Quantifying Actual and Theoretical Ethanol Yields

for Switchgrass Strains Using NIRS Analyses

Kenneth P. Vogel & Bruce S. Dien & Hans G. Jung &

Michael D. Casler & Steven D. Masterson &

Robert B. Mitchell

# US Government 2010

Abstract Quantifying actual and theoretical ethanol yields

from biomass conversion processes such as simultanteous

saccharification and fermentation (SSF) requires expensive,

complex fermentation assays, and extensive compositional

analyses of the biomass sample. Near-infrared reflectance

spectroscopy (NIRS) is a non-destructive technology that

can be used to obtain rapid, low-cost, high-throughput, and

accurate estimates of agricultural product composition. In

this study, broad-based NIRS calibrations were developed

for switchgrass biomass that can be used to estimate over

20 components including cell wall and soluble sugars and

also ethanol production and pentose sugars released as

measured using a laboratory SSF procedure. With this

information, an additional 13 complex feedstock traits can

be determined including theoretical and actual ethanol

yields from hexose fermentation. The NIRS calibrations

were used to estimate feedstock composition and conversion information for biomass samples from a multi-year

switchgrass (Panicum virgatum L.) biomass cultivar evaluation trial. There were significant differences among

switchgrass strains for all biomass conversion and composition traits including actual ethanol yields, ETOHL

(L Mg−1

) and theoretical ethanol yields, ETOHTL

(L Mg−1

), based on cell wall and non-cell wall composition

NIRS analyses. ETOHL means ranged from 98 to

115 L Mg−1 while ETOHTL means ranged from 203 to

222 L Mg−1

. Because of differences in both biomass yields

and conversion efficiency, there were significant differences

among strains for both actual (2,534–3,720 L ha−1

) and

theoretical (4,878–7,888 L ha−1

) ethanol production per

hectare. It should be feasible to improve ethanol yields per

hectare by improving both biomass yield and conversion

efficiency by using NIRS analyses to quantify differences

among cultivars and management practices.

Keywords Switchgrass. Biomass. Ethanol . NIRS . Quality

Introduction

High-yielding perennial grasses such as switchgrass (Panicum

virgatum L.) have the potential to become valuable biomass

energy crops that can be grown on marginal cropland.

Agricultural products in general vary in composition and

For abbreviations used in this paper, please refer to Table 1.

USDA neither guarantees nor warrants the standard of the product,

and the use of the name by USDA implies no approval of the product

to the exclusion of others that may also be suitable.

Ms. Patricia O’Bryan and Mr. Ted Joe are acknowledged for their

excellent technical support in conducting the laboratory fermentation

and composition experiments.

K. P. Vogel (*) : S. D. Masterson : R. B. Mitchell

Grain, Forage, and Bioenergy Research Unit,

Agricultural Research Service,

US Department of Agriculture (USDA-ARS),

University of Nebraska,

137 Keim Hall, P.O. Box 830937, Lincoln, NE 68583-0937, USA

e-mail: Ken.Vogel@ars.usda.gov

B. S. Dien

Fermentation Biotechnology Research Unit,

National Center for Agricultural Utilization Research,

USDA-ARS,

1815 N. University Street,

Peoria, IL 61604, USA

H. G. Jung

Plant Science Research Unit, USDA-ARS,

411 Borlaug Hall, 1991 Upper Buford Circle,

St. Paul, MN 55108-6026, USA

M. D. Casler

US Dairy Forage Research Center,

1925 Linden Dr. West,

Madison, WI 53706-1108, USA

DOI 10.1007/s12155-010-9104-4

Bioenerg. Res. (2011) 4:96–110

Published online: 21 August 2010
other properties due to genetics, growth environment, crop

management, harvest practices, and post-harvest storage and

processing. Biomass produced for conversion to bioenergy

will vary due to the same factors. The quality of agricultural

products affects their use for food, animal feed, fiber, and

industrial products. Likewise, biomass quality will affect its

value for conversion to energy at a biorefinery. Maize (Zea

mays L.) grain, which is more homogenous in properties and

composition and is much simpler to process to ethanol than

herbaceous biomass, is routinely assayed for quality at the

biorefinery gate. Quantifying the actual and theoretical

ethanol yields from biochemical conversion routes of

biomass, such as simultaneous saccharification and fermentation (SSF), requires expensive and complex assays. Fast,

inexpensive methods with high accuracy are needed for

predicting maximum ethanol yields and actual conversion

efficiencies by agronomists for optimizing feedstock quality

via production management, by breeders and geneticists for

developing cultivars or hybrids with improved processing

capabilities and energy yields per ton, and by biorefineries

for quickly measuring quality of each truck load of biomass

at the biorefinery gate [1].

Near-infrared spectroscopy (NIRS) is widely applied in

agriculture for determining the quality of forages, grains and

grain products, oilseeds, coffee, tea, spices, fruits, vegetables,

sugarcane, beverages, fats and oils, dairy products, eggs,

meat, and other agricultural products [2]. NIRS is also widely

used to quantify the composition of agricultural products

because it meets the criteria of being accurate, reliable, rapid,

non-destructive, and inexpensive. NIRS analysis is an

analytical method where analytes are quantified in a sample

based on the spectral characteristics of the sample. An

analyte is defined here as a substance or chemical constituent

of interest [3]. The amount of an analyte is predicted based

on the sample’s near-infrared reflectance spectra using

equations fitted to a calibration set representative of the

future samples. Laboratory analyses of the samples in the

calibration set are conducted for each of the analytes of

interest, the near-infrared reflectance spectra profiles of the

samples are determined, and utilizing the laboratory and

spectral data, prediction equations are developed and

validated using mathematical and statistical procedures

described by Shenk and Westerhaus [4], Westerhaus et al.

[3] and Wold et al. [5, 6].

Most current commercialization efforts for converting

lignocelluloses into liquid biofuels rely on biochemical

processing. The complex plant cell wall structure requires

initial pre-treatment of the biomass either mechanically,

thermally, and possibly in the presence of chemical

catalysts to expose the carbohydrates. Following pretreatment, the carbohydrates are enzymatically hydrolyzed and

fermented into ethanol using microorganisms capable of

fermenting both hexoses and pentoses. The pretreated

biomass can be saccharified and fermented into ethanol

[7, 8]. NIRS analysis procedures have been used previously

to determine composition of biomass feedstocks [9–13] but

to date, NIRS analyses has not been used to determine

SSF biomass conversion efficiency. Sanderson et al. [9]

determined the chemical composition of both woody and

herbaceous biomass feedstocks including switchgrass

samples and attempted to development NIRS calibrations

for ash, lignin, uronic acid, cell wall sugars, C, H, N, O,

and ethanol extractives which were not defined. Their

results indicated that a broad-based NIRS calibration

could be developed for use with an array of feedstocks

but separate NIRS calibrations for different classes of

biomass would result in improved accuracy and precision.

Lorenz et al. [13] used NIRS analyses to predict the

composition of maize (Z. mays L.) corn stover. The maize

stover compositional values were then used to estimate

theoretical ethanol yield of stover from an array of maize

hybrids and lines by using the National Renewable Energy

Laboratory’s theoretical ethanol yield calculator. The

maize hybrids and lines differed significantly for all

evaluated traits including theoretical ethanol yields on a

mass (L Mg−1

) and production area (L ha−1) basis, thereby

demonstrating that variability in corn stover composition

directly affects potential ethanol production [13]. However, actual sugar or ethanol yields, which will be less than

100% efficient, were not determined experimentally. The

theoretical ethanol yield calculator used by Lorenz et al.

[13] does not account for variability in pretreatment

effects, enzymatic hydrolysis, or ethanol fermentation

yields as indirect consequence of changes in biomass

composition and properties. Recently, Lorenzana et al.

[14] reported maize stover cell wall composition and

efficiency of cell wall glucose release by dilute acid/high

temperature pretreatment and enzymatic hydrolysis of

recombinant inbred lines using a NIRS calibration developed as part of their study, but again actual ethanol

production was not measured.

This study had two primary objectives. The first was

to develop broad-based NIRS calibrations for all major

compositional components of switchgrass biomass and

switchgrass biomass ethanol and released pentose yields

from a laboratory SSF process using commercial cellulases and Saccharomyces cerevisiae. Composition information was needed to estimate theoretical maximum

ethanol yields. The second objective was to determine if

switchgrass cultivars and experimental strains adapted to

the Midwest USA differ significantly in their actual and

potential ethanol yields per ton and hectare. To address

this objective, switchgrass samples from a multi-year

biomass yield trial were analyzed using the NIRS

prediction equations to obtain estimates of the biomass

constituents and ethanol and released pentose yields. The

Bioenerg. Res. (2011) 4:96–110 97
biomass yield data from the trial along with the ethanol

conversion data were used to determine ethanol yields per

hectare.

Materials and Methods

Development of NIRS Calibrations

The switchgrass biomass samples that were used to develop

broad-based NIRS calibrations are representative of the

potential range in switchgrass biomass quality that could be

produced under different management and harvesting

systems with currently available cultivars and experimental

strains. The biomass samples were from the following

studies and had been analyzed previously for forage quality

traits.

(a) A 2-year study at Mead, NE and Ames, IA in which 6 N

fertilizer rates (0, 60, 120, 180, 240, and 300 kg ha−1 N)

and eight maturity stages at harvest, which ranged from

boot stage harvest in late June to an end-of-growing

season harvest after a killing frost, were evaluated [15].

(b) A study in which 20 upland and lowland cultivars or

experimental strains were evaluated for biomass yield

at five locations (Spooner and Arlington, WI, Mead,

NE, Manhattan, KS, and Stillwater, OK) over a 2-year

period [16] when harvest maturity ranged from R1

(late boot stage) to R5 (post-anthesis) as described by

Moore et al. [17].

(c) A long-term C sequestration study near Mead, NE in

which switchgrass samples were collected from two

switchgrass cultivars grown with 3 N rates (0, 60, and

120 kg ha−1 N) and harvested either at anthesis or after

a killing frost [18].

(d) Samples from a field trial near Mead, NE in which

switchgrass hybrids were harvested at the RO (boot

stage) to R3 maturity stage [19].

(e) Biomass samples from a half-sib family breeding

nursery located near Mead, NE which were harvested

at the R2 to R4 maturity stages.

Samples were prepared following the sample preparation

guidelines outlined by Murray and Cowe [20]. Specifically,

for each of the above studies, biomass samples were

collected at harvest and dried in forced-air ovens at 60°C.

The switchgrass biomass samples were ground through a

2-mm screen in a Wiley mill and then re-ground in a

cyclone-type mill to pass a 1-mm screen. Ground samples

were scanned using a Model 6500 near-infrared spectrometer

(NIRSystems, Silver Springs, MD; now FOSS NIRSystems,

Inc., Laurel, MD). A set of 482 samples were selected from

the above experiments which represented the range of plant

maturities, cultivars, ecotypes, fertility rates, and environments of the samples in the five experiments. NIRS software

analyses procedures were used to select a calibration set of

112 samples from these experiments that represented the

spectral diversity of the samples using procedures described

by Shenk and Westerhaus [4] and Westerhaus et al. [3]. This

was accomplished using the “Center” and “Select” modules

of WINISI II1 version 1.04 software from Infrasoft International LLC, State College, PA which is available through

Foss NIRSystems Inc1

. A switchgrass standard sample from

the Lincoln ARS Forage Quality Laboratory was also

included in the calibration set.

The calibration set of samples were analyzed for chemical

composition, ethanol and pentose sugar yields following pretreatment and SSF, and forage quality traits on an oven dry

weight basis. Dry matter concentration was determined on

subsamples that were not used for other analyses by heating in

a forced-air oven for 24 h at 105°C. Analytical methods and

calculations and abbreviations for all traits are shown in

Table 1. Depending on the specific analysis, each sample was

analyzed in duplicate or triplicate and the laboratory mean

value for that sample was used to develop the NIRS

calibration. Additional details and discussion on the laboratory procedures are available in Dien [21].

Compositional Analysis

Total nitrogen (N) and carbon (C) concentration were

determined in the University of Nebraska Agronomy and

Horticultural Department’s analytical laboratory by the

LECO combustion method (Model FP 428 and FP 2000,

LECO Corp., St. Joseph, MI) [22, 23]. Lipid content (EE)

was determined by exhaustive extraction with diethyl ether

[24]. Total mineral or ash content (ASH) was measured as

loss of weight after combustion at 450°C for 16 h in a

muffle furnace. Carbohydrates and lignin were determined

using a sequential procedure as outlined by Dien et al. [25].

Soluble carbohydrates were extracted with 80% v/v ethanol

at 60°C overnight [26]. The supernatant was analyzed by

high-performance liquid chromatography (HPLC) for

monosaccharides (glucose and fructose) and oligosaccharides (sucrose, stachyose, and raffinose) using a refractive

index detector [25]. Fructans were not measured on the

samples because no fructans were found in switchgrass by

Dien et al. [25]. The alcohol-insoluble residue was treated

with heat-stable α-amylase and amyloglucosidase in 0.1 M

acetate buffer, pH 5, to release glucose from starch [27].

Sufficient 95% v/v ethanol was added to reach an alcohol

concentration of 80%, after which the supernatant was

removed and analyzed by HPLC for glucose released from

starch. The remaining crude, alcohol-insoluble cell wall

residue was subjected to a two-stage sulfuric acid hydrolysis using the Uppsala Total Dietary Fiber Method [27]. An

aliquot from the first stage of the acid hydrolysis was

98 Bioenerg. Res. (2011) 4:96–110
Table 1 Switchgrass biomass composition, and actual and potential ethanol yield traits determined by laboratory analysis or calculation, and

references for the methods

Variable Abbreviation Units Reference or equation

Composition variables

Dry matter DM mg g−1 Vogel et al. [34]

Carbon C mg g−1 Watson and Isaac [23]

Nitrogen N mg g−1 Watson and Isaac [23]

Extracted fat EE mg g−1 Padmore [24]

Minerals (total ash) ASH mg g−1 450°C muffle furnace for 6 h.

Klason lignin KL mg g−1 Theander et al. [27]

Uronic acidsa UA mg g−1 Theander et al. [27], Ahmed and Labavitch [28]

Rhamnose a RHA mg g−1 Theander et al. [27]

Fucose a FUC mg g−1 Theander et al. [27]

Arabinose a ARA mg g−1 Theander et al. [27]

Xylose a XYL mg g−1 Theander et al. [27]

Mannose a MAN mg g−1 Theander et al. [27]

Galactose a GAL mg g−1 Theander et al. [27]

Glucose a GLC mg g−1 Theander et al. [27]

p-Coumarate esters PCA mg g−1 Jung and Shalita-Jones [30]

Esterified ferulates FEST mg g−1 Jung and Shalita-Jones [30]

Etherified ferulates FETH mg g−1 Iiyama et al. [29]

Cell wall concentration CWC mg g−1 KL+UA+RHA+FUC+ARA+XYL+MAN+GAL+GLC+

PCA+FEST+FETH

ARA+XYL+Man+GAL AXMG mg g−1 ARA+XYL+MAN+GAL

ARA+XYL AX mg g−1 ARA+XYL

Sucrose SUC mg g−1 Dien et al. [25]

Soluble glucose GLCS mg g−1 Dien et al. [25]

Fructose FRU mg g−1 Dien et al. [25]

Total soluble carbohydrates SC mg g−1 SUC+GLCS+FRU

Starch STA mg g−1 Dien et al. [25]

Non-structural carbohydrates (starch+SC) NSC mg g−1 SC+STA

Total hexoses HEX mg g−1 ð Þþ ð Þ MAN þ GAL þ GLC ð Þ 180=162 NSC

Total sugars SUG mg g−1 HEX þ ð Þ ð Þ AX ð Þ 150=132

Ethanol and potential ethanol

Ethanol/g dry forage ETOH mg g−1 SSFb

, see “Materials and Methods” section for details.

Pentose sugars released/g dry forage PENT mg g−1 SSFb

, see “Materials and Methods” section for details

Calculated ethanol traits

Proportion of hexoses that are

non-structural or soluble

PSOL % (NSC/HEX)100

Pentose proportion of total carbohydrates PPEN % ð Þ 1 ð Þ HEX=SUG 100

Theoretical ethanol from hexoses

(excluding starch)

HEXE mg g−1 1⁄2 þ ð Þ MAN þ GAL þ GLC 0:57 1⁄2 þ 0:51 GLCS ð Þ þ FRU 1⁄2  0:537SUC

Estimated ethanol from non-structural

carbohydratesc

NSCE mg g−1 1⁄2  0:51 GLCS ð Þþ þ FRU 0:537SUC þ 0:57STAÞ 0:9

Cell wall ethanol CWE mg g−1 ETOH-NSCE

Theoretical ethanol conversion efficiency

from cell wall hexosansd

CWEP % 1⁄2  CWE=ð Þ ð Þ MAN þ GAL þ GLC 0:57 0:9Þ 100

Pentoses extraction efficiency PENTP % 1⁄2  0:88PENT=ð Þ ARA þ XYL 100

Hexose ethanol extraction efficiency HEXEP % (ETOH/HEXE)100

Forage quality composition

Neutral detergent fiber NDF mg g−1 Vogel et al. [34]

Acid detergent fiber ADF mg g−1 Vogel et al. [34]

Acid detergent lignin ADL mg g−1 ANKOM Technology -9/99

Bioenerg. Res. (2011) 4:96–110 99
analyzed for uronic acids [28] using glucuronic acid as the

standard. Neutral sugars from the two-stage acid hydrolysis

were analyzed as alditol-acetate derivatives by GC-FID.

The acid-insoluble residue provided the Klason lignin

concentration estimate after correction for ash.

Total (ester and ether linked) ferulates in the cell wall

were extracted with 4 M NaOH for 3 h at 160°C from

starch-free, alcohol-insoluble residues [29]. Ester-linked

ferulates and p-coumarates were extracted from similar

starch-free, alcohol insoluble residues with 2 M NaOH at

39°C for 24 h [30]. Ferulic and p-coumaric acid residues in

the alkaline extracts were quantified by HPLC [30]. Etherlinked ferulate was calculated as the difference between

total and ester-linked ferulic acid concentrations of each

sample [29].

Pretreatment and Fermentation

Flask fermentations were conducted using a simultaneous

saccharification and fermentation scheme based upon a

modified method [31]. For each sample, 1.5 g of biomass

was mixed with 8.5 ml of sulfuric acid solution (1.75% w/v)

and reacted at 121°C for an hour using an autoclave.

Samples were pretreated in bottles closed with screw cap

lids to prevent water loss. Hydrolysates were adjusted to

pH 4.5 by adding Ca(OH)2 to neutralize the mineral acid

and 1 M sodium citrate buffer stock diluted to 50 mM.

Hydrolysates were supplemented with 1 ml of a 10×YP

stock (200 g/L peptone and 100 g/L yeast extract). The

hydrolysates were fermented using a simultaneous saccharification and fermentation scheme. Filter sterilized cellulase

(5.0 FPU per dry gram biomass, GC220, Genencor Inc.,

Rochester, NY) and β-glucosidase (15 U per dry gram

biomass, Novo188, Novozymes, Denmark) were added and

the cultures were inoculated to an O.D.600 of 0.5 using S.

cerevisiae D5A. The pre-fermentation culture was serially

transferred twice, first on YP supplemented with 20 g/L

glucose and next on YP supplemented with 50 g/L glucose.

Each time the pre-culture was grown for 18 h at 35°C under

agitation (150 rpm) in an incubator shaker. The final culture

was concentrated to an O.D.600 of 50. The bottles were

topped with screw cap holders with fitted silicone septa,

which were pierced with 22 g stainless steel needles to

allow for CO2 exhaust. The fermentations were conducted

at 35°C for 72 h while being mixed at 100 rpm using an

incubator shaker. Ethanol, sugars, and organic acids were

analyzed using a HPLC system equipped with an organic

acids column (Bio-Rad Laboratories, CA) and a refractive

index detector, as previously described [32]. All samples

were analyzed in triplicate.

Forage Quality Analyses

Crude protein concentration was calculated as N×6.25 [33].

In vitro rumen dry matter digestibility (IVDMD), neutral

detergent fiber, acid detergent fiber, and acid detergent

lignin (ADL) were determined using the ANKOM Fiber

Analyzer (ANKOM Technology Corp., Fairport, NY1

) and

the procedures described by Vogel et al. [34] and the

ANKOM ADL procedure (ANKOM Technology -9/99,

Method for Determining Acid Detergent Lignin in

Beakers).

NIRS

The laboratory data were used to develop prediction

equations using the procedures described by Westerhaus

et al. [3]. The number of samples for which complete

laboratory data was available was typically 111 or 112 but

for some analytes the number of samples ranged from 102

to 108. The samples were divided into calibration and

validation sets with the validation set having 17 or 18

samples depending upon the analyte. Validation samples

were selected to represent the range of the entire sample set.

The remaining samples were in the calibration group.

Calibration equations for predicting unknowns were developed using the WinISI software modified PLS procedure.

The calibration equations were tested using the WinISI

software validation procedures by predicting analyte values

of the validation set. Calibration equations were selected

Table 1 (continued)

Variable Abbreviation Units Reference or equation

In vitro dry matter digestibility IVDMD mg g−1 Vogel et al. [34]

Crude protein CP mg g−1 N×6.25

a Cell wall carbohydrates

b SSF simultaneous saccharification and fermentation using modified procedure of Dowe and McMillan [31] and quantified using HPLC as described by

Dien et al. [32]

c Assumed that yeast converts all soluble sugars to ethanol with efficiency of 0.90

d Assumed that yeast could covert cell wall hexoses to ethanol with an efficiency of 0.90. To obtain estimates of 100% conversion efficiency, multiply by

1.11

100 Bioenerg. Res. (2011) 4:96–110
using criteria described by Westerhaus et al. [4] and Shenk

and Westerhaus [3]. Once the calibration equation was

selected, the validation samples were included in the

calibration set and calibration equations were redeveloped

using the same math treatments and options as in the

original calibrations. This additional calibration work was

done to improve the precision and robustness of the

calibrations. Calibrations statistics included the coefficient

of determination (r

2

), standard error of calibration (SEC),

standard error of cross-validation (SECV), and standard

error of prediction (SEP) values. The r

2 represents the

proportion of the total variation among the samples for a

trait that is explained by the calibration. The SEC is the

standard error of the difference between the laboratory

value and the NIRS-predicted value for the same sample.

The SEP is the standard error of the difference between the

laboratory value and the NIRS-predicted value of the

validation set. SECV is the standard error of the difference

between predicted and laboratory values when samples are

sequentially removed from the calibration process. Acceptable NIRS calibration equations should have r

2 values

greater than 0.80, and preferably greater than 0.90, with

small SEC, SEP, and SECV values.

Switchgrass Biomass Cultivar Trial

The switchgrass cultivar and experimental strain evaluation

trial was located in eastern Nebraska at the University of

Nebraska’s Agricultural Research and Development Center

about 50 km west of Omaha, NE. The trial was a typical

small plot trial with 21 cultivars or experimental strains.

The trial was planted in 2002 and biomass was harvested in

2003, 2004, and 2005. The experimental design was a

randomized complete block with six replicates. Seeded

plots were 1.5×3.0 m and were separated on the ends by a

1.5-m wide alley that was also seeded to switchgrass. No

fertilizer was applied the establishment year. In the postestablishment years, the study was fertilized in the spring

with 112 kg ha−1 N (NH4NO3). Herbicides were used for

weed control. The residual biomass on the plots from the

previous year was removed before spring growth initiated

by mowing or burning. Plots were harvested for biomass

yield in mid-August when most cultivars or experimental

strains in the nursery were headed. Maturity of the strains in

the nursery ranged from R1 to R4 using the staging system

of Moore et al. [17]. Plots were harvested with a plot flail

harvester with a cutting height of 10 cm. The harvester cut

a 0.91 m swath down the middle of each plot. Plots were

sampled prior to harvest to obtain samples for biomass

composition analysis and to determine dry matter concentration. Wet sample weights were added to plot yields to

nullify the effect of sub-sample size on biomass yield.

Yields are reported on a dry weight basis. The single

harvest treatment and N fertilization rates were based on the

results of Vogel et al. [15]. The harvested samples were

dried, ground, and used for NIRS analysis with the

procedures described above. Prior to predicting the composition of the samples, the NIRS spectra of the samples

were analyzed for fitness to the calibration set of samples

by using the Global H statistic to test for outliers [20]. All

samples in the application set of samples had acceptable H

values (H<3.0). The analytes listed in Table 1 were

predicted for the samples from the yield test. The feedstock

quality data and the yield data was used to calculate ethanol

yield, theoretical ethanol yield, and potentially fermentable

substrate yield (L Mg−1

) Mg and production per hectare

(L ha−1

) using equations listed below. All data were

analyzed using the PROC GLM procedure of PC-SAS for

Windows [35]. Strain or cultivars were considered to be

fixed effects. With perennials, mean performance over

years is of primary importance; therefore, plot means over

years were used in the statistical analyses.

Equations for Determining Yield (L) per Ton (Mg)

and Production per Hectare (L Ha−1

)

1. Specific volume of ethanol is 0.789 g mL−1 so

1/0.789 g mL−1

=1.267 mLg−1 ethanol. On a unit basis,

mg g1 1⁄4 g kg1 1⁄4 kg Mg1.

2. Ethanol from glucan (mL g−1

) ðor hexosan sugarÞ 1⁄4

1⁄2ð180 g of glucose=162 g of glucanÞð0:51 g ethanol=

g glucoseÞ=0:789 g ethanol per mL [21]. For equation 5

below, note that 180 ð Þ1⁄4 ð Þ =162 0:51 0:57.

3. Ethanol from xylan (or arabinan) ðmL g1Þ 1⁄4

1⁄2 ð 150 g xylose=132 g of xylan Þ  0:51 g ethanol=g

xyloseÞ=0:789 g ethanol per ml [21].

The trait and associated units listed in Table 1 can be

inserted into the equations below. Sugars were expressed on

an anhydrous basis. Actual ethanol yield via SSF was

calculated using the ETOH result. Because the yeast that

was used to ferment the released sugars cannot ferment

pentose sugars, ETOH was only from reduced hexoses;

1 ha=10,000 m2

.

4. Ethanol yield from SSF released glucose from biomass:

ETOHL L Mg1   1⁄4 ETOH  1:267:

5. Theoretical ethanol yield from all biomass hexoses:

HEXEL ðL Mg1Þ 1⁄4 ðððMAN þ GAL þ GLC þ STAÞ  0:57Þ

þððGLCS þ FRUÞ  0:51ÞþðSUC  0:537ÞÞ

1:267Þ; assuming 100% conversion:

Bioenerg. Res. (2011) 4:96–110 101
6. Ethanol yield from SSF released pentose sugars:

PENTEL L Mg1 ð Þ1⁄4 PENT  0:51  1:267

 0:8; assuming 80% conversion:

7. Theoretical ethanol yield from pentose sugars:

PENTETL L Mg1   1⁄4 ð Þ ARA þ XYL 0:579  1:267:

8. Total ethanol yield (ETOHTL) from SSF:

ETOHTL L Mg1   1⁄4 ETOHL þ PENTEL:

9. Total ethanol production per ha from SSF:

ETOHTLH L ha1   1⁄4 ETOHTL  biomass production Mg ha1  :

10. Total theoretical ethanol yield from all biomass sugars:

ETOHTLT L Mg1   1⁄4 HEXEL þ PENTETL:

11. Total theoretical ethanol production from all biomass

sugars:

ETOHTLTH L ha1   1⁄4 ETOHTLT

 biomass production yield Mg ha1  :

12. Conversion ratio of actual to theoretical ethanol on a

liter-to-liter basis.

ERATIO 1⁄4 ETOHTL=ETOHTLT:

To convert to units used in production agriculture in the

USA, the following conversion factors can be used: L ethanol

ha−1

/9.35=gallons acre−1; L Mg1 ethanol  0:24 1⁄4 gallons

U:S ton1; 1 ha=2.47 acres.

Results and Discussion

Switchgrass NIRS Calibrations

The calibration set used to develop the NIRS method

represented a diverse population as evidenced by the large

range in compositional properties (Table 2). Notable

examples of properties with wide values include Klason

lignin (92–256 mg g−1

), cell-wall associated glucans (190–

320 mg g−1

), and xylan (129.75–238.36 mg g−1). These

components represent 62.3% of the average switchgrass

biomass. Glucose and xylose content are particularly

important because only carbohydrates can be biochemically

converted into ethanol. Solubles sugars (e.g., glucose,

fructose, and sucrose) were also significant and on average

comprised 15% of the hexose sugars present in switchgrass.

Most other major cell wall and soluble carbohydrates also

exhibited significant variation. Exceptions included the

more minor cell wall components, fucose (FUC) and

rhamnose. Most samples did not have detectable levels of

fucose. Variation in total carbon (C) was much smaller than

for specific carbohydrates, illustrating that while total C

variation is limited, plants display plasticity in the specific

form it takes.

Differences in the partitioning of C between carbohydrates

and lignin directly affect the theoretical ethanol yield, which is

solely dependent upon total carbohydrates. The theoretical

ethanol yield can be calculated for all biomass hexoses,

pentoses, and all biomass sugars (see Eqs. 5, 7, and 10) from

the carbohydrate composition. Likewise, the partitioning of

C among different carbohydrates can affect the expected

ethanol yield depending upon how efficiently the carbohydrates can be extracted (soluble vs. structural) and fermented

(hexoses vs. pentoses). In this study, the hexoses were

directly fermented to ETOH using a S. cerevisiae yeast strain

selected for fermentation of cellulosic feedstocks. However,

because Saccharomyces yeast does not ferment pentoses,

xylose was measured directly in the spent fermentation

broth. Approximately, a twofold difference was observed

between low and high yielding switchgrass samples for

ETOH (60.83–127.34 g mg−1

) and pentose (PENT, 116.61 to

255.37 g mg−1

) yields. Non-structural sugars (e.g., soluble

and storage carbohydrates) are readily extractable and

fermented in comparison to those associated with the cell

wall. The contribution of ETOH from the non-structural

carbohydrates (NSCE) was subtracted from total ETOH to

give cell wall associated ethanol (CWE). NSCE was

calculated by using an estimated 90% conversion efficiency

for glucose and starch yeast fermentations. The CWE had a

significantly lower mean value (66.88 g mg−1

) than ETOH

(91.86 g mg−1

), indicating the significant affect of the more

easily converted soluble and storage carbohydrates. Ethanol

yield is affected by both the amount of carbohydrate present

and the efficiency of their extraction and (for hexoses)

conversion into ethanol. Conversion efficiency was directly

measured by calculating the ratio of the ethanol or pentose

yields and the theoretical yields. Pentoses were extracted by

the dilute-acid pretreatment with 78.13% efficiency. By

contract, the conversion efficiency of hexoses into ethanol

was 50.2% and we estimate only 47.0% conversion

efficiency for cell wall hexoses which are primarily from

cellulose. The lower efficiencies for glucans compared to

pentoses reflect in part the yield lost to ethanol production by

102 Bioenerg. Res. (2011) 4:96–110
Table 2 Mean, standard deviation, and range values for switchgrass samples in the bioenergy NIRS calibration data set

Composition variablesa Units Mean Standard deviation Minimum Maximum

DM mg g−1 905 9 858 929

C mg g−1 438.7 6.4 417.2 450.1

N mg g−1 9.5 4.8 2.0 22.8

EE mg g−1 10.2 3.9 2.0 23.0

ASH mg g−1 76.9 15.7 1.1 118.3

KL mg g−1 166.1 32.6 92.0 256.3

UA mg g−1 15.8 1.8 12.03 23.1

RHA mg g−1 1.4 0.7 0.4 3.7

FUC mg g−1 0.0 0.1 0.00 0.5

ARA mg g−1 30.0 2.6 24.5 36.8

XYL mg g−1 190.8 22.9 129.8 238.4

MAN mg g−1 6.6 3.2 2.7 23.6

GAL mg g−1 9.5 1.5 4.25 14.7

GLC mg g−1 265.7 26.7 190.4 319.8

PCA mg g−1 5.76 1.6 2.2 11.0

FEST mg g−1 1.4 0.5 0.4 3.0

FETH mg g−1 0.8 0.6 0.0 2.4

CWC mg g−1 693.4 60.4 543.8 816.4

AXMG mg g−1 234.7 31.7 178.2 287.2

AX mg g−1 220.7 23.6 158.2 272.4

SUC mg g−1 27.5 13.7 2.08 54.8

GLCS mg g−1 8.3 4.9 0.3 21.4

FRU mg g−1 10.0 6.7 0.0 34.9

SC mg g−1 45.8 20.9 8.1 99.5

STA mg g−1 7.5 8.0 0.00 47.5

NSC mg g−1 54.8 26.2 10.2 119.7

HEX mg g−1 366.8 26.6 302.0 430.2

SUG mg g−1 617.8 47.1 507.1 703.5

PSOL % 15 7 3 32

PPEN % 41 2 34 46

Ethanol and potential ethanol

ETOH mg g−1 91.96 14.9 60.8 127.3

PENT mg g−1 196.4 27.7 116.6 255.4

HEXE mg g−1 184.4 12.2 154.0 221.1

NSCE mg g−1 25.2 2.12 4.7 55.0

CWE mg g−1 66.9 10.3 38.7 90.7

CWEP % 47.0 9.4 25.3 73.3

PENTP % 78.1 6.4 58.2 93.1

HEXEP % 50.2 9.05 33.3 73.6

Forage quality composition

NDF mg g−1 712.7 44.0 587.7 807.9

ADF mg g−1 381.8 45.2 269.3 482.0

ADL mg g−1 54.6 12.9 10.9 84.34

IVDMD mg g−1 495.3 81.3 290.0 714.8

CP mg g−1 59.4 30.0 12.2 142.8

a Definitions for all abbreviations are listed in Table 1.

Bioenerg. Res. (2011) 4:96–110 103
Table 3 Summary of switchgrass NIRS calibration statistics for bioenergy trait characterization sample set

Abbreviation Equation type Mean N SEC r

2 SECV SEP RPD

gkg−1 gkg−1 gkg−1 gkg−1

Biomass bioenergy variables

C Combineda 439.18 104 2.14 0.86 4.17

Cal & Val.b 439.02 88 3.65 0.62 5.07 4.49 1.42

N Combined 9.46 107 0.42 0.99 0.61

Cal & Val. 9.20 89 0.52 0.99 0.63 0.72 6.66

EE Combined. 10.26 111 1.82 0.78 2.80

Cal & Val. 10.08 94 2.19 0.69 3.46 3.30 1.18

ASH Combined 77.45 107 5.54 0.84 8.60

Cal & Val. 76.40 87 10.13 0.35 10.50 8.89 1.77

KL Combined 165.21 109 10.56 0.89 20.63

Cal & Val. 168.45 92 10.34 0.89 22.26 25.61 1.27

UA Combined 15.73 108 0.75 0.76 0.87

Cal & Val. 15.67 91 0.73 0.74 0.88 1.03 1.74

RHA Combined 1.44 107 0.24 0.86 0.27

Cal & Val. 1.45 89 0.26 0.83 0.31 0.24 2.91

FUC Combined 0.10 12 0.01 0.96 0.07

Cal & Val. 0.10 9 0.03 0.60 0.06 0.08 1.25

ARA Combined 29.85 109 1.03 0.85 1.32

Cal & Val. 29.99 92 1.02 0.85 1.33 1.31 1.98

XYL Combined 191.00 110 6.00 0.93 8.41

Cal & Val. 192.24 93 6.65 0.91 9.18 8.45 2.71

MAN Combined 6.53 105 0.47 0.98 0.87

Cal & Val. 6.65 88 0.51 0.97 1.02 1.02 3.13

GAL Combined 9.55 107 0.38 0.93 0.61

Cal & Val. 9.54 91 0.30 0.95 0.64 1.23 1.21

GLC Combined 265.90 107 5.70 0.95 7.82

Cal & Val. 267.45 89 6.12 0.95 8.11 11.61 2.3

PCA Combined 5.66 108 0.36 0.95 0.49

Cal & Val. 5.69 90 0.36 0.95 0.51 0.73 2.19

FEST Combined 1.42 110 0.16 0.91 0.21

Cal & Val. 1.41 91 0.15 0.92 0.22 0.27 1.85

FETH Combined 0.82 98 0.24 0.78 0.38

Cal & Val. 0.78 82 0.24 0.74 0.37 0.54 1.11

CWC Combined 693.60 110 17.90 0.91 28.98

Cal & Val. 698.87 89 12.70 0.95 25.42 37.56 1.6

AXMG Combined 237.18 109 6.32 0.92 8.76

Cal & Val. 238.52 92 6.69 0.91 9.38 11.05 2.87

AX Combined 220.95 109 6.62 0.92 9.04

Cal & Val. 222.29 92 6.05 0.93 9.00 10.53 2.24

SUC Combined 27.53 106 3.59 0.93 5.50

Cal & Val. 26.55 89 3.77 0.92 5.59 6.00 2.28

GLCS Combined 8.14 108 1.71 0.87 2.25

Cal & Val. 8.15 93 2.05 0.80 2.69 3.19 1.53

FRU Combined 10.13 108 2.02 0.91 2.77

Cal & Val. 10.30 91 2.05 0.91 3.03 3.21 2.08

SC Combined 45.93 109 4.72 0.95 7.23

Cal & Val. 45.48 89 4.41 0.95 7.64 7.01d 2.98

STA Combined 7.08 106 1.52 0.95 2.79

104 Bioenerg. Res. (2011) 4:96–110
Table 3 (continued)

Abbreviation Equation type Mean N SEC r

2 SECV SEP RPD

gkg−1 gkg−1 gkg−1 gkg−1

Cal & Val. 6.50 88 1.60 0.91 2.76 4.51 1.77

ETOH Combined 91.91 102 3.50 0.94 4.96

Cal & Val. 91.62 86 3.98 0.92 5.68 6.48 2.3

PENT Combined 196.22 104 6.47 0.95 10.40

Cal & Val. 199.14 86 7.41 0.92 10.59 12.35 2.24

NSC Combined 54.17 101 3.31 0.98 5.05

Cal & Val. 54.57 84 4.45 0.97 6.18 6.97 3.76

HEX Combined 367.14 103 7.36 0.92 10.33

Cal & Val. 367.69 86 7.65 0.90 10.39 15.88 1.67

SUG Combined 618.56 102 11.36 0.94 13.92

Cal & Val. 621.04 85 12.54 0.92 14.53 28.40 1.65

PSOL Combined 0.15 101 0.01 0.98 0.01

Cal & Val. 0.15 86 0.01 0.97 0.02 0.02 350

PPEN Combined 0.41 103 0.01 0.94 0.01

Cal & Val. 0.41 86 0.01 0.87 0.01 0.01 200

HEXE Combined 184.71 103 4.15 0.88 6.11

Cal & Val. 185.31 88 4.75 0.83 6.55 10.26 1.18

NSCE Combined 25.51 107 1.80 0.98 2.58

Cal & Val. 25.28 91 1.89 0.97 3.14 2.68 0.79

CWE Combined 66.18 105 4.73 0.79 6.17

Cal & Val. 66.18 87 4.81 0.76 6.27 5.91 1.74

CWEP Combined 46.59 102 3.12 0.89 4.08

Cal & Val. 46.56 84 2.99 0.87 3.67 4.04 2.32

PENTP Combined 78.13 105 3.55 0.69 4.10

Cal & Val. 78.41 86 3.52 0.64 4.06 4.37 1.46

HEXEP Combined 50.38 104 2.59 0.91 3.40

Cal & Val. 50.11 86 2.60 0.91 3.27 3.18 2.84

Forage quality traits

IVDMD Combined 494.76 109 12.45 0.98 22.68

Cal & Val 491.16 94 17.06 0.95 27.18 28.35 2.86

NDF Combined 713.59 109 11.98 0.93 16.42

Cal & Val. 715.31 92 12.11 0.93 18.31 22.21 1.98

ADF Combined 382.76 108 9.71 0.95 12.79

Cal & Val. 382.73 94 8.95 0.96 14.46 13.12 3.44

ADL Combined 55.56 109 4.65 0.84 6.20

Cal & Val. 55.55 92 5.18 0.79 6.61 10.65 1.21

DM Combined 906.09 103 3.33 0.77 4.07

Cal & Val. 905.64 89 3.09 0.82 4.16 5.74 1.56

SEC standard error of calibration, RSQ coefficient of determination, SECV standard error of cross validation, and SEP standard error of prediction,

RPD ratio of SD/SEP

a

The upper row (combined) for each variable contains the revised calibration equation on which both calibration and validation samples were included to

improve the robustness of the prediction equations

b The lower row for each variable contains the calibration statistics and validations statistics (Cal & Val) for the calibration set of samples from which the

validation set of samples were removed for use as validation samples

Bioenerg. Res. (2011) 4:96–110 105
Table 4 Means and range values for biomass yield, forage composition, and energy yield traits of switchgrass cultivars and selected experimental

strains from a biomass yield test grown in eastern Nebraska in 2002–2005

Entry Traits

Yield, forage quality, lignin

Yield IVDMD NDF ADF ADL N EE ASH KL

Mg ha−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1

Summer 13.0 506 731 403 55 9 9.9 65.6 157.2

Trailblazer 14.0 514 732 395 53 10 11.9 68.6 165.6

Shawnee 14.6 530 715 388 53 10 11.1 65.9 149.9

Kanlow 18.4 529 737 407 50 9 9.2 58.4 133.1

NE 2229 15.0 538 719 388 51 10 10.8 65.1 151.3

NE 2234 16.6 525 726 391 50 10 11.4 69.1 154.6

Mean 14.7 523 728 396 52 10 10.9 67.2 154.0

F 7.9* 5.38** 6.3* 4.6* 2.2* 3.9* 8.9* 7.8* 10.1*

LSD 0.05 0.8 8.2 5.6 5.5 1.7 0.5 0.5 2.6 4.7

Range min 11.7 506 709 384 50 9 9.2 58.4 133.1

Max 18.4 558 741 407 55 11 12.4 75.6 165.6

Cell wall carbohydrates, total C

FUC ARA XYL MAN GAL GLC UA RHA C

g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1

Summer 0.1 29.9 208.2 4.9 8.5 281.9 15.9 1.2 445.4

Trailblazer 0.1 30.7 206.7 6.1 8.9 280.7 16.4 1.3 445.5

Shawnee 0.1 29.3 198.2 5.6 8.7 269.4 16.3 1.2 444.6

Kanlow 0.2 29.5 206.1 5.4 8.7 299.3 16.1 1.0 444.6

NE 2229 0.1 30.7 205.7 5.2 8.8 280.2 16.6 1.3 443.9

NE 2234 0.1 31.0 207.2 5.5 9.0 280.9 16.6 1.3 444.3

Mean 0.12 30.3 205.6 5.5 8.8 282.1 16.3 1.2 444.5

F 10.6* 7.8* 8.9 5.2* 3.8* 16.0* 5.4* 14.7* 4.4*

LSD 0.05 0.01 0.5 2.0 0.3 0.2 2.8 0.2 0.04 0.8

Range min 0.01 28.0 193.1 4.9 8.4 268.2 15.9 1.0 441.2

Max 0.02 31.4 208.9 6.5 9.3 299.3 16.8 1.4 445.8

Other biomass constituents

PCA FEST FETH SUC GLCS FRU STA

g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1

Summer 6.31 1.46 1.16 27.4 8.1 6.1 7.3

Trailblazer 5.45 1.30 1.11 21.6 6.3 5.4 6.7

Shawnee 5.87 1.43 0.95 31.4 9.6 8.9 9.4

Kanlow 8.83 2.53 0.76 27.1 6.3 6.4 3.0

NE 2229 5.64 1.39 1.07 27.2 8.2 8.5 7.6

NE 2234 5.64 1.43 1.09 22.1 6.5 6.3 4.8

Mean 6.1 1.5 1.0 24.3 6.9 6.4 6.1

F 76.9* 77.6* 8.2* 17.6* 6.9* 10.9* 8.1*

LSD 0.05 0.2 0.1 0.1 1.5 0.7 0.6 1.0

Range min 5.2 1.2 0.8 19.5 6.2 4.4 2.4

Max 8.8 2.5 1.2 33.2 8.1 8.9 9.4

Biomass major components

CWC AXMG AX SC NSC HEX SUG PSOL PPEN

g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 g kg−1

Summer 716.7 251.5 238.0 41.6 48.9 375.0 645.5 13.0 41.9

106 Bioenerg. Res. (2011) 4:96–110
the yeast but primarily the more recalcitrant nature of the

cellulose polymer vs. hemicelluloses.

Good-to-excellent prediction equations were obtained

for most traits as indicated by the high r

2 and low SEC,

SEP, and SECV values for the best predictive equations that

were developed for each trait (Table 3). Initially, a subset of

samples (approximately 20) were removed from the sample

set to serve as validation samples. The remaining samples

were used to develop the calibrations which were validated

using the validation subset (see “Cal & Val” row for each

Table 4 (continued)

Entry Traits

Trailblazer 724.4 252.4 237.4 33.3 40.0 366.5 636.3 10.9 42.4

Shawnee 687.1 241.9 227.5 49.8 59.2 372.6 631.2 15.9 41.0

Kanlow 711.4 249.7 235.6 39.7 42.7 388.7 656.4 11.0 40.8

NE 2229 708.0 250.5 236.5 44.0 51.6 376.5 645.2 13.7 41.6

NE 2234 714.4 252.7 238.2 35.0 39.8 366.0 636.7 10.8 42.5

Mean 712.6 250.1 235.9 37.6 43.7 370.9 639.0 11.7 42.0

F 9.8* 10.1* 10.0* 14.5* 12.4* 8.6* 6.6* 14.2* 13.1*

LSD 0.05 6.7 2.2 2.1 2.4 3.1 3.7 4.9 0.8 0.3

Range min 669.1 234.6 221.1 29.7 34.6 363.9 622.5 9.4 40.4

Max 725.8 254.8 239.9 51.1 60.2 388.7 656.4 16.2 42.7

Saccrification and fermentation products

ETOH PENT HEXE NSCE CWE CWEP PENTP HEXEP

g kg−1 g kg−1 g kg−1 g kg−1 g kg−1 %%%

Summer 82.9 210.1 190.3 23.5 59.4 39.3 77.7 43.7

Trailblazer 81.8 209.4 186.1 19.2 62.5 41.3 77.6 44.0

Shawnee 87.9 199.2 188.0 28.5 59.5 40.9 77.1 46.8

Kanlow 80.4 197.3 199.6 20.4 60.0 37.4 73.7 40.4

NE 2229 90.5 207.1 190.9 24.8 65.7 43.6 77.1 47.4

NE 2234 83.9 207.2 186.8 19.1 64.8 42.9 76.5 45.0

Mean 82.9 205.1 188.7 21.0 61.9 40.8 76.6 44.0

F 13.7* 10.4* 14.8* 12.3* 8.3* 10.8* 8.1* 18.2*

LSD 0.05 1.6 2.6 1.7 1.5 1.3 0.9 0.8 0.8

Range min 77.5 192.5 184.6 16.6 57.7 37.4 72.9 40.4

Max 90.5 210.9 199.6 28.9 65.7 43.6 78.6 48.0

Ethanol yield and theoretical yields per Mg and hectare

ETOHL HEXEL PENTEL ETOHTL ETOHTLH PENTETL ETOHTLT ETOHTLTH E-Ratio

L Mg−1 L Mg−1 L Mg−1 L Mg−1 L ha−1 L Mg−1 L Mg−1 L ha−1

Summer 105.1 246.4 108.6 213.7 2,762.8 174.6 421.0 5,479.5 50.8

Trailblazer 103.6 240.6 108.2 211.8 2,960.4 174.2 414.8 5,790.1 51.1

Shawnee 111.4 245.0 102.9 214.4 3,133.2 166.9 411.9 6,037.0 52.1

Kanlow 101.8 255.0 102.0 203.8 3,719.8 172.8 427.8 7,887.9 47.7

NE 2229 114.6 247.3 107.1 221.7 3,317.6 173.5 420.8 6,330.1 52.7

NE 2234 106.3 240.2 107.1 213.4 3,521.8 174.7 414.9 6,874.4 51.5

Mean 105 243.65 106.0 211.1 3,091.2 173.0 416.6 6,128.6 50.7

F 13.7* 8.2* 10.4* 9.6* 5.9* 10.0* 6.3* 8.6* 20.0

LSD 0.05 2.0 2.5 1.4 2.7 183.9 1.6 3.2 353.5 0.6

Range min 98.1 239.0 99.5 203.5 2,533.8 162.2 406.3 4,877.9 47.7

Max 114.6 255.0 109.0 221.7 3,719.8 176.0 427.8 7,887.9 52.7

See Table 1 for abbreviation definitions

** p=0.01, indicate significance

Bioenerg. Res. (2011) 4:96–110 107
trait in Table 3). With these initial calibrations, the r

2 values

were only less than 0.80 for ASH, EE, C, UA, FUC, FETH,

CWE, pentose extraction efficiency (PENTP), and ADL.

ASH is typically difficult to predict with NIRS. Based on

our experience using NIRS for forage quality traits, it is

more difficult to obtain r

2 values greater than 0.8 if the

calibration set has limited variation for the calibration

analyte, for analytes that are measured with low precision

such as ADL, and for complex traits that include a

biochemical reaction step in the analysis procedure such

as IVDMD. As discussed previously, there was limited

variation for total C in switchgrass biomass for which the

range was 858–939 mg g−1

. CWE and PENTP are complex

traits because of the SSF process. The SEP values were

acceptable for most bioenergy traits, but exceeded 1.5% of

the SEC by a limited extent for some traits (Table 3). To

improve the accuracy of the final prediction equations, the

validation samples were included in the calibration set and

calibration equations were redeveloped using the same

math treatments and options as in the original best

calibrations (see rows “Combined” in Table 3). After

including these additional samples, r

2 values improved to

be greater than 0.8 for all traits except EE, UA, FETH, and

CWE (all with r

2

>0.76) and PENTP (r

2

=0.69). For the

major sugars, PCA, FEST, and SSF products (ETOH and

PENT) the r

2 values were greater than 0.90. Combining the

calibration and validations samples into a single combined

calibration set also resulted in reduced SEC and SECV

values for most traits. Another measure of the utility of

calibrations is the ratio (RPD) of the standard deviation of the

calibration samples (SD) to the standard error of prediction

[36]. The RPD values greater than 2 indicate that the SEP is

less than 0.5 the SD of the reference samples. The RPD

ratios were typically greater than 2.2 for all major cell wall

sugars, soluble sugars, ETOH, and PENT. The calibration

results demonstrate that acceptable NIRS calibrations can be

obtained for switchgrass biomass composition and SSF

ethanol and soluble pentose yield.

Switchgrass Strain Differences for Actual and Theoretical

Ethanol Yields

There were significant differences among the four check

cultivars and experimental strains in the biomass yield trail

for all traits in the strain evaluation trial including actual and

theoretical ethanol yield per megagram and production per

hectare (Table 4). The lowland cultivar, Kanlow had the

highest biomass yields per hectare but its biomass also had the

lowest SSF conversion efficiency. Ethanol yield from SSF

(ETOHL) ranged from 98 to 115 L Mg−1

. Potential ethanol

yield from released pentoses, assuming 80% conversion

efficiency, ranged from 100 to 109 L Mg−1

. ETOHTL from

Table 5 Means and standard deviations (in parentheses) and correlations between calculated and NIRS predicted complex switchgrass biomass

composition traits

Trait Means (SD) r

Calculated from component values NIRS-predicted values

g kg−1 g kg−1

CWC 712.6 (22.16) 703.0 (22.7) 0.88**

AXMG 250.1 (9.8) 252.2 (9.7) 0.99**

AX 235.9 (10.1) 237.7 (7.7) 0.84**

SC 37.6 (10.7) 40.5 (11.5) 0.97**

NSC4 43.7 (13.0) 43.1 (14.4) 0.96**

HEX 370.9 (13.7) 374.6 (15.0) 0.95**

SUG 639.0 (14.5) 640.2 (15.6) 0.82**

HEXE 188.7 (7.3) 187.6 (7.0) 0.92**

NSCE 21.0 (6.3) 19.7 (7.0) 0.96**

CWE 61.9 (5.7) 64.1 (4.5) 0.77**

% %

PSOL 11.7 (3.3) 11.7 (3.5) 0.99**

PPEN 41.9 (1.6) 42.1 (1.6) 0.93**

CWEP 40.9 (4.6) 40.4 (4.04) 0.95**

PENTP 76.6 (3.3) 76.8 (2.3) 0.88**

HEXEP 44.0 (4.6) 43.8 (4.3) 0.95**

N=378

** p=0.01, indicate significance

108 Bioenerg. Res. (2011) 4:96–110
both glucose and released pentoses ranged from 204 to

222 L Mg−1

. An experimental upland strain (NE 2229) that

was bred for improved forage yield and IVDMD had the

largest ETOHL and ETOHTL values. With the assay that was

used in this study, NE 2,229 produced over 12 L more ethanol

per megagram from SSF glucose than the cultivar Kanlow.

Because of the differences in biomass yield per hectare

and actual and theoretical ethanol yield per megagram

among the cultivars and experimental strains, there were

very large differences in ethanol production per hectare

(Table 4). The range in SSF ethanol production

(ETOHTLH) from the hexoses was 2,533–3,719 L ha−1

while the range in total theoretical ethanol production from

all potential biomass sugars ranged from 4,877 to

7,887 L ha−1

. These large differences in ethanol production

per hectare illustrate the need to optimize both biomass

yield and conversion yield from biomass (L Mg−1

) in

switchgrass production systems. None of the cultivars and

strains in this trial was bred for improved SSF ethanol

yield, but several including Shawnee and NE 2229 were

bred for improved forage digestibility which had a positive

effect on SSF ethanol yield. The results demonstrate that

genetic differences exist among switchgrass for conversion

efficiency and indicate that a focused breeding effort should

result in additional improvements in conversion efficiency.

Plant breeders and agronomists need to analyze a large

number of biomass samples to develop improved plants and

management practices that result in increased ethanol yields

and production per hectare. In the ARS switchgrass

breeding programs at Lincoln and Madison, WI, 5,000–

10,000 samples are generated per year per location for

which composition and bioenergy conversion potential data

is needed to efficiently bred for improved conversion

efficiency. If done with conventional laboratory analyses,

the wet laboratory work alone for the data listed in Table 4

for the switchgrass biomass yield test would have an

approximate cost of over $110,000 ð approximately

$ 300 per sample  21 entries  6 replicates  3 yearsÞ

without including equipment costs. NIRS analysis costs

including equipment and technical support would be about

$5 per sample for a total of approximately $2,000 for this

experiment. Good NIRS calibrations provide the capacity to

conduct such bioenergy analyses economically and rapidly.

Composition and conversion efficiency data obtained from

NIRS analyses of biomass samples also will enable

researchers to identify which biomass components have

the largest effect on conversion efficiency and to determine

which are the most amenable to genetic or cultural practice

modification.

Biomass composition traits such as cell wall concentration, total sugars, or hexose extraction efficiency listed in

Table 4 were determined by calculation using NIRS

predicted constituents of these traits and the equations

listed in Table 1. It is also feasible to develop NIRS

calibrations for these calculated traits (Table 3). For the

samples from the switchgrass yield test, we determined the

concentration of these multi-constituent traits by both

calculations from NIRS-predicted component data and

direct NIRS prediction of the composite traits. The

calculated and direct NIRS predicted means and standard

errors of these complex biomass components were very

similar and were highly correlated (Table 5). This comparison demonstrates the accuracy and precision of the NIRS

calibrations that were developed for switchgrass biomass

composition and also the power of NIRS technology to

predict complex feedstock traits. As biomass conversion

technology is developed, it should be possible to develop

NIRS calibrations for biorefinery products and co-products.

Summary

NIRS calibrations were developed for switchgrass biomass

that can be used to accurately estimate over 20 biomass

components including cell wall and soluble sugars, and

ethanol and released pentose sugars from a laboratory SSF

procedure. With this information, it is feasible to calculate an

addition 13 complex feedstock traits including theoretical

ethanol yield from hexoses and SSF hexose ethanol

conversion efficiency. Using this NIRS-derived biomass

data, we demonstrated that switchgrass cultivars and

experimental strains adapted to the Midwest USA differ

significantly for all biomass composition traits analyzed, and

actual and theoretical ethanol yield per megagram and per

hectare. These calibrations and their improved future

versions can be used in all aspects of switchgrass research

including basic genetics, breeding, production, harvest, and

storage. It should be feasible to develop switchgrass cultivars

with improved ethanol yields per megagram and production

per hectare. With good NIRS calibrations, it should be

possible for switchgrass biomass feedstock to be marketed

and utilized on the basis of its ethanol yield potential.

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