Rapid Detection of Ash and Inorganics in Bioenergy Feedstocks Using

Apr 30, 2017 - The utilization of multiple biomass sources including forest and agricultural residues, wood waste, and dedicated bioenergy crops will ...
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Rapid Detection of Ash and Inorganics in Bioenergy Feedstocks Using Fourier Transform Infrared Spectroscopy Coupled with Partial Least-Squares Regression Charles W. Edmunds, Choo Hamilton, Keonhee Kim, Nicolas André, and Nicole Labbé* Center for Renewable Carbon, The University of Tennessee, 2506 Jacob Drive, Knoxville, Tennessee 37996, United States ABSTRACT: The rapid determination of ash and inorganic elements in biomass is critical for feedstock screening for thermochemical conversion processes. In this study, 225 lignocellulosic biomass samples composed of switchgrass and hybrid poplar (wood and bark fractions and wood/bark blends) were used to construct feedstock agnostic predictive models using Fourier transform infrared (FTIR) spectroscopy coupled with partial least-squares regression for ash content and the concentration of inorganic elements. The models for ash, potassium, calcium, magnesium, sulfur, and silicon performed well with validation correlation coefficient (rval) values of 0.94−0.98 and residual predictive deviation (RPD) values of 2.75−5.18. The phosphorus model was not as robust, with a RPD of 2.50 and a rval of 0.91; however, the model may be suitable for screening purposes. This work shows that FTIR combined with a multivariate regression technique is a viable tool for the rapid determination of ash and inorganics in multiple feedstocks.

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INTRODUCTION The growing awareness of global greenhouse gas emissions and the finite supply of petroleum resources motivates research and ultimately the commercial deployment of lignocellulosic biomass for biofuel and biochemical applications. For these biomass sources to be utilized as a suitable feedstock, their chemical and physical characteristics must be known. Biomass properties such as cellulose, hemicellulose, lignin, and ash content have a significant impact on both biochemical and thermochemical conversion processes.1 For example, ash and especially alkali and alkaline earth metals such as K, Na, Mg, and Ca have been shown to catalyze undesirable pathways during pyrolysis, resulting in reduced yield and quality of biooil.2−4 Other problems associated with inorganic constituents in biomass during pyrolysis include slagging, corrosion of equipment, and deactivation of catalysts.5 The utilization of multiple biomass sources including forest and agricultural residues, wood waste, and dedicated bioenergy crops will be required to feed biorefineries for a large-scale bioeconomy to be realized in the United States.6 However, there are significant differences in chemical properties between different biomass species.7,8 Even within the same species, variations in ash content based on environmental conditions, soil type, agricultural practices, harvesting technique, transportation, preprocessing method, and other factors have been observed. 9,10 Therefore, the incoming feedstock for a biorefinery will likely have a broad range of properties and may be derived from different biomass sources (i.e., forest residues and dedicated biomass crops). In addition, it is possible that a biorefinery may receive feedstocks that are composed of blended, mixed, or even unknown biomass species. In this scenario, feedstock agnostic models (meaning that the models can be applied to any lignocellulosic biomass) that can provide rapid and low-cost detection of ash and inorganics would be ideal. © XXXX American Chemical Society

Many spectroscopic techniques have been utilized for highthroughput measurements of biomass properties such as ultraviolet/visible, fluorescence, near- and mid-infrared, and Raman spectroscopy.11 Near- and mid-IR spectroscopies combined with multivariate statistical techniques such as partial least-squares (PLS) regression for rapid detection of chemical and physical properties have received much attention due to the inherent benefits of IR spectroscopy such as rapid data acquisition, minimal sample preparation, its nondestructive nature, and its relatively low cost of analysis.12 Infrared spectroscopy combined with PLS regression has been employed for the chemical characterization of bioenergy feedstocks to predict carbohydrates, lignin, ash, and extractives content, as well as other biomass quality metrics, in various straw and grass species,13−18 corn stover,19 hybrid poplar,20 and pine.21 More information regarding infrared spectroscopy combined with multivariate analysis techniques can be found in the reviews by Xu et al.12 and Xiao et al.22 Despite numerous reports of infrared spectroscopy combined with multivariate methods for the rapid determination of biomass properties, there is a dearth of work regarding the prediction of specific inorganics in bioenergy feedstock species. This is likely due to the difficulty of detecting the infrared absorption bands or inorganic constituents (minerals and ions) in plant biomass. Inorganic elements occur in biomass in many different forms including salts such as phosphates, sulfates, chlorides, and carbonates; organic minerals such as oxalates; and inorganic minerals such as oxides, silicates, and hydrides; as well as in ionic associations with the organic molecules found in biomass.8,23,24 Organic molecules and functional groups containing C, O, and H have strong IR absorption bands, making the use of IR spectroscopy challenging for the detection Received: January 23, 2017 Revised: April 26, 2017 Published: April 30, 2017 A

DOI: 10.1021/acs.energyfuels.7b00249 Energy Fuels XXXX, XXX, XXX−XXX

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water and filtered using syringe filters (PTFE 0.45 μm) before analysis with a 7300DV ICP-OES (PerkinElmer, USA). The ash and inorganic composition was reported on an air-dry basis, and all analyses were performed in triplicate. FTIR Spectral Acquisition. FTIR spectra were collected using a Spectrum One spectrometer (PerkinElmer, USA) equipped with a diamond attenuated total reflectance (ATR) accessory (Golden Gate Sampling Accessory, PerkinElmer, USA). The milled biomass sample was placed on the ATR element, and spectra were immediately collected after the anvil was tightened by hand to apply pressure to the sample in contact with the ATR element. Care was taken to apply a consistent amount of pressure for each sample. Spectra were scanned at a resolution of 4 cm−1 in the range from 4000 to 600 cm−1 in absorbance mode with 8 scans per spectrum. A total of 10 spectra per sample were collected. The spectra underwent ATR correction at a setting of 0.5 using PerkinElmer Spectrum software. Multivariate Analysis. The Unscrambler software version 9.0 (CAMO Software Inc., Woodbridge, NJ, USA) was used for multivariate analyses. After the FTIR spectra data were mean normalized and baseline corrected, principal component analysis (PCA) was performed to visualize outliers, trends, or clusters in the data. The spectral resolution of the FTIR data was reduced to 4 cm−1, and the 10 replicate spectra for each sample were averaged to produce a single spectrum for each biomass sample. PLS models were built using the pretreated FTIR spectra as input/predictor variables and a reference biomass parameter (i.e., values determined by laboratory procedures) as the response variable to predict total ash content and the concentration of potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), sulfur (S), and silicon (Si). Three-fourths of the biomass samples (169 samples) were randomly selected for the calibration set and used to build the PLS regression models. The predictive performance of the models was then tested with an independent validation data set, which consisted of the remaining onefourth of the samples (56 samples).

of inorganics.25 However, progress has been made toward the rapid determination of ash and inorganics in several types of biomass. For example, Fourier transform infrared (FTIR) spectroscopy with PLS regression was used to determine nitrogen content and alkali index in several species of energy grasses26 and ash content in switchgrass and corn stover.19 In addition, FTIR combined with PLS regression has been utilized in the food/agriculture industry to predict macro- and micronutrients in grapevine petioles.27 The goal of this work was to develop and demonstrate robust FTIR−PLS models that are capable of predicting ash content and specific inorganics in several different types of lignocellulosic biomass (i.e., feedstock agnostic models). Models were constructed using a total of 225 field-grown switchgrass, hybrid poplar wood, hybrid poplar bark, and hybrid poplar wood/bark blended samples representing a large range in ash content and specific inorganics. As will be demonstrated further, these models accurately predict the inorganic composition of these feedstocks and have the potential to perform high-throughput detection of ash content and inorganics, thus reducing analysis time and cost. Applications for such “feedstock agnostic” models include detecting the quality of multiple, blended, and possibly unknown bioenergy feedstocks that are being fed into a thermochemical conversion process. This will in turn inform feedstock preprocessing requirements to improve biomass quality and consistency and allow for the optimization of the biorefinery operating conditions.

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MATERIALS AND METHODS

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Sample Preparation. Biomass used for this study included 70 senesced switchgrass samples, 85 hybrid poplar wood samples, 60 hybrid poplar bark samples, and 10 hybrid poplar wood/bark blended samples grown in the southeastern United States. The switchgrass samples were composed of three different varieties (Alamo, EG1101, and EG1102) and were grown on 11 different farms covering more than 588 ha near Vonore, TN, under contract of the Tennessee BioFuels Initiative. The switchgrass stands were harvested annually, and these samples were harvested after the second year of stand development. After harvesting, the switchgrass was baled and stored for 75 days prior to analysis. The hybrid poplar trees were grown at the East Tennessee Research and Education Center (ETREC) in Alcoa, TN, on a 3.5 ha research plot and were harvested after a 2-year growing period. The hybrid poplar species included Populus trichocarpa × Populus deltoides and P. deltoides × Populus maximowiczii. A cookie (50 mm thickness) was taken from each tree, and the bark was manually removed, producing clean wood and bark samples that were used for this study. To obtain samples containing intermediate values for ash and inorganics content, 10 samples of hybrid poplar wood/bark blends were prepared by mixing wood and bark from the same tree in a 1:1 ratio. Prior to analysis, all biomass samples were dried at 40 °C for 4−5 days and then milled using a Wiley mill (Thomas Scientific, Swedesboro, NJ, USA) to pass through a 40 mesh (0.425 mm) screen. Inorganic Components Quantification. Total ash content of the biomass was determined following the National Renewable Energy Laboratory (NREL) standard laboratory analytical procedure (NREL/ TP-510-42622).28 The concentration of inorganic elements in the biomass samples was measured after microwave digestion followed by detection using an inductively coupled plasma optical emission spectrometer (ICP-OES).29 Briefly, 0.5 g of milled biomass was digested in a solution of 4 mL of HNO3 (trace metal grade, 67−70%), 3 mL of 35% H2O2, and 0.2 mL of 48% HF at 180−210 °C for 60 min at 1200 W using a Multiwave 3000 microwave (Anton Paar, Ashland, VA, USA). Following digestion, 1 mL of 1% H3BO4 was added to each sample to complex any remaining HF and to dissolve any precipitated fluorides. Next, the reaction solution was diluted to 50 mL with DI

RESULTS AND DISCUSSION Ash Content and Inorganics. The three biomass types (switchgrass, hybrid poplar wood, and hybrid poplar bark) were utilized because they are potentially suitable bioenergy feedstocks.6 Figure 1 shows box plots illustrating the

Figure 1. Box plots showing the distribution of ash content in the switchgrass, hybrid poplar bark, and hybrid poplar wood samples.

distribution of ash content between switchgrass, hybrid poplar bark, and hybrid poplar wood samples. A box plot depicts the median as the horizontal line in the middle of the box and the second and third quartiles as the outside of the box; the first and fourth quartiles are represented by the whiskers, and the dots show the outliers. Ash content ranged from 0.1 to 6.7% B

DOI: 10.1021/acs.energyfuels.7b00249 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 1. Ash Content and Inorganic Composition of Biomass Samples inorganic composition (mg/kg, dry basis) biomass type

a

% ash

Ca

K

Mg

P

S

Si

switchgrass n = 70

a

min maxb mean SDc

1.5 5.1 2.5 0.7

1188 3200 2326 387

880 8063 2653 1419

812 3630 1741 622

222 1273 686 246

294 1433 639 232

2132 9428 4774 1306

hybrid poplar bark n = 60

min max mean SD

3.0 6.7 4.7 0.8

1878 19767 12516 2706

3470 11064 6603 1464

1250 2801 2033 312

897 2073 1436 256

650 1415 927 157

417 4347 2073 732

hybrid poplar wood n = 85

min max mean SD

0.1 1.4 0.8 0.2

531 5298 2126 975

751 2806 1476 429

199 831 520 161

121 969 586 241

97 464 222 59

177 1791 1021 525

hybrid poplar wood/bark blend n = 10

min max mean SD

1.6 3.0 2.2 0.4

3763 10152 6172 1915

2570 4109 3291 524

762 1604 1120 215

588 1163 798 173

348 648 503 97

0 1624 514 593

combined n = 225

min max mean SD

0.1 6.7 2.4 1.6

531 19767 5323 4877

751 11064 3401 2387

199 3630 1380 765

121 2073 869 435

97 1433 572 325

0 9428 2660 1933

Minimum. bMaximum. cStandard deviation.

Figure 2. Box plots showing the distribution of (a) calcium, (b) potassium, (c) magnesium, (d) phosphorus, (e) sulfur, and (f) silicon content in the switchgrass, hybrid poplar bark, and hybrid poplar wood samples.

over the 225 biomass samples tested. Of the three biomass types tested, hybrid poplar wood had the lowest ash content

with a mean of 0.8% and a range of 0.1−1.4%; switchgrass was intermediate with a mean of 2.5% and a range of 1.5−5.1%, and C

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The major bands in Figure 3 are typical of lignocellulosic biomass and include 1736 cm−1 (CO stretching in acetyl groups on hemicellulose), 1510 cm−1 (aromatic skeletal vibration in lignin), 1316 cm−1 (CO in syringyl lignin), 1240 cm−1 (syringyl ring and CO stretch in lignin and hemicellulose), 1035 cm−1 (CO stretch and aromatic CH deformation in cellulose, hemicellulose, and lignin), and 896 cm−1 (CH deformation in cellulose).30−32 The large peak at 1620−1600 cm−1 seen in the hybrid poplar bark is likely due to increased lignin and/or phenolic extractive content compared to hybrid poplar wood and switchgrass, caused by aromatic skeletal vibration and CO stretch.30,31,33 Because of the strong infrared absorbance of the organic constituents in biomass, absorbance bands caused by inorganics are not apparent in the spectra of raw biomass samples. Similarly, Suárez-Garcı ́a et al.25 reported that FTIR spectra of olive cake and olive tree branches and leaves were dominated by organic components and that organic matter had to be removed to probe the inorganic constituents using FTIR spectroscopy. Suárez-Garcı ́a et al.25 measured groups of minerals such as sulfates, nitrates, and phosphates with FTIR spectroscopy in biomass residue after a low-temperature oxidation treatment with oxygen plasma. This highlights that the strong IR absorbance of the organic biomass components is an obstacle toward detecting the inorganics constituents in biomass. It is likely that the inorganics in biomass have minor effects on the IR spectra of biomass such as subtle peak shifting. The use of the multivariate statistical techniques PCA and PLS is required to probe these subtle perturbations in the IR spectrum, which the inorganics impart on the organic compounds and the corresponding IR absorption peaks. PCA was performed to identify the main sources of variation in the FTIR spectra between the samples (Figure 4). In the scores plot, the main separation occurs along the PC1 axis, which explains 78% of the variation in the FTIR data (Figure 4a). The hybrid poplar bark samples are located on the positive portion of the PC1 axis, the hybrid poplar wood samples are located on the negative portion of the PC1 axis, and the switchgrass samples are grouped on the negative portion and near the origin of the PC1 axis. As expected, the hybrid poplar wood/bark blends are located on the PC1 axis between the pure wood and bark samples. The loadings plot for PC1 (Figure 4b) explains which particular absorbance bands are

hybrid poplar bark had the highest mean ash content at 4.7% with a range of 3.0−6.7%. Hybrid poplar wood and bark were blended with a 1:1 ratio to produce biomass samples with an intermediate range of ash and inorganics content; these samples had a mean ash content of 2.2% with a range of 1.6−3.0% (Table 1). The values reported here for ash content of switchgrass, hybrid poplar bark, and hybrid poplar wood fall within the range previously reported by Vassilev et al.7 Figure 2 shows box plots representing the distribution of the inorganics in the three feedstocks tested. The mean concentration for the inorganic elements Ca, K, Mg, P, and S, in order of highest to lowest, was hybrid poplar bark > switchgrass > hybrid poplar wood (Table 1). The only measured inorganic that exhibited a different trend was Si, for which the highest mean concentration was observed in switchgrass, followed by hybrid poplar bark and then hybrid poplar wood. High concentrations of Si have been previously reported in switchgrass.7 FTIR Spectra and Principal Component Analysis. Midinfrared spectra of all biomass samples were collected in the 4000−600 cm−1 range. The 1800−760 cm−1 fingerprint region (which is rich in chemical information) of the FTIR spectra of switchgrass, poplar bark, and poplar wood is shown in Figure 3.

Figure 3. Fingerprint region of FTIR spectra of switchgrass, hybrid poplar bark, and hybrid poplar wood.

Figure 4. Principal component analysis of FTIR spectra for switchgrass, hybrid poplar bark, and hybrid poplar wood samples showing (a) the scores plot of PC1 versus PC2 and (b) the corresponding loadings plot for PC1. D

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approach and consider RPD values greater than 3 as acceptable for analytical purposes.38,39 The PLS technique is well suited for complex data sets in which there are a large number of variables, and the relationship between the predictor variables and the response variables is not well understood. The PLS method works by utilizing the variation among the sample set to correlate all of the spectral variables (i.e., the signal at each wavenumber measured) with the measured reference data (ash and inorganics concentrations) to build a multivariate regression equation. One benefit of using the PLS method to formulate a multivariate regression is that it is not required that we understand which particular functional groups and corresponding wavenumbers are affected by the presence of ash or a particular inorganic compound in the biomass. The more significant spectral bands are identified in the regression coefficients plots produced during the PLS regression analysis. This is important because the heterogeneous and complex nature of biomass, in addition to the various forms in which inorganics can occur in biomass, results in ambiguity in defining specific spectral bands that correspond to specific inorganic elements. The performance metrics for our FTIR−PLS models are shown in Table 2. All models, with the exception of the P

responsible for the variation (and hence the sample grouping) along the PC1 axis on the scores plot (Figure 4a). In the loadings plot of PC1, a large positive peak occurs at 1620−1600 cm−1, which is assigned to aromatic ring vibration, CO stretch, and aryl CC stretch in lignin and phenols.30,31,34 The large negative peak that appears at 1035 cm−1 corresponds to CO stretching in cellulose and hemicellulose and CH deformation in lignin, whereas a shoulder at 1048 cm−1 is assigned to CO stretching in cellulose and hemicellulose.31,32 In addition, the small negative peak in the loadings plot at 896 cm−1 is assigned to CH deformation in cellulose.31 Hence, the differences in lignin and carbohydrates content among the different biomass feedstocks drive the separation along the PC1 axis. Phenolic extractives and lignin content are typically high in bark,8,33 which explains its location on the positive PC1 axis and the large positive peak at 1620−1600 cm−1 corresponding to aromatic ring vibration and CO stretch in lignin and phenols. Similarly, wood and grass species typically have less lignin and greater carbohydrates content compared to bark,8,35 explaining the grouping of these samples along the negative portion of the PC1 axis, and the negative bands at 1035 cm−1 (corresponding to cellulose and hemicellulose), 1048 cm−1, and 896 cm−1 (corresponding to cellulose and hemicellulose). The fact that hybrid poplar wood and switchgrass are grouped more closely to each other compared to the hybrid poplar bark is not surprising given the drastic difference in the cellulose content of bark versus wood or switchgrass as reported by Vassilev et al.8 In addition, switchgrass contains greater extractives content than wood, and these extractives contain phenolic compounds that may explain its relatively more positive placement on PC1 (compared to wood) and the associated 1620−1600 cm−1 peak in the loadings for PC1.8,36 As expected, the main variation in the IR spectral data was explained by the major organic components (carbohydrates, lignin, and other phenolic compounds) found in lignocellulosic biomass, whereas the influence of the inorganic constituents is imperceptible compared to the strong absorbance bands of the organic components. Herein lies the challenge of utilizing IR spectroscopy for the detection of inorganics. The multivariate statistical technique PLS regression is required to extract the chemical information contained in the IR spectra and build regression models to predict ash and the concentration of the different inorganic elements of the biomass. PLS Models. Partial least-squares models were built using only the fingerprint region (1800−760 cm−1) of the FTIR spectra. The predictive performance of the FTIR−PLS models was assessed on the basis of the following metrics: correlation coefficient (r), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), and residual predictive deviation (RPD). The RMSEC indicates the average prediction error for the calibration data set, whereas the RMSEP indicates the average prediction error using the validation sample set; both of these parameters have the same units as the reference and predicted variable of interest. The RMSEP is typically higher than the RMSEC because it is based on the prediction error of samples not used for calibration model development. The RPD was calculated as the standard deviation of the reference data divided by the standard error of prediction (SEP) and is used to evaluate the robustness of the predictive model. Several authors state that RPD values greater than 2 or 2.5 are acceptable for most purposes,16,27,37 whereas others take a more conservative

Table 2. FTIR−PLS Models for Calibration and Validation Sample Sets of Mixed Feedstocks To Predict Biomass Ash Content and Inorganics Content model parameter

ash

ncala rcalb RMSECc no. of factors % X variance explained % Y variance explained

167 0.97 0.40 5 97.9

nvala rvalb RMSEPd SEPe RPDf % X variance explained % Y variance explained

K

Ca

Mg

P

S

Si

Calibration Data 152 154 0.94 0.99 783 789 4 5 97.6 98.0

Set 154 0.95 228 6 98.3

159 0.90 190 4 97.7

153 0.95 90 3 97.0

140 0.93 711 4 97.6

93.8

88.1

89.4

81.4

90.7

86.5

57 0.98 0.36 0.35 4.63 98.3

Validation Data 50 51 0.94 0.98 773 934 773 942 3.09 5.18 92.4 97.9

Set 49 0.95 231 233 3.28 98.3

48 0.91 176 174 2.50 97.6

51 0.94 118 118 2.75 96.3

47 0.95 684 617 3.13 96.8

95.4

97.8

90.6

82.5

87.4

87.4

97.0

96.1

Number of samples. Subscripts “cal” and “val” indicate calibration and validation data sets, respectively. bCorrelation coefficient. Subscripts “cal” and “val” indicates calibration and validation data sets, respectively. cRoot-mean-square error (RMSE) of the calibration data set. dRoot-mean-square error (RMSE) of the validation data set. e Standard error of prediction. fResidual predictive deviation. The units for RMSEC, RMSEP, and SEP are the same as the ones used for the reference measurement (wt % for ash, mg/kg for K, Ca, Mg, P, S, and Si). a

model, have rcal values of 0.94 or greater, meaning that the calibration models have good correlations between the predicted and reference values, and demonstrate that the FTIR data contain significant information about ash and inorganics content in the biomass. More specifically, the ash content model performed well with correlation coefficients of 0.97 and 0.98 for the calibration and validation data sets (rcal E

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Figure 5. FTIR−PLS regression for ash content showing (a) measured versus predicted ash content of the calibration and validation data sets and (b) the corresponding regression coefficients.

Figure 6. Predicted versus measured values and corresponding regression coefficients plots for FTIR−PLS models built using switchgrass, hybrid poplar wood, and hybrid poplar bark samples to predict (a, b) potassium, (c, d) calcium, and (e, f) magnesium. F

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Figure 7. Predicted versus measured values and corresponding regression coefficients plots for FTIR−PLS models built using switchgrass, hybrid poplar wood, and hybrid poplar bark samples to predict (a, b) phosphorus, (c, d) sulfur, and (e, f) silicon.

0.694 (R2 = 0.482) and RMSEP of 0.867%. The greater prediction performance reported in our study is likely due to the large sample size and wide range in ash content among the samples tested. The models for K, Ca, Mg, S, and Si all performed well with rcal and rval values ≥0.94. The RMSEP values were 773, 942, and 233 mg/kg for K, Ca, and Mg models, respectively. Similar to the ash content model, the prediction error is likely acceptable for all but samples in the very low range of concentrations of these inorganic elements. A good strategy for implementation of these predictive models may be to determine a lower threshold for the parameter of interest; this threshold would depend on the RMSEP of the particular model and the amount of error deemed acceptable in the predicted value. The model that predicts P did not perform as well as the other models with an rcal of 0.90, an rval of 0.91, and a RMSEP of 174 mg/kg. The RPD value for this model was 2.50,

and rval), respectively. The RMSEC was 0.40%, whereas the RMSEP was 0.35%. Considering the range of ash content of 0.1−6.7% for the samples used to construct the model, this predictive performance is likely sufficient for all but samples falling in the lowest range of ash content values where the prediction error becomes large. The RPD for this model was 4.63, indicating good predictive performance. These results indicate better model performance for predicting ash content than several reported previously. For example, Liu et al.19 reported a FTIR−PLS model to predict ash content in corn stover and switchgrass suitable for screening with comparatively lower correlation coefficient of 0.88 for their validation model and higher RMSEP of 0.61%. In addition, Tamaki et al.13 reported an r of 0.902 (R2 = 0.813) for prediction in triticale and wheat straws, a RMSE of 0.404%, and a RPD value of 2.32, whereas Allison et al.26 reported lower values for their ash content model (for switchgrass and canary grass) with an r of G

DOI: 10.1021/acs.energyfuels.7b00249 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

absorbance spectrum due to the CC and CO bonds, and the oxalate-associated cation has been shown to influence its absorbance spectrum.43 The loadings plot for the Ca model shows positive peaks at 1615−1606 and 1315 cm−1 (Figure 6d). Both of these bands have been identified in calcium oxalate containing crystals that were isolated from different plant species;43 therefore, the presence of these bands in the PLS regression coefficients plot for the Ca model indicates that calcium in the switchgrass and hybrid poplar samples occurs, at least in some fraction, as calcium oxalate. The PLS regression coefficients for ash, K, and Ca all show a positive band in the range of 1620−1600 cm−1, which, as discussed earlier, is assigned to aromatic ring vibration and C O stretch in lignin and may be elevated due to other phenolic compounds found in bark extractives. This is also in agreement with the PCA loadings plot (Figure 4b), which has a large positive peak in the 1620−1600 cm−1 range. The reoccurrence of the positive regression coefficient at 1620−1600 cm−1 may be due to the fact that the hybrid poplar bark samples likely have a higher content of phenolic compounds (lignin and extractives) and also have higher concentrations of ash and inorganics, with the exception of Si, compared to the switchgrass and hybrid poplar samples (Figures 1 and 2). Considering the complex and heterogeneous nature of lignocellulosic biomass and the fundamental infrared absorption behavior of the organic components in biomass, many of the FTIR bands that are identified as high-ranking regression coefficients could not be identified. The major benefit of utilizing infrared measurements with PLS regression models for prediction is the capability of rapid detection with little sample preparation. The absorbance spectrum of a biomass sample can be collected with FTIR in a few minutes compared to standard chemical degradation methods for ash and inorganic elemental analysis, which can take several days. On the other hand, an inherent drawback in utilizing IR−PLS regression models to predict biomass properties is that the accuracy is reduced compared to the standard laboratory procedures. Using IR−PLS regression models is not suitable when very accurate measurements are required; instead, the benefits of rapid detection will be more apparent for large sample sets or if online or at-line sensing is required, for example, for screening incoming feedstocks at a biomass-processing facility or in a biorefinery. The implication of utilizing IR sensors combined with PLS models in a biomass-processing facility or biorefinery is the constant monitoring of feedstock composition to maintain required feedstock quality specifications. Further research to extend infrared detection and PLS regression modeling in the near-IR range is currently in progress in our laboratories. The near-IR spectral region represents overtones and vibrational combinations of molecular bonds, whereas the fundamental molecular vibrations are measured in the mid-IR region. Although there are challenges associated with the near-IR detection, the potential benefits include robust sensor technology and easier online implementation.

indicating that the model may be suitable for screening purposes, but accurate quantitative predictions are not attainable without further improvement of the model. Xu et al.40 reported less precise PLS models to predict P as compared to the carbon and nitrogen models in their study using NIR spectroscopy. The authors attribute the reduced performance of the P model to the inability of P to absorb infrared radiation, the variability of the various forms in which P can occur in biomass, and the variability among different plant species and plant developmental stages. Smith et al.27 reported prediction models using FTIR with PLS for macro- and micronutrients in grapevine petioles. Their results show similar findings for K, Mg, and S with high predictive model correlation coefficient (r) of 0.957−0.980 (R2 = 0.915−0.961) using segmented crossvalidation and RPD values of 2.52−3.4. A noted difference, as observed by Smith et al.,27 is the lower model performance for Ca with a RPD of 3.5 and the increased model performance for the prediction of P, which had an r of 0.957 (R2 = 0.915) and a RPD of 3.8. The calibration models used a reasonably low number of PLS factors (between 3 and 6), indicating that models were not overfitted by picking up noise in the FTIR spectra. The optimal number of factors used by each model was chosen by The Unscrambler software based on the point when the addition of an additional factor did not significantly increase the explained Y variance. The explained predictors (X) variance and explained response (Y) variance for each model are shown in Table 2. When considering the number of factors used, there is a tradeoff between using enough factors to obtain high predictive performance and not using too many factors, which can result in the inclusion of spectral information that is not correlated to the predicted variable (including spectral noise) into the model’s regression coefficients. When too many factors are used, and the latter situation occurs, this will likely result in model instability and is apparent by poor prediction performance when the PLS model is applied to a new sample population.41 In the context of this work, when the PLS models are applied to a new sample population (i.e., the validation data set), the values for the correlation coefficient (r) and rootmean-square error are not significantly lower than those for the calibration data set (Table 2). It is expected that the correlation coefficient and root-mean-square error values for the validation data set will vary slightly compared to that of the calibration model because a new sample set is being applied, and the fact that these values are not significantly reduced when the validation data set is applied indicates that these models are stable and do not suffer from overfitting of the spectral data. In addition, for these PLS regression models to be utilized on new samples, similar sample drying and milling procedures should be used, as this could affect the performance of the models. The regression coefficients (Figures 5−7) indicate which specific wavenumbers in the FTIR spectra are most important to predict the parameter of interest (i.e., ash content, K, Ca, etc.). As previously discussed, the inorganics in biomass are difficult to detect using IR spectroscopy because many inorganic components may not absorb in the infrared range or their absorption bands may be hidden by the larger intensity of the signals of the organic components in biomass. In addition, the presence and interactions of closely associated inorganic components may influence the organic molecules, allowing for indirect detection with infrared spectroscopy.42 Although many forms of inorganics are not easily detectable by infrared spectroscopy, the oxalate anion produces an infrared

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CONCLUSIONS Feedstock agnostic PLS regression models to predict the content of ash and inorganics (K, Ca, Mg, S, and Si) in switchgrass and hybrid poplar were constructed using FTIR spectra and reference measurements of 225 unique biomass samples. Our findings indicate that the information contained in the FTIR data is ubiquitous among these different biomass H

DOI: 10.1021/acs.energyfuels.7b00249 Energy Fuels XXXX, XXX, XXX−XXX

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feedstocks, and, within the confines of our study, these feedstock agnostic models have been shown to work on several types of biomass. Due to the time-consuming and costly nature of conventional methods to measure ash and inorganics content, rapid and inexpensive FTIR−PLS models could prove useful. This work demonstrates progress toward developing feedstock agnostic FTIR−PLS models capable of detecting ash and inorganics in bioenergy feedstocks. Such models could be beneficial for screening incoming feedstocks, informing preprocessing requirements, and allowing for process optimization in a biorefinery setting.

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AUTHOR INFORMATION

Corresponding Author

*(N.L.) E-mail: [email protected]. Phone: (865) 946-1126. ORCID

Charles W. Edmunds: 0000-0002-2356-7312 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by the Integrated Biomass Supply Systems (IBSS) Partnership, which is supported by Agriculture and Food Research Initiative Competitive Grant 2011-6800530410 from the USDA National Institute of Food and Agriculture.

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ABBREVIATIONS FTIR = Fourier transform infrared PC = principal component PCA = principal component analysis PLS = partial least-squares r = correlation coefficient RMSEC = root-mean-square error of calibration RMSEP = root-mean-square error of prediction RPD = residual predictive deviation SEP = standard error of prediction

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REFERENCES

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DOI: 10.1021/acs.energyfuels.7b00249 Energy Fuels XXXX, XXX, XXX−XXX