HAL Id: hal-00895352 https://hal.archives-ouvertes.fr/hal-00895352 Submitted on 1 Jan 2001 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Relationships between the sensory characteristics, neutral volatile composition and gross composition of ten cheese varieties J. Ben Lawlor, Conor Delahunty, Martin Wilkinson, Jeremiah Sheehan To cite this version: J. Ben Lawlor, Conor Delahunty, Martin Wilkinson, Jeremiah Sheehan. Relationships between the sensory characteristics, neutral volatile composition and gross composition of ten cheese varieties. Le Lait, INRA Editions, 2001, 81 (4), pp.487-507. 10.1051/lait:2001147. hal-00895352
Transcript
HAL Id: hal-00895352https://hal.archives-ouvertes.fr/hal-00895352
Submitted on 1 Jan 2001
HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.
Relationships between the sensory characteristics,neutral volatile composition and gross composition of
ten cheese varietiesJ. Ben Lawlor, Conor Delahunty, Martin Wilkinson, Jeremiah Sheehan
To cite this version:J. Ben Lawlor, Conor Delahunty, Martin Wilkinson, Jeremiah Sheehan. Relationships between thesensory characteristics, neutral volatile composition and gross composition of ten cheese varieties. LeLait, INRA Editions, 2001, 81 (4), pp.487-507. �10.1051/lait:2001147�. �hal-00895352�
Relationships between the sensory characteristics,neutral volatile composition and gross composition
of ten cheese varieties
J. Ben LAWLORa, Conor M. DELAHUNTYa*,Martin G. WILKINSONb, Jeremiah SHEEHANb
a Department of Food Science and Technology, Nutritional Sciences,University College, Cork, Ireland
b Dairy Products Research Centre, Moorepark, Fermoy, Co. Cork, Ireland
(Received 6 November 2000; accepted 9 January 2001)
Abstract — Relationships between the odour, flavour and texture sensory attributes and the neutralvolatile composition and gross composition of ten varieties of cheese were determined. Sensory eval-uation was carried out by fifteen trained assessors who used a vocabulary of nine odour, twenty-oneflavour and ten texture terms. Volatile compounds were isolated in a bench top purge and trap modelsystem, trapped on Tenax-TA and analysed using gas chromatography coupled with mass spectrome-try. Gross composition was measured using standard methods. Results of Principal ComponentsAnalysis on the sensory data showed that the first seven Principal Components significantly discrimi-nated between cheeses and accounted for 86% of the variation. Partial Least Squares regression wasused to determine the relationship between significant sensory attributes and thirty identified volatilecompounds and the gross compositional data. Six odour and eleven flavour attributes were positivelycorrelated with subsets of volatile compounds and gross compositional data. Seven texture attributeswere shown to be correlated to subsets of gross compositional measurements. Overall, the presentstudy illustrated that individual cheese flavour and texture attributes are the result of complex interac-tions of specific volatile compounds and compositional variables.
Résumé— Corrélation entre les caractéristiques sensorielles, la fraction volatile neutre et lesprincipaux paramètres de composition de dix variétés de fromages.Les caractéristiques senso-rielles concernant l’odeur, le goût et la texture de dix variétés de fromages ont été corrélées à la com-position de leurs fractions volatiles neutres et à leurs principaux paramètres de composition. Une
évaluation sensorielle a été réalisée par quinze sujets entraînés avec un vocabulaire de neuf descrip-teurs pour l’odeur, vingt et un pour le goût et dix pour la texture. Les composés volatils ont été isolés àl’aide d’un système de concentration des effluves sur polymère Tenax-TA et ont ensuite été analyséspar chromatographie en phase gazeuse couplée à la spectrométrie de masse. Les principaux paramè-tres de composition ont été déterminés à l’aide de méthodes standards. Les analyses en composantesprincipales des données sensorielles ont montré que les sept premières composantes principales si-gnificativement discriminantes comptaient pour 86 % de la variation totale. Une méthode statistiquede régression multidimensionnelle a été utilisée pour déterminer les corrélations entre les donnéessensorielles, trente composés volatils des différents fromages et leur composition. Six descripteursd’odeur et onze de goût ont été positivement corrélés avec des groupes de composés d’arômes etcertains paramètres de composition. Sept descripteurs concernant la texture ont été corrélés à desparamètres de composition. Cette étude a montré que l’arôme et la texture d’un fromage résultentd’interactions complexes entre la fraction volatile et les principaux paramètres de composition.
Increasing demand for non-Cheddar va-rieties of cheese is predicted as a growingnumber of consumers seek new flavour andtexture varieties. However, although theimportance of cheese flavour and texture toconsumer preference has been shown [26,35], there still exists inadequate scientificunderstanding of the specific contributionof cheese composition to flavour and tex-ture development, and to individual flavourand texture perceptions in cheese when it isconsumed.
Differing cheese manufacturing prac-tices result in differences in gross composi-tion, volatile composition, and ultimatelydifferences in flavour and texture betweencheese varieties. For example, fat content,which can now be standardised by thecheesemaker, plays an important role in theperceived flavour and texture of cheese.The breakdown of fat in the cheese matrix,by lipolysis, results in the liberation of freefatty acids (FFA’s). FFA’s, and theircatabolites such as alkan-2-ones (2-methylketones), can act as important sources offlavour in mould-ripened cheeses [38],Italian hard cheeses and Swiss-typecheeses [25]. Fat also dissolves and absorbsmost organic flavour compounds, influenc-
ing their release during consumption andsubsequent contribution to perception.Finally, fat content plays a significant rolein mouth feel, or texture, of cheese, and thisis apparent in the texture differences be-tween normal and reduced fat cheeses.Other compositional variables, such as theSalt-in-Moisture (S/M), Moisture-in theNon-Fat-Substances (MNFS) and Fat inDry Matter (FDM) also influence cheeseflavour and texture. For example, varia-tions in S/M levels have been shown tohave a marked effect on the rate of proteoly-sis in cheese [24, 27]. Such differences, inturn, may lead to differing rates of curdsoftening (due to casein breakdown) andvariation in the rates of formation of flavourcomponents derived from the catabolism offree amino acids, such as aldehydes, alco-hols and sulphur compounds betweencheese types.
By relating information on flavour andtexture perceptions of cheeses obtainedfrom descriptive sensory analysis, to chem-ical and/or physical parameters of the samegroup of cheeses, one could identify whatMoskowitz [39] called the “true” or “opera-tive” stimuli for flavour and texture percep-tion. Such information could be used tooptimise product formulations and focustechnology inputs to influence productquality.
488 J. Ben Lawlor et al.
For flavour, Vangtal and Hammond [43]correlated the flavour notes, evaluated by aflavour profile method, of fifteen Swiss-type cheeses to pH, moisture, fat and saltmeasurements. They found both positiveand negative associations. For example,“fruity” flavour was positively correlatedwith pH and salt content, while “nutty” fla-vour was negatively correlated to the samecompositional variables. Relationships be-tween volatile composition and cheese fla-vour have been more difficult to determineby statistical correlation. Banks et al. [1],related measures of FFA’s, methyl ketones,esters, lactones, terpenes and indicators ofprotein breakdown to sensory attributes ofCheddar cheese using Partial Least Squares(PLS) regression, but only found that “fla-vour intensity” and “acid flavour” were re-lated to combinations of these. “Flavourintensity” was associated positively withthe extent of protein breakdown and withthe concentration of 2-pentadecanone, andnegatively with 3-hydroxy-2-butanone,phytene A and phytadiene. However, theybelieved that these relationships showedflavour association rather than causality.Virgilli et al. [45], who also used PLS re-gression, related individual odour and tasteattributes of Parmigiano-Reggiano cheeseto volatile and non-volatile components.They showed that certain odour and flavourattributes were related to groups of volatileand non-volatile compounds. For example,“intense” odour correlated positively withesters, free fatty acids, hexadecanol andtwo branched aldehydes. This approach,where volatile and non-volatile data aretaken together to enable understanding offlavour, appears to be most valid.
To our knowledge, few other studieshave investigated the relationship betweenindividual sensory attributes and both vola-tile composition and gross composition ofcheese at the same time. Gross compositionreflects free amino acid and peptide compo-sition, and often reflects FFA’s and volatilecomposition. In addition, gross composi-
tion determines the release of volatile com-pounds during consumption [32, 33].Therefore it may be most appropriate toconsider both of these measures together inattempts to understand flavour perception.
For texture, Chen et al. [8] found that“hardness”, “cohesivness”, “adhesiveness”and “chewiness” were closely correlatedwith composition and pH of eleven variedcheese types, ranging from Parmesan toprocessed cheese. Casiraghi et al. [7] found“chewiness” to be correlated with both totalsolids and protein, and “hardness” to becorrelated with total solids for a range ofItalian cheeses. Hort et al. [16] investigatedthe relationship between six perceived tex-tural characteristics of seventeen samplesof Cheddar cheese, of different age and ori-gin, and their chemical and rheologicaldata. It was found that the rheological datawas much better than compositional data inrelation to sensory texture. However, au-thors did acknowledge that the limitedrange of cheeses, covering only a smallrange of compositional contents, couldhave limited the study. Most recently, Noëlet al. [40] investigated relationships betweensensory data and rheological, proteolysisand gross composition, using ten differentsamples of two very different cheese types,namely, Appenzeller and Parmigiano-Reggiano. These workers not only foundsignificant relationships between grosscomposition and sensory texture proper-ties, but also, by using PLS regression, wereable to predict some of the texture charac-teristics quite well and recommended thisapproach for similar investigations.
The aim of the present study was, firstlyto investigate the relationship betweengross composition and neutral volatilecomposition, and the odour and flavoursensory attributes of ten cheeses of very dif-ferent type. Secondly, to determine rela-tionships between gross composition andthe texture attributes of these cheeses. Bydetermination of these relationships, it washoped to provide a broad understanding of
Sensory properties and composition of cheese 489
the compositional differences betweencheeses, and of how these differences arereflected in specific sensory attributes.
2. MATERIALS AND METHODS
2.1. Samples
Ten cheeses (Tab. I), chosen to representa range of flavour and texture types, werepurchased from a local delicatessen(Horgan’s delicatessen, Mitchelstown, CoCork, Ireland). Samples for descriptive sen-sory analysis were stored at 4oC prior toevaluation. Samples for analysis of volatilecomposition were vacuum packed, using aWebomatic type D-463 (WebomaticVacuum Packaging Systems, Werner Bonk,Mausegatt 59, D-463 Bochum 6,Germany), and stored at –20oC prior toanalysis. The vacuum packaging materialconsisted of Cryovac (W. R. Grace EuropeInc., Av. Montchoisi 35, 1001 Lausanne,Switzerland) (45 cm3/m2/24 h at STP).Samples for analysis of gross compositionwere stored at 4oC prior to duplicate evalu-ation.
2.2. Materials
All authentic standards used for identifi-cation of volatile compounds were“AnalaR” grade obtained from BDHChemicals Ltd., Broom road, Poole,Dorset, UK; Sigma-Aldrich Ireland Ltd.,Airlon Road, Tallagh, Dublin 24, Ireland;Merck Chemical Co. Ltd., D-6100,Darmstadt, Germany and Lancaster Syn-thesis Ltd. Eastgate, White Lund,Moorecambles, Lancashire, LA3 3DY, UK.Tenax TA (60/80) was purchased fromSigma-Aldrich Ireland Ltd. α-Amylase(from porcine pancreas) was purchasedfrom Merck Chemical Co. Ltd.
2.3. Descriptive sensory analysis
A panel of 15 trained assessors (11 fe-male, 4 male, aged between 20 and30 years) described the sensory character ofthe cheeses. Prior to analysis assessors at-tended a number of group discussions dur-ing which the current list of descriptiveattributes were developed [26]. Descriptivesensory analysis was carried out using a vo-cabulary of 9 odour, 21 flavour and 10 tex-
490 J. Ben Lawlor et al.
Table I. Cheeses investigated.
Code Cheese Cheese type Origin
1 Mahón lipolysed aromatic cheese Spain
2 Cambozola soft/semi-hard mould cheese Germany
3 Gruyère sweet cheese with eyes Switzerland
4 Wensleydale high acid crumbly type UK
5 Blue Shropshire soft/semi-hard mould cheese UK
6 Tetilla high fat and high acid Spain
7 Ambassedeur aromatic semi-hard The Netherlands
8 Fontina high fat and high acid Italy
9 Appenzeller semi-hard smear ripened Switzerland
10 Chaumes soft smear ripened France
ture terms (Tab. II). On the morning ofassessment the outer 5 mm of each cheesewas removed and cheeses were cut into 5 gcubes. Each sample was equilibrated toroom temperature (21oC) and presented ina glass tumbler covered with a clock glassand coded with a randomly selected three-digit number. Each panellist was providedwith water, non-salted crackers and tooth-picks to cleanse their palate between sam-ples. Each panellist was also provided witha full list of definitions for each of the attrib-utes. Cheeses were scored for attributes onunstructured 100 mm line scales anchoredat both ends with extremes of each attribute.To account for first order and carry-overeffects order of tasting for between andwithin days was balanced [34]. Data wererecorded and stored using the PSA-system(Oliemans, Punter and Partners Inc. 3508SG Utrecht, The Netherlands). All sampleswere analysed in duplicate.
2.4. Volatile composition
Prior to evaluation, samples werethawed from frozen at 4oC overnight. Theouter layer (2 cm) of each cheese was re-moved to minimise the possibility of com-pounds from the package, which mighthave migrated into the cheese sample, be-ing mistaken for cheese compounds. Thecheese was then grated and a 10 g sampletransferred into a sample flask (100 mL) ofa bench-top purge and trap system. Artifi-cial saliva (15 mL) [44] was then added andthe sample mixture, held at 37oC in a waterbath, was purged through with purified N2(100 mL.min–1) for 30 min to trap volatileson Tenax TA.
The Tenax-TA traps were thermallydesorbed using a Tekmar 3000 concentrator(JVA Analytical Ltd. Unit 1, LongmileBusiness Centre, Longmile Road, Dublin
Sensory properties and composition of cheese 491
Table II. List of descriptive terms used by trained assessors to characterise cheeses.
Odour attributes
pungent sweaty/sour dairy sweet
caramel fruity sweet
silage mouldy creamy
Flavour attributes
buttery nutty acidic
caramel smoky bitter
dairy sweet soapy pepper
rancid silage burnt after-taste
mushroom processed astringent
oily sweet strength
mouldy salty balanced
Texture attributes
firmness moist grainy
rubbery oily mouth-coating
crumbly chewy
smooth slimy
12, Ireland) for 6 min at 230oC. Desorbedvolatiles were identified and quantified us-ing a Saturn GC-3400cx Gas Chromatograph-Saturn 3 Mass Spectrometer (GC-MS)(JVA Analytical). The GC column used wasan Rtx-502.2, 60 m× 0.53 mm id, filmthickness 3µm (Interscience, BarrenscourtLane, Belfast, 13T8 8RR, Northern Ire-land). The GC oven was programmed from40 oC (5 min), increased to 80oC at 2oCper min–1 (20 min), increased to 200oC at3 oC min–1 (40 min) and increased to240oC at 10oC min–1(4 min) to give a totalrun time of 69 min. All analyses were per-formed in duplicate.
2.5. Gross composition and pH
Gross composition was analysed usingthe following methods, moisture [17], pro-tein [21], fat [19], salt [20], ash [18], cal-cium [18] and pH [5]. All analyses wereperformed in duplicate.
2.6. Data treatment
Descriptive sensory data was analysedas follows. Duplicate sample scores of as-sessors were averaged for each attribute andanalysed using one-way analysis of vari-ance (ANOVA) (SPSS v 6.1, SPSS Inc.Chicago, IL 60611, USA).
Descriptors which did not significantlydiscriminate between cheeses were re-moved from further analysis. Data weresubsequently standardised (1/standard de-viation) and analysed using Principle Com-ponents Analysis (PCA; [41]). PCA wascarried out using Unscrambler v 6.1(CAMO AS, N-7041 Trondheim, Norway).PCA is a bilinear modelling method thatgives an interpretable overview of the maininformation in a multidimensional data ma-trix. The information carried by the originalvariables is projected onto a smaller num-ber of underlying “latent” variables calledPrincipal Components (PC’s). How each
PC discriminated between the sensorycharacter of the cheeses was then investi-gated using one-way analysis of variance(ANOVA) (SPSS v 6.1).
Data on volatile composition were ana-lysed in the following manner. Thirty com-pounds were chosen, none of which weredetected in substantial quantities in blankanalyses of the artificial saliva. The peakarea of each compound was determined ei-ther by total ion, or in the case of overlap-ping peaks, by single ion plots. Compoundswere identified by comparison to retentiontimes and mass spectra of authentic stan-dards and with comparison with biblio-graphic data (NIST92). The log peak areaof each compound was calculated and datawere then standardised (1/standard devia-tion) and analysed by PCA. How each PCdiscriminated between the volatile compo-sition of the cheeses was then investigatedusing one-way ANOVA (SPSS v 6.1, SPSSInc.).
Relationships between both volatile andgross compositional data, and the sensoryattributes were investigated using PartialLeast Squares (PLS) regression (PLS;[37]), using Unscrambler v 6.1 (CAMOAS). PLS regression is a bilinear modellingmethod where information in the originalX-data is projected onto a small number ofunderlying (“latent”) variables called PLScomponents. TheY-data are actively used inestimating the “latent” variables to ensurethat the first components are those that aremost relevant for predicting theY-variables.Interpretation of the relationship betweenX-data andY-data is then simplified as thisrelationship is concentrated on the smallestpossible number of components. PLS1,which regresses oneY-(sensory) variable ata time, was used in the current study. Twoseparate analyses were performed. The firstPLS analysis related both volatile com-pounds and gross composition (X-matrix)to individual odour and flavour attributes(Y-matrix). The second analysis relatedgross composition (X-matrix) to individual
492 J. Ben Lawlor et al.
texture attributes (Y-matrix). For each anal-ysis a plot of the regression coefficients ofthe PLS model illustrated the contributionof each of theX-variables (volatile com-pounds and/or compositional measure-ments) in predicting individualY-variables(sensory attributes).X-variables with asmall negative or positive coefficient thatcontributed little information were re-moved and the model was recalculated. Se-lection of the optimum model was based onthe minimum value of the Root MeanSquare Error of Prediction (RMSEP). Thisvalue, expressed in the same units as theY-variable (i.e., on a scale of 1–100), showsthe average uncertainty that can be ex-pected when predictingY (sensory) valuesfor new samples. The strength of the model(calibration coefficient) and its ability topredict the sensory attributes of future sam-ples (validation coefficient) were alsotested using full cross validation.
3. RESULTS AND DISCUSSION
3.1. Descriptive sensory analysis
The attributes “dairy sweet” odour and“caramel”, “dairy sweet”, “smoky” and“soapy” flavour did not significantly(P > 0.05) distinguish between cheeses andwere consequently not included in thePCA. The PCA, carried out on significantattributes, found that seven PC’s, account-ing for 86% of the experimental variance,significantly (P < 0.05) discriminated be-tween cheeses. The scores and loadings ofcheeses and descriptive sensory attributeson the first seven PC’s, as well as the per-centage variance accounted for by each PC,can be seen in Tables III and IV, respectively.
PC1 distinguished Wensleydale andGruyère from Chaumes and Fontina. Theformer cheeses were characterised by a“caramel” odour and a “firmness” and
Sensory properties and composition of cheese 493
Table III. Results of PCA on the sensory attributes of ten cheese varieties showing the scores and thepercentage variance (% var.) accounted for by the first seven PC’s.
Principal component
Cheese 1 2 3 4 5 6 7
Mahón 2.05 2.54 –0.94 –3.59 1.24 –1.63 –0.67
Cambozola –2.54 –0.91 –4.17 1.57 –0.57 0.01 0.93
Gruyère 5.25 –0.47 1.98 3.76 –1.04 –0.53 –0.86
Wensleydale 5.61 0.01 –1.13 –2.43 –1.44 1.77 0.68
Blue Shropshire –2.27 5.03 –2.21 1.70 –0.62 0.16 –0.75
The most important loadings on each PC are in bold.
“grainy” texture. Both Chaumes andFontina on the other hand were described ashaving a “pungent” odour, a “rancid”,“oily” and “burnt after-taste” flavour and a“smooth”, “moist”, “oily” and “slimy” tex-ture (Tabs. III and IV). PC2 distinguishedBlue Shropshire from Tetilla cheese. BlueShropshire was described by assessors ashaving a “salty”, “acidic”, “astringent” and“strength” flavour and a “mouth-coating”texture. Tetilla cheese, on the other hand,was characterised by a “creamy” odour, anda “processed” and “balanced” flavour. Suc-cessive PC’s highlighted other differencesin sensory character between cheeses. Forexample, PC3 distinguished between the“creamy”, “mouldy” odour and “buttery”,“mouldy”, and “bitter” flavour ofCambozola and Blue Shropshire and the“pepper” flavour “rubbery” and “chewy”texture of Appenzeller and Ambassedeurcheeses.
3.2. Volatile composition
The volatile compounds identified in thepresent study (Tab. V) were as follows: twoalcohols; four aldehydes; one anisole; tenesters; eight ketones; two sulphur com-pounds and three terpenes. Coefficients ofvariation ranged from 0.22 to 36.76%, how-ever, due to space limitations these data arenot shown. Due to the diverse nature of thecheeses, most compounds were not foundin all cheeses, however the data are shownas log transformed peak areas in Table V.PCA of this data found that the first six PC’sdiscriminated significantly (P < 0.05) be-tween cheeses, and accounted for 76% ofexperimental variance. The scores andloadings of cheeses as well as the percent-age variance accounted for by each PC, arepresented in Tables VI and VII, respec-tively. In the following explanation ofcheese composition, the discrimination ob-served on each PC will be interpreted in re-lation to the raw data shown in Table V.
PC 1 separated Blue Shropshire from allother cheeses (Tab. VI). In fact, Blue Shrop-shire was a considerable outlier on this PCas it contained nine compounds not foundin any of the other cheeses (Tab. V). Thefour ester compounds with negative load-ings on this PC were all found exclusivelyin Blue Shropshire cheese (Tab. V). Estercompounds result from esterification reac-tions between fatty acids and alcohols. Hy-drolysis of milk fat to fatty acids is essentialfor flavour in blue cheeses and is particu-larly extensive in blue cheeses due to thelipase activity of the mould during ripen-ing. Fatty acids can also act as importantprecursors of 2-methyl ketones. BlueShropshire cheese was also characterisedby a high concentration of 2-methyl ke-tones, including, 2-pentanone, 2-hexanone,2-octanone and 2-nonanone. The mould,e.g. Penicillium roqueforti, oxidises satu-rated fatty acids toβ-ketoacids that are sub-sequently decarboxylated to methylketones. Gallois and Langlois [12] foundthat methyl ketones represented 50–75% ofthe total odorous profile of five French bluecheeses. Although not obvious by interpre-tation of the PCA result, it was found thatthe other blue mould cheese analysed in thisstudy, Cambozola, contained relativelylarge quantity of 2-methyl ketones (Tab. V).However, the quantities of methyl ketonesfound were not near as those found in BlueShropshire. The contribution of the straight-chain aldehydes, pentanal, hexanal andheptanal, to Tetilla and Fontina on this PCcan also be seen in Table VII. Straight chainaldehydes are formed duringβ-oxidation ofunsaturated fatty acids and their presencehas been reported in Swiss cheese [22], Par-mesan [6] and a number of European“appelation d’origine contrôlée” cheeses[4]. Engels et al. [11] reported relativelyhigh concentrations of straight chain alde-hydes in the water-soluble fraction ofGruyère and Parmesan cheeses that had ahigh occurrence of lipolysis.
Sensory properties and composition of cheese 495
496 J. Ben Lawlor et al.
Table V. Result of volatile compositional analysis of ten cheese varieties showing the log transformedpeak areas of compounds. For identification of cheese codes refer to Table I.
PC2 separated Chaumes and Cambozolafrom Fontina. The 2-methyl ketone, 2-heptanone, that characterised Chaumes andCambozola on this PC was also found in allother cheeses apart from Fontina. Fontinacheese was characterised by butanal,dimethyl trisulphide and five ester com-pounds, namely, ethyl butyrate, ethylhexanoate, propyl acetate, propyl butyrateand 2-methyl ethyl butyrate. The high oc-currence of ester compounds in Fontinacheese, an Italian high fat/high acid-typecheese, may be accounted for by the factthat Italian cheeses are made from rennetpasts containing pregastric esterases whichcause extensive lipolysis [36]. PC3 sepa-rated Mahón and Tetilla from Ambassedeurand Appenzeller. Mahón and Tetillacheeses, both Spanish cheeses, were char-acterised on this PC by the following com-pounds: the alcohols 3-methyl-1-butanoland 3-hexen-2, 5-diol (in Tetilla); the alde-hydes butanal and hexanal; the esters ethylacetate and ethyl butyrate; the ketone2-butanone and the two monoterpenecompounds α-pinene and limonene.Ambassedeur and Appenzeller, on the otherhand were characterised by the sulphurcompound dimethyl disulphide and the
monoterpeneα-phellandrene (in Ambasse-deur). Dimethyl disulphide and the othersulphur compound dimethyl trisulphide(important on PC2) result from the degra-dation of sulphur containing amino acids.These two sulphur compounds have beenreported in aged Brie and Swiss cheeses[23, 46], Parmesan cheese [6], Fontinacheese [4] and a number of hard typecheeses [11]. Mariaca et al. [31] investi-gated the terpenoid content of forty-sevenplants from lowland and highland pasturesfrom which cheeses were made. Authorsreported the presence of a diverse range ofterpene compounds including limonene,α-pinene andα-phellandrene, in thirteen, nineand four different plant species, respec-tively. The occurrence of these threemonoterpenoids in plants could explaintheir presence in the present cheeses.Terpene compounds have been identified inGruyère [3], Parmesan [6], Cheddar [30],Swiss Emmental [22] and a number ofhard-type cheeses [11].
Other PC’s also separated cheeses basedon their volatile composition. For exam-ple, PC4 separated Ambassedeur fromAppenzeller. Ambassedeur cheese wascharacterised by butanal and hexanal,
Sensory properties and composition of cheese 497
Table VI. Results of PCA on the volatile composition of ten cheese varieties showing the scores andthe percentage variance (% var.) accounted for by the first six PC’s.
The most important loadings on each PC are in bold.
2-butanone, dimethyl disulphide,α-phellandrene and limonene. Appenzellerwas distinguished on this PC by containingthe two esters ethyl acetate and ethyl butyr-ate.
It should be noted that in the presentwork only one sample from each cheese va-riety was studied. Differences in volatilecomposition between cheeses, within a va-riety, may occur due to variations in starterorganisms, milk composition and/or flora(see Grappin, [13]).
3.3. Gross composition of cheeses
The results of compositional analysisdemonstrated that the present range ofcheeses covered a wide range of values foreach compositional variable (Tab. VIII).
Fat and Fat in the Dry Matter (FDM)contents ranged from 25 g.100 g–1 and45.36, respectively, in Ambassedeur to41.71 g.100 g–1 and 70.28, respectively, inCambozola. Fat not only solves lipophilicflavour compounds produced from the hy-
drolysis of fats and proteins, but also, pre-vents the casein network of cheese from de-veloping into a tough, rubbery matrix.Protein, which represents the continuoussolid phase of cheese, ranged from13.81 g.100 g–1 in Cambozola to a maxi-mum of 29.08 g.100 g–1 in Mahón cheese.The pH of cheeses ranged from 4.73 (Wens-leydale) to 6.92 (Blue Shropshire). pH isimportant for flavour as it influencesgrowth of Non Starter Lactic Acid Bacteria(NSLAB) in Cheddar and Gouda, growthof Propionibacteriumsp. in Swiss-typecheese, and the dissociation and odour ac-tivity of volatile compounds e.g. FFA [2].pH is also important for cheese texture, aspH changes are directly related to calciumcontent and protein network [27, 43].
Salt and Salt-in-Moisture (S/M) levelsranged from 1.12 g.100 g–1 and 2.63, re-spectively in Wensleydale to 2.75 g.100 g–1
and 10.49, respectively in Mahón cheese.The important influence of salt on cheesehas been reviewed by Guinee and Fox [14].Kelly et al. [24] demonstrated that the S/Mlevel of Cheddar-like cheese has a marked
Sensory properties and composition of cheese 499
Table VIII. Compositional analysis of ten cheese varieties.
N/D = Not determined.avalues expressed as g.100 g–1.
effect on the rate and extent of proteolysis.Moisture in the Non-Fat Substances(MNFS) ranged from 41.60 in Mahón to69.74 in Cambozola cheese. MNFS is es-sentially the relative amounts of moistureand protein in the cheese and this value ismore relevant than the percentage moisturesince it is in the interface of moisture andcasein that enzymic reaction responsiblefor ripening take place [28]. Calcium con-tent ranged from 379 mg.100 g–1 inCambozola to 902 mg.100 g–1 in Mahón(this was also the case for ash content). Cal-cium content of cheese is largely deter-mined at the point at which the curd isdrained from the whey and has an importantrole in texture development.
3.4. Relationship between odourand flavour attributes and volatileand gross composition
Six odour and eleven flavour attributeswere correlated to subsets of both volatilecompounds and compositional measure-ments (Tabs. IX and X). Calibration coeffi-cients (strength of the current models) wereall ≥ 0.68, while the validation coefficients(ability to predict new samples) were all≥ 0.46. RMSEP values ranged from 1.43 to7.26 (on a scale of 1–100) indicating thatthe current models all had good predictivepower for the current set of cheeses. How-ever, since each volatile compound was notdetected in all of the cheeses, and becausemodels were attribute based (rather thancheese based), some cheeses described by aparticular characteristic may not contain allof the compounds identified in the model.Therefore to interpret attributes in any indi-vidual cheese, rather than in a broad cheesecontext, it would be best to refer also to Ta-ble V. However, despite this consideration,the models in the current work are valid andindeed have been validated by PLS itself.
The “mouldy” odour, found in particularin Blue Shropshire and Cambozolacheeses, was positively associated with fat,
FDM and pH, 2-pentanone, 2-hexanone, 2-heptanone and 2-octanone, and negativelycorrelated with calcium content (Tab. IX).It has been known for some time that 2-methyl ketones are a dominant part of bluecheese flavour [12, 25], particularly 2-heptanone which has been described aspossessing a “blue flavour” note [15]. Thepresence of these compounds can be attrib-uted to the high degree of lipolysis, due tothe action of the mould, e.g.Penicilliumroqueforti, on the fat during the ripening ofthese two cheese types. Furthermore, thede-acidifying activity of the mould duringripening may account for the positive asso-ciations between pH and “mouldy” odour,while the high rate of acid production dur-ing the manufacture of blue cheese with re-sulting solubilisation of colloidal calciumphosphate and consequent low mineralcontent is reflected in the negative associa-tion between calcium content and thisodour attribute. Of these two mouldcheeses, Blue Shropshire had the higherpeak areas for all four methyl ketones andwas also scored higher than Cambozola for“mouldy” odour (raw panel data notshown). This may have been due to mouldtype, more prolific mould growth, or to thecomposition, or more specifically, the fatcontent of these two cheeses. In compari-son to Cambozola, Blue Shropshire had thelower fat content (41.71 and 33.67 g.100 g–1,respectively) and FDM (70.28 and 54.56,respectively) (Tab. VIII). This lower fatcontent may have resulted in a higher rate ofrelease of 2-methyl ketones from BlueShropshire, lowering flavour thresholdconcentrations needed in the cheese, andhence, this cheese being described as hav-ing a more “mouldy” odour. Furthermore,the lack of 2-octanone in Cambozola mayalso be due to it’s higher fat content, and thehigher hydrophobicity of 2-octanone whencompared to the other methyl ketones, re-sulting in a reduced partitioning into the gasphase. Delahunty et al. [10] found that re-lease of higher molecular weight methylketones was lower in full-fat cheese (35%
500 J. Ben Lawlor et al.
fat) in comparison to reduced and low fat(23 and 16% fat respectively) cheeses.
“Sweet” odour, that helped describe theSwiss-type cheese Gruyère, and to a lesserextent Appenzeller (see also Lawlor andDelahunty [26]), was positively associatedwith protein content and negatively corre-lated with salt and S/M contents. InGruyère cheese, ethyl acetate, ethyl butyr-
ate and dimethyl trisulphide were posi-tively correlated with this attribute. InAppenzeller cheese, on the other hand,“sweet” odour was positively associatedwith ethyl acetate, ethyl butyrate, dimethyltrisulphide, and propyl acetate. Vangtal andHammond [43] showed that “sweet” fla-vour Emmental cheeses was correlatedwith the production of short chain acids and
Sensory properties and composition of cheese 501
Table IX. Result of PLS1 analysis between the odour attributes (Y-matrix) and volatile compositionand gross composition (X-matrix) of ten cheese varieties showing the relationship between the odourattributes and volatile composition and gross compositional measurements. The fit of the currentmodel, calibration coefficient (Cal. Coef), its ability to predict, validation coefficient (Val. Coef.), andthe Root Mean Error Square Error of prediction (RMSEP) are shown. The companion of this tableshowing results for flavour attributes can be seen as Table X.
low salt concentration (which favours thegrowth ofPropionibacteriumsp. a salt sen-sitive organism). The “sweet” odour char-acter of both Swiss-type cheeses in thepresent work were associated with esters,compounds derived from lipolysis and alsoa low salt and S/M content. However, itshould also be noted that fatty acid precur-sors of ester compounds may also be syn-thesised by the cheese microflora, or, resultfrom the breakdown of amino acids (seeMcSweeney, [36]). The flavour attribute“salty”, that characterised Mahón cheese,was positively associated with S/M, ethylhexanoate and 2-pentanone and negativelyassociated with moisture content (Tab. X).This cheese had the highest S/M (10.49) ofall cheeses investigated, therefore, it wasnot surprising that this cheese was de-scribed as having a “salty” flavour. The “si-lage” flavour that described Fontina cheese(see PC6, Tab. IV) was positively associ-ated with pH, ethyl hexanoate, propylacetate, propyl butyrate and dimethyldisulphide. It was negatively associatedwith fat content. As mentioned previouslythe manufacture of Italian hard cheeses in-volves the addition of rennet pastes con-taining pregastric esterase that producesstrong fatty acid flavours. The resultinghigh rate of lipolysis in these cheeses(although not as high as in blue-mouldcheeses) may account for the associationbetween esters and the “silage” flavour ofFontina cheese.
Considered individually, families ofcompounds, such as esters and sulphurcompounds etc., possess characteristic fla-vours. For example, esters in cheese havebeen described as having floral, fruity notes[38], while sulphur compounds are de-scribed by a strong garlic, very ripe cheeseodour [9, 42]. However, when these com-pounds are mixed in the same cheese matrixtheir behaviour is quite different and mayindeed result in the relationships observedin the present work. Bosset and Gauch [4],in their study on the volatile fractions of six
different cheese types, concluded thatcheese flavour seemed not to depend on anyparticular key component, but rather on abalance or a “weighted ratio” of a numberof components acting together. Engels [11]came to similar conclusions. The presentstudy demonstrated that individual odourand flavour attributes of cheese not only de-pend on interactions between specific vola-tile compounds, but also, on interactionsbetween specific compositional variables.However, it must be stressed that the cur-rent models are “associative” rather than“causeative”, and just because variables arerelated in a statistical sense does not imply acause-and-effect situation. Clearly, morework is required to support these findings.Based on the results of this study and a pre-vious study [26] this will involve analysinga smaller-subset of cheeses, namely, Swiss-type cheeses and blue mould cheeses. In ad-dition, the volatile compounds of thesecheeses will be isolated using a modelmouth system that will include a mastica-tion device, while the contribution of theWater Soluble Fraction (WSF) and FreeFatty Acids to flavour in these two cheese-types will also be determined. Both theWSF and FFA are known to contribute tocheese flavour [11, 36] and their absencefrom the current data set may account forcertain flavour terms in the present work re-maining unexplained.
3.5. Relationship between textureattributes and gross composition
Seven out of the original ten texture at-tributes were shown to be correlated to sub-sets of gross compositional measurements(Tab. XI). Calibration coefficients were all≥ 0.77, validation coefficients were all≥ 0.52 and the RMSEP’s ranged from 2.7 to13.6 (on a scale of 1–100) indicating thatthe current models had good predictivepower.
The texture attribute “firmness” that de-scribed Gruyère, (raw data not shown), a
504 J. Ben Lawlor et al.
Swiss-type cheese, was positively associ-ated with protein and calcium content andnegatively associated with moisture con-tent, salt content, S/M and pH (Tab. XI).During Swiss-type cheese manufacture,high cooking temperatures (50–53oC)have the effect of inactivating most of thechymosin in the curd (the morethermostable plasmin is not as affected[29]. The result of this is a general decreasein the rate of proteolysis and hence firmertextured cheese is obtained. Furthermore,the concentration of calcium has a major ef-fect on texture and the high calcium content(764.5 g.100 g–1) of this cheese could helpexplain its “firmness”. The high mineralcontent of the curd is achieved by a low per-centage of starter and a high scalding tem-perature [29]. “Moist” texture, whichcharacterised Cambozola cheese, a surfacemould ripened cheese, was positively asso-ciated with moisture, salt and ash content,pH and MNFS and negatively associated
with protein content. Cambozola had thehighest MNFS of all cheeses studied (69.74)which is an indication of the relative amountof moisture to protein in the cheese. As pro-tein represents the only continuous solidphase of the cheese this may explain whyCambozola cheese was described as pos-sessing a “moist” texture. The attribute“oily” texture, describing Chaumes andCambozola, was positively associated withmoisture, MNFS, salt content and pH. BothChaumes and Cambozola are classified assoft cheeses and both had the highest MNFSratio and lowest protein contents. “Chewy”texture, characterising Ambassedeur cheese,was associated with most compositionalvariables, in particular calcium content (posi-tive) and fat content and FDM (negative).Ambassedeur was also described as “rub-bery” in texture; this attribute was also nega-tively associated with fat content and FDMand positively associated with calcium con-centrations. Fat in cheese has the effect
Sensory properties and composition of cheese 505
Table XI. Result of PLS1 analysis between the sensory attributes (Y-matrix) and gross composition (X-matrix) of ten cheese varieties showing the relationship between the sensory attributes and gross com-position. The fit of the current model, calibration coefficient (Cal. Coef), its ability to predict,validation coefficient (Val. Coef.), and the Root Mean Error Square Error of prediction (RMSEP) areshown.
Firmness protein, Ca, S/M, salt, moisture, pH 0.92 0.69 12.81
Rubbery Ca, ash moisture, protein, fat,FDM, S/M
0.98 0.81 9.45
Moist moisture, MNFS, ash, salt, pH protein 0.89 0.60 11.88
Oily moisture, salt, MNFS, pH protein 0.77 0.52 13.61
Chewy moisture, MNFS, protein, ash,Ca
fat, FDM, salt, S/M, pH 0.99 0.94 2.76
Slimy salt moisture, protein, MNFS,FDM, S/M
0.99 0.68 10.02
Mouth-coating
protein, fat, FDM, ash,S/M, pH
moisture, MNFS, Ca, salt 0.98 0.92 2.83
of preventing the protein (casein) networkof the cheese matrix from shrinking into atough inedible structure, while calciumconfers a “firmness” to cheese texture,which explains why Ambassedeur was de-scribed as having a “chewy” and “rubbery”texture.
In conclusion, the relationships betweenodour, flavour and texture sensory charac-teristics and volatile composition and grosscomposition were determined. Results in-dicated that cheese flavour and texture aremulti-dimensional phenomenon resultingfrom complex interactions between manu-facturing method, volatile composition andthe gross composition of cheese.
ACKNOWLEDGEMENTS
Authors would like to thank Dr. S. Hulin-Bertaud and Dr. M.A. Posthumus for their assis-tance. This work was funded by the Departmentof Agriculture, Food and Rural Development ofIreland, under the Non-Commissioned Food Re-search Programme 1994–2000
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