Abstract

Loblolly (Pinus taeda) and slash (Pinus elliottii) pine tree biomass are significant renewable energy resources for bioenergy industries in the Southeastern United States. There is a great need for evaluation of their properties (relevant to production and evaluation-screening for bioenergy extraction) by rapid but accurate analytical methods like near infrared reflectance (NIR) spectroscopy. However, no attempts have been made so far to develop useful NIR spectroscopic calibration models for analyzing biomass of these two species. In this study, acceptable NIR spectroscopic calibration models were developed for: gross calorific value, GCV (n = 181; R²= 0.83; ratio of standard error of cross-validation to deviation, RSCD = 2.32; ratio of standard error of cross-validation to inter-quartile distance, RSCIQ = 2.68); net calorific value, NCV (n = 184; R²= 0.83; RSCD = 2.31; RSCIQ = 2.69); ash-free calorific value, AFCV (n = 184; R²= 0.83; RSCD = 2.24; RSCIQ = 2.49); moisture (n = 181; R²= 0.85; RSCD = 2.54; RSCIQ = 2.30); ash (n = 180; R²= 0.86; RSCD = 2.65; RSCIQ = 2.62); total carbon, C (n = 101; R²= 0.95; RSCD = 3.01; RSCIQ = 5.66); total nitrogen, N (n = 87; R²= 0.95; RSCD = 4.24; RSCIQ = 4.76); and total sulfur, S (n = 83; R²= 0.97; RSCD = 4.17; RSCIQ = 3.05) contents of these biomasses though the calibration models for NCV and AFCV are indirect calibration. Prediction of the independent validation sets yielded good agreement between the NIR spectroscopic predicted values and the laboratory reference values for each of: GCV (n = 92; r²= 0.89; ratio of performance to deviation; RPD = 3.01; ratio of performance to inter-quartile distance, RPIQ = 3.16); NCV (n = 91; r²= 0.83; RPD = 2.43; RPIQ = 3.06); AFCV (n = 91; r²= 0.80; RPD = 2.25; RPIQ = 2.83); moisture (n = 92; r²= 0.82; RPD = 2.38; RPIQ = 2.40); ash (n = 89; r²= 0.81; RPD = 2.30; RPIQ = 2.66); C (n = 43; r²= 0.90; RPD = 3.14; RPIQ = 3.23); N (n = 44; r²= 0.95; RPD = 4.33; RPIQ = 5.96); and S (n = 42; r²= 0.93; RPD = 3.67; RPIQ = 3.24) contents, indicating that all eight calibration models had good quantitative information. The standard errors of prediction for all models were less than twice the corresponding standard error of laboratory. Therefore, precise, accurate, and rapid analysis of calorific values and C, N, S contents of these biomasses can be done using these novel NIR spectroscopic calibration models developed.

© 2017 The Author(s)

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