisture content. The method is very rapid and reliable. It can be used for numerous materials without preparation of the sample and integrated with other process control elements.

Technical Abstract: NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63and 14.16percent (wet basis) and the moisture concentration was verified using a standard oven method. Samples from the 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 spectral data and spectral data with preprocessing of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (Rsquare). SEC is the estimation of calibration procedure and R2 allowed determining the amounts of variation in data predicted by calibration models. The preprocessing and its PLS model that yielded the best fit were used to predict the pellets moisture concentration from the spectral data of the validation group of pellets. Predicted and reference moisture contents were compared. 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 Correction (MSC) corrected reflectance spectra with 1st derivative model gave the highest RPD value of 4.46 and Rsquare 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. Baseline and MSC corrected absorbance spectra model also gave RPD of 3.9 with Rsquare of 0.94 close to the reflectance model. Therefore, these two models were selected as the best model for moisture content prediction of wood pellets sample.