研究目的
To employ FTIR spectroscopy coupled with partial least squares (PLS) regression to rapidly predict the chemical composition, thermal reactivity, and energy content of logging residue of loblolly pine.
研究成果
FTIR spectroscopy coupled with PLS regression can effectively predict the chemical and thermochemical properties of forest logging residue, with good performance for extractives, lignin, volatile matter, and fixed carbon. Reducing the wavenumber range to the fingerprint region did not compromise predictive ability and often improved it. This method offers a high-throughput alternative to conventional laborious techniques, facilitating the optimization of biomass conversion processes in biorefineries.
研究不足
The study had a relatively small sample size (n=40), which may limit the generalizability of the models. Poor performance was noted for predicting some monomeric sugars (e.g., galactose, mannose) and higher heating value, attributed to similar molecular structures of carbohydrates and secondary correlations. The models for ash content also performed poorly, possibly due to the nature of inorganic compounds not producing strong IR vibrations.
1：Experimental Design and Method Selection:
The study used FTIR spectroscopy with PLS regression for predictive modeling. Spectra were collected and treated with first derivatives for baseline correction and to reduce nonlinearity and multicollinearity.
2：Sample Selection and Data Sources:
Lignocellulosic biomass from loblolly pine plantations in southern Alabama, USA, was used, comprising whole tree, wood and bark, slash (limbs and foliage), and clean wood chips. Ten biomass sets were sampled for each type.
3：List of Experimental Equipment and Materials:
Equipment included a PerkinElmer Spectrum 400 FT-IR/FT-NIR spectrometer with a diamond crystal attenuated total reflectance device and a torque knob, Soxhlet apparatus, vacuum oven, autoclave, UV/vis spectrophotometer, Bio-Rad Aminex HPX-87P column equipped HPLC, bomb calorimeter. Materials included acetone, sulfuric acid, deionized water, distilled water.
4：Experimental Procedures and Operational Workflow:
Samples were ground to pass an 80-mesh screen, oven-dried at 40°C for 4 hours, placed on the diamond plate with 70±2 psi pressure, and scanned 32 times at 4 cm?1 resolution. Background correction was applied. Conventional methods were used for chemical composition (extractives, lignin, carbohydrates via hydrolysis and HPLC), thermal reactivity (proximate analysis for ash, volatile matter, fixed carbon), and energy content (bomb calorimetry for HHV).
5：Data Analysis Methods:
PLS regression was performed using PerkinElmer's Spectrum Quant+ software with leave-one-out cross-validation. Models were evaluated using SEC, SECV, R2, and RPD. ANOVA and Tukey tests were used for statistical comparisons.
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