Prediction of Hardwood and Softwood Contents in Blends of Wood

Article pubs.acs.org/EF

Prediction of Hardwood and Softwood Contents in Blends of Wood Powders Using Mid-Infrared Spectroscopy Daniele Duca, Andrea Pizzi, Giorgio Rossini, Chiara Mengarelli, Ester Foppa Pedretti, and Manuela Mancini* Agricultural, Food and Environmental Science, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy ABSTRACT: Biofuel quality control plays an important role considering the recent European policy about renewable energy source promotion. Origin and source of the raw material are often required to be stated by biofuel chain operators for traceability and sustainability issues. Being fast, non-destructive, and low-cost, infrared spectroscopy coupled with chemometrics is already applied to several sectors and could also be employed in the solid biomass sector. The result of this work is a tool for the prediction of hardwood and softwood contents in blend samples by means of Fourier transform infrared spectroscopy coupled with partial least squares regression. A total of 61 samples of fir, pine, sessile oak, and beech and four series of binary blends (28 samples) from wood powders of one hardwood species and one softwood species were analyzed. The infrared prediction model was full cross-validated. The results of this work showed the good performance of the model with a standard error of a few percentage points (3.8%). As a consequence, the development of an analytical instrument based on such techniques could be useful to support the bioenergy chain stakeholders, such as solid biofuel producers, traders, and customers, for traceability, process tuning, and quality control issues.

1. INTRODUCTION European policy currently promotes renewable energy sources to reach specific sustainability targets, in particular for greenhouse gas reduction. Solid biofuels in the form of pellets, woodchips, briquettes, and others can contribute significantly to this aim, with a growing market in the last few years.1,2 The biofuel quality control plays an important role, especially regarding traceability, production, and sustainability issues.3−5 This control is important because it affects efficiency in energy conversion systems and related emissions.6,7 The quality is checked by technical standards listing analytical parameters, which correlations have already been studied by the authors.8,9 The origin and source of the raw material (e.g., hardwood and softwood) are often required to be stated (EN ISO 17225-110) by biofuel chain operators for traceability and sustainability issues. This information is difficult to check by conventional analysis or microscopy techniques because they are time- and money-consuming. Infrared spectroscopy (IRS) could be a suitable technique to overcome these issues. Being fast, non-destructive, and lowcost, IRS is applied to not only the biofuel sector11,12 but also other fields, such as the agricultural food sector,13,14 pharmaceutical sector,15 and industrial sector.16,17 It could be useful to verify biomass quality parameters directly in the production line to support the production process monitoring and tuning as studied by Lestander and co-workers.18,19 When IRS is combined with chemometric tools, such as principal component analysis (PCA),20−22 soft independent modeling of class analogies,23,24 and partial least squares (PLS),19,25 it is possible to extract valuable information from the spectral database. Fourier transform infrared (FTIR) spectroscopy and PCA have already been used to discriminate hardwood and softwood by other authors;26 however, the sample size is not comparable, © XXXX American Chemical Society

and the clustering of spectral data from different blends in the PCA space has not been analyzed. No study has been carried out using PLS regression techniques to evaluate hardwood and softwood contents in wood samples. Authors are aware of the solid biofuel chain necessities at different levels to have such a tool to improve the quality control and have information about sustainability and traceability. With IRS, it is possible to acquire a huge amount of information with fast and low-cost response directly available for the operators. Therefore, the first purpose of this study was the discrimination between softwoods, hardwoods, and the related blends by means of FTIR coupled with PCA. Over 60 samples belonging to some of the most common European species, especially employed in the energy pellet sector, were collected and analyzed by the Biomass Lab of Università Politecnica delle Marche. The second purpose was the prediction of hardwood and softwood contents in blend samples by PLS analysis of FTIR spectra. To this aim, a total of 28 blends at different concentration levels were used.

2. MATERIALS AND METHODS 2.1. Sample Collection and Preparation. In this study, 29 samples of hardwood and 32 samples of softwood of the most common European species in the energy pellet sector, i.e., fir, pine, sessile oak, and beech, have been selected and collected (Table 1). Only whole pieces of wood-like beams or boards from sawmills and debarked tree log disk wood slices were taken to have samples with known origin and non-chemically treated. The material was first of all stabilized at 45 °C for 24 h; in this way, all of the samples have reached the same moisture content of 6−7%. Subsequently, the samples were Received: December 23, 2015 Revised: February 22, 2016

A

DOI: 10.1021/acs.energyfuels.5b02994 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 1. Dataset of Hardwood and Softwood Samples hardwood

data set (n = 29)

softwood

data set (n = 32)

sessile oak (Quercus petraea) beech (Fagus sylvatica)

15 14

pine (Pinus spp.) fir (Abies spp.)

16 16

ground by means of a cutting mill (model SM 2000, Retsch), sieved under 0.25 mm, stored in plastic containers, and analyzed in medium infrared. A total of 28 blends were prepared and analyzed in the same way as samples at different concentrations (Table 2). Observation space (pyramid) for blends of wood was also reported in Figure 1. The preparation of the blends was performed by means of average samples (B0, S0, P0, and F0), and they were obtained mixing the same quantities of material by 10 previously prepared samples (stabilized and ground) of the same species; in this way, the representativeness of the material was increased. The components were combined and mixed in plastic containers. 2.2. Mid-Infrared Spectroscopy. To collect mid-infrared spectra, a FTIR spectrometer equipped with a ZnSe single reflection (FTIR model Nicolet iS 10, Thermo) attenuated total reflectance accessory (model Smart iTR, Thermo) has been used. To perform the analysis, a small amount of sample (0.2−0.3 g) was pressed onto the crystal using a high-pressure tower. The instrument was set to acquire the spectrum at a wavenumber range between 600 and 4000 cm−1, 64 scans per sample, and spectral resolution of 4 cm−1. A blank spectrum was acquired before each analysis to exclude the signals not associated with the sample (e.g., environment). All samples were analyzed in triplicates. 2.3. Data Processing and PCA. To find relevant information among a huge amount of data, i.e., spectral data, several multivariate chemometric methods are available. In this study, PCA was performed for the discrimination of the two classes of wood and for a preliminary evaluation of hardwood and softwood contents in blends. PCA was performed by means of MATLAB (version 7.10.0, MathWorks) on a pretreated data matrix of samples and mean-centered data. Before PCA computation, the spectral database was subjected to different pretreatments on a selected range with high spectral information (i.e., the wavenumber range between 650 and 1850 cm−1). Standard normal variate (SNV) was performed to reduce the scatter effect and first derivatives to enhance spectral information. The derivative spectra were smoothed by the Savitzky−Golay method with a segment size of 13 points and second polynomial order. At last, the three replicates of each sample were averaged in one representative spectrum. All pretreatments were carried out using The Unscrambler (version 9.7, Camo Process AS). 2.4. Data Processing and PLS Regression. The infrared prediction model was obtained by PLS regression and cross-validated with the leave-one-out method. In this type of validation, n validation models were created from the original data set to assess the performance of the prediction model. For each validation model, the test set was obtained taking out a sample in the original data set, while the remaining samples represented the training set, which was used to build up each statistical model. Then, the value for the test set was predicted, and the prediction residuals were calculated. The procedure was repeated for each validation model. As a result, all prediction residuals were combined to calculate validation residual variance and the root-mean-square error of cross-validation (RMSECV). The

Figure 1. Observation space (pyramid) for blends of wood from the four species B, S, F, and P. Dots indicate observation of 28 binary blends (FB, BP, PS, and SF) (B, beech; F, fir; S, sessile oak; and P, pine).

models PLS developed without the average of replicates [i.e., no treatment, SNV, and multiplicative scatter correction (MSC)] were validated taking out not just one spectra of the three replicates but all of them at the same time. PLS finds the relationship between the y value and the spectral data matrix computing latent variables, i.e., a new smaller set of variables, which are linear combinations of the spectral data. The number of latent variables was chosen considering the lowest value of RMSECV, and the accuracy of the model can be evaluated using different regression coefficients. The determination coefficient (R2) indicates how well data are replicated by the statistical model: values between 0.50 and 0.65 allow for the discrimination between high and low concentrations; values between 0.66 and 0.81 indicate approximate quantitative predictions; and values between 0.82 and 0.90 reveal good predictions. Calibration models with values for R2 above 0.91 are considered to be excellent.25,27 The range error ratio (RER) is used to evaluate the prediction accuracy of the model. It is calculated by dividing the range of a given parameter by the prediction error for that parameter (RMSECV). RER values of less than 6 indicate very poor classification and are not recommended for any application. RER values between 7 and 20 classify the model as poor to fair and could be used for screening purposes. RER values between 21 and 30 indicate a good classification, suggesting that the model would be suitable for quality control applications.25,27,28 In this study, PLS was performed in R software (version 3.1.2, R Development Core Team)29 equipped with the PLS package30 on the pretreated data matrix of samples and mean-centered data. The aim of the regression analysis was the prediction of hardwood or softwood contents in ground wood blends. Before PLS computation, the spectral database was subjected to different pretreatments, such as SNV, MSC, spectral average of replicates, and derivatives (smoothed with Savitzky−Golay). Pretreatments were carried out on all wavelength ranges between 600 and 4000 cm−1 (IR1) and the wavelength range between 650 and 1850 cm−1 (IR2), with the range selected because it contained high spectral information. All pretreatments were performed in R software equipped with the PROSPECTR package31 and PLS package.30 The best prediction model was evaluated comparing the regression coefficients and choosing the model with the highest RER value and lowest RMSECV value.

Table 2. Percentage of Hardwood and Softwood Blends (%, w w−1)a

a

hardwood−softwood

100−0%

5−95%

15−85%

25−75%

50−50%

75−25%

85−15%

95−5%

0−100%

beech−fir beech−pine sessile oak−fir sessile oak−pine

F0 P0 F0 P0

BF1 BP1 SF1 SP1

BF2 BP2 SF2 SP2

BF3 BP3 SF3 SP3

BF4 BP4 SF4 SP4

BF5 BP5 SF5 SP5

BF6 BP6 SF6 SP6

BF7 BP7 SF7 SP7

B0 B0 S0 S0

B, beech; F, fir; S, sessile oak; and P, pine. B


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Energy & Fuels

Figure 2. Raw average spectra of pure wood samples and 50/50% blends (B, beech; F, fir; S, sessile oak; and P, pine).

3. RESULTS AND DISCUSSION 3.1. Spectra Analysis. Figure 2 compares the raw average spectra of pure wood and 50% blends. Spectral differences between hardwood and softwood are evident, and the most relevant wavenumbers are reported in Table 3. The discrimination between the two groups are mainly due to lignin and hemicellulose, which are different in chemical composition. No relevant spectral differences are connected to the cellulose compound. In more detail, the softwood lignin is a polymer consisting mostly of guaiacyl units with only a small amount of phydroxyphenyl and syringyl units. The hardwood lignin is

composed of syringyl and guaiacyl units, with a small amount of p-hydroxyphenyl units.32 Absorption bands correlated to syringil units, i.e., 1594, 1326, 1228, and 1422 cm−1, can be easily detected in hardwood spectra, while characteristic guaiacyl unit absorption bands (1264 and 1239 cm−1) are more evident in softwood. Hemicelluloses are mainly composed by 80−90% of 4-Omethylglucoronoxylan in hardwoods, while 60−70% glucomannans and 15−30% arabinogalactan are the main constituents in softwoods.33 Softwood shows a characteristic peak at 807 cm−1 because of glucomannan absorption, which is not evident in hardwood spectra. On the other hand, the presence C


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Energy & Fuels

Table 3. Main Infrared Absorption Bands Found in the Three First PLS Loadings Responsible for Hardwood and Softwood Discrimination (a) measured wavenumber (cm−1) 807 (F, P) 1143 (F, P) 1228 (S), 1234 (B)

1264 1316 1326 1422

(F), 1263 (P) (F, P) (B), 1324 (S) (B, S, F, P)

1461 (S, B) 1505 (B, S), 1510 (F), 1509 (P)

1594 (S), 1593 (B)

1694 (P) 1732 (S, B, P), 1731 (F) a

bibliography wavenumber (cm−1) 810 1140 1240 1233 1226 1269 1317 1326 1424 1425 1420 1423 1460 1460 1506−1513 1505 1510 1595 1596−1600 1594 1690 1697 1730 1734

assignment

compound

glucomannan, hydrogen on the C2 atom in the mannose residue37 (b) aromatic C−H in plane of guaiacyl units38,39 (s) C−O in the OC−O group in xylose37 syringyl nuclei of plane at positions 2, 5, and 6 in guaiacyl units34 syringil ring vibration; (s) C−O of lignin and xylan32 (s) guaiacyl ring plus (s) carbonyl group34 CH2 wagging37 ring breathing of syringyl units40 (b) C−OH of the CH2−OH group in cellulose37 (s,b) C−H and C−O characteristics of different groups for lignin and carbohydrates41 vibration of the aromatic ring of lignin; (b) C−H in cellulose39 aromatic ring vibrations of lignin40 (b) CH2 on the xylose ring37 asymmetric deformation of the C−H bond of xylan39 (s) asymmetric vibration for aryl ring42 (s) guaiacyl and syringil (hardwood)39 (s) guaiacyl (softwood)39 lignin absorption band43 (s) symmetric vibration for aryl ring42 (s) CC of the aromatic ring (syringyl), (s) CO, and (b) C−H44 (s) CO of unsaturated acids and ketones (resin acids)45 typical band of resin acids46,47 (s) CO by the ester carbonyl resin34 (s) CO in the OC−OH group of the glucuronic acid units (hardwood)37

H L L, H

L C L L, C

H L

L

E E, H

s, stretching; b, bending; B, beech, F, fir, S, sessile oak, P, pine; L, lignin; H, hemicellulose; C, cellulose; and E, extractives.

of an acetyl group in hardwood xylan causes an higher absorption than softwood at 1732 cm−1. 3.2. Spectral Data Exploration. PCA was carried out on the spectral database to investigate the possibility to discriminate hardwood and softwood samples and to recognize blends at different levels of concentration. Accounting for 85.9% of the total initial variability, the two first principal components (PCs) were taken into account for further elaborations. The scores plot of the first two PCs shows that hardwood and softwood samples are well-separated along PC1; softwoods have negative PC1 scores, whereas hardwoods have positive PC1 scores (Figure 3). It is also possible to observe a separation between the different species along PC2;

pine and sessile oak have mainly positive PC2 scores, and beech and fir have negative PC2 scores. In Figure 4, projections on PC space of blends and average samples for each species are shown. The blends are positioned

Figure 4. PCA scores plot of hardwoods, softwoods, and blends at different levels of concentration. Species_av, average samples, being the mean of the 10 pooled samples for each species; %_hard, percentage of hardwood in the blend.

mainly along the lines of connection between the average sample species (B0, F0, S0, and P0) used to form that specific blend. It is worth noting that there is a linear trend related to hardwood/softwood contents. The binary blends at 50% of the hardwood/softwood concentration (BF4, BP4, SF4, and SP4) were found in the middle between the two groups, while the blends at 25% (BF3, BP3, SF3, SP3, BF5, BP5, SF5, and SP5), 15% (BF2, BP2, SF2,

Figure 3. Scores plot of the first two PCs. D


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Energy & Fuels Table 4. Summary of PLS Prediction Results treatmenta

spectral region

Lb

R2

RMSECV (%)

slope

bias

variance xc (%)

variance yc (%)

RER

no treatment SNV MSC SNV av SNV 1der13 av SNV 1der21 av SNV 2der21 av no treatment SNV MSC SNV av SNV 1der13 av SNV 1der21 av SNV 2der21 av

IR1 IR1 IR1 IR1 IR1 IR1 IR1 IR2 IR2 IR2 IR2 IR2 IR2 IR2

9 6 6 5 4 4 1 7 5 5 4 10 10 5

0.95 0.96 0.96 0.99 0.98 0.98 0.97 0.95 0.96 0.96 0.99 0.99 0.99 0.99

8.0 7.4 7.4 4.2 4.8 4.5 5.8 8.1 7.3 7.3 4.4 3.8 3.8 4.5

0.96 0.96 0.96 0.99 0.99 0.99 0.96 0.96 0.96 0.96 0.99 1.00 1.00 0.98

−0.16 0.04 0.03 0.05 −0.12 −0.12 0.05 −0.04 0.04 0.04 0.12 0.07 0.01 0.10

99.9 97.9 97.9 98.9 96.5 98.0 69.5 99.8 97.1 97.1 98.5 99.1 99.5 93.7

96.8 96.8 96.8 99.2 99.3 99.2 97.8 96.7 96.8 96.8 99.1 100.0 100.0 99.8

12.5 13.5 13.5 23.5 20.9 22.4 17.3 12.4 13.7 13.6 23.0 26.4 26.4 22.5

a MSC, multiplicative scatter correction; SNV, standard normal variate; 1derX, first derivative with X number of smoothing points; 2derX, second derivative with X number of smoothing points; and av, average. bL = number of latent variables. cPercentage explained of variance using latent variables for x (spectra) and y (reference data).

SP2, BF6, BP6, SF6, and SP6), and 5% (BF1, BP1, SF1, SP1, BF7, BP7, SF7, and SP7) of hardwood or softwood contents are proportionally positioned in the right and left sides, respectively. The linear distribution of blends based on the hardwood/softwood content suggests that the methodology could be efficient also in identifying the blend percentage, and further elaborations were carried out using PLS regression. 3.3. Prediction of Hardwood and Softwood Contents. Table 4 shows PLS prediction results using different types of pretreatments. The number of smoothing points does not considerably influence the prediction accuracy; therefore, 13 points were chosen. It is to be noted that, in general, the spectral region IR2 returns results better than IR1 and the model accuracy increases using average as pretreatment. The best prediction of hardwood and softwood contents was developed on IR2 using SNV, first derivative (Savitzky-Golay method, with 13 smoothing points), and average of three replicates. Figure 5 shows the best prediction model for hardwood and softwood contents, which has RMSECV of 3.8%, R2 of 0.99, and RER of 26.4. All values indicate that the results are excellent and that the model could be used in any quality control application where binary blends of the same hardwood and softwood species are used. The first three PLS loadings in the model account for 80.6, 6.6, and 8.3% of the variance in the spectral data and 93.0, 0.4, and 0.4% variance in the reference data, respectively. The PLS loadings for the best model shown in Figure 6 were analyzed to identify the compounds associated with the most important wavenumbers linked to blend percentage prediction. To make an easier interpretation of the PLS loadings, a comparison to raw average spectra of pure wood (Figure 2) was also carried out. The interpretation of derivative spectra is more complex than raw spectra. A peak of maximum absorbance on the original spectra corresponds to zero in the first derivative and to a trough, alongside which are positive satellite bands, on the second derivative.25 Furthermore, zero point data around the highest peaks in PLS loading, corresponding to the maximum of the raw peak spectra, were selected and compared to the literature.

Figure 5. PLS regression model for the prediction of hardwood and softwood contents.

Other authors found that a band shift in PLS loading can be observed around the original peak position.25,34,35 As shown in Figure 6, the most relevant wavenumber are at 1018, 1143, 1270, and 1512 cm−1 for the first PLS loading, 1188, 1454, and 1697 cm−1 for the second PLS loading, and 1068, 1244, and 1420 cm−1 for the third PLS loading. Besides bands at 1018, 1068, and 1188 cm−1, all of the most relevant wavenumbers were confirmed by spectral differences previously reported in Table 3. Although these former bands are important for the model, they are not evident in Figure 2, because the first derivative pretreatment enhances spectral information. The bands at around 1018 cm−1 was attributable to the glycosidic (C−O−C) stretching of carbohydrates (1008 cm−1).34 The band at around 1068 cm−1 was assigned to arabinose (1065−1068 cm−1).36 Deformation of O−H in cellulose and hemicellulose (1187 cm−1) is related to the absorption band at 1188 cm−1 found in the second PLS loading. E


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(3) Tzoulis, I.; Andreopoulou, Z. Emerging Traceability Technologies as a Tool for Quality Wood Trade. Procedia Technology 2013, 8, 606−611. (4) Katers, J. F.; Snippen, A. J.; Puettmann, M. E. Life-Cycle Inventory of Wood Pellet Manufacturing and Utilization in Wisconsin. Forest Products Journal 2012, 62 (4), 289−295. (5) van Dam, J.; Junginger, M.; Faaij, A.; Jürgens, I.; Best, G.; Fritsche, U. Overview of recent developments in sustainable biomass certification. Biomass Bioenergy 2008, 32 (8), 749−780. (6) Toscano, G.; Duca, D.; Amato, A.; Pizzi, A. Emission from realistic utilization of wood pellet stove. Energy 2014, 68, 644−650. (7) Gehrig, M.; Pelz, S.; Jaeger, D.; Hofmeister, G.; Groll, A.; Thorwarth, H.; Haslinger, W. Implementation of a firebed cooling device and its influence on emissions and combustion parameters at a residential wood pellet boiler. Appl. Energy 2015, 159, 310−316. (8) Toscano, G.; Riva, G.; Foppa Pedretti, E.; Corinaldesi, F.; Mengarelli, C.; Duca, D. Investigation on wood pellet quality and relationship between ash content and the most important chemical elements. Biomass Bioenergy 2013, 56, 317−322. (9) Duca, D.; Riva, G.; Foppa Pedretti, E.; Toscano, G. Wood pellet quality with respect to EN 14961−2 standard and certifications. Fuel 2014, 135, 9−14. (10) European Committee for Standardization (CEN). Solid BiofuelsFuel Specifications and ClassesPart 1: General Requirements (ISO 17225-1:2014); CEN: Brussels, Belgium, 2014; EN ISO 172251:2014. (11) Chadwick, D. T.; McDonnell, K. P.; Brennan, L. P.; Fagan, C. C.; Everard, C. D. Evaluation of infrared techniques for the assessment of biomass and biofuel quality parameters and conversion technology processes: A review. Renewable Sustainable Energy Rev. 2014, 30, 672− 681. (12) Xu, F.; Yu, J.; Tesso, T.; Dowell, F.; Wang, D. Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review. Appl. Energy 2013, 104, 801−809. (13) Porep, J. U.; Kammerer, D. R.; Carle, R. On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. 2015, 46 (2), 211−230. (14) Núñ ez-Sánchez, N.; Martínez-Marín, A. L.; Polvillo, O.; Fernández-Cabanás, V. M.; Carrizosa, J.; Urrutia, B.; Serradilla, J. M. Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats. Food Chem. 2016, 190, 244−252. (15) Kalinkova, G. N. Infrared spectroscopy in pharmacy. Vib. Spectrosc. 1999, 19 (2), 307−320. (16) Iyakwari, S.; Glass, H. J.; Rollinson, G. K.; Kowalczuk, P. B. Application of near infrared sensors to preconcentration of hydrothermally-formed copper ore. Miner. Eng. 2016, 85, 148−167. (17) Macias-Melo, E. V.; Aguilar-Castro, K. M.; Alvarez-Lemus, M. A.; Flores-Prieto, J. J. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Trans. 2015, 58, 667−673. (18) Lestander, T. A.; Finell, M.; Samuelsson, R.; Arshadi, M.; Thyrel, M. Industrial scale biofuel pellet production from blends of unbarked softwood and hardwood stemsthe effects of raw material composition and moisture content on pellet quality. Fuel Process. Technol. 2012, 95, 73−77. (19) Lestander, T. A.; Johnsson, B.; Grothage, M. NIR techniques create added values for the pellet and biofuel industry. Bioresour. Technol. 2009, 100 (4), 1589−1594. (20) Bergman, P. C. A.; Boersma, A. R.; Zwart, R. W. R.; Kiel, J. H. A. Torrefaction for Biomass Co-firing in Existing Coal-Fired Power Stations “BIOCOAL”; ECN Biomass: Petten, Netherlands, 2005; ECN-C--05013. (21) Santoni, I.; Callone, E.; Sandak, A.; Sandak, J.; Dirè, S. Solid state NMR and IR characterization of wood polymer structure in relation to tree provenance. Carbohydr. Polym. 2015, 117, 710−721. (22) Sandak, A.; Sandak, J.; Negri, M. Relationship between nearinfrared (NIR) spectra and the geographical provenance of timber. Wood Sci. Technol. 2011, 45 (1), 35−48.

Figure 6. First three PLS loading lines for prediction of hardwood and softwood contents.

With confirmation of raw average spectra of pure wood interpretation, the main wood compounds that contribute to the PLS prediction model are hemicellulose (1018, 1068, 1188, 1244, and 1454 cm−1), lignin (1143, 1244, 1270, 1420, and 1512 cm−1), and extractives (1697 cm−1).

4. CONCLUSION The results of this work show that it is possible to clearly discriminate between hardwood and softwood by means of FTIR spectroscopy and PCA. Furthermore, the method can be employed to discern a difference among the species analyzed. FTIR analysis coupled with PLS regression shows a good performance to predict hardwood and softwood contents in blend samples. The best model obtained can predict the blend percentage with a standard error of a few points (3.8%). The methodology is based on the chemical differences between hardwood and softwood, in particular related to lignin and hemicellulose compounds. Some of the most important species traded in the solid biofuels market, such as fir, pine, sessile oak, and beech, are considered. As a consequence, the development of an analytical instrument based on this statistical tool could be useful to support the bioenergy chain stakeholders, such as solid biofuel producers, traders, and customers. At the production level, it could also provide useful information for biofuel traceability, process tuning, and quality control at the production line. Moreover, traders and customers could take advantage of this tool for sustainability and traceability issues.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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REFERENCES

(1) Eurostat. Share of Renewables in Energy Consumption up to 15% in the EU in 2013; Eurostat: Luxembourg, Luxembourg; March 10, 2015. (2) Proskurina, S.; Heinimö, J.; Mikkilä, M.; Vakkilainen, E. The wood pellet business in Russia with the role of North-West Russian regions: Present trends and future challenges. Renewable Sustainable Energy Rev. 2015, 51, 730−740. F


Article

Energy & Fuels (23) Sgarbossa, A.; Costa, C.; Menesatti, P.; Antonucci, F.; Pallottino, F.; Zanetti, M.; Grigolato, S.; Cavalli, R. A multivariate SIMCA index as discriminant in wood pellet quality assessment. Renewable Energy 2015, 76, 258−263. (24) Bächle, H.; Zimmer, B.; Wegener, G. Classification of thermally modified wood by FT-NIR spectroscopy and SIMCA. Wood Sci. Technol. 2012, 46 (6), 1181−1192. (25) Fagan, C. C.; Everard, C. D.; McDonnell, K. Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy. Bioresour. Technol. 2011, 102 (8), 5200−5206. (26) Chen, H.; Ferrari, C.; Angiuli, M.; Yao, J.; Raspi, C.; Bramanti, E. Qualitative and quantitative analysis of wood samples by Fourier transform infrared spectroscopy and multivariate analysis. Carbohydr. Polym. 2010, 82 (3), 772−778. (27) Williams, P. C. Implementation of Near-Infrared Technology. In Near-Infrared Technology in the Agriculture and Food Industries, 2nd ed.; Williams, P. C., Norris, K., Eds.; AACC International: St. Paul, MN, 2001. (28) Fagan, C. C.; Everard, C.; O’Donnell, C. P.; Downey, G.; Sheehan, E. M.; Delahunty, C. M.; O’Callaghan, D. J. Evaluating MidInfrared Spectroscopy as a New Technique for Predicting Sensory Texture Attributes of Processed Cheese. J. Dairy Sci. 2007, 90 (3), 1122−1132. (29) R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014; http://www.R-project.org/. (30) Mevik, B. H.; Wehrens, R.; Liland, K. H. pls: Partial Least Squares and Principal Component Regression, R Package Version 2.4-3; R Foundation for Statistical Computing: Vienna, Austria, 2013; http:// CRAN.R-project.org/package=pls. (31) Stevens, A.; Ramirez-Lopez, L. An introduction to the prospectr package. R Package Vignette, R Package Version 0.1.3; R Foundation for Statistical Computing: Vienna, Austria, 2013. (32) Santoni, I.; Callone, E.; Sandak, A.; Sandak, J.; Dirè, S. Solid state NMR and IR characterization of wood polymer structure in relation to tree provenance. Carbohydr. Polym. 2015, 117, 710−721. (33) Toscano, G.; Duca, D.; Rossini, G.; Mengarelli, C.; Pizzi, A. Identification of different woody biomass for energy purpose by means of Soft Independent Modeling of Class Analogy applied to thermogravimetric analysis. Energy 2015, 83, 351−357. (34) Zhou, C.; Jiang, W.; Cheng, Q.; Via, B. K. Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy. J. Anal. Methods Chem. 2015, 2015, 1−9. (35) Zhou, C.; Jiang, W.; Via, B. K.; Fasina, O.; Han, G. Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR. Carbohydr. Polym. 2015, 121, 336−341. (36) Hori, R.; Sugiyama, J. A combined FT-IR microscopy and principal component analysis on softwood cell walls. Carbohydr. Polym. 2003, 52 (4), 449−453. (37) Simonović, J.; Stevanic, J.; Djikanović, D.; Salmén, L.; Radotić, K. Anisotropy of cell wall polymers in branches of hardwood and softwood: a polarized FTIR study. Cellulose 2011, 18 (6), 1433−1440. (38) Schwanninger, M.; Rodrigues, J. C.; Pereira, H.; Hinterstoisser, B. Effects of short-time vibratory ball milling on the shape of FT-IR spectra of wood and cellulose. Vib. Spectrosc. 2004, 36 (1), 23−40. (39) Esteves, B.; Velez Marques, A.; Domingos, I.; Pereira, H. Chemical changes of heat treated pine and eucalypt wood monitored by FTIR. Maderas: Cienc. Tecnol. 2013, 15, 245−258. (40) Xiao, L.-P.; Lin, Z.; Peng, W.-X.; Yuan, T.-Q.; Xu, F.; Li, N.-C.; Tao, Q.-S.; Xiang, H.; Sun, R.-C. Unraveling the structural characteristics of lignin in hydrothermal pretreated fibers and manufactured binderless boards from Eucalyptus grandis. Sustainable Chem. Processes 2014, 2, 9. (41) Popescu, M.-C.; Popescu, C.-M.; Lisa, G.; Sakata, Y. Evaluation of morphological and chemical aspects of different wood species by spectroscopy and thermal methods. J. Mol. Struct. 2011, 988 (1−3), 65−72.

(42) Lupoi, J. S.; Singh, S.; Parthasarathi, R.; Simmons, B. A.; Henry, R. J. Recent innovations in analytical methods for the qualitative and quantitative assessment of lignin. Renewable Sustainable Energy Rev. 2015, 49, 871−906. (43) Bjarnestad, S.; Dahlman, O. Chemical Compositions of Hardwood and Softwood Pulps Employing Photoacoustic Fourier Transform Infrared Spectroscopy in Combination with Partial LeastSquares Analysis. Anal. Chem. 2002, 74 (22), 5851−5858. (44) Popescu, C.-M.; Singurel, G.; Popescu, M.-C.; Vasile, C.; Argyropoulos, D. S.; Willför, S. Vibrational spectroscopy and X-ray diffraction methods to establish the differences between hardwood and softwood. Carbohydr. Polym. 2009, 77 (4), 851−857. (45) Mamleeva, N. A.; Autlov, S. A.; Bazarnova, N. y. G.; Lunin, V. V. Delignification of softwood by ozonation. Pure Appl. Chem. 2009, 81 (11), 2081−2091. (46) Rautkari, L.; Properzi, M.; Pichelin, F.; Hughes, M. Properties and set-recovery of surface densified Norway spruce and European beech. Wood Sci. Technol. 2010, 44 (4), 679−691. (47) Nuopponen, M.; Vuorinen, T.; Jämsä, S.; Viitaniemi, P. The effects of a heat treatment on the behaviour of extractives in softwood studied by FTIR spectroscopic methods. Wood Sci. Technol. 2003, 37 (2), 109−115.

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