The Use of Near-Infrared (NIR) Spectroscopy and Principal Component Analysis (PCA) To Discriminate Bark and Wood of the Most Common Species of the Pellet Sector (original) (raw)
Related papers
Fast measurement by infrared spectroscopy as support to woody biofuels quality determination
Journal of Agricultural Engineering, 2016
The increase in the demand for energy supply during the past few decades has brought and will bring to a growth in the utilisation of renewable resources, in particular of solid biomasses. Considering the variability in the properties of biomass and the globalisation of the timber market, a chemical and physical characterisation is essential to determine the biomass quality. The specific international standards on solid biofuels (ISO 17225 series) describe proper specification and classification of wood chip and pellet, to ensure appropriate quality. Moreover, standard requires information about origin and source of the biomass, normally only to be declared by the producers. In order to fulfill the requirements for the biomass quality, the origin and the source should be assessed, even if currently is hard to determine, in particular on milled or densified biomass. Infrared spectroscopy can provide information on the biomass at the chemical level, directly linked also to its origin ...
Fuel, 2018
The increasing concern regarding energy supply and the consequent rapid growth of the pellet market lead to the need to classify the product quality. To this aim, chemical-physical parameters and qualitative attributes are defined by the technical standards EN ISO 17,225 to classify the quality of biofuels, but, while the former can be determined by traditional chemical analysis, no methodologies have been set for the latter one. Hence, nearinfrared spectroscopy was tested to obtain information about the origin and the source of the pellet, at the moment only declared by the producers and difficult to be achieved by conventional analysis. In fact, the great strength of the technique is based on the fact that biomass features could be read simultaneously with a rapid and cheap NIR measurement. Checking the presence of treated wood (e.g. residues from wood processing industry) especially in densified products, such as pellets and briquettes, is particular important since in several European countries, e.g. Italy, these materials are considered as waste. In this study more than a hundred samples of virgin and treated wood (residues from wood processing industries) were analysed by means of FT-NIR. Partial Least Square regression-Discriminant Analysis was used in order to classify samples between the two classes and different variables selection methods were tested in order to improve the classification performance of the models. The results obtained demonstrated that near infrared analysis coupled with multivariate analysis can be used in screening applications to classify virgin wood from glue-laminated wood and treated wood. In particular, the model for the discrimination of treated wood (except glue-laminated samples) from virgin wood performs 100% correct classification and the model for the discrimination between virgin wood and glue-laminated wood only has a 3.6% misclassification rate. The methodology can be considered as the first one able to provide information about the origin of the biomass in a rapid and cheap way.
Methodology for Identification of High-Value Biomass Feedstocks - Final Report
The objective of the Phase I project was to develop an advanced methodology for the assessment of biomass materials based on the combination of thermal analysis with Fourier Transform Infrared (FT-IR) spectroscopy (TG-FTIR) and advanced data analysis methods. This work was accomplished in four tasks: 1) sample selection and TG-FTIR modification; 2) testing on a suite of biomass materials; 3) data analysis using chemometrics and other advanced methods; 4) preliminary design of Phase II prototype. The Phase I project has yielded promising results. For example, good agreement was obtained for the prediction of bio-oil yields when compared to data from TG-FTIR analysis of 46 biomass materials. These predictions were based on an Artificial Neural Net (ANN) model with the following inputs: 1) elemental composition (C,H,N,O,S); 2) volatile matter content; 3) ash content. In addition, similar results were obtained for the prediction of several other major products. Finally, a preliminary de...
Bioresource technology, 2010
This paper is the first of a two series papers on the use of near infrared (NIR) coupled with multivariate data analysis (MVDA) as a process analytical technology (PAT) tool for the rapid characterization of physical and chemical properties of two common West Virginian hardwood species, northern red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera L.). These two wood species are potential feed stock for the bio-refinery industry. In Part 1, we report our results on yellow-poplar. The results of this study demonstrated that some preprocessing operations on the NIR spectra (first derivative) greatly improved all the prediction models developed in the study. Predictive PLS1 models developed using selective spectra regions, 1300-1800 nm and the full NIR region (800-2400 nm), were similar. The selective spectra region, 1300-1800 nm, included the first and second overtone of the NIR spectrum (1300-1800 nm). Measured and predicted physical and chemical properties of yellow-poplar yielded moderate to high correlation (R2).
In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O mode...
IM Publications Open LLP eBooks, 2019
With the aim to reduce the dependence on fossil fuels and mitigate climate change, biomass for energy use is becoming more and more important. In particular, wood pellets are gaining greater attention because of the easy logistics and their high energy density in comparison to other solid biomasses. This is also demonstrated by the rapid growth of its demand in Europe. For pellets, traceability is a very important and complex issue, since the feedstock employed is de-structured by grinding and densification and thus losing qualitative information. As a consequence, a multitude of wood sources can participate to their blend in a concealed way, modifying the quality. The international standard EN ISO 17225-2 defines different quality classes for woody pellets taking into consideration chemical-physical parameters and the provenance traceability and composition of the material. In particular, the European standard considers the possibility of using by-products and residues from the wood processing industry, i.e. wood containing glue residues, for pellet production, but Italian national legislation considered these materials like waste. This work aimed at verifying the ability of Fourier Transform−Near-Infrared (FT-NIR) spectroscopy to discriminate between treated and virgin wood. For this purpose, more than one hundred samples of virgin and treated wood deriving from the wood processing industry were collected and analyzed by FT-NIR. The results obtained showed that this technique is able to provide qualitative information about pellet traceability. Therefore, the methodology should be considered as a valid tool for pellet quality control, because it allows to obtain information about the origin of the material used for its production.
Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy
Biomass and Bioenergy, 1996
Near infrared reflectance spectroscopy (NIRS) has been used extensively in lignocellulose analysis of forages and should be useful in predicting the chemical composition of biomass feedstocks. We determined the chemical composition of several woody and herbaceous feedstocks (121 samples in total) in the laboratory and used these data to calibrate an NIR spectrometer. Samples were analysed for ethanol extractives, ash, lignin, uranic acids, arabinose, xylose, mannose, galactose, glucose, C, H, N and 0. A modified partial-least-squares statistical technique was used to develop calibration equations. Twenty samples not used in the calibration were used for independent validation of the prediction equations. Calibration equations were developed successfully for concentrations of all constituents except H and 0. When the equations were applied to the 20 validation samples, only extractives, lignin and arabinose had validation statistics within the control limits. Mannose., galactose., C, H and 0 could not be predicted with any precision or accuracy. These results indicate that NIRS can be used to predict the chemical composition of a broad range of biomass feedstocks. Increasing the population size for calibration and (or) developing more narrowly based calibrations may improve prediction ability and result in a technique that should be useful in rapid analysis of biomass feedstocks for research and industry.
Journal of analytical methods in chemistry, 2016
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., R(2) > 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 compositi...