the most common procedures for characterizing the chemical components 
 of lignocellulosic feedstocks use a two - stage sulfuric acid hydrolysis 
 to fractionate biomass for gravimetric and instrumental analyses . 

these methods have been developed and refined since their original 
 use in the pulp and paper industry . for a review of the lineage of 
 these methods ,
these analytical methods have enabled complete summative 
 analysis of biomass feedstocks to become a routine part of biofuels 
 research and development .
the data from these methods are used to calculate mass balance 
 and process yields and for technoeconomic analysis .
these results 
 affect evaluations of process configuration , reactor design , and process 
 performance . at the national renewable energy laboratory ( nrel )
we 
 use these data to support technoeconomic analyses of biomass to ethanol 
 processes ( 2 ) .
therefore , the uncertainty 
 ( i.e. , dispersion of values from repeated measurement ) in the primary 
 data is of general interest to those with technical or financial interests 
 in biomass conversion technology .
earlier versions of these methods 
 were used in a collaborative international study of biomass reference 
 materials nearly two decades ago ( 3 ) . in 
 our previous paper ( 1 )
, we described our 
 current laboratory analytical procedures ( laps ) that are used to determine 
 the component concentrations in lignocellulosic biomass at nrel .
these biomass compositional analysis methods are empirical in nature ; 
 the final results depend on how the method is run . empirical methods 
 can be compared using reference materials such as the national institute 
 of standards and technology ( nist ) biomass reference materials ( rms 
 84914 ) ( 4 ) .
it is difficult to compare 
 method errors ( measured difference from a  true 
a 
 measurement result is complete only when accompanied by a quantitative 
 statement of its uncertainty .
the uncertainty is required in order 
 to decide if the result is adequate for its intended purpose and to 
 ascertain if it is consistent with other similar results  ( 5 ) . for this work , we measured the composition of a homogenized corn 
 stover feedstock ( 154 replicate samples in 13 batches , by 7 analysts 
 in 2 laboratories ) along with a nist reference bagasse , run as a control , 
 using our suite of analytical methods ( nrel laps ) .
we evaluate the 
 uncertainty of our methods by the statistical analysis of these data , 
 and we report the standard deviations of each component measurement .
the corn stover sample ( pioneer hybrid 33b51 ) was harvested in 
 the fall of 2003 from a farm in northeastern colorado .
we acquired 
 a large quantity of this material , and it has been used as a feedstock 
 for numerous pretreatment , saccharification , and fermentation experiments 
 at nrel for several years .
( 6.35 mm ) round screen ( reduction technologies , 
 model 10  12 , leeds , al ) and stored in totes .
a 5 gal ( 18.9 
 l ) sample was taken from one of the totes and ground in a wiley knife 
 mill to pass through a 2 mm round screen .
we coned and quartered 
 about 900 g of this sieved corn stover three times and then distributed 
 the well - mixed material into 24 glass bottles . to assess the homogeneity 
 of the material , we selected four bottles at random and predicted 
 the composition of eight subsamples of each bottle were using a near - infrared 
 ( nir ) calibration model ( 6 ) .
we saw no statistically 
 significant differences in predicted composition among the four bottles . 

the sugar cane bagasse sample was purchased from nist as rm 8491 ( 4 ) and was analyzed as received .
complete biomass compositional analysis of the feedstocks was performed 
 using methods described in detail in our companion paper ( 1 ) , available from astm international ( 7 ) and on the web ( 8) . 

briefly , batches of 12 replicate biomass samples ( approximately 1.5 
 g in an 11 ml cell ) were sequentially extracted with water and ethanol 
 using automated solvent extractors ( ase200 , dionex , sunnyvale , ca ) . 

the batch size ( 12 corn stover plus 1 bagasse ) was set by the available 
 number of cells in the extractor .
the insoluble portion from that 
 extraction was air - dried and then subjected to a two - stage sulfuric 
 acid hydrolysis ( 1 h at 30 c/72 wt % sulfuric acid , followed 
 by 1 h at 121 c/4 wt % sulfuric acid in an autoclave ) .
sulfuric 
 acid ( 72 wt % , r8191600 - 1a , ricca chemical co. , arlington , tx ) was 
 used as received .
sugar recovery standards ( srs ) were included in 
 the second stage of the hydrolysis to account for the loss of carbohydrates 
 to degradation products .
we used glass pressure vessels ( part 8648 - 30 ) 
 and teflon caps ( 5845 - 47 , ace glass inc . ,
we separated the lignin - rich residue from the analytical hydrolysate 
 liquor using vacuum filtration through ceramic filtering crucibles 
 ( 60531 , coorstek , golden , co ) .
the filter crucibles are connected 
 to the filter flask using a rubber gasket ( 24065 - 000 , vwr , west chester , 
 pa ) after removal of the accompanying glass stem .
carbohydrates and 
 acetyl content were determined by high - performance liquid chromatography 
 ( hplc ) .
lignin was measured by combining a uv measurement for acid - soluble 
 lignin and a gravimetric method for measuring acid - insoluble residue .
all hplcs ( model 1100 , agilent , 
 santa clara , ca ) were equipped with an inline degassing unit , chilled 
 autosampler , and refractive index detector ( ri ) .
we used a lead cation 
 ( pb ) exchange column ( shodex sp0810 , showa denko k.k .
, 
 kawasaki , japan ) , running at 85 c , using 18 m water as 
 the mobile phase at 0.6 ml / min .
we used deashing guard columns ( 125 - 0118 , 
 bio - rad , sunnyvale , ca ) located outside the heating compartment to 
 keep the guard columns within their manufacturer s temperature 
 tolerance .
this reduces the appearance of ghost peaks from the monomer 
 sugars that can interfere with quantitation .
we purchased the calibration 
 standards and independent calibration verification standards from 
 absolute standards inc .
calibration standards for sugars 
 were run at four levels ranging from 0.01 mg / ml to 6.00 mg / ml for d-(+)-cellobiose , d-(+)-glucose , d-(+)-xylose , d-(+)-galactose , and l-(+)-arabinose , with a 50 l 
 injection volume into the hplc .
acetyl separation was performed using 
 a cation h column ( hpx-87h bio - rad ) , running at 55 c , using 
 0.01 n sulfuric acid in 18 m water as the mobile phase at a 
 flow rate of 0.6 ml / min .
calibration standards for acetyl ranged from 
 0.02 to 1.08 mg / ml and were also run at four levels with a 50 l 
 injection size .
the measured 
 concentrations of monomer sugars released from the biomass are corrected 
 using srs autoclaved with the samples .
mixed solutions of biomass 
 sugars , prepared in - house and similar in concentration to the samples , 
 are analyzed before and after the autoclave hydrolysis to account 
 for the fraction of the liberated monosaccharides that are degraded 
 during the hydrolysis . in practice , the glucose concentration in the 
 srs samples is reduced to approximately 95% of its original value 
 after autoclaving , so the corresponding glucose concentrations in 
 the biomass samples are multiplied by the ratio 1.0/0.95 to correct 
 for this loss .
typical srs recoveries for xylose are approximately 
 85% , because xylose is more labile than glucose .
we performed these 
 analyses in two similarly equipped laboratories on the nrel campus . 

the two laboratories ( identified below as a and b ) were equipped with 
 similar equipment and reagents ; with the following exceptions , the 
 materials and methods should be considered the same for both laboratories . 

laboratory a was equipped with a thermolyne ( barnstead thermolyne , 
 hampton , nh ) 30400 furnace set to ramp to 575 c , whereas laboratory 
 b s furnace was set to a constant 575 c and samples were 
 ignited using a bunsen burner prior to placement in the furnace .
laboratory 
 a had two large autoclaves with external steam generators ( consolidated 
 sr-24c with mark ii controllers , consolidated stills and sterilizers , 
 boston , ma ) available for the secondary acid hydrolysis .
laboratory 
 b was equipped with a benchtop autoclave that generated steam pressure 
 internally ( sterilmatic model stm_e , market forge , everett , ma ) .
the 
 only common materials between the laboratories were common batches 
 of srs solutions and hplc standards for both carbohydrate and acetyl 
 measurements .
a total of 11 primary components 
 were measured : glucan , xylan , galactan , arabinan ( the structural carbohydrates ) , 
 acetyl , lignin ( the combined acid - insoluble residue and acid - soluble 
 lignin ) , ash ( apportioned between structural and nonstructural ) , protein 
 ( apportioned between structural and nonstructural ) , water and ethanol 
 extractives , and the sucrose content within the water extractives 
 fraction . on the basis of these primary data , we calculated the values 
 of the following components : water extractives ( others ) , total extractives , 
 total structurals , and total .
water extractives ( others ) is calculated 
 as the amount of water - soluble material minus the soluble ash , soluble 
 protein , and sucrose .
the total extractives value is the sum of the 
 water and the ethanol - soluble material .
the structurals value is calculated 
 as the sum of all material not solubilized during extraction ; it consists 
 of the glucan , xylan , galactan , arabinan , lignin , acetyl , structural 
 protein , and structural ash .
the total value is the sum of all components 
 in the biomass . to measure the overall method uncertainty
, we 
 had a homogenized corn stover sample analyzed in different laboratories , 
 by different analysts , and in multiple batches run by the same analyst . 

each analyst performed complete analysis on 12 replicate corn stover 
 samples and 1 sugar cane bagasse sample as a batch .
the data presented 
 below are the results of 13 separate batches performed by 7 analysts . 

each analyst was assigned a laboratory such that the number of batches 
 was divided approximately evenly between the two laboratories .
to 
 evaluate the effect of multiple batches run by a single analyst , two 
 researchers each analyzed four separate batches , two batches in each 
 laboratory .
all tests of statistical 
 significance were performed at the 95% significance level ( p < 0.05 ) .
we chose corn stover for this experiment , because it is a widely 
 and currently available herbaceous feedstock .
an analyst homogenized 
 and distributed a 900 g sample of corn stover into 24 bottles .
we 
 randomly selected four bottles , scanned the contents by near - infrared 
 ( nir ) spectroscopy , and predicted the stover compositions . in a one - way 

anova of these data , no significant compositional differences between 
 the bottles were found .
a total of 13 batches of 
 12 corn stover samples were analyzed by 7 analysts , resulting in a 
 total of 156 analyses .
one sample each from two batches was lost during 
 the analysis , leaving 154 complete analyses . as a control ,
one sample 
 of nist bagasse reference material ( rm 8491 ) was analyzed with each 
 batch for a total of 13 replicates .
the analysts performed the compositional analyses in batches ( 12 
 corn stover samples and 1 bagasse ) over the course of several weeks 
 ( dec 2008jan 2009 ) . here
we report both the censored ( without 
 tukey outliers ) and uncensored ( all data ) sets .
we removed individual 
 measurements after applying tukey outlier tests in conjunction with 
 observed experimental anomalies .
[ we applied the tukey outlier test 
 to the major components ( water extractives , lignin , glucan , and xylan ) . 

this test identifies any sample having a value > 1.5 times the interquartile 
 range less than the lower hinge or greater than the upper hinge .
a 
 total of 17 samples out of 154 were identified as low outliers for 
 water extractives .
all of these low water extractives were run in 
 laboratory b. during this experiment , analysts noted occasional variability 
 in the volume of the solution in the water collection vial after extraction , 
 along with minor instrument warnings from the automated solvent extractor 
 in laboratory b. after all experiments were completed , we discovered 
 and repaired a mechanical problem with the automated solvent extractor 
 in laboratory b that we believe was responsible for the low outlier 
 water extractive values .
the exclusion of these 17 samples reduced 
 the water extractives uncertainty considerably , but even after exclusion 
 of these samples , the relative standard deviation of the water extractives 
 value for samples run in laboratory b was still larger than that in 
 laboratory a ( 3.5 vs 2.5% ) , a statistically significant difference . 

we identified a total of 13 tukey outliers for glucan and/or xylan 
 ( 7 for both glucan and xylan , 5 for glucan only , and 1 for xylan only ) . 

six of these 13 samples were also outliers for water extractives but 
 were not flagged as carbohydrate outliers on an extractives - free basis . 

thus , the underlying carbohydrate data were likely correct , and the 
 low extractives value computationally biased the final carbohydrate 
 result high .
six of the other 7 were from laboratory a. these samples 
 showed high carbohydrate values and normal water extractives values , 
 which suggests a problem in the carbohydrate analysis separate from 
 the water extractives test .
we had identified a problem with the gasket 
 seals used for filtration after analytical hydrolysis in laboratory 
 a. we hypothesize that excessive air flow through the flask caused 
 evaporation of the filtrate , leading to artificially high carbohydrate 
 values .
the tukey outlier test also identified 8 lignin outliers , 
 all but 1 of which were associated with an outlier extractives value , 
 which again suggests the low extractives result computationally biased 
 the final lignin result high . ] in summary , of 154 analyses , we identified 
 as outliers 17 individual water extractives measurements , 12 glucan 
 measurements , 8 xylan values , and 8 lignin values .
we believe the 
 censored data represent the  best case  results of our 
 analyses , whereas the uncensored data show how small method changes 
 can strongly affect the uncertainties of these empirical methods . it could be argued that we are simply excluding the 5% 
 of the data that always fall outside the range covered by 2 
 standard deviations from the mean . however , we believe these outliers 
 are not representative of the typical results of the compositional 
 analysis procedure for two reasons .
first , all of the water extractives 
 outliers were below the mean , and all of the lignin and glucan / xylan 
 outliers were above the mean , suggesting a systematic error in both 
 cases .
second , we have working hypotheses to explain the sources of 
 both errors : a malfunctioning solvent extractor in laboratory b for 
 the water extractives and associated lignin and glucan / xylan outliers , 
 and a leaking gasket in laboratory a during filtration for the independent 
 carbohydrate outliers .
we show in table 1 the censored and uncensored 
 compositional data for the corn stover sample .
we report both the 
 overall and the pooled ( weighted average within the batches ) standard 
 deviations ( sd ) for this set .
the pooled sds are less than the overall 
 sds for all components , suggesting that the uncertainty within the 
 batches is lower than the uncertainty between batches .
we show in 
 table 2 the compositional data for the nist 
 bagasse ( rm 8491 ) control sample , which we ran once with each batch . 

we report only the overall sd , as the 
 pooled sd is not meaningful for single replicates .
the average total 
 mass closures were 96.9% ( sd 1.0% ) and 100.8% ( sd 1.0% ) for the corn 
 stover and the bagasse sample , respectively .
a component closure near 
 100% suggests that most components are accounted for and little double 
 counting of components is occurring .
figure 1 shows the average censored compositional 
 data for the corn stover material ( figure 1a ) and the corresponding data for the nist sugar cane 
 bagasse ( figure 1b ) .
the constituents 
 are shown in the same order in both figures and are sorted by percent 
 dry weight in the corn stover samples .
the error bars in this figure 
 represent 1 sd about the mean of each constituent .
almost 90% 
 of the total mass is accounted for by glucan , xylan , lignin , and total 
 extractives .
the corn stover feedstock contains much more total ( water 
 + ethanol ) extractives than the sugar cane bagasse ( > 22 vs < 6% ) . 

the amount of extractives in corn stover depends on the variety , harvest 
 time , and postharvest handling .
sugar cane bagasse is a byproduct 
 of an industrial process to extract the cane juice , and this process 
 leaves behind little extractable material .
summary 
 compositional analysis results on a dry weight basis from this study 
 for ( a ) corn stover and ( b ) nist standard 
 reference material ( rm ) 8491 ( bagasse ) . data were produced by 7 analysts 
 working in 2 different laboratories .
the corn stover material is slightly lower in 
 glucan and xylan , much lower in lignin , and much higher in total extractives 
 than the nist rm .
uncertainties for each constituent are similar between 
 the corn stover and the bagasse rm .
the effect of the censoring process on the uncertainty of the measurements 
 is illustrated in figure 2 .
panels a and b of figure 2 show the sd 
 and the relative standard deviation ( rsd = sd / mean ) of the uncensored 
 compositional data for the main constituents of corn stover , whereas 
 panels c and d show the corresponding censored 
 data .
the sds for the censored components are 3050% lower 
 than for the uncensored data , which shows the outsized effect of the 
 excluded samples .
because the total extractives value is used to correct 
 the measurement of structural components to a whole , dry weight basis , 
 uncertainties in the extractives value affect all of the results . 

the rsd values are larger for the minor components , due largely to 
 smaller mean values rather than the larger sd values .
summary 
 compositional analysis uncertainty results from this study on dry 
 weight basis for the corn stover ( a ) standard deviation 
 ( sd ) and ( b ) relative standard deviation ( rsd , sd / mean ) 
 for the uncensored data and ( c ) sd and ( d ) rsd ( sd / mean ) for the censored data . the constituents are shown 
 in the same order as in figure 1 .
the overall 
 uncertainty is driven to a large extent by total extractives and glucan 
 and drops substantially when outlier data are removed .
figure 3 shows control charts of the censored corn stover 
 compositional data for each of the individual samples analyzed for 
 glucan , xylan , lignin , and total extractives . in each plot
, the solid 
 line indicates the mean and the dashed lines indicate 3 sds 
 above and below the mean .
no trends in constituent values are evident , 
 suggesting that the analyses were performed consistently over time .
control 
 chart plots of individual corn stover sample compositional analysis 
 results from this study for glucan , xylan , lignin , and total extractives . 

these four constituents represent almost 90% of the total mass of 
 the corn stover .
figure 4 shows the uncensored corn stover 
 compositional data for water extractives and glucan , noted by laboratory , 
 for each of the samples .
many of the glucan outliers ( figure 4b ) correspond with water extractives 
 outliers ( figure 4a ) . as discussed 
 above , this is due to the computational effect of using the extractives 
 value to convert the primary measurement of extractives - free glucan 
 to a whole , dry weight basis .
a few of the glucan high outliers do 
 not correspond with water extractives outliers , and as we mentioned 
 previously , we believe these hydrolysate solutions became concentrated 
 during the vacuum filtration step . ( a )
water extractives and ( b ) glucan content of 
 154 samples in the round robin study , generated by seven analysts 
 working in two different laboratories . in both plots the data are 
 separated by laboratory , with tukey outliers highlighted . all sample 
 results with water extractives data flagged as tukey outliers ( n = 17 ) were from the same solvent extraction instrument 
 in laboratory b and were excluded from the data analysis . a total 
 of 12 carbohydrate outliers
were also identified , but 5 of these were 
 flagged as extractives outliers as well . all but 1 of the remaining 
 7 outliers
were analyzed in laboratory a. as discussed above , 
 srs are included in the secondary ( autoclave ) hydrolysis step .
the 
 use of srs as a proxy to estimate the extent of degradation of the 
 structural ( polymer ) carbohydrates may overestimate the amount of 
 sugars lost during hydrolysis , because the structural carbohydrates 
 are present as oligomers ( not monomers as in the srs ) at the beginning 
 of the secondary hydrolysis .
although imperfect , until a good model 
 polysaccharide is identified , hydrolyzing monosaccharides simultaneously 
 with biomass samples is the best available method to correct for lost 
 sugars . because the srs procedure accounts for the degradation 
 of structural carbohydrates during a specific batch , it can correct 
 for slight differences between autoclaves
this is shown in figure 5 , which shows box plots of the average srs recoveries 
 for glucose ( figure 5a ) and xylose 
 ( figure 5b ) in the two laboratories 
 as well as the corresponding glucan and xylan composition values for 
 the corn stover material .
analysts working in laboratory a used one 
 of two large autoclaves routinely used by life science researchers 
 to sterilize glassware and biological media , and analysts working 
 in laboratory b used a benchtop autoclave used only for analytical 
 hydrolysis .
however , the srs data suggest that the actual 
 conditions in the autoclaves were different .
there are statistically 
 significant differences between the two laboratories for srs recoveries 
 for both glucose and xylose .
however , when these srs recovery data 
 were then used to correct the corresponding values for glucose and 
 xylose in the corn stover samples , no statistically significant differences 
 were seen between the two laboratories for either glucan or xylan 
 composition of the corn stover samples .
thus , a small but consistent 
 systematic bias in the autoclave steps between the two laboratories 
 was eliminated by using the srs procedure .
box plots 
 of the sugar recovery standard ( srs ) recoveries for ( a ) glucose and ( c ) xylose in the two laboratories ( a 
 and b ) where the compositional analysis results were generated and 
 the corresponding calculated ( b ) glucan and ( d ) xylan composition of the corn stover material .
the thick lines 
 show the average values , the boxes cover the interquartile range ( iqr , 
  middle 50  ) , the whiskers denote the 1st and 4th quartile 
 ranges , and any outliers are noted with circles .
statistically significant 
 differences between the two laboratories were seen in srs recoveries 
 for both glucose and xylose .
when these srs recovery data were then 
 used to correct the corresponding values for glucose and xylose in 
 the corn stover samples , no statistically significant differences 
 were seen between the two laboratories for either constituent .
figure 6 shows control charts of the measured 
 glucan , xylan , lignin , and total extractives content of the sugar 
 cane bagasse rm that was analyzed with each batch of corn stover . 

the lines in this figure are the mean ( solid ) and the 3 sd ( dotted ) 
 values .
the sds seen with the corn stover and nist bagasse materials 
 were similar , which suggests the uncertainty reported here is typical 
 of the method and will be similar across different feedstocks .
it 
 is useful to track the measured composition of such a control method 
 over time , because large differences between the overall mean and 
 any individual measurement may indicate a problem with the analysis 
 of the associated laboratory samples .
individual 
 nist rm 8491 compositional analysis results from this study for glucan , 
 xylan , lignin , and extractives .
one nist rm sample was analyzed with 
 each batch of 12 corn stover samples .
this figure shows box plots of each analyst s 
 data with the overall median , shown as a bold line , and the overall 
 interquartile range ( iqr ,  middle 50  ) marked by the 
 shaded region .
analysts 1 and 5 ran four batches each , and their iqr 
 boxes are the widest among analysts .
the rest of the analysts ran 
 only one batch each and show narrower iqrs .
the wider iqrs for analysts 
 1 and 5 are consistent with the data in table 1 , showing that the uncertainty between batches is higher than the 
 uncertainty between analysts .
for example , the 
 median glucan values ( figure 7a ) determined by analysts 2 and 6 were 0.5 and 0.8 percentage points 
 ( % dry weight ) below the median value of all analysts .
these biases 
 are small and based on a single batch , so we can not draw conclusions 
 about possible biases among analysts .
box plots 
 of the glucan , xylan , lignin , and extractives content of corn stover 
 analyzed during this study , separated by analyst .
the median value 
 of the entire data set is shown as a solid horizontal line , and the 
 interquartile range ( iqr ,  middle 50  ) of the entire 
 data set is shown in gray .
analysts 1 and 5 ran four batches each , 
 whereas the other analysts ran a single batch .
the corn stover 
 used for this study was not chosen to have a representative composition , 
 but rather it was prepared to be compositionally homogeneous .
the 
 composition seen here is within the range of stover values reported 
 elsewhere . for a detailed discussion of corn stover compositional 
 variability , see templeton et al .
we included the nist rm 8491 as a control sample with each analytical 
 batch .
the data we report here agree reasonably with previous data . 
 a round robin analysis sponsored by the iea , nrel , and
nist reported 
 values ( all % dry weight ) of 38.6 , 23.1 , 20.4 , and 94.3 for glucan , 
 lignin , xylan , and total component closure , respectively ( 3 ) .
the authors did not report uncertainties with 
 their whole biomass compositional data as they did with their extractives - free 
 compositional data .
we report bagasse compositions ( all % dry weight ) 
 of 39.0 ( sd 0.5 ) , 24.8 ( 0.5 ) , 21.8 ( 0.4 ) , and 100.8 ( 1.0 ) for glucan , 
 lignin , xylan , and total component closure , respectively . our component 
 closure is higher than the iea data because we report acetyl [ 3.3% 
 ( 0.2% ) ] and protein [ 2.2% ( 0.1% ) ] , whereas the iea round robin did 
 not , although the iea reported glucuronic acid ( 1.3% ) , whereas we 
 did not .
a new interlaboratory study is currently being performed 
 to recertify the compositions of the four nist biomass rms .
these empirical 
 methods require attention to detail and can be problematic , with many 
 sample manipulations and therefore opportunities for mistakes .
we 
 have shown that it is possible to get reproducible feedstock compositional 
 data among different analysts and laboratories using these compositional 
 analysis methods .
we have also shown , in the censored and uncensored 
 data ( figure 2 ) , that small differences in 
 the analytical technique can have a great effect on the sample uncertainty . to generate high - quality data ,
it is important to follow the methods 
 closely , although simply following the written protocol does not guarantee 
 good data .
in addition 
 to good laboratory practices ( pipet and balance calibration , proper 
 hplc standards , etc . ) , we ensure quality compositional data by analyzing 
 srs with each sample batch , running a reference material with each 
 batch ( nist bagasse , in this case ) , and analyzing samples in duplicate 
 ( or more ) .
evidence of good compositional analyses includes replicate 
 data within these reported method uncertainties , good analytical component 
 closure ( 95105% ) , and good component closure around a given 
 unit operation ( pretreatment , conditioning , saccharification , fermentation , 
 etc . ) . on the basis of our experience ,
these include improperly 
 dried samples and incomplete wetting or sample stirring during the 
 primary hydrolysis , leading to incomplete hydrolysis and biasing the 
 carbohydrate measurement low .
other technique mistakes included raising 
 the ph of the analytical hydrolysate to > 6 during neutralization with 
 caco3 and difficulties in interpreting and integrating 
 the hplc chromatograms , because baseline resolution of all sugars 
 is not possible with current columns .
we have also seen concentration 
 of the hydrolysate during filtration of the acid - insoluble residue 
 and incomplete extraction of samples prior to analytical hydrolysis , 
 which tend to bias the carbohydrate results high . in general , there 
 are many possible causes to bias the carbohydrate measurements low 
 and fewer causes for high biases .
the uncertainties associated with these methods can be reduced 
 by having well - trained analysts running the methods ; because the methods 
 are so manually intensive , it is easy for an analyst to fall out of 
 practice .
we recommend having one analyst work up each sample from 
 beginning to end , which allows the analyst to spot problems in the 
 data and correct his or her technique .
even with all 
 of these safeguards in place , we noted larger compositional uncertainties 
 in the uncensored data due to problems in the extraction step .
poor 
 extractions led to poor corrections from extractives - free data to 
 a whole , dry weight basis , and we excluded the poorly extracted samples 
 from the censored data set .
we noted but did not appreciate differences 
 in the volume of water extract solution and minor instrumental errors . 

in hindsight , this should have alerted us to mechanical problems with 
 the instrument .
we now measure the amount of water extract solution 
 to ensure more consistent extractions .
the outlier water extractives 
 were easy to spot with so many replicates run in this data set ( figure 4a ) .
even if few replicates were run , 
 an alert analyst should note unusually low total mass closure , unusually 
 low extraction volumes , or unusually high carbohydrate values , which 
 indicate problems with the analytical data .
we believe these are caused by concentrating the 
 analytical hydrolysate during the solid / liquid vacuum filtration step , 
 because these samples tended to have high concentrations for all of 
 the sugars and acetyl simultaneously .
we have replaced the filtration 
 gaskets used in that laboratory to limit the amount of air drawn past 
 the analytical hydrolysate . as discussed previously , we recommend that a srs be run with each 
 batch .
this allows for a direct correction of the samples with the 
 specific hydrolysis conditions and can be used to track changes in 
 autoclave performance . because the srs recovery is specific to each 
 autoclave , we do not recommend using srs values from one autoclave 
 to correct for data run in a different autoclave .
the uncertainty values 
 presented here combine the uncertainty contributions from laboratory 
 operations ( e.g. , weighing , dilutions ) , instrumental operations ( ase , 
 hplc ) , different analysts , and different laboratories . using a homogenized 
 corn stover sample ,
estimates of errors , 
 in the form of 98% confidence intervals , for these methods have been 
 published elsewhere , and are reported to range from 0.5 to 1.5% ( absolute 
 basis ) for all components ( 11 , 12 ) .
in contrast , we report 
 the method uncertainties of replicate analyses on a homogenized corn 
 stover sample .
we report method uncertainties for the major biomass 
 components ( all % rsd ) as glucan ( 1.6% ) , lignin ( 1.4% ) , xylan ( 1.8% ) , 
 and extractives ( 2.7% ) . not surprisingly , we found higher uncertainties 
 for the lesser components including protein ( 4.7% ) , whole ash ( 4.4% ) , 
 minor sugars ( 6.8% ) , and acetyl ( 9.8% ) .
these uncertainties are based 
 on results from 7 analysts , run in 13 batches , analyzed in 2 laboratories . 

we recognize that these feedstock compositions 
 are used for conversion and yield calculations , and the uncertainties 
 in these primary measurements propagate into these calculations .
we 
 are currently examining the effect of this uncertainty propagation 
 ( manuscript in preparation ) .
iea , international energy agency ; hplc , high - performance liquid 
 chromatography ; lap , laboratory analytical procedure ; nist , national 
 institute of standards and technology ; nrel , national renewable energy 
 laboratory ; rm , reference material ; rsd , relative standard deviation ; 
 srs , sugar recovery standard .
 the most common procedures for characterizing the chemical components 
 of lignocellulosic feedstocks use a two - stage sulfuric acid hydrolysis 
 to fractionate biomass for gravimetric and instrumental analyses . 
 the uncertainty ( i.e. , dispersion of values from repeated measurement ) 
 in the primary data is of general interest to those with technical 
 or financial interests in biomass conversion technology . 
 the composition 
 of a homogenized corn stover feedstock ( 154 replicate samples in 13 
 batches , by 7 analysts in 2 laboratories ) was measured along with 
 a national institute of standards and technology ( nist ) reference 
 sugar cane bagasse , as a control , using this laboratory 's suite of 
 laboratory analytical procedures ( laps ) . 
 the uncertainty was evaluated 
 by the statistical analysis of these data and is reported as the standard 
 deviation of each component measurement . censored and 
 uncensored versions 
 of these data sets are reported , as evidence was found for intermittent 
 instrumental and equipment problems . 
 the censored data are believed 
 to represent the  
 best case  results of these analyses , 
 whereas the uncensored data show how small method changes can strongly 
 affect the uncertainties of these empirical methods . 
 relative standard 
 deviations ( rsd ) of 13% are reported for glucan , xylan , lignin , 
 extractives , and total component closure with the other minor components 
 showing 410% rsd . 
 the standard deviations seen with the corn 
 stover and nist bagasse materials were similar , which suggests that 
 the uncertainties reported here are due more to the analytical method 
 used than to the specific feedstock type being analyzed . 
Introduction
Materials and Methods
Results and Discussion
Abbreviations Used