investigation into the use of pyrolysis-elemental analysis for the measurement of carbohydrates in...

Analytica Chimica Acta 555 (2006) 175–180

Investigation into the use of pyrolysis-elemental analysis for themeasurement of carbohydrates in foodstuffs

M.J. Dennis∗, K. Heaton, C. Rhodes, S.D. Kelly, S. Hird, P.A. BreretonCentral Science Laboratory, Food Safety and Quality Group, Sand Hutton, York YO41 1LZ, UK

Received 4 April 2005; received in revised form 24 August 2005; accepted 25 August 2005Available online 9 November 2005

Abstract

The development of a method for the determination of carbohydrate based upon pyrolysis-elemental analysis (Py-EA) is described. The methoduses a more “direct” approach to determine carbohydrate compared to traditional “by difference” techniques such as the Weende system. Thecarbohydrate content of foods is quantified by comparison of the carbon monoxide (CO) released by pyrolysis with that released by a knownmass of sucrose. “Carbohydrate” response from other food components (fat, protein and fibre), normalised against sucrose were 0.2, 0.47 and 0.9,respectively. Although differences were observed between the new procedure and data obtained using the Weende procedure, they were withinr oods rangingf ilar resul

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ange when including measurement uncertainty. The new procedure gave similar values to the Weende procedure when analysing 65 from trace to high content carbohydrate foods. The results demonstrated the broad applicability of the procedure and its ability to get simts.

Repeatability obtained using the new techniqe ranged from R.S.D. 1 to 13%. Improvements in the precision of the procedure couhrough replicate analysis, improvements in instrumentation and the use of targeted reference materials for specific matrices that are tod.

2005 Published by Elsevier B.V.

eywords: Carbohydrate; Nutrition; Labelling; Pyrolysis; Elemental analyser

. Introduction

Carbohydrates are major components of food. Sugars, dex-rins and starches have nutritional value to man and are termedavailable carbohydrate”, whereas pectins, hemicelluloses, cel-uloses and lignin have negligible nutritional value and hencere termed “unavailable carbohydrate” or dietary fibre.

The EC Nutrition Labelling Rules Directive (90/496/EEC)overns the presentation of nutrition information on food pack-ging. Where nutrition labelling is provided, the informationiven must include the amount of carbohydrate expressed as thectual weight rather than monosaccharide equivalents.

Measurement of carbohydrate in food is based upon theeende proximate system[1] in which carbohydrate is mea-

ured by difference. Protein, fat, ash, water and dietary fibrere measured by standard methods and subtracted from 100%;

he remainder is considered carbohydrate. This approach can benaccurate as errors in proximate analysis are propagated.

∗ Corresponding author. Tel.: +44 1904 462677; fax: +44 1904 462133.

There is a need for an accurate, rapid, universally acceprocedure for the direct measurement of carbohydrate in aof foods that can be used by industry and the regulatory auities to ensure compliance with labelling requirements. Tniques such as the Kjeldahl and Dumas methods are usprotein determination. In these it is common practise to msure total nitrogen and employ correction factors to providanalysis, which, though not perfectly specific, is quick, chand suitable for purpose.

This paper describes a simple, rapid, method for determthe carbohydrate content of foods together with an evaluatiits accuracy and precision. The method can be easily automThe principle of the method is that, when pyrolysed, carbdrates, give a high yield of carbon monoxide (CO) becausratio of oxygen-to-carbon molecules (O:C) is close to uOther components in food (e.g. fats and protein) have a lratio of O:C so yield less CO when pyrolysed. Using thiscedure, the carbohydrate content of foods can be quantificalibration of the CO response against known masses of suA correction needs to be made by subtracting the contribof protein, fat and fibre from the CO signal. This contribut

E-mail address: [email protected] (M.J. Dennis). is calculated by the measurement of protein, fat and fibre in

003-2670/$ – see front matter © 2005 Published by Elsevier B.V.oi:10.1016/j.aca.2005.08.067

176 M.J. Dennis et al. / Analytica Chimica Acta 555 (2006) 175–180

the food (using existing methods) and application of previouslymeasured response factors.

2. Experimental

Samples were freeze dried overnight (where the water con-tent exceeded 5%) and packed into silver containers; these werethen placed in the autosampler where they were purged withhelium. The samples were dropped into a vertical ceramic tubemaintained at a temperature of 1150◦C in an elemental anal-yser. The pyrolysis products were separated by chromatographyon molecular sieve after which carbon dioxide and water wereremoved from the gas stream by a trap containing Carbosorb(calcium oxide, GPR grade BDH) and anhydrous magnesiumperchlorate (GPR grade BDH). The responses of the separatedgases (H2, CH4, N2 and CO) were measured using a thermal

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ultrouswePy-pleencg-

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Carrier gas HeliumCarrier gas flow rate 60–100 kPa giving a flow rate of

80–120 ml min−1

Filament temperature 190◦CPneumatic air pressure 350 kPaReference circuit helium pressure 35–45 kPa

2.2. Calculation of carbohydrate (CHO) content of food

The %sucrose in each sample was calculated as:

%sucrose= apparent mass of sucrose

mass of sample× 100 (1)

Allowance for water content lost was made for samples that werefreeze-dried.

Measured %CHO (w/w)= %sucrose× %water content

100(2)

Response factors were calculated through the measurement ofthe apparent carbohydrate response from a range of differentproteins, fats and fibres. Using the mean of the response factorsreported here, adjustment was made for the contribution of theapparent %CHO from protein, fat and fibre in the sample usingthe data:

protein CHO= %protein× 0.47 (3)

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conductivity detector. The electrical signal produced bydetector was proportional to the concentration of carbon monide. The area of the signal was recorded using a compuintegrator and compared to standards of known carbohydconcentration.

A homogeneous sucrose calibration standard (Sigmagrade, 99.5%, Sigma–Aldrich Ltd.) was stored over anhydrsilica gel in a sealed container under vacuum. Sub-samplesremoved for preparation for pyrolysis-elemental analyser (EA) analysis. A second sample of beet sugar was sub-samand stored in the same way to provide a sugar in-house refermaterial (IHRM). This material was used to monitor the lonterm repeatability of carbohydrate determinations.

Each batch started with the analysis of a sucrose ‘dumsample to condition the pyrolysis tube. A complete set of cibration standards was analysed before and after the samto ensure that both were analysed under the same conditEach batch included at least duplicate measurements of theIHRM alongside the samples. Batches were accepted if theues for carbohydrate content of the sugar IHRM were within±3standard deviations of the mean.

2.1. Pyrolysis-elemental analyser (Py-EA) conditions

Reactor tube packing (from base) Quartz wool (40 mm), Sigradur G g(160 mm), quartz wool (20 mm)

Reactor temperature 1150◦CGC column Molecular sieve 5A, stainless steel

column (1.5 m× 4 mm i.d., 6 mm o.d.,80–100 mesh)

GC oven temperature 100◦C

-ge

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ss.arl-

at CHO= %fat× 0.20 (4)

bre CHO= %fibre× 0.91 (5)

CHO= measured CHO− protein CHO

− fat CHO− fibre CHO (6)

range of carbohydrates, proteins, fats and oils and fibrebtained (Sigma–Aldrich) for the measurement of CO resp

actors from a series of pure chemical entities. Measuremere compared against a sample of sucrose, used as aontrol standard and analysed with each batch.

The procedures were evaluated using a wide range oftuffs gathered from retail sources and the data compared ahe nutritional label and against data produced from an aeasurement of carbohydrate in some of the samplesroximate analysis and calulation by difference. Proficiencyaterials, with assigned values for proximate analysis,btained from the food analysis performance assessment sFAPAS). These were used to assess the accuracy and pref the method. Carbohydrate values were calculated “by dnce” from the assigned values for the other proximates.

. Results and discussion

.1. Determination of response factors for protein,arbohydrate, dietary fibre and fat

Response factors for a series of food components were cated in order to correct for the contribution to the CO resprom components other than carbohydrate. The respons

series of carbohydrates (free sugars, dextrin and sta

M.J. Dennis et al. / Analytica Chimica Acta 555 (2006) 175–180 177

Table 1Response factors for carbohydrates

Carbohydrate Type Factor

Dextran Dextrin 0.94Amylopectin Starch 0.95Amylose (potato) Starch 0.95Starch (ACS reagent) Starch 0.92Rice starch Starch 1.05Wheat starch Starch 1.01Corn starch Starch 1.05Potato starch Starch 1.02Arabinose Free sugar 0.98Fructose Free sugar 0.90Galactose Free sugar 0.89Lactose Free sugar 0.93Maltose Free sugar 0.85Mannose Free sugar 0.97Ribose Free sugar 0.96Sucrose Free sugar 1.00 (defined)

Mean 0.96S.D. 0.06R.S.D. (%) 6

were compared with that of sucrose (Table 1). Good agreementwas found for all carbohydrates analysed (response factor 0.96,R.S.D. 6%).

Response factors were calculated for fat (0.20), protein (0.47)and dietary fibre (0.90) (Tables 2–4). These proved remark-ably consistent within a series of different proteins, fats anddietary fibre and hence the mean values were used when mak-ing allowance for the presence of these materials to determinecarbohydrate in more complex foods.

3.2. Metrology

A Py-EA gas chromatogram showing the separation of CO,CH4 and N2 is shown inFig. 1. The average run time is∼5 minwith CO retention time∼4.2 min. The repeatability of the Py-EA method was assessed through the replicate analysis of aselection of food samples, in a single batch (n = 7). The resultsgiven inTable 5demonstrate the repeatability (R.S.D.≤ 6%) ofthe method.

Table 2Response factors for fats and oils

Oil or fat Origin Factor

Refined lard Animal 0.18Refined and deodorised beef fat Animal 0.21Crude beef fat Animal 0.20SBHHHHV

MSR

Table 3Response factors for proteins

Protein Factor

Casein 0.44 (S.D. 0.03,n = 4)Albumin 0.41Gelatin 0.54Gliadin 0.43Globulins 0.41Gluten 0.51Lactalbumin 0.56Pepsin 0.47Fibrinogen 0.43

Mean 0.47S.D. 0.06R.S.D. (%) 12

Table 4Response factors for dietary fibre

Dietary fibre Type Factor

B-glucan Reserve polysaccharide 0.99 (S.D. 3.05,n = 3)Mannan Reserve polysaccharide 0.77Lichenan Polysaccharide 0.86Agar Algal polysaccharide 0.90Laminarin Algal polysaccharide 0.98 (S.D. 3.61,n = 3)Carbomethyl cellulose Structural polysaccharide 1.07Cellulose Structural polysaccharide 1.08Gum arabic Structural polysaccharide 0.92Gum tragacanth Structural polysaccharide 0.93Pectin Structural polysaccharide 0.82Pectin (citrus) Structural polysaccharide 0.93Pectin (d.f. content) Structural polysaccharide 0.80Xylan (beechwood) Structural polysaccharide 0.98Xylan (oat) Structural polysaccharide 0.77Carrageenan Algal polysaccharide 0.76

Mean 0.90S.D. 0.10R.S.D. (%) 11

The reproducibility of the Py-EA method was assessedthrough the replicate analysis of the sugar quality control stan-dard with each analysis batch. The mean value determinedfrom 90 measurements was 100% (standard deviation 8%). Theresults are displayed as a control chart inFig. 2. For eachbatch the quality control standard was within the action limitset (mean± 3 standard deviations). The relatively high variabil-ity of the sugar standard demonstrates the overlying limits in

Table 5Repeatability of the Py-EA measurement of carbohydrate in foods (n = 7)

Sample R.S.D. (%)

Milk powder (T2502) 1Breakfast cereal (T2405) 3Dairy ration (T1018) 6Extruded cereal (T2403) 3Pea (retail) 6Apple (retail) 7Potato (retail) 3

T2502, T2405, T1018, T2403 are FAPAS proficiency test materials that areknown to be homogeneous and have assigned values from the study.

pecial tallow Animal 0.20utter oil Dairy 0.25ardened fish oil Fish 0.19ydrogenated palm oil Vegetable 0.21ydrogenated groundnut oil Vegetable 0.22ydrogenated rapeseed oil Vegetable 0.20egetable oil (rapeseed) Vegetable 0.15 (S.D. 0.017,n = 4)

ean 0.20.D. 0.03.S.D. (%) 13


Fig. 1. Example of a chromatogram for Py-EA analysis of food (milk powderIHRM).

terms of precision of the method. While some improvements inmethodology may be possible the precision obtained probablyreflects the inherent precision of the particular model.

The accuracy of the Py-EA method for the determination ofcarbohydrate content of foods was assessed through the analy-sis of food IHRMs. Further assessment of accuracy was madefrom the comparison of the estimated carbohydrate values withthose provided by West Yorkshire Analytical Services, deter-mined “by difference”. The results are given inTable 6. Goodagreement was recorded between estimated and assigned valuesfor food IHRMs. It should be emphasised that no direct measure-ments of carbohydrate content in these foods were made and theassigned values were all determined using the traditional “by dif-ference” approach. There was no significant difference betweenthe results obtained through the new procedure and the assignedvalues for the IHRM when the uncertainty of the results are takeninto account (Table 6).

Table 6Comparison of Py-EA measurement of carbohydrate in foods vs. “by differencevalue” from proximate analyses

Sample Py-EA value (n = 7)±uncertainty

By difference value±uncertainty

Reference materialsMilk powder (T2502) 43± 4.6 39± 2.0a

Breakfast cereal (T2405) 86± 7.1 82± 2.1a

Dairy ration (T1018) 47± 4.8 43± 1.6a

Extruded cereal (T2403) 85± 7.0 81± 1.1a

Comparison of resultsBreakfast cereal (retail) 66 581Apple (retail) 9 12b

Dessert topping (retail) 27 32b

Infant formula (retail) 47 55b

Cheese sauce mix (retail) 30 40b

Oxtail soup mix (retail) 65 77b

Dried peach (retail) 45 56b

Sprouts (retail) 3 5b

a Assigned value for reference material from multiple proximate analyses.b Value from a single proximate analysis of retail samples by West Yorkshire

Analytical Services.

3.3. Determination of carbohydrate content of a range offoodstuffs

The applicability of the Py-EA method has been assessedby analysis of a range of different foodstuffs containinglow medium and high amounts of carbohydrate. These data(Tables 7–10) have been compared against other sources of infor-mation on the carbohydrate content of foods. For some samples,we have undertaken proximate analysis measurements of thesefoods or compared the data against food label information orinformation from food databases. The food label informationfor protein, fat and fibre content was used to generate the appro-priate correction for non-carbohydrate CO production.

The method demonstrated the absence of carbohydrate fromfoodstuffs known to contain minimal amounts of carbohydrate.

Table 7Estimated carbohydrate (%) in foods with trace carbohydrate content

Sample Py-EA Label Reported

UKa USAb

Vegetable oil −10 0 0 0Cheese −4 Tr Tr 1Bacon −5 0 0 N/aPork, cooked −4 N/a 0 N/aPork, raw −4 0 0 N/aBeef, cooked −2 N/a 0 N/aBLLCCTSSM

Fig. 2. Control chart for sugar quality control standard.

eef, raw −2 0 0 N/aiver, cooked 4 N/a 4 N/aiver, raw 2 2 2 N/ahicken, cooked 0 N/a 0 N/ahicken, raw 0 0 0 N/auna 0 0 0 0hrimp 0 0 Tr 1quid 2 0 1 N/aackerel −3 0 0 N/a

a McCance and Widdowson’s the composition of foods.b USDA.

M.J. Dennis et al. / Analytica Chimica Acta 555 (2006) 175–180 179

Table 8Estimated carbohydrate (%) in foods with low carbohydrate content


UKa USAb

Sprouts 3 4 4 N/aCabbage 3 4 4 5Carrot 4 8 8 10Can peach 8 11 10 12Plum 9 9 9 13Orange 9 9 9 12Potato 7 17 17 N/aApple 11 12 12 15Sausage 10 14 12 N/aPeas 13 14 14 N/a


Table 9Estimated carbohydrate (%) in foods with medium carbohydrate content


UKa USAb

Dessert topping 25 31 40 17Cheese sauce mix 30 38 41 N/aStock cube 38 38 12 N/aInstant tea with milk 40 43 N/a N/aMushroom soup mix 42 48 N/a N/aMexican mix 42 34 N/a N/aMilk powder 42 43 39 49Dried peach 45 37 53 61


Table 10Estimated carbohydrate (%) in foods with high carbohydrate content


UKa USAb

Pitta bread 48 58 58 56Gravey granules 55 55 41 N/aWhite bread 56 45 49 50Bread sauce mix 57 63 N/a N/aParsley sauce 57 51 60 N/aChoc orange dessert 64 72 N/a N/aOxtail soup 65 54 51 N/aInstant dessert 65 72 60 N/aBreakfast cereal 66 59 70 N/aMarzipan 68 69 68 N/aJam 68 63 69 69Instant custard 71 76 N/a N/aDrinking chocolate 71 72 77 90Beverage 72 72 78 N/aHoney 74 76 74 82Beverage 75 74 79 N/aCocoa 82 85 79 N/aBreakfast cereal 86 82 85 87Beverage 98 97 N/a N/a


Table 11Comparison of the estimated carbohydrate content (%) as calculated by differ-ence using both the EC directive and traditional methods for converting totalgrams of nitrogen (N) in foods to protein

Commodity Estimated carbohydrate content

N × 6.25 N (specific factor)

Cheese −3 −3 (6.38)Milk powder 47 48 (6.38)White bread 61 60 (5.70)Pitta bread 48 48 (5.70)Breakfast cereal 66 65 (5.83)Marzipan 68 68 (5.18)

The response factors used appeared to over-estimate the con-tribution to the Py-EA signal, resulting in negative values forcarbohydrate content (e.g. cheese,−4). For vegetable oil, thecalculated value was completely derived from the oil correctionfactor (no protein or fibre present) and gave the most nega-tive value (−10). This may reflect the inherent variability ofthe method which could be corrected for by using a referencematerial which has greater chemical similarity to the analyte.

The carbohydrate content of foods with low levels of carbo-hydrate (<20%) generally agreed well with typical values for thefood type (as indicated by the label). The exception to this waspotato. Replicate analyses were carried out which confirmed thatthe value from the Py-EA method (7%) deviated significantlyfrom the 17% quoted in[2]. This could reflect differing moisturecontents and or carbohydrate contents between our sample andthe reference samples used in the publication.

There was generally good agreement between labelling andestimated values from Py-EA for foods with medium and highcarbohydrate.

In Tables 8–11, the carbohydrate values estimated from thePy-EA measurements are compared with those supplied on thelabelling and with values from Holland et al.[2] and the USDA[3]. These values have been obtained by direct analysis and“by difference” respectively and are expressed in slightly dif-ferent ways (monosaccharide equivalent versus actual weight).In developing the pyrolysis technique for the direct measure-m p am piri-c racyo ea-s w thatt ch isb pur-p an bee

epli-c ecificf ed toa ove-m orms logy.T teinc rage

ent of carbohydrate, it was not the intention to develoethod which would give precisely the same data as the em

al “by difference” approach since its is the potential inaccuf the latter which its is intend to resolve through direct murement. Hence, these comparisons are intended to shohe method permits a direct analysis of carbohydrate whiroadly comparable with existing procedures and is fit forose. These experiments demonstrate that the method cffectively applied to a broad range of food types.

The precision of the method could be improved through rate analysis and a more targeted approach using matrix spactors. It could for example be easily automated and appli

common matrix such as cereals with concomitant imprents in precision. In addition, it could be possible to perf

imultaneous nitrogen determination using this methodohis would have the benefit of not having to correct for proontent using current by difference techniques. With an ave


run time of∼5 min as opposed to several hours of wet chemistryfor the kjeldahl method.

Acknowledgement

We acknowledge with thanks the provision of proximateanalysis data by West Yorkshire Analytical Services, RoyMacarthur’s help in the statistical analysis, and funding for thisproject from the Food Standards Agency.

References

[1] W. Henneberg, F. Stohmann, J. Landw. 3 (1859) 485–552, cited by South-gate (1991).

[2] B. Holland, A. Welsch, I. Unwin, D. Buss, A. Paul, D. Southgate,McCance and Widdowson’s: The Composition of Foods, 5th ed., RSC,Cambridge, UK, 1991.

[3] http://www.nal.usda.gov/fnic/foodcomp/Data/SR14/sr14doc.htm.

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