Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

Bioresource Technology 102 (2011) 5200–5206

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Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy Colette C. Fagan ⇑, Colm D. Everard, Kevin McDonnell Biosystems Engineering, Bioresources Research Centre, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland

a r t i c l e

i n f o

Article history: Received 29 September 2010 Received in revised form 26 January 2011 Accepted 28 January 2011 Available online 24 February 2011 Keywords: Bioenergy crop Near infrared spectroscopy Miscanthus Short rotational coppice willow Quality

a b s t r a c t The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus x giganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R2 = 0.99), 0.13 MJ/kg (R2 = 0.99), 0.42% (R2 = 0.58), and 0.57% (R2 = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Economies worldwide face two central energy challenges: securing the supply of reliable and affordable energy, and achieving the transformation to a low-carbon, high-efficiency, and sustainable energy system (Anon, 2008; Orts et al., 2008). An important step in decreasing the levels of greenhouse gases in our atmosphere is to increase the contribution of renewable energy to our energy supply (IEA, 2004). Biomass, a key renewable energy source, contributed approximately 5.7% to the EU’s energy consumption in 2008 (European Union, 2009), predominately through heat and power applications. The sustainable utilization of agricultural crops and residues as sources of renewable energy will require optimization of operations within the biomass-to-bioenergy chain. The conversion of biomass to energy is influenced by the type of feedstock, its physical characteristics and chemical composition (Gil et al., 2010; Hanaoka et al., 2005; Obernberger and Thek, 2004). Therefore chemical composition of biomass fuels can influence the choice of conversion technology and process control in the selected energy conversion pathway. Sluiter et al. (2010) stated that accurate feedstock compositional analysis will enable evaluation of conversion yields and process economics due to changes in feedstock or process design. Energy crops and agricultural crop residues are inherently heterogeneous and additional variability can be introduced at many stages of the biomass-to-energy chain i.e. crop management, ⇑ Corresponding author. Tel.: +353 1 7167440; fax: +353 1 7167415. E-mail address: [email protected] (C.C. Fagan). 0960-8524/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2011.01.087

harvesting, storage and, conversion (Bullard et al., 2002; Landström et al., 1996; Lewandowski and Heinz, 2003). The development of integrated biomass management systems from ‘field-toconversion’ would facilitate reduced variability in bioenergy end products while concurrently increasing conversion efficiency. However for the value of such data to be fully exploited data collection, processing and decision taking must be integrated. This is in keeping with the characteristics of viable precision agriculture systems which Kitchen (2008) defined. Such integrated management systems will require sensing technologies to support and enable the optimization of biomass-to-energy conversion processes. These sensing technologies need to provide accurate, repeatable and timely measures of biomass indices throughout the biomassto-energy chain. Application of appropriate sensing technology will be key to the development of a successfully integrated biomass management system. Near-infrared (NIR) spectroscopy has been investigated to determine its potential to predict qualitative and quantitative attributes in the food, pharmaceutical and agricultural industries (Benito et al., 2008; Borjesson et al., 2007; Fagan et al., 2009, 2008; Woodcock et al., 2008). A limited number of studies have also investigated the application of NIR spectroscopy to the prediction of biomass composition. Hames et al. (2003) examined the use of NIR spectroscopy to determine the composition of corn stover feedstocks and process intermediates from ethanol production and found that it should be possible to save time and money with no loss of precision or accuracy relative to the calibration methods. The possibility to employ NIR spectroscopy and chemometrics to predict calorific value, moisture and ash of a single biomass source,

C.C. Fagan et al. / Bioresource Technology 102 (2011) 5200–5206

i.e. Norway spruce (Picea abies (L.) Karst.), trees have also been reported (Lestander and Rhen, 2005). The resulting models have a coefficient of variation of 2.4% to 2.8% for moisture content, 6.7% to 9.8% for ash content and 0.5% to 0.6% for gross calorific value. Allison et al. (2009) reported it was possible to determine the nitrogen content and alkali index of two dried grass species grown under a range of agronomic conditions using Fourier transform infrared spectra (R2 = 0.94, 0.94, respectively). The study also predicted carbon and ash content but less successfully (R2 = 0.58, 0.48, respectively). The energy crops Miscanthus and Short Rotational Coppice Willow (SRCW) have been identified as promising energy crop species for Ireland (Styles and Jones, 2007a,b; Van Den Broek et al., 1997) To date no study has examined the potential of NIR spectroscopy to predict the composition of these energy crops simultaneously. The objective of this study was to determine the potential of NIR spectroscopy in conjunction with chemometrics to predict the moisture, calorific value, ash and carbon content of harvested Miscanthus and Short Rotational Coppice Willow (SRCW). 2. Methods 2.1. Sample preparation Samples of Miscanthus (Miscanthus x giganteus) and two varieties of SRCW i.e. Tora (Salix schwerinii x Salix viminalis) and Karin ((Salix schwerinii x Salix viminalis) x Salix burjatica) were subjected to different fertilising treatments before harvesting. Four replicates of each treatment were harvested for testing. Miscanthus treatments consisted of fertilising at three levels 60,000 120,000 and 180,000 l/ha with waste water from a dairy facility, while for SRCW four treatment levels were used i.e. 90,000, 180,000, 270,000 and 360,000 l/ha of waste water. The biomass was harvested in October (SRCW) and January (Miscanthus) manually and the bottom 1.5 m of the stem with branches and leaves removed were used for analysis. Ranges of moisture for each species was achieved by dividing each sample using a secateurs and drying in a convection oven at 105 °C for varying periods of time. Miscanthus was dried for 0, 2 and 24 h; Tora was dried for 0, 0.5, 1, 3 and 24 h; and Karin was dried for 0, 0.5 and 24 h. The samples (n = 164) were then cut into <20 mm lengths using a secateurs and ground to <3 mm using a grinder (Glen Creston Ltd., Equipment No: 16 151, Serial No: 980615). Samples were then stored in 500 ml air tight containers at room temperature before and during analysis. 2.2. Compositional analysis Moisture content of ground Miscanthus, Tora and Karin samples (n = 164) were determined according to the British Standards (BS EN 14774-1) using the oven method at 105 °C for 24 h. A subset of the samples (n = 44) was selected for calorific value, carbon and ash analysis. Gross energies (calorific value) of the ground samples were determined in an adiabatic bomb calorimeter (Parr Instruments, Moline, IL, USA). Total carbon content on a dry basis was determined using carbon analyser (Primacs SLC TOC Analyser, Model CS22, Skalar Analytical B.V., 4800 DE Breda, The Netherlands). Carbon content on a wet basis was calculated for analysis. Ash content on a wet basis of the ground samples was determined according to the European Committee for Standardisation (CEN/TS 15370-1:2006). Ash on a dry basis was then calculated. The resulting range in the reference data was 35.3%, 3.2%, 3.4%, 5.9%, 6.7% and 5.3 MJ/kg for moisture, ash on a wet basis, ash on a dry basis, carbon on a dry basis, carbon on a wet basis, and calorific value respectively.

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2.3. Near-infrared spectroscopy Spectra were collected in reflectance mode using a scanning spectrophotometer (NIR Systems 6500, Foss NIRSystems, Denmark) and a standard circular quartz reflectance cell over the wavelength range 400–2498 at 2 nm intervals. Samples were scanned at room temperature (18–20 °C). Data were recorded as absorbance (log 1/R), converted to JCAMP-DX format and imported directly into The Unscrambler software (v 9.2; CAMO AS, Oslo, Norway). Spectra were recorded for each sample in duplicate and the mean of these replicates was used in subsequent calculations. NIRS3 (v. 1.05) software (ISI International, Port Matilda, USA) was used for spectrophotometer control and spectral file manipulation.

2.4. Data processing and analysis An NIR spectrum frequently contains data points carrying overlapping information hence powerful statistical techniques such as chemometrics must be employed. Chemometric techniques such as partial least squares (PLS) regression can be employed to compute a new smaller set of variables i.e. latent variable, which are linear combinations of the spectral data and can be used in the regression equation. Therefore redundancies are removed from the data and only the most relevant variation in the spectra is used in the regression. The near-infrared calibration models were developed using PLS regression and confirmed using full cross-validation. In full crossvalidation one sample is left out from the calibration data set and the model is calibrated on the remaining data points. Then the values for the left-out sample are predicted and the prediction residuals are computed. The process is repeated until each sample has been left out once; then all prediction residuals are combined to compute the root mean square error of cross validation (RMSECV). Prior to PLS regression the spectral database was subjected to a number of pre-treatments including multiplicative scatter correction (MSC), standard normal variant (SNV), first and second derivative steps. The derivative spectra were obtained using the Savitzky–Golay method with segment sizes of 5, 9 and 21 smoothing points (Alciaturi et al., 1998; Vaiphasa, 2006). All pretreatments were carried out using The Unscrambler software. Calibration models were developed using five wavelength ranges i.e. 400 to 2500 nm (IR1), 400 to 750 nm (IR2), 400 to 1100 nm (IR3), 750 to 1100 nm (IR4), and 1100 to 2500 nm (IR5). The accuracy of each calibration model can be evaluated using the coefficients of determination (R2) between the predicted and measured values as stated by Williams (2003). A value for R2 between 0.50 and 0.65 indicates that discrimination between high and low concentrations can be made. A value for R2 between 0.66 and 0.81 indicates approximate quantitative predictions, whereas, a value for R2 between 0.82 and 0.90 reveals good predictions. Calibration models having a value for R2 above 0.91 are considered to be excellent (Williams, 2001). Practical utility of the validation models were assessed using the range error ratio (RER), calculated by dividing the range of a given compositional parameter by the prediction error for that parameter. RER is a method of standardising the RMSECV by relating it to the range of the reference data. For example RER values of less than six 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 indicate the model could be used in a screening application. RER values between 21 and 30 indicate a good classification suggesting the model would be suitable for a role in a quality control application (Fagan et al., 2007; Williams, 2001). The ratio of standard error of prediction to stan-

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dard deviation of the reference data (RPD) was also calculated. Ideally the RPD should be five or higher (Williams, 2001). 3. Results and discussion 3.1. Spectra Fig. 1 shows typical NIR spectra of Miscanthus and SRCW samples. Two major spectral regions where differences can be detected visually are at wavebands 672 and 1940 nm. The peak observed at 672 nm in SRCW spectra is absent in Miscanthus spectra. This peak is most likely the result of absorption due to the presence of chlorophyll a in the SRCW samples. Chlorophyll is not present in the Miscanthus samples as they were harvested at the end of their growing season (January). A clear difference between the low and high moisture samples was observed at 1940 nm due to the stretching and deformation of the O–H bond in water. Principal component analysis was carried out to investigate the relationship between samples and to provide information regarding patterns in the sample set. Principal component (PC) 1 and PC2 explained 75% and 17% of the variation in the data, respectively. It was observed that samples could be discriminated on the basis of moisture content along PC1, with low moisture content samples located at the negative extreme of PC1 and high moisture content samples located at the positive extreme of PC2. Sample distribution was also investigated in terms of the fertiliser application rate, however no effect of treatment level was observed. It was also not possible to predict fertiliser treatment rate using PLS regression. This suggests that there was limited impact of fertiliser rate on the spectra of the bioenergy crops in relation to the impact of other variables. 3.2. Prediction of moisture content Table 1 shows the best prediction models for moisture content for each of the spectral regions. The strongest moisture prediction models were developed using MSC plus a 1st derivative step, with the exception of the IR3 model which used solely MSC spectra. The weakest model was developed using the visible spectrum (IR2). It was also found that eliminating this region from the full spectral range (i.e. IR5) resulted in the strongest model (RMSECV = 0.90%, R2 = 0.99, RER = 39.3, RPD = 13.5) (Fig. 2a). The R2, RER and RPD all indicate an excellent prediction model which could be used in any application. The first three loadings in the model (Fig. 2b) account for 92%, 6%, and 1% of the variance in the spectral and 92%, 6%, and 1% variance in the reference data respectively. Absorption at individual wavelengths has been previously assigned to various chemical structures (Osborne and Fearn, 1986). The loadings

0.8

Willow LM

Willow HM

Miscanthus LM

Miscanthus HM

Absorbance (log 1/R)

1940 nm 0.6

672 nm 0.4

0.2

0 400

900

1400

1900

2400

Wavelength (nm)

Fig. 1. Typical near-infrared spectra of low moisture (LM) and high moisture (HM) SRCW and Miscanthus samples.

Table 1 Summary of PLS prediction results for moisture content (%) (MC) and calorific value (MJ/kg) (CV) using near infrared spectroscopy. Preferred models in bold. Parameter

Spectral Regiona

Treatmentb

RMSECVc

R2

Ld

Variancee

RERf

X

Y

MC MC MC MC

IR1 IR2 IR3 IR4

0.99 2.95 1.62 1.56

0.99 0.93 0.98 0.98

6 6 10 5

98 98 100 100

98 93 98 98

35.8 12.0 21.9 22.7

MC

IR5

MSC 1der2 MSC 1der2 MSC MSC 1der10 MSC 1der2

0.90

0.99

7

99

99

39.3

CV CV CV CV CV

IR1 IR2 IR3 IR4 IR5

1der10 SNV 1der10 2der4 1der10

0.17 0.52 0.36 0.27 0.13

0.98 0.87 0.94 0.97 0.99

3 2 9 6 5

98 99 99 97 100

99 90 98 99 99

30.5 10.2 14.5 19.5 39.9

a IR1: 400–2500 nm, IR2: 400–750 nm, IR3: 400–1100 nm, IR4: 750–1100 nm, IR5: 1100–2500 nm. b MSC: multiplicative scatter correction, 1derX: 1st derivative with X number of smoothing points, SNV: standard normal variate, 2derX: 2nd derivative with X number of smoothing points. c Root mean square error of cross validation. d Number of loadings. e % Explained variance using L for X (spectra) and Y (reference data). f Range error ratio.

shown in Fig. 2b are derived from 1st derivative spectra. Derivative spectra are more complex to interpret than the original spectra. The 1st derivative will pass through zero at the wavelength at which the maximum absorbance occurs, while on the 2nd derivative a trough, alongside which are positive satellite bands, will relate to a peak in original function. However, due to the overlapping present in the NIR spectra some shift around the original peak position is inevitable. The important spectral regions for the prediction were identified (Fig. 2b). Absorbance bands in the region of 1396 nm correspond to C–H stretching and deformation of CH3 (1360 nm), CH2 (1395, 1415 nm). The spectral region between 1888 to 1956 nm has been associated with C–H stretching (1st overtone) (1780 nm) and O–H stretching and C–O stretching (1820 nm) of cellulose, as well as C@O stretching (2nd overtone) of –CO2H, (1900 nm), O–H stretching (1st overtone) of POH (1980 nm), O–H stretching and deformation of H2O (1940 nm) and C@O stretching (2nd overtone) of –CO2R (1950 nm). Finally absorbance bands in the regions of 2034, 2242, and 2318 nm have been associated with C@O stretching (2nd overtone) of CONH2 (2030 nm), N–H stretching and NH3 deformation of amino acid (2242 nm), and C–H stretching and deformation of CH2 (2310 nm). 3.3. Prediction of calorific value Calorific value was also successfully predicted using NIR spectroscopy and PLS regression (Table 1). The best model was developed using the full NIR range (IR5) although using the full visible-NIR spectra (IR1) also provided an excellent model and used two less loadings. The RMSECV (0.13 MJ/kg), R2 (0.99), RER (39.9) and RPD (70.8) (Fig. 2c) indicated that as with moisture, the developed model (IR5) had an excellent fit with the data and could be used in any application. This model was developed using 1st derivative spectra and unlike the moisture prediction model did not employ scatter correction. Fig. 2d shows the first three loadings employed in the model. The first three loading in the model account for 97%, 2%, and 1% of the variance in the spectra and 94%, 4%, and 1% variance in the reference data respectively. In addition to spectral regions that were seen to be important in the prediction of moisture (Fig. 2b), absorption in the regions of 1148 and 1524 nm were important in the prediction of calorific value. Absorption at 1152 and 1142 nm has previously been assigned

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Loading 1 0.20

Spectral Region: 1100 – 2500 nm Pre-treatment: MSC + 1st derivative (5 point) RMSECV: 0.90 % R2: 0.99 Loadings 7 30

Loading 2

Loading 3

1888 nm

2242 nm 2318 nm 1956 nm

1396 nm 0.10

Loading value

Predicted Moisture Content (% w/w)

40

20

0.00

-0.10

2034 nm 10 -0.20 1904 nm

(a) 0 0

10

20

30

-0.30 1100

40

Measured Moisture Content (% w/w)

(b) 1300

1500

1700

1900

2100

2300

2500

Wavelength (nm)

Loading 1

0.20

Spectral Region: 1100 – 2500 nm Pre-treatment: 1st derivative (21 point) RMSECV: 0.13 MJ/kg R2: 0.99 Loadings 5

Loading 2

Loading 3

1914 nm 1352 1416 nm

0.10

2036 nm

1148 nm

2250 nm

1524 nm

17 Loading value

Predicted Calorific Value (MJ/kg)

19

0.00

1614 nm

15

2292 nm 1398 nm

-0.10

2018 nm

(c) 13 13

15

17

19

-0.20 1100

(d)

1888 nm 1300

Measured Calorific Value (MJ/kg)

1500

1700

1900

2100

2300

2500

Wavelength (nm)

Fig. 2. Linear regression plots of predicted versus actual (a) moisture content and (c) calorific value of SRCW and Miscanthus samples and PLS loadings plot for loadings 1( ), 2 ( ) and 3 ( ) for PLS models of (b) moisture content and (d) calorific value prediction.

to the C–H stretching (2nd overtone) of aromatic and CH3 structures respectively.

inorganic materials are broader, fewer in number and appear at lower wavenumbers than those observed for organic materials. However the carbonate ion has absorption bands in the NIR region (1900, 2000, 2160 and 2350 nm), in this study those regions of the

3.4. Prediction of ash content Ash content was predicted on both a wet and dry basis (Table 2). No difference in the accuracy or molecular basis of the models were observed with either ash on a dry basis or ash on a wet basis, with the best predicted fit obtained using the NIR region (750 to 1100 nm) which had been pre-treated using MSC and a 1st derivative step. Therefore the model for the prediction of ash on a dry basis will not be discussed further. The ash on a wet basis prediction model was the weakest model developed for any parameter. (RMSECV = 0.42%, R2 = 0.58, RER = 7.7, RPD = 3.6) (Fig. 3a). The model statistics suggest a poor model only suitable for very rough screening. However previous studies have shown that it is possible to determine inorganic compounds using NIR spectroscopy (Chen et al., 2003). The first three loading in the model (Fig. 3b) account for 50%, 46%, and 3% of the variance in the spectral data and 27%, 12%, and 22% variance in the reference data respectively. For ash content prediction the spectral regions of interest were 970 to 974 nm and 1100 nm (Fig. 3b). Simple inorganic constituents will not absorb in the infrared region however inorganic complexes can. For example diatomic molecules produce one vibration along the chemical bond, while monatomic ligands, where metals coordinate with atoms such as halogens, H, N or O, produce characteristic infrared bands (Stuart, 2005). However the infrared bands for

Table 2 Summary of PLS prediction results for ash content on a wet basis (%) (Awb) and ash content on a dry basis (%) (Adb) using near infrared spectroscopy. Preferred model in bold. Parameter

Spectral Regiona

Treatmentb

RMSECVc

R2

Ld

Variancee

RERf

X

Y

Awb Awb Awb Awb Awb

IR1 IR2 IR3 IR4 IR5

2der10 2der10 2der10 MSC 1der2 MSC 1der2

0.45 0.46 0.46 0.42 0.44

0.51 0.48 0.49 0.58 0.52

2 2 2 5 3

96 99 99 100 98

60 57 58 72 62

7.19 6.98 7.04 7.73 7.27

Adb Adb Adb Adb Adb

IR1 IR2 IR3 IR4 IR5

2der10 2der10 2der10 MSC 1der2 MSC 1der2

0.47 0.49 0.48 0.44 0.47

0.80 0.48 0.49 0.57 0.52

2 2 2 5 3

96 99 99 99 98

60 57 44 72 63

7.18 6.98 7.03 7.67 7.27

a IR1: 400–2500 nm, IR2: 400–750 nm, IR3: 400–1100 nm, IR4: 750–1100 nm, IR5: 1100–2500 nm. b 2derX: 2nd derivative with X number of smoothing points, MSC: multiplicative scatter correction, 1derX: 1st derivative with X number of smoothing points. c Root mean square error of cross validation. d Number of loadings. e % Explained variance using L for X (spectra) and Y (reference data). f Range error ratio.

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Loading 1 Spectral Region: 750 – 1100 nm Pre-treatment: MSC + 1st derivative (5 point) RMSECV: 0.42 R2: 0.58 Loadings: 5

4

Loading 2

Loading 3 1100 nm

0.4

3

Loading value

Predicted Ash Content wb (% w/w)

5

2

0.2

0.0 1

(a)

0

0

1

2

3

(b)

970 - 974 nm

4

-0.2 750.00

5

Measured Ash Content wb (% w/w)

800.00

850.00

900.00

950.00

1000.00

1050.00

1100.00

Wavelength (nm)

Loading 1 51

49

1888, 1916 nm

1366, 1401, 1412, 1432 nm

Loading 3

0.1

2226 2268 nm

1996 2086 nm

1678, 1724 nm

2330 nm

48 Loading values

Predicted Carbon Content wb (% w/w)

50

Loading 2

0.2

Spectral Region: 1100 - 2500 nm Pre-treatment: 2nd derivative (21 point) RMSECV: 0.64 R2: 0.89 Loadings: 4

47 46

0

45

1212 nm -0.1

44 43

42

43

44

45

46

47

48

49

50

51

-0.2 1100

1300

1500

1994, 2038 nm 2222, 2270, 2298 nm

1334, 1404, 1436, 1442 nm

(c)

42

1724, 1746 nm

(d)

1882, 1920 nm 1700

1900

2100

2300

2500

Wavelength (nm)

Measured Carbon Content wb (% w/w)

Fig. 3. Linear regression plots of predicted versus actual (a) ash content on a wet basis and (c) carbon content on a wet basis of SRCW and Miscanthus samples and PLS loadings plot for loadings 1 ( ), 2 ( ) and 3 ( ) for PLS models of (b) ash content on a wet basis and (d) carbon content on a wet basis prediction.

spectra did not significantly improve the ash prediction models which were best predicted using 750 to 1100 nm. Silica in biological materials can be written as [Si(Si„)n(OH)4 n] which allows for

some Si(OSi„)3(OH) units and many Si(OSi„)4 species (Mann et al., 1983). The second overtone of the OH stretch of a silanol group is known to be centred on 940 nm, with a third overtone near 725 nm. This could account for the loading plot in Fig. 3b.

Table 3 Summary of PLS prediction results for carbon content on a wet basis (%) (Cwb) and carbon content on a dry basis (%) (Cdb) using near infrared spectroscopy. Preferred model in bold.

3.5. Prediction of carbon content

Parameter

Spectral Regiona

Treatmentb

RMSECVc

R2

Ld

Variancee

RERf

X

Y

Cwb Cwb Cwb Cwb Cwb

IR1 IR2 IR3 IR4 IR5

2der10 1der10 1der10 1der2 2der10

0.66 0.67 0.66 0.64 0.57

0.84 0.84 0.84 0.85 0.88

3 2 2 6 4

96 97 97 99 99

92 89 89 89 93

8.96 8.88 8.91 9.31 10.39

Cdb Cdb Cdb Cdb Cdb

IR1 IR2 IR3 IR4 IR5

1der10 2der10 1der10 1der10 2der10

0.69 0.80 0.73 0.73 0.64

0.87 0.83 0.86 0.86 0.89

3 2 2 6 4

92 98 97 99 98

88 87 87 89 92

9.73 8.37 9.09 9.10 10.37

a IR1: 400–2500 nm, IR2: 400–750 nm, IR3: 400–1100 nm, IR4: 750–1100 nm, IR5: 1100–2500 nm. b 2derX: 2nd derivative with X number of smoothing points, 1derX: 1st derivative with X number of smoothing points. c Root mean square error of cross validation. d Number of loadings. e % Explained variance using L for X (spectra) and Y (reference data). f Range error ratio.

Carbon content was also predicted on both a dry and wet basis (Table 3). In both cases very similar models were developed and hence prediction of carbon content on a dry basis will not be further discussed. Carbon content on a wet basis was best predicted using 2nd derivative spectra in the range 1100 to 2500 nm. The prediction statistics for the model (RMSECV = 0.57%, R2 = 0.88, RER = 10.4, RPD = 4.6) (Fig. 3c) suggests that the accuracy of the model is fair and it could be used in a screening application. The first three loading in the model (Fig. 3d) account for 72%, 8%, and 17% of the variance in the spectral and 75%, 10%, and 1% variance in the reference data respectively. Numerous wavelengths were found to be important in the prediction of carbon content (Fig. 3d). These wavelengths were primarily associated with C–H stretching and/or deformation of CH, CH2, CH3, aromatics, –CHO and cellulose (1215, 1360, 1395, 1400, 1685, 1725, 2200 and 2336 nm); O–H stretching and deformation of ROH at 1410 and 2080 nm respectively; C@O stretching (2nd overtone) of –CO2H, CONH, and CONH (1900, 1920 and 2030 nm); N–H stretching (1st overtone) of CONH2 (1430 nm); and finally S–H stretching (1st overtone) of –SH (1740 nm).

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3.6. Implementation in biomass-to-energy systems It is important to note that of the spectral range studied (400– 2500 nm) the region most important for the prediction of SRCW and Miscanthus quality indices was the NIR region 750 to 2500 nm. However FTIR spectroscopy may also have the potential to predict bioenergy crops, and may have the potential to improve the prediction of ash content. FTIR spectroscopy has previously been employed to predict wood component concentrations (e.g. lignin) in decayed wood samples (Ferraz et al., 2000). The potential to exclude the visible spectrum is positive as potential future colour variation in the biomass will not influence the developed model. Ideally the sensor technology would employ multi-spectral approach rather that a full spectrum in order to reduce costs. The potential to develop a model which could be used to predict both SRCW and Miscanthus composition, and potentially other feedstocks, is also an advantage. This would simplify the adoption of such technology as, in a country such as Ireland, it is likely that for a sustainable biomass utilization system to exist there would need to be a supply of a range of feedstocks with different quality ratings, at different times of the year from a number of suppliers. Another advantage is that, while the models were developed one at a time, only one NIR spectra of each sample was required. In an industrial application one spectrum would therefore result in the prediction of a number of parameters simultaneously for example, moisture, calorific value and ash content. Such approaches have been widely adopted and a number of commercial equipment manufacturers sell instruments, to the food and feed industries, which simultaneously determine a number of material parameters. The models developed compare favourably with previously reported studies examining NIR prediction of Spruce tree composition (Lestander and Rhen, 2005). Lestander and Rhen (2005) found that the 780 to 1350 nm region was influenced by the variation between the two sources of Spruce i.e. stem and branch. Interestingly this was the spectral region which was most successful in prediction ash content of the SRCW and Miscanthus samples. In comparing the RER of the models developed by Lestander and Rhen (2005) with those of the current study it was clear that both the moisture and calorific value models were fairly comparable with all the models having a RER greater than 20. However the RER of the ash prediction model (8) did not compare favourably with that of Lestander and Rhen (2005) (17). The ash content of SRCW and Miscanthus will vary considerably. Biomass fuels can be divided into three groups on the basis of their ash composition; (1). biomass with Ca, K, rich and Si lean ash, (2). biomass with Si rich and Ca, K lean ash, (3).biomass with Ca, K, and P rich ash (Hitunen et al., 2008). SRCW falls into category 1 while Miscanthus falls into category 2. Ryu et al. (2006) found that Si dominated Miscanthus ash (74%), with Ca Fe and K dominating the remainder; in contrast SRCW ash was dominated by Ca, with additional significant Fe, P and K composition. This variation in the inorganic elemental composition of the two biomasses, and ultimately in the ash composition, may be one factor resulting in the weak prediction of ash, however this would need to be further explored. The results from the current study indicate that NIR spectroscopy could be successfully employed to predict the moisture, calorific value and carbon content of a dual biomass resource stream. However while the moisture and calorific value prediction models were classed as being excellent all the models will require further validation in large scale trials. Composition of the bioenergy crop is a critical factor to both the producer and end user. High moisture content, for example, will adversely impact heating of the harvested biomass during storage, decrease the economic efficiency of transportation, while decreasing the heating value of the fuel (Lewandowski and Kicherer,

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1997). Therefore producers are usually paid on the basis of the moisture content of the biomass. However moisture content at harvesting can be affected by numerous factors including weather conditions, and the timing of the harvest. Hence development of low cost rapid sensing technologies which allow producers to make real-time decisions in harvesting will improve the efficiency of the production systems. Indeed large-scale sustainable deployment of biomass-to-energy systems will be reliant on optimized provision of precision feedstocks to conversion facilities. As biomass could be used to produce biofuels, heat, and/or electricity there will also potentially be multiple uses for the available biomass. In reality large-scale use of biomass-to-energy systems will require the supply of numerous biomass resources of varying quality, at different times of the year for a range of applications. To date there has been limited study of the concept of multi-biomass utilization (Rentizelas et al., 2009). The integration of non-invasive sensing technologies, such as near infrared spectroscopy, into a multi-biomass utilization system would assist in achieving the large-scale implementation of biomass-to-energy systems. There are a number of potential points within the biomass-to-energy chain where NIR sensing would have a role. For example a feedstock supplier could employ the technology after harvesting in order to optimize any post-harvest handling operations that might be required for example storage. Conversion facilities could also employ this technology to ensure incoming raw material is within specifications or to adjust processing conditions to ensure a consistent outcome to the conversion process.

4. Conclusions Models to predict the moisture, calorific value, carbon and ash content of a dual bioenergy crop resource stream were developed. Moisture, calorific value, carbon and ash content of SRCW and Miscanthus were predicted with RMSECV of 0.90%, 0.13 MJ kg 1, 0.42% and 0.64%, respectively. The moisture and calorific value models were deemed to be excellent and suitable for use in any application, the carbon prediction model was fair and could be used in a screening application, while the ash prediction model was poor model and only suitable for very rough screening.

Acknowledgement This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant Number 06/CP/E001. The authors also acknowledge Dr Gerry Downey (Teagasc, Ireland) for assisting with access to the spectrophotometer.

References Alciaturi, C.E., Escobar, M.E., De La Cruz, C., 1998. A numerical procedure for curve fitting of noisy infrared spectra. Analytica Chimica Acta 376, 169–181. Allison, G.G., Morris, C., Hodgson, E., Jones, J., Kubacki, M., Barraclough, T., Yates, N., Shield, I., Bridgwater, A.V., Donnison, I.S., 2009. Measurement of key compositional parameters in two species of energy grass by Fourier transform infrared spectroscopy. Bioresource Technology 100, 6428–6433. Anon, 2008. Building Ireland’s smart economy: A framework for sustainable economic renewal. Department of the Taoiseach, Dublin. Benito, M.T.J., Ojeda, C.B., Rojas, F.S., 2008. Process analytical chemistry: Applications of near infrared spectrometry in environmental and food analysis: An overview. Applied Spectroscopy Reviews 43, 452–484. Borjesson, T., Stenberg, B., Schnurer, J., 2007. Near-infrared spectroscopy for estimation of ergosterol content in barley: A comparison between reflectance and transmittance techniques. Cereal Chemistry 84, 231–236. Bullard, M.J., Mustill, S.J., McMillan, S.D., Nixon, P.M.I., Carver, P., Britt, C., 2002. Yield improvements through modification of planting density and harvest frequency in short rotation coppice Salix spp.-1. Yield response in two morphologically diverse varieties. Biomass and Bioenergy 22, 15–25.

5206

C.C. Fagan et al. / Bioresource Technology 102 (2011) 5200–5206

Chen, J.Y., Matsunaga, R., Ishikawa, K., Zhang, H., 2003. Main inorganic component measurement of seawater using near-infrared spectroscopy. Applied Spectroscopy 57, 1399–1406. European Union, 2009. Eurostat Statistics Energy, Energy ststistics – supply, transformation, consumption. Accessed: September 2009. http://epp.eurostat. ec.europa.eu. Fagan, C., Castillo, M., O’Callaghan, D., Payne, F., O’Donnell, C., 2009. Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing. Sensing and Instrumentation for Food Quality and Safety 3, 62–69. Fagan, C.C., Everard, C., O’Donnell, C.P., Downey, G., Sheehan, E.M., Delahunty, C.M., O’Callaghan, D.J., 2007. Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese. Journal of Dairy Science 90, 1122–1132. Fagan, C.C., O’Donnell, C.P., Rudzik, L., Wüst, E., 2008. Milk and dairy products. In: Sun, D.-W. (Ed.), Infrared Spectroscopy for Food Quality Analysis and Control. Academic Press/Elsevier, San Diego, USA, pp. 241–273. Ferraz, A., Baeza, J., Rodriguez, J., Freer, J., 2000. Estimating the chemical composition of biodegraded pine and eucalyptus wood by DRIFT spectroscopy and multivariate analysis. Bioresource Technology 74, 201–212. Gil, M.V., Oulego, P., Casal, M.D., Pevida, C., Pis, J.J., Rubiera, F., 2010. Mechanical durability and combustion characteristics of pellets from biomass blends. Bioresource Technology 101, 8859–8867. Hames, B.R., Thomas, S.R., Sluiter, A.D., Roth, C.J., Templeton, D.W., 2003. Rapid biomass analysis – New tools for compositional analysis of corn stover feedstocks and process intermediates from ethanol production. Applied Biochemistry and Biotechnology 105, 5–16. Hanaoka, T., Inoue, S., Uno, S., Ogi, T., Minowa, T., 2005. Effect of woody biomass components on air–steam gasification. Biomass and Bioenergy 28, 69–76. Hitunen, M., Barišic´, V., Coda Zabetta, E., 2008. Combustion of different types of biomass in CFB boilers 16th European Biomass Conference. Valencia, Spain. IEA, 2004. World Energy Outlook 2004. IEA Publications, Paris, France. Kitchen, N.R., 2008. Emerging technologies for real-time and integrated agriculture decisions. Comput. Electron. Agric. 61, 1–3. Landström, S., Lomakka, L., Andersson, S., 1996. Harvest in spring improves yield and quality of reed canary grass as a bioenergy crop. Biomass and Bioenergy 11, 333–341. Lestander, T.A., Rhen, C., 2005. Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using bi-orthogonal partial least squares regression. Analyst 130, 1182–1189. Lewandowski, I., Heinz, A., 2003. Delayed harvest of miscanthus–influences on biomass quantity and quality and environmental impacts of energy production. European Journal of Agronomy 19, 45–63.

Lewandowski, I., Kicherer, A., 1997. Combustion quality of biomass: Practical relevance and experiments to modify the biomass quality of Miscanthus x giganteus. European Journal of Agronomy 6, 163–177. Mann, S., Perry, C.C., Williams, R.J.P., Kicherer, A., Fyfe, C.A., Gobbi, G.C., Kennedy, G.J., 1983. The characterisation of the nature of silica in biological systems. Journal of the Chemical Society, Chemical Communications, 168–170. Obernberger, I., Thek, G., 2004. Physical characterisation and chemical composition of densified biomass fuels with regard to their combustion behaviour. Biomass and Bioenergy 27, 653–669. Orts, W.J., Holtman, K.M., Seiber, J.N., 2008. Agricultural chemistry and bioenergy. Journal of Agricultural and Food Chemistry 56, 3892–3899. Osborne, B.G., Fearn, T., 1986. Near infrared spectroscopy in food analysis. John Wiley & Sons, New York. Rentizelas, A.A., Tolis, A.J., Tatsiopoulos, I.P., 2009. Logistics issues of biomass: The storage problem and the multi-biomass supply chain. Renew. Sust. Energ. Rev. 13, 887–894. Ryu, C., Yang, Y.B., Khor, A., Yates, N.E., Sharifi, V.N., Swithenbank, J., 2006. Effect of fuel properties on biomass combustion: Part I. Experiments–fuel type, equivalence ratio and particle size. Fuel 85, 1039–1046. Sluiter, J.B., Ruiz, R.O., Scarlata, C.J., Sluiter, A.D., Templeton, D.W., 2010. Compositional analysis of lignocellulosic feedstocks. 1. Review and description of methods. Journal of Agricultural and Food Chemistry 58, 9043– 9053. Stuart, B.H., 2005. Inorganic Molecules. John Wiley & Sons, Ltd. Styles, D., Jones, M.B., 2007a. Current and future financial competitiveness of electricity and heat from energy crops: A case study from Ireland. Energy Policy 35, 4355–4367. Styles, D., Jones, M.B., 2007b. Energy crops in Ireland: Quantifying the potential lifecycle greenhouse gas reductions of energy-crop electricity. Biomass and Bioenergy 31, 759–772. Vaiphasa, C., 2006. Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 60, 91–99. Van Den Broek, R., Faaij, A., Van Wijk, A., Kent, T., Bulfin, M., Healion, K., Blaney, G., 1997. Willow firing in retrofitted Irish peat power plants. Biomass and Bioenergy 12, 75–90. Williams, P., 2003. Near-Infrared Technology – Getting the best out of light. A short course in the practical implementation of near infrared spectroscopy. 1.1 ed. Nanaimo, Canada. Williams, P.C., 2001. Implementation of near-infrared technology. 2nd ed. In: Williams, P.C., Norris, K. (Eds.), Near Infrared Technology in the Agriculture and Food Industries. American Association of Cereal Chemists, Minnesota, USA. Woodcock, T., Fagan, C.C., O’Donnell, C.P., Downey, G., 2008. Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food and Bioprocess Technology 1, 117–130.