Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees.

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( R c 2 = 0.834, RMSEC = 0.262, RPD c = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( R c 2 = 0.816, RMSEC = 0.276, RPD c = 2.331) and raw spectra ( R c 2 = 0.822, RMSEC = 0.271, RPD c = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality.

Near infrared spectroscopy calibration for wood chemistry: which chemometric technique is best for prediction and interpretation?

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins.

Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set ( R p 2 ) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

Fast Pyrolysis Bio-Oil-Based Epoxy as an Adhesive in Oriented Strand Board Production.

The objectives of this study were to utilize bio-oil-based epoxy resin in oriented strand board (OSB) production and investigate the effect of bio-oil substitution in epoxy resin as an adhesive for OSB production. Bio-oil was produced by the fast pyrolysis (FP) process using southern yellow pine (Pinus spp.). Bio-oil-based epoxy resin was synthesized by the modification of epoxy resin with FP bio-oil at various substitution levels. Acetone extraction using a Soxhlet process indicated a superior cured reaction of bio-oil and epoxy resin at 20% bio-oil substitution. FTIR spectra corroborated the Soxhlet extraction with the removal of the epoxide peak signature within the cross-linked polymer. Images from the scanning electron microscopy suggested bulk phase homogeneity. OSB panels were tested according to ASTM D1037-12. The modulus of rupture (MOR), modulus of elasticity (MOE), internal bond strength, and water resistance (thickness swell and water absorption) properties of the OSB panels were feasible at bio-oil substitution up to 30% in the epoxy resin system.

Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density.

Visible and near infrared (Vis-NIR) spectroscopy is a mature analytical tool for qualitative and quantitative analysis in various sectors. However, in the face of "curse of dimensionality" due to thousands of wavelengths for a Vis-NIR spectrum of a sample, the complexity of computation and memory will be increased. Additionally, variable optimization technique can be used to improve prediction accuracy through removing some irrelevant information or noise. Wood density is a critical parameter of wood quality because it determines other important traits. Accurate estimation of wood density is becoming increasingly important for forest management and end uses of wood. In this study, the performance of two-dimensional (2D) correlation spectroscopy between wavelengths of various spectral transformations, i.e., reflectance spectra (R), reciprocal (1/R), and logarithm spectra (log (1/R)), were analyzed before optimizing spectral variable. The spectra of optimal transformation were decomposed using biorthogonal wavelet family from 3rd to 8th decomposition level based on lifting wavelet transform (LWT). The optimal wavelet coefficients of LWT were selected based on the performance of calibration set using partial least squares (PLS). Two frequent variable selection methods including uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were also compared. The results showed that the dimensionality of spectral matrix was reduced from 2048 to 16 and the best density prediction results of Siberian elm (Ulmus pumila L.) were obtained (Rp2R = 0.899, RMSEP = 0.016) based on LWT.

Preparation and Characterization of Epoxy Resin Cross-Linked with High Wood Pyrolysis Bio-Oil Substitution by Acetone Pretreatment.

The use of cost effective solvents may be necessary to store wood pyrolysis bio-oil in order to stabilize and control its viscosity, but this part of the production system has not been explored. Conversely, any rise in viscosity during storage, that would occur without a solvent, will add variance to the production system and render it cost ineffective. The purpose of this study was to modify bio-oil with a common solvent and then react the bio-oil with an epoxy for bonding of wood without any loss in properties. The acetone pretreatment of the bio-oil/epoxy mixture was found to improve the cross-linking potential and substitution rate based on its mechanical, chemical, and thermal properties. Specifically, the bio-oil was blended with epoxy resin at weight ratios ranging from 2:1 to 1:5 and were then cured. A higher bio-oil substitution rate was found to lower the shear bond strength of the bio-oil/epoxy resins. However, when an acetone pretreatment was used, it was possible to replace the bio-oil by as much as 50% while satisfying usage requirements. Extraction of the bio-oil/epoxy mixture with four different solvents demonstrated an improvement in cross-linking after acetone pretreatment. ATR-FTIR analysis confirmed that the polymer achieved a higher cross-linked structure. DSC and TGA curves showed improved thermal stability with the addition of the acetone pretreatment. UV-Vis characterization showed that some functional groups of the bio-oil to epoxy system were unreacted. Finally, when the resin mixture was utilized to bond wood, the acetone pretreatment coupled with precise tuning of the bio-oil:epoxy ratio was an effective method to control cross-linking while ensuring acceptable bond strength.

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass.

The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2-0.92; RPD- 3.58) and lignin (R2-0.82; RPD- 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.

Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression.

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e., R2 > 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

The effect of ethanol on hydroxyl and carbonyl groups in biopolyol produced by hydrothermal liquefaction of loblolly pine: (31)P-NMR and (19)F-NMR analysis.

The goal of this study was to investigate the role of ethanol and temperature on the hydroxyl and carbonyl groups in biopolyol produced from hydrothermal liquefaction of loblolly pine (Pinus spp.) carried out at 250, 300, 350 and 390°C for 30min. Water and water/ethanol mixture (1/1, wt/wt) were used as liquefying solvent in the HTL experiments. HTL in water and water/ethanol is donated as W-HTL and W/E-HTL, respectively. It was found that 300°C and water/ethanol solvent was the optimum liquefaction temperature and solvent, yielding up to 68.1wt.% bio-oil and 2.4wt.% solid residue. (31)P-NMR analysis showed that biopolyol produced by W-HTL was rich in phenolic OH while W/E-HTL produced more aliphatic OH rich biopolyols. Moreover, biopolyols with higher hydroxyl concentration were produced by W/E-HTL. Carbonyl groups were analyzed by (19)F-NMR, which showed that ethanol reduced the concentration of carbonyl groups.

Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR.

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.

Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy.

This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm(-1) from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry.

Facile synthesis of soluble graphene quantum dots and its improved property in detecting heavy metal ions.

An effective approach to produce graphene quantum dots (GQDs) has been developed, which based on the cutting of graphene oxide (GO) powder into smaller pieces and being reduced by a green approach, using sodium polystyrene sulfonate (PSS) as a dispersant and l-ascorbic acid (l-AA) as the reducing agent, which is environmentally friendly. Then the as-prepared GQDs were further used for the detection of heavy metal ions Pb(2+). This kind of GQDs has greater solubility in water and is more biocompatible than GO that has been reduced by hydrazine hydrate. The few-layers of GQDs with defects and residual OH groups were shown to be particularly well suited for the determination of metal ions in the liquid phase using an electrochemical method, in which a remarkably low detection limit of 7×10(-9)M for Pb(2+) was achieved.

Effect of liquefaction temperature on hydroxyl groups of bio-oil from loblolly pine (Pinus taeda).

Loblolly pine was liquefied with ethylene glycol at 100, 150, 200 and 250 °C in order to analyze the effect of liquefaction temperature on hydroxyl groups of bio-oil, and to determine the source and variation of hydroxyl groups. The optimum temperature was found to be 150-200 °C. Hydroxyl number (OHN) of the bio-oil was ranged from 632 to 1430 mg KOH/g. GC-MS analysis showed that 70-90% of OHN was generated from unreacted EG. (31)P NMR analysis showed that the majority of hydroxyl groups were aliphatic, and none of the bio-oil exhibited any detectable hydroxyl groups from phenolic sources. Finally, it was found that all bio-oils were stable in terms of OHN for 2 months when stored at -10 °C.

Modeling for proximate analysis and heating value of torrefied biomass with vibration spectroscopy.

The goal of this study was to characterize the changes in biomass with torrefaction for near infrared reflectance (NIR) and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for sweetgum, loblolly pine, and switchgrass. Calibration models were built for the prediction of proximate analysis after torrefaction. Two dimensional (2D) correlation spectroscopy between NIR and FTIR was found to precisely explain the depolymerization at key functional groups located within hemicellulose, cellulose, and lignin. This novel 2D technique also demonstrated the possibility of assigning key NIR wavenumbers based on mid IR spectra. Hemicellulose based wavenumbers were found to be most sensitive to torrefaction severity with complete degradation at 250-275°C. Lignin associated wavenumbers exhibited the least degradation to severity but was still detected with 2D correlation spectroscopy. Finally, calibration models for proximate analysis were performed and while both systems could be used for rapid monitoring, NIR performed better than FTIR.