Abstract

Gross calorific value (GCV) has been predicted by building models based on near infrared (NIR) spectroscopy and multivariate analysis; however, to date, the impact of feedstock chemical composition on the models has not been directly assessed. In the present study, 20 longleaf pine trees were sampled at two positions (breast and mid-height) for calorimetric and spectroscopic analyses. The GCVs, which ranged from 20 MJ kg⁻¹ to 24 MJ kg⁻¹, showed a strong correlation with the wide-ranging values of acetone-soluble extractives content. After extraction of the samples with acetone, the range for the GCV was both lower and slightly narrower (19–21 MJ kg⁻¹) and was poorly correlated to lignin content with its narrow range of values. Near infrared (NIR) spectroscopy coupled with multivariate analysis was applied to the samples and provided a strong coefficient of determination (R²) between the values predicted by NIR and those determined by calorimetry for the unextracted, but not the extracted, samples. Plotting the regression coefficients validated the results by showing very similar plots for GCV and extractives content, thereby indicating that the same molecular features are driving the models. NIR spectroscopy coupled with multivariate analysis can predict GCV for bioenergy feedstocks and also provide insight into chemical features with the greatest impact on fuel value.

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