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    InstitutfrTierwissenschaften

    Predictiveshelflifemodelfortheimprovementofquality

    managementinmeatchains

    InauguralDissertation

    zurErlangung

    des

    Grades

    DoktorderErnhrungs undHaushaltwissenschaften

    (Dr.oec.troph.)

    der

    HohenLandwirtschaftlichenFakultt

    der

    RheinischenFriedrichWilhelmsUniversitt

    zuBonn

    vorgelegtam

    06.07.2010

    von

    StefanieBruckner

    aus

    Leer

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    Referent: Prof.Dr.BrigittePetersen

    1.Korreferent: Prof.Dr.RainerStamminger

    2.Korreferent: PDDr.JudithKreyenschmidt

    Tag

    der

    mndlichen

    Prfung:

    27.

    August

    2010

    Erscheinungsjahr: 2010

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    Meinen Eltern

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    Essentially,

    allmodels

    are

    wrong,

    butsomeareuseful.

    GeorgeE.P.Box

    (Britishstatistician,born1919)

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    Abstract

    Predictiveshelflifemodelfortheimprovementofqualitymanagementinmeatchains

    Theobjectiveofthisthesiswasthedevelopmentofacommonpredictiveshelflifemodelfor

    freshpork

    and

    fresh

    poultry

    based

    on

    the

    growth

    of

    Pseudomonas

    sp.

    as

    specific

    spoilage

    organism (SSO). As an element of a decision support system the model should provide

    predictive information to improve quality management in meat chains. To define the

    relevantparametersoftheshelflifemodel,thespoilageprocessesofbothmeattypeswere

    characterisedandcomparedunderconstantanddynamicenvironmentalconditions.

    Altogether,638porksamplesand600poultrysampleswere investigated in42timeseries.

    The growthofPseudomonas sp. on fresh pork and fresh poultrywas investigated at five

    different constant and nine dynamic temperature scenarios to quantify the influence of

    temperatureon

    shelf

    life.

    Additionally,

    several

    intrinsic

    factors

    (pH

    value,

    aw

    value,

    Warner

    Bratzlershearforce,Dglucose,Llacticacid,fatandproteincontent)wereanalysedduring

    storageat4CandcorrelatedtothecountsofPseudomonassp.Thecollectedgrowthdata

    havebeenthebasisforthedevelopmentofthecommonpredictiveshelflifemodel.

    The results showed a good correlation between the counts of Pseudomonas sp. and the

    sensory characteristicsunder constant aswell asdynamic temperature conditions. Itwas

    possibletodetermineacommonspoilagelevelat7.5log10cfu/gforbothmeattypeswhich

    defines the end of shelf life based on the growth of Pseudomonas sp. Temperaturewas

    identifiedas

    the

    most

    important

    influencing

    factor

    on

    the

    growth

    of

    Pseudomonas

    sp.

    and

    thusontheshelflifeofbothmeattypes.Theinvestigatedintrinsicfactorshadonlyminoror

    no influence and were therefore not considered in the predictive shelf life model.

    Investigationofthe influenceofdynamictemperatureconditionsonshelf lifeoffreshpork

    and poultry revealed similar spoilage patterns for both meat types under dynamic

    temperatureconditionswithremarkableshelf lifereductionsofuptotwodays(uptoover

    30%)causedbyshorttemperatureabusesatthebeginningofstorage.

    Basedontheresults,acommonpredictiveshelflifemodelwasdevelopedbycombiningthe

    Gompertzand

    the

    Arrhenius

    model.

    The

    predictive

    information

    from

    the

    model

    can

    be

    used

    inspecificsituationsofdecisionmaking,forexamplebyoptimisingthestoragemanagement

    from theFIFOconcept (First In,FirstOut) to theLSFOconcept (LeastShelf life,FirstOut).

    Furthermore, the predictive model can be combined with risk assessment tools which

    enables the development of a range of novel comprehensive quality and cold chain

    managementsystems.

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    Kurzbeschreibung

    VorhersagemodellfrdieBestimmungderHaltbarkeitzurVerbesserungdes

    QualittsmanagementsinFleischerzeugendenKetten

    Zielder

    vorliegenden

    Arbeit

    war

    die

    Entwicklung

    eines

    gemeinsamen

    Vorhersagemodells

    fr

    dieBestimmungderHaltbarkeitvon frischemSchweine undGeflgelfleischbasierendauf

    dem Wachstum des Hauptverderbniserregers Pseudomonas sp. Als Teil eines Decision

    Support Systems stellt das Modell Informationen bereit, die zur Verbesserung des

    Qualittsmanagements in Fleisch erzeugenden Ketten beitragen. Zur Ermittlung der

    Modellparameter erfolgte die Charakterisierung des Frischeverlustes beider Fleischsorten

    unterstatischenunddynamischenUmweltbedingungen.

    Fr die Erstellung des Vorhersagemodells erfolgte die Untersuchung von insgesamt 638

    Schweinekotelettsund

    600

    Geflgelbrustfilets

    in

    insgesamt

    42

    Zeitreihenmessungen.

    Das

    Wachstum des Hauptverderbniserregers Pseudomonas sp. wurde unter fnf konstanten

    sowie neun dynamischen Temperaturszenarien untersucht, um den Einfluss der

    Lagertemperatur auf die Haltbarkeit zu ermitteln. Die Analyse weiterer mglicher

    Einflussfaktoren auf das Wachstum von Pseudomonas sp. (pHWert, awWert, Warner

    BratzlerScherkraft, DGlukosegehalt, LMilchsuregehalt, Fettgehalt und Proteingehalt)

    erfolgtebeieiner konstanten Lagertemperatur von4C.Dieerhobenenmikrobiologischen

    WachstumsdatenbildetendieBasisfrdieEntwicklungdesVorhersagemodells.

    DieEignung

    von

    Pseudomonas

    sp.

    als

    Frischeparameter

    fr

    beide

    Fleischsorten

    wurde

    durch

    hoheKorrelationenmitdensensorischenCharakteristikaunterkonstantenunddynamischen

    Temperaturbedingungenbesttigt.DasEndederHaltbarkeitwurde frbeideFleischsorten

    bei einer Keimzahl von 7,5 log10 KbE/g festgelegt. Als Haupteinflussfaktor auf den

    Frischeverlust bzw. das Wachstum von Pseudomonas sp. wurde die Lagertemperatur

    identifiziert,wohingegendieuntersuchten intrinsischenFaktorennureinengeringenbzw.

    keinenEinflussaufdasWachstumvonPseudomonas sp.hatten.Daher fanden siebeider

    Modellentwicklung keine weitere Bercksichtigung. Beide Fleischsorten zeigten ein

    vergleichbares Verderbsmuster unter dynamischen Temperaturbedingungen, wobei

    kurzzeitigeTemperaturerhhungenzuBeginnderLagerungzuHaltbarkeitsverkrzungenvon

    biszu2Tagen(biszu30%)fhrten.

    Basierend auf den erhobenen Daten erfolgte die Entwicklung eines gemeinsamen

    prdikitiven Haltbarkeitsmodells durch Kombination zweier mathematischer Modelle

    (GompertzmodellundArrheniusmodell).DasentwickelteModellermglichtdieVorhersage

    desWachstumsvonPseudomonassp.und somitderHaltbarkeitvonbeidenFleischsorten

    unter wechselnden Temperaturbedingungen. Die Haltbarkeitsvorhersagen des Modells

    knnen in spezifischen Entscheidungssituationen verwendet werden, um z.B. durch den

    schnellerenVertriebvonProduktenmitkrzererHaltbarkeitszeitdasLagermanagementzu

    optimieren.DesWeiterenisteineKombinationmitRisikobewertungssystemendenkbar.

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    I

    Contents

    Contents I

    Listoftables III

    Listoffigures IV

    1 GeneralIntroduction 1

    1.1 Meatspoilage 2

    1.2 Shelflifemodelling 4

    1.3 Researchobjectivesandoutlineofthethesis 8

    References 9

    2 Characterisationandcomparisonof spoilageprocesses in freshporkand

    poultry

    15

    2.1 Introduction 16

    2.2 Materialandmethods 17

    2.2.1 Sampledescriptionandexperimentaldesign 17

    2.2.2 Microbiologicalanalysis 18

    2.2.3 Sensoryanalysis 18

    2.2.4 Measurementofphysicalandchemicalproperties 18

    2.2.5 Measurementofnutrients 19

    2.2.6 Statisticalmethods 20

    2.3 Resultsanddiscussion 20

    2.3.1 Influenceoftheextrinsicparametertemperature 20

    2.3.2 Influenceofintrinsicparameters 23

    References 30

    3 Influenceofcoldchaininterruptionsontheshelflifeofporkandpoultry 34

    3.1 Introduction 35

    3.2

    Material

    and

    methods

    36 3.2.1 Sampledescription 36

    3.2.2 Experimentaldesign 36

    3.2.3 Samplepreparationandmicrobiologicalanalysis 38

    3.2.4 Sensoryanalysis 38

    3.2.5 Statisticalanalysisandfitting 38

    3.3 Resultsanddiscussion 39

    References 45

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    II

    4 Model for shelf lifepredictionasa tool forqualitymanagement inpork

    andpoultrychains48

    4.1 Introduction 49

    4.2 Materialandmethods 50

    4.2.1Experimentaldescription 50

    4.2.2Statisticalanalysisandmodelling 51

    4.3 Resultsanddiscussion 54

    References 64

    5 Summary 68

    ListofPublications 72

    Curriculum

    Vitae

    74

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    III

    Listoftables

    Table1.1: Averagecompositionofmeat(%)(Belitzetal.,2009) 2

    Table1.2: Selectionofprimary,secondaryandtertiarymodelsusedfordescribing

    microbialgrowth(modifiedafterMcDonald&Sun,1999)

    5

    Table2.1: Microbialandsensorydeterminedshelf livesoffreshporkandpoultry

    atdifferentconstantstoragetemperatures

    22

    Table2.2: Intrinsicparameters(meanvaluesstandarddeviation)duringstorage

    infreshporkandpoultrymeatat4C

    23

    Table2.3: Correlationsbetween intrinsicandmicrobiologicalparametersaswell

    assensoryindexforfreshporkat4C

    25

    Table2.4: Correlationsbetween intrinsicandmicrobiologicalparametersaswell

    assensoryindexforfreshpoultryat4C

    25

    Table3.1: Dynamictemperaturescenariosforfreshporkandpoultry 37

    Table3.2: Calculated shelf life timesandshelf life reductions for freshporkand

    freshpoultryindifferentdynamicstoragetrials

    43

    Table4.1: Nonisothermaltemperaturescenariosusedformodelvalidation 51

    Table4.2: Growth parameters obtained with the Gompertz model for

    Pseudomonas sp. on fresh pork and poultry at different isothermal

    storagetemperatures

    54

    Table4.3: Bias and accuracy factor for the developed model at different non

    isothermaltemperaturescenariosforfreshporkandfreshpoultry

    60

    Table4.4: Observedandpredictedshelf lives for freshporkand freshpoultryat

    differentnonisothermaltemperaturescenarios

    61

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    IV

    Listoffigures

    Figure1.1: Generalpatternofmicrobialspoilage (modifiedafterDalgaard,1993).

    SSO: specific spoilageorganism; () totalmicroflora; ( )SSO; ()

    metabolites. Microbial growth phases: lag phase, exponentialphase,stationaryphase

    4

    Figure2.1: GrowthofPseudomonassp.on freshpork (left)andpoultry (right)at

    constantstoragetemperaturesfittedwiththeGompertzmodel

    21

    Figure2.2: Sensory index for fresh pork (left) and poultry (right) at different

    constantstoragetemperatures(endofshelflifeatSI 1.8)

    21

    Figure2.3: Microbial shelf life of fresh pork ( ) and poultry ( ) at different

    constantstoragetemperatures

    22

    Figure2.4: Changes in mean values ( standard deviation) for pH in fresh pork

    ()andpoultry()duringstorageat4C.( )microbialendofshelf

    lifepork,( )microbialendofshelflifepoultry

    24

    Figure2.5: Changes inmean values ( standarddeviation) forDglucose in fresh

    pork()andpoultry()duringstorageat4C.( )microbialendof

    shelflifepork,( )microbialendofshelflifepoultry

    25

    Figure2.6: Changesinmeanvalues(standarddeviation)forLlacticacidinfresh

    pork()andpoultry()duringstorageat4C.( )microbialendof

    shelflifepork,( )microbialendofshelflifepoultry

    27

    Figure3.1: GrowthofPseudomonassp. intrialAfittedwiththeGompertzmodel:

    a)onpork,b)onpoultry; ( ) scenarioA0at4C constant, ( )

    scenarioA1withshiftsto7C, ( )scenarioA2withshiftsto15C

    (solidgrey line:temperatureprofileA1,dashedgrey line:temperature

    profileA2).

    40

    Figure3.2: GrowthofPseudomonassp. intrialB fittedwiththeGompertzmodel

    on pork (left) and poultry (right), a) and b): during the complete

    storage,

    c)

    and

    d):

    during

    the

    first

    60

    h

    of

    storage;

    (

    )

    scenario

    B0

    at

    4Cconstant,( )scenarioB1withshiftsto7C,( )scenarioB2

    withshiftsto15C(solidgreyline:temperatureprofileN1,dashedgrey

    line:temperatureprofileB2).

    40

    Figure3.3: GrowthofPseudomonassp. in trialC fittedwith theGompertzmodel

    on pork (left) and poultry (right), a) and b): during the complete

    storage,c)andd):duringthefirst60hofstorage;( )scenarioC0at

    4Cconstant,( )scenarioC1withshiftsto7C,( )scenarioC2

    withshiftsto15C(solidgreyline:temperatureprofileC1,dashedgrey

    line:temperatureprofileC2).

    41

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    V

    Figure3.4: GrowthofPseudomonassp. intrialD fittedwiththeGompertzmodel

    on pork (left) and poultry (right), a) and b): during the complete

    storage,c)andd):duringthefirst60hofstorage;( )scenarioD0at

    4Cconstant,( )scenarioD1withshiftsto7C,( )scenarioD2

    withshiftsto15C(solidgreyline:temperatureprofileD1,dashedgrey

    line:temperatureprofileD2).

    42

    Figure4.1: ModellingtemperaturedependencyoftherelativegrowthrateBwith

    theArrheniusequationforfreshpork(left)andfreshpoultry(right)

    54

    Figure4.2: Linear fitofreversalpointMagainst temperature for freshpork (left)

    andfreshpoultry(right)

    55

    Figure4.3: Linearfitforln(Bpoultry)versusln(Bpork)(left)aswellasforMpoultryversus

    Mpork (right)

    55

    Figure4.4: ObservedandpredictedgrowthofPseudomonassp.onfreshpork(left)

    and poultry (right) under dynamic temperature conditions in Trial E;

    ( ) observed growth; () predicted growth; () 10%, (grey line:

    temperatureprofile).

    56

    Figure4.5: Observed and predicted growth of Pseudomonas sp. on fresh pork

    under dynamic temperature conditions (Trial A D); ( ) observed

    growth; () predicted growth; () 10 %, (grey line: temperature

    profile).

    57

    Figure4.6: ObservedandpredictedgrowthofPseudomonas sp.on freshpoultry

    under dynamic temperature conditions (Trial A D); ( ) observedgrowthdata;()predictedgrowth;()10%,(greyline:temperature

    profile).

    59

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    CHAPTER1

    GENERALINTRODUCTION

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    Generalintroduction 2

    1.1 Meatspoilage

    From the legislativeperspective,meat isdefinedasallpartsofwarmblooded animals, in

    fresh or processed form, which are suitable for human consumption in the

    Regulation(EC)

    853/2004.

    Colloquially

    speaking

    the

    skeletal

    muscle

    with

    embedded

    fat

    and

    connectivetissueismeantbythetermmeat(Belitzetal.,2009).Basedonthecolour,meat

    can be divided between red meat (e.g. pork, beef, lamb) and white meat (poultry). The

    difference in colour is caused by a different content of myoglobin in the muscle. Red

    musclestendtohaveahigherproportionofnarrow,myoglobinrichfibreswhereaswhite

    muscles have a greater proportion of broad, myoglobinpoor fibres (Lawrie et al., 1998;

    Belitzetal.,2009).However,thebasiccompositionofbothmeattypes iscomparable.The

    maincomponentiswater(>70%),followedbyprotein(around20%),lipids(

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    Generalintroduction 3

    contaminated with a variety of microorganisms (Krmer, 2002; Kleer, 2007) such as

    Flavobacterium sp., Enterobacteriaceae, Pseudomonas sp.,Aeromonas sp.,Acitenobacter

    sp.,Moraxellasp.andStaphylococcussp.(Barnes&Thornley,1966;Blickstad&Molin,1983;

    Gallo et al., 1988; Olsson et al., 2003). Generally, only one of these microorganisms is

    responsiblefor

    the

    spoilage

    of

    fresh

    meat

    during

    chill

    storage.

    This

    organism

    is

    called

    specific

    spoilageorganism(SSO)(Gram&Huss,1996;Grametal.,2002).Thegrowthandselectionof

    theSSO is influencedbyseveral factors,whicharedivided into intrinsic (propertiesof the

    food), extrinsic (storage environment), processing (treatments during processing) and

    implicitfactors(microbialinteractions)(Mossel,1971).Amoredetailedexplanationofthese

    factorsandtheirrelevanceforfreshmeatisgiveninchapter2.1.

    From the initial microflora of fresh meat, less than 10 % is capable of growing at

    refrigerationtemperature

    while

    the

    proportion

    of

    the

    SSO

    is

    even

    lower

    (Gill,

    1986;

    Nychas

    etal.,1988;Borchetal.,1996).ButduringstoragetheSSOgrowsfasterthantherestofthe

    microfloraproducingmetabolitesresponsiblefore.g.offodoursorslime,whichleadstothe

    sensoryrejectionofthemeat(Huis intVeld,1996,Koutsoumanis&Taoukis,2005).Atthe

    pointofspoilage,which is thepointofsensory rejection, thecellconcentration is termed

    minimalspoilage level.Shelf life isdefinedasthetime frombeginningofstorageuntilthe

    SSOreachestheminimalspoilage level (Dalgaard,1993).Besides thedeterminationofthe

    minimalspoilage level,theconcentrationofthemetabolitesproducedbytheSSOcanalso

    beused

    for

    the

    estimation

    of

    shelf

    life.

    The

    metabolite

    which

    corresponds

    to

    spoilage

    caused

    bythegrowthoftheSSOcanberegardedasbeingachemicalspoilageindex(CSI)(Dalgaard,

    1993).Thegeneralpatternofmicrobialspoilagewithdifferentgrowthphasesaswellasthe

    relationsbetweenchanges inthetotalmicroflora,theSSOandthemetabolites isshown in

    Figure1.1.

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    Generalintroduction 4

    Figure1.1:Generalpatternofmicrobialspoilage(modifiedafterDalgaard,1993).

    SSO:specificspoilageorganism,() totalmicroflora;( )SSO;()metabolites.

    Microbialgrowthphases: lagphase, exponentialphase,stationaryphase

    Forfreshaerobicallystoredporkandpoultry,Pseudomonassp.havebeenidentifiedasSSO

    (Gill&Newton,1977;Pooni&Mead,1984;Coatesetal.,1995;Kreyenschmidt,2003;Raab

    et al., 2008). Ps.fragi, Ps.fluorescens and Ps. lundensis are the main species which are

    detectedduring theaerobicspoilageofmeat.Additionally,Ps.putidawas found (Molin&

    Ternstrm,1986;Nychasetal.,1988;Gennari&Dragotto,1992;GarcaLpezetal.,1998;

    Krmer,2002).GlucoseisthefirstsubstrateutilisedbyPseudomonassp.duringthespoilage

    offreshmeat.Thedominanceofthesebacteriacanpartlybeexplainedbyitsmetabolismof

    glucosevia

    the

    Entner

    Doudoroff

    metabolic

    pathway

    (an

    alternative

    to

    glycolysis).

    In

    this

    pathway glucose is converted to the less commonly used 2ketogluconate or gluconate

    which provides an extracellular energy source for Pseudomonas sp. and can not be

    metabolisedbyotherbacteria(Farber&Idziak,1982;Nychasetal.,1988;Dainty&Mackey,

    1992; Montville & Matthews, 2007). After the depletion of glucose, the pseudomonads

    sequentially catabolise lactate, pyruvate, gluconate and in the end amino acids. The

    metabolism of nitrogenous compounds such as amino acids finally leads to the sensory

    changes(e.g.offodours)whichoccuratthepointofspoilage(Nychasetal.,2008).

    1.2 Shelflifemodelling

    Generally, shelf life is understood as the time period for the product to become

    unacceptable from sensory, nutritional or safety perspectives (Fu & Labuza, 1993) which

    has been shown in Figure 1.1. Traditionally, the shelf life of a product has been mainly

    determined via challenge tests. In these tests, the effects of specific conditions on the

    growth and proliferation of the SSO were tested. To estimate the shelf life in real meat

    supplychains,

    challenge

    tests

    are

    mainly

    too

    expensive,

    labour

    intensive,

    time

    consuming

    and only valid for the product and conditions tested (Walker, 1994; Roberts, 1995;

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    Generalintroduction 5

    McDonald&Sun,1999;Wilsonetal.,2002).Butdatagenerationwithchallengetestsarethe

    basisformathematicalmodelswhichcanpredictthegrowthordeclineofmicroorganisms.

    This field of research is called predictive microbiology or predictive food microbiology

    (McMeekin et al., 1993; Whiting, 1995; McDonald & Sun, 1999). According to Buchanan

    (1993)predictive

    models

    can

    be

    classified

    in

    several

    ways,

    e.g.

    based

    on

    the

    microbiological

    event studied (microbial growth or inactivation), the mathematical approach (probability

    based or kineticsbased) and if they are mechanistic or empirical. Another generally

    acceptedclassificationofpredictive foodmodels is theclassification inprimary,secondary

    andtertiarymodelsproposedbyWhitingandBuchanan(1993).Primarymodelsdescribethe

    change of microbial numbers with time. The response can be measured directly by the

    microbialcount,substratelevelsormetabolicproductsandindirectlybyabsorbance,optical

    densityorimpedance(Whiting,1995).Thechangeofmicrobialcount,especiallythecountof

    the SSO, can then be described by plotting the data with a primary model, for which

    particularly various sigmoidal functions are used. During the last years, several primary,

    secondaryandtertiarymodelshavebeenusedtodescribethegrowthofmicroorganismsas

    correlatedtoenvironmentalfactors.AselectionofthesemodelsislistedinTable1.2.

    Table 1.2: Selection of primary, secondary and tertiary models used for describing microbial growth

    (modifiedafterMcDonald&Sun,1999)

    Classification Model Source

    Primarymodels Gompertzfunction Gibsonetal.(1987)

    ModifiedGompertz

    Zwietering

    et

    al.

    (1990)

    Logisticmodel Jason(1983)

    Baranyimodel Baranyietal.(1993),Baranyi&Roberts(1994)

    Threephaselinearmodel Buchananetal.(1997)

    Schnutemodel Schnute(1981)

    Secondarymodels Belehradekmodel(squarerootmodel) Belehradek(1930)

    Ratkowskymodel(squarerootmodel) Ratkowskyetal.(1982)Arrheniusmodel Arrhenius(1889)

    ModifiedArrheniusmodel

    (DaveyorSchoolfield)

    Davey(1989)

    Schoolfieldetal.(1981)

    Probabilitymodels Hauschild(1982)

    Polynomialorresponsesurfacemodels Gibsonetal.(1988)

    Tertiarymodels USDAPathogenModellingProgram Buchanan(1991)http://pmp.arserrc.gov/

    FoodSpoilage

    Predictor

    (PseudomonasPredictor)Neumeyer

    et

    al.

    (1997)

    Blackburn(2000)

    SeafoodSpoilageandSafetyPredictor(SSSP) Dalgaardetal.(2008)http://sssp.dtuaqua.dk/

    SymPrevius Thuault&Couvertetal.(2008)

    http://www.symprevius.net/

    ComBase Baranyi&Tamplin(2004)http://www.combase.cc/

    TemperatureHistoryEvaluationforRawMeats(THERM) Inghametal.(2007,2009)

    http://www.meathaccp.wisc.edu/therm/

    Growthpredictor&PerfringensPredictor http://www.ifr.ac.uk/Safety/GrowthPredictor

    The most widely used primary models are the Logistic model and the Gompertz model,

    which

    are

    comparable

    in

    applicability

    and

    accuracy.

    Both

    include

    four

    parameters

    to

    describethesigmoidalgrowthcurve.Thedifference is thattheLogisticcurve issymmetric

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    Generalintroduction 6

    aboutthereversalpointM,whereastheGompertzisnot(Gibsonetal.,1987).Zwieteringet

    al. (1990) statistically compared several sigmoidal functions to describe the growth of

    Lactobacillus plantarum, including the Logistic and the Gompertz function. They

    reparameterisedtheequationstoincludebiologicallyrelevantparameterse.g.lagtimeand

    specificgrowth

    rate.

    The

    modified

    Gompertz

    function

    was

    found

    to

    be

    statistically

    adequate

    to describe the microbiological growth and was easy to use. Buchanan (1993)also stated

    thattheGompertzfunctioniseasytousewithgoodcurvefittingsoftware.

    Asecondarymodelisthenusedtodescribetheresponseofoneormoreparametersofthe

    primary model to changes in environmental conditions (e.g. temperature, pH, aw)

    (Buchanan,1993;Whiting&Buchanan,1993;Whiting,1995).Astemperatureisconsidered

    asthemostimportantinfluencefactor,secondarymodelsmainlydescribethetemperature

    dependencyof

    model

    parameters

    (Labuza

    &

    Fu,

    1993;

    McDonald

    &

    Sun,

    1999).

    Well

    known

    secondary models are the Arrhenius model and the square root model as well as their

    modified forms (Table 1.2). The simple Arrhenius equation is often used in predictive

    microbiologybutitisnormallyonlyaccurateoveralimitedtemperaturerangeformicrobial

    growth wherefore modified versions have been developed to achieve better fits with

    extremetemperatureranges(Buchanan,1993;Fu&Labuza,1993;McDonald&Sun,1999).

    However,thesemodified formshavebeenreportedasbeingcomplexandcumbersome in

    use (Buchanan, 1993) and successful applications of the simple Arrhenius model are

    available

    for

    many

    different

    meat

    types

    and

    meat

    products

    (Giannuzzi

    et

    al.,

    1998;

    Kreyenschmidt,2003;Moore&Sheldon,2003;Mataragasetal.,2006;Kreyenschmidtetal.,

    2010). Another often used secondary model is the Square root model which was first

    successfully applied by Ratkowsky et al. (1982) to describe satisfactorily the relationship

    betweenmicrobialgrowthrateandtemperaturewithover50datasets.

    Theincorporationofprimaryand/orsecondarymodelsinuserfriendlycomputersoftware

    to provide a complete prediction tool is called tertiary model (Buchanan, 1993; Whiting,

    1995).Theycanbeseenasan interfacebetweenthescientistandtheenduserwherethe

    enduser can enter a set of product characteristics and receive a prediction of growth

    parameters (Betts & Walker, 2004). However, only a few predictive tertiary models are

    available foruse in the industry.Generalpredictivemicrobiology softwarewithdatabases

    are for example ComBase (http://www.combase.cc) and the Seafood Spoilage and Safety

    Predictor (SSSP) (http://sssp.dtuaqua.dk/), whichcanbe freelyaccessedworldwidevia the

    internet.Theuseoftertiarymodelsforthepredictionofshelflifeandremainingshelflifein

    theindustryfacilitatesthemakingofbetterinformeddecisionsbyactorsinthesupplychains

    regarding

    further

    storage

    and

    the

    distribution

    of

    the

    product.

    This

    results

    in

    an

    improvement

    of quality management by the optimisation of the storage management from the FIFO

    concept (First In,FirstOut) to theLSFOconcept (LeastShelf life,FirstOut)which reduces

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    Generalintroduction 7

    productwasteand thuseconomic losses (Giannakourou etal.,2001; Koutsoumanisetal.,

    2005).

    For the successful development of a predictive shelf lifemodel applicable for freshmeat,

    severalpointshavetobeconsidered.Atfirst,adetailedknowledgeofthespoilageprocessis

    requiredasitrelatestovariousinfluencingfactors(McMeekinetal.,1993;Blackburn,2000).

    ThisincludestheknowledgeoftheSSOoftheproductaswellasthepopulationlevelofthe

    SSO at which spoilage occurs (minimal spoilage level) and the range of environmental

    conditionsoverwhichaparticularSSOisresponsibleforspoilageDalgaard(1995).Togather

    this information for the model development, storage tests are conducted at specified

    environmentalconditions.Thesestoragetestsareoftencarriedoutinlaboratorymedialike

    nutrientbroth(e.g.Willocxetal.,1993;Baranyietal.,1995;Greeretal.,1995).Theproblem

    is,that

    models

    based

    on

    microbiological

    growth

    data

    generated

    in

    broth

    often

    under

    or

    overestimatemicrobialgrowthinrealfood.Forexample,Pin&Baranyi(1998)developeda

    microbialgrowthmodelbasedongrowthdataofamixedmicrobialpopulationinbroth.Ina

    laterstudy,theyshowedthattheoverallerrorofthismodelwas53.5%whencomparingthe

    predictionsof themodelwith theobservations innaturallyspoiled food (Pinetal.,1999).

    Gill et al. (1997) also stated that their models derived from the cultivation ofAeromonas

    hydrophilaandListeriamonocytogenesincommercialbrothsappeartobehighlyunreliable

    guidestothebehavioursofthoseorganismsonpork.Therefore,predictivemodels forthe

    usein

    the

    meat

    industry

    should

    be

    developed

    using

    microbiological

    growth

    data

    obtained

    in

    naturallycontaminatedfoodproducts(Dalgaard,1995;Koutsoumanis&Nychas,2000).

    Another important aspect is the validation of the model under dynamic environmental

    conditions since in meat chains major variations in temperature during storage and

    distributionareoftenobserved(Raab&Kreyenschmidt,2008;Koutsoumanisetal.,2010).As

    forthedevelopmentofthemodel,themicrobiologicalgrowthdatausedforvalidationunder

    dynamic temperature conditions should be generated in storage tests with the naturally

    contaminated products (Dalgaard et al., 1997; McMeekin et al., 1997; Shimoni & Labuza,

    2000;Membr&Lambert,2008).

    However,onlyafewmodelshavebeenpublishedwhichwere,ontheonehanddevelopedin

    freshmeatormeatproductsinsteadoflaboratorymedia,andontheotherhandvalidatedin

    these products under dynamic temperature conditions. These are e.g. the model of

    Koutsoumanisetal.(2006)forthegrowthofPseudomonassp.ingroundmeat,themodelof

    Gospavicetal. (2008) forPseudomonassp. inpoultryand themodelsofMataragasetal.

    (2006)aswellasKreyenschmidtetal.(2010)forlacticacidbacteriainmodifiedatmosphere

    packed(MAP)

    cooked

    sliced

    ham.

    But

    all

    these

    models

    were

    only

    developed

    and

    validated

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    Generalintroduction 8

    forjustonetypeofmeatormeatproduct.Predictivemodelsthatareapplicablefordifferent

    typesoffreshmeat(e.g.freshporkandpoultry)areunavailable.

    1.3 Researchobjectivesandoutlineofthethesis

    Themainobjectiveofthisthesisisthedevelopmentofacommonpredictiveshelflifemodel

    forfreshporkandpoultry.Inthecontextofqualitymanagementinmeatchainsthemodel

    shouldbeacoreelementofadecision support system toprovidepredictive information.

    Theseobjectivesleadtothefollowingresearchquestions:

    Howarethespoilageprocessesoffreshporkandpoultrycharacterisedanddothey

    followsimilarpatterns?

    HowisthegrowthofPseudomonassp.influencedbycoldchaininterruptionsinfresh

    porkandpoultryandwhatistheeffectonshelflife?

    Isitpossibletodevelopandvalidateacommonpredictivemodelforfreshporkand

    freshpoultrybasedonthegrowthofPseudomonassp.toestimatetheshelflifeand

    remainingshelflifeunderdifferenttemperatureconditions?

    In the first part of this thesis (chapter 2), the spoilage processes of fresh pork and fresh

    poultry are characterised and compared. For this purpose, intrinsic factors (pHvalue, aw

    value, WarnerBratzler shear force (WBSF), Dglucose, Llactic acid, fat and protein) are

    analysedconcerning

    their

    effect

    on

    Pseudomonas sp. growth for fresh pork and poultry

    during storage at 4C. Additionally, the growth of Pseudomonas sp. is investigated at

    different constant storage temperatures and the minimal spoilage level is determined for

    fresh pork and fresh poultry to work out similarities and differences in the spoilage

    processes.

    In chapter 3 several storage trials are conducted with different dynamic temperature

    scenarios to figure out the influence of short cold chain interruptions on the spoilage

    processesand

    thus

    shelf

    life

    of

    fresh

    pork

    and

    poultry.

    Especially

    the

    influence

    of

    different

    amplitudes and durations of short temperature abuses, which can occur in the real cold

    chainoffreshmeat,areanalysedandcomparedforbothmeattypes.

    In the lastchapter (chapter4),modelparameters for freshporkand freshpoultryderived

    from storage experiments at constant storage temperatures in chapter 2 are presented.

    Basedon thisdata,apredictive shelf lifemodel isdevelopedwhich isapplicable forboth

    meattypes.Themodelisvalidatedunderdynamictemperatureconditionsusingthegrowth

    data of chapter 3 and previous investigations. Furthermore, the improvement of quality

    management resulting from the implementation of the model in meat supply chains is

    discussed.

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    Generalintroduction 9

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    measuringtheshelflifeoffood,pages319.WoodheadPublishingLimitedandCRCPress,Cambridge,UKandBocaRaton,US.

    Thuault,D.andCouvert,O.(2008).Symprevious,predictivemicrobiologytoolsforcoldchainmanagement.InKreyenschmidt, J. (ed.), Proceedings of the 3rd International Workshop Cold ChainManagement,02.03.June2008,Bonn,Germany,pages6571.BonnerUniversittsdruckerei,Bonn,Germany.

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    Generalintroduction 14

    Walker,S.J.(1994).Theprinciplesandpracticeofshelflifepredictionformicroorganisms.InMan,C.M.D.andJones,A.A.(eds.),Shelf lifeevaluationoffoods,pages4051.BlackieAcademic&Professional,London,

    UK.

    Whiting,R.C.(1995).Microbialmodellinginfoods.CriticalreviewsinFoodScienceandNutrition,35(6):467494

    Whiting,

    R.C.

    and

    Buchanan,

    R.

    L.

    (1993).

    Letter

    to

    the

    editor:

    A

    classification

    of

    models

    in

    predictive

    micobiology areplytoK.R.Davey.FoodMicrobiology,10(2):175177.

    Willocx, F., Mercier, M., Hendrickx, M. and Tobback,P. (1993).Modelling the influence of temperature andcarbondioxideuponthegrowthofPseudomonasfluorescens.FoodMicrobiology,10(2):159173.

    Wilson, P. D.G., Brocklehurst, T.F., Arino, S., Thuault, D., Jakobsen, M., Lange, M., Farkas, J., Wimpenny,J.W.T. and Impe, J. F.V. (2002). Modelling microbial growth in structured foods: towards a unifiedapproach.InternationalJournalofFoodMicrobiology,73(23):275289.

    Zwietering,M.H.,Jongenburger,I.,Rombouts,F.M.andvantRiet,K.(1990).Modelingofthebacterialgrowthcurve.AppliedandEnvironmentalMicrobiology,56(6):18751881.

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    CHAPTER2

    CHARACTERISATIONANDCOMPARISONOF

    SPOILAGEPROCESSESINFRESHPORKAND

    POULTRY

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    2Characterisationandcomparisonofspoilageprocesses 16

    2.1 Introduction

    Fresh pork and poultry are highly perishable commodities due to their nutritional

    composition. The main cause for their loss of freshness during storage is microbial growth

    (Singh & Anderson, 2004), especially the growth of the specific spoilage organism (SSO)

    Pseudomonassp.(Blickstad&Molin,1983;Pooni&Mead,1984;Galloetal.,1988;Coateset

    al.,1995;Kreyenschmidtetal.,2007;Liuetal.,2006b;Raabetal.,2008).

    Duringstorage,thegrowthoftheSSOisaffectedbyseveralfactors,whicharedivided into

    four groups according to Mossel (1971): (i) intrinsic factors, which are the expression of

    physicalandchemicalpropertiesofthefood itself(e.g.wateractivity,nutrients,structure),

    (ii) extrinsic factors, which are parameters of the storage environment of the food (e.g.

    storage temperature, gas atmosphere), (iii) processing factors, which are physical or

    chemical treatments during processing of the food (e.g. heat treatment) and (iv) implicit

    factors,whichdescribesynergisticorantagonisticinfluencesamongtheprimaryselectionof

    organisms (e.g. specific rate of growth). Factors which areconsidered as beingrelevant for

    fresh meat are the intrinsic factors initial number of psychotrophs present on the meat

    surface,wateractivity(aw),inherentpHofthemeatsurfaceandnutritionalcontentaswell

    astheextrinsicfactorsstoragetemperatureandoxygenavailability(McDonald&Sun,1999;

    Cervenyetal.,2009).

    Changes of these parameters influencing microbiological growth during storage have been

    investigatedbyseveralauthors.HuffLonerganetal.(2002)analyseddifferentqualitytraits

    including intrinsic parameters, such as pHvalue, glycolytic potential and their mutual

    correlations in fresh pork, but without considering their relationship to microbiological

    growthand thusshelf life.Moreemphasishasbeen laidon improvingthesecharacteristics

    by rearing in the pig meat industry (Hovenier et al., 1993). Byun et al. (2003) compared

    different parameters in fresh pork and beef during storage and found high correlations

    between D glucose as well as Llactate and bacterial counts (total plate count and

    psychotrophic bacterial count) in fresh pork. Correlations between pH and bacterial counts

    wereonlyofmediummagnitudeandtheSSOwerenot investigated intheirstudy.Allenet

    al.(1997)describedsignificantcorrelationsbetweenthepsychotrophicplatecountandthe

    pHaswellasthecapacitancedetectiontime(forenumerationofPseudomonasfluorescens)

    and pH in poultry breasts, but these correlations were very low. In a later study, no

    significant correlations between these parameters were observed (Allen et al., 1998).

    Despiteallthesestudiesit isunclearwhichfactorshavethegreatestinfluenceontheshelf

    lifeoffreshporkandpoultryandwhetherthesefactorsarethesameordifferentforfresh

    porkandpoultrymeat.

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    2Characterisationandcomparisonofspoilageprocesses 17

    Therefore,theobjectiveofthestudywastoinvestigatedifferentintrinsicparametersduring

    storage in fresh pork and poultry to attempt to establish similarities as well as differences

    between fresh pork and poultry and to identify relevant factors influencing the growth of

    Pseudomonas sp. Additionally, the influence of the extrinsic parameter temperature was

    analysed.

    2.2 Materialandmethods

    2.2.1 Sampledescriptionandexperimentaldesign

    Porkloins(M.longissimusdorsi)weretransportedfromlocalbutchersandslaughterhouses

    tothelaboratoryundertemperaturecontrolledconditions.Inthelaboratory,everyloinwas

    divided

    into

    150

    200

    g

    slices

    (chops)

    under

    sterile

    conditions.

    Skinless

    chicken

    breast

    fillets

    (150 170g)weretransportedfromapoultryslaughteringandprocessingplantinGermany

    to a wholesaler and forwarded to the laboratory under temperaturecontrolled conditions.

    Each pork chop and each chicken breast fillet was placed in an individual tray and over

    wrapped with a low density polyethylene (LDPE) film (aerobe packaging). For all storage

    experiments, high precision low temperature incubators (MIR 153, Sanyo Electric Co., Ora

    Gun,Gumma,Japan)wereused.Timebetweenslaughteringandthefirstinvestigationwas

    24hforbothmeattypes.

    In the first step of the study, the microbiological growth data from previous investigations

    (Raabetal.,2008)weretakenasabasisanddatafromnewmeasurementswereaddedto

    identify the influence of the extrinsic parameter temperature onPseudomonas sp. growth

    and hence shelf life. Altogether, microbiological data of 147 pork chops and 124 poultry

    filletswereconsidered,whichwerestoredatfivedifferentisothermaltemperatures(2,4,7,

    10 and 15C). Three samples were analysed for total viable count (TVC),Pseudomonas sp.

    countandsensorycharacteristicsatappropriatetimeintervals.

    In

    the

    second

    step,

    loins

    of

    5

    pig

    carcasses

    (25

    chops)

    and

    25

    skinless

    chicken

    breast

    fillets

    werepreparedandpackagedasdescribedpreviously.Sampleswerestoredat4Cforabout

    14 days. Five samples of each meat type were analysed for microbial counts (TVC and

    Pseudomonassp.), sensory characteristics and the intrinsic parameters pHvalue, awvalue,

    Dglucose,LlacticacidandWarnerBratzlershearforce(WBSF)atfivesamplepointsduring

    storage. Sample points were chosen based on the spoilage processes at 4C determined in

    step 1of the study. For pork, chopsfrom thesame loinswere chosenat all sample points,

    whichmeansthatparameterchangesinoneloinwastrackedduringstorage.Additionally,5

    poultry fillets and 5 pork chops were frozen at 20C at day 0 of the study for analysis of

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    2Characterisationandcomparisonofspoilageprocesses 18

    initialfatandproteincontent.Thestorageexperimentwasrepeatedtwice,sothatatotalof

    90porkchopsand90chickenbreastfilletswereinvestigated.

    2.2.2 Microbiologicalanalysis

    For microbial analysis, a representative product sample of 25g was extracted using a cork

    borer and transferred to a filtered stomacherbag, which was filled with saline peptone

    diluents (0.85 % NaCl with 0.1 % peptone; Oxoid, Basingstoke, United Kingdom) to a final

    weightof250g.Thecontentswerehomogenisedfor60susingaStomacher400(Kleinfeld

    Labortechnik,Gehrden,Germany).A10folddilutionseriesofthehomogenatewasprepared

    using saline peptone diluents. Appropriate dilutions were transferred to the following

    media: plate count agar (PCA, Oxoid, Basingstoke, United Kingdom) for TVC, incubated at

    30C for 72 h as wellasPseudomonas Agar Base(OxoidBasingstoke, United Kingdom) plus

    CFC supplement (Oxoid, Basingstoke, United Kingdom) forPseudomonas sp., incubated at

    25Cfor48h.TVCwasenumeratedusingthepourplatetechnique,forPseudomonassp.the

    spreadplatetechniquewasused.

    2.2.3 Sensoryanalysis

    Sensoryevaluationofeachsamplewasassessedbyatrainedsensorypanel.Odour,texture

    and colour were evaluated using a 3point scoring system where 3 = very good and 1 =

    unacceptable.Aweightedsensoryindex(SI)wascalculatedusingthefollowingequation2.1.

    5

    122 TOCSI

    ++= (2.1)

    withSI:sensoryindex,C:colour,O:odour,T:texture

    Sensoryacceptancewasdescribedasafunctionoftimebylinearregression.Themeatwas

    considered spoiled when the SI reached 1.8 (Kreyenschmidt, 2003). From the shelf life

    times obtained based on the decrease in sensory acceptance, a spoilage level for

    Pseudomonassp.wasderivedtospecifytheendofshelflife.

    2.2.4 Measurementofphysicalandchemicalproperties

    ThepHvaluewasmeasuredonthreedifferentpointsineachsamplebylancingapHmeter

    (Testo 206; Testo, Lenzkirch, Germany) directly in the product. From these three

    measurements, an average pH value was calculated for each poultry fillet and each pork

    chop. The water activity (awvalue) was measured with the AquaLab CX3 TE (Decagon

    Devices Inc., Pullman, USA). The sample dish was filled with sample materials according to

    theinstructions.Measurementswereconductedatroomtemperature(20C)andrepeated

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    2Characterisationandcomparisonofspoilageprocesses 19

    three times. An average awvalue was calculated from these three measurements for each

    sample.

    For the measurement of the WBSF, five cores were removed from each poultry fillet and

    each pork chop using a cork borer to ensure comparability of the samples. Each core was

    placed in a Texture Analyser TA.XT plus (Stable Micro System, Surrey, UK) with a notched

    WarnerBratzlerShearblade.Assayparameterswere:pretestspeed:2mm/s,testspeed:2

    mm/s, posttest speed: 10 mm/s, down stroke distance: 20 mm and trigger force: 250g.

    Afteramanualstart,themeasurementitselfanditsdocumentationwasperformedwiththe

    relatedsoftwareTextureExponent32.Peakshearforce(kg)wasrecordedforeachcore.An

    average WBSF value for every meat sample was then calculated from the five cores per

    sample. After the investigation of microbial and physicochemical parameters, remaining

    sampleswerefrozenat 20CforDglucoseandLlacticacidanalyses.

    2.2.5 Measurementofnutrients

    Dglucose and Llactic acidconcentrations were assayedwith the respective enzyme kit for

    the determination of these concentrations in foodstuffs and other materials (Dglucose:

    TestCombination 10 716 251 035, rbiopharm, Darmstadt, Germany; Llactic acid: Test

    Combination 10 139 084 035, rbiopharm, Darmstadt, Germany). Remaining samples from

    previous investigations were thawed at 4C in the refrigerator for 24 h prior to the

    investigation. 5 g of the thawed sample was weighed in a stomacher bag, 35 ml double

    distilledwaterwasaddedandthemixturehomogenisedfor10minusingaStomacher(IUL,

    Knigswinter,Germany).AfterCarrezclarificationofthesamplesolution,pHwasadjustedto

    8.08.5byusingsodiumchloride.100mldoubledistilledwaterwasadded,thesuspension

    wasshakenwellandfiltered(Whatmanfiltertype595;WhatmanInt.Ltd.,Maidstone,UK).

    The filtrate was used for the enzymatic analysis of Dglucose and Llactic acid according to

    the instructions of the enzyme kits. Extinctions were measured with a UVVis photometer

    (Genesys

    6,

    Thermo

    Scientific,

    Waltham,

    USA)

    at

    a

    wavelength

    of

    340

    nm.

    Measurements

    wereperformedinduplicate.

    Porkchopsandpoultryfilletsbeingexaminedforfatandproteinanalysiswerethawedina

    refrigerator at 4C and homogenised in a mixer (Moulinex/Groupe SEB, Ecully Cedex,

    France).Foursamplesof1gand5geachwereforwardedtoexternalinstitutesforfatand

    protein content analysis. Analysis of the protein content was conducted according to the

    official analytical methods specified in the German Food Law (Lebensmittel und

    Futtermittelgesetzbuch; LFGB), which is based on the nitrogen determination by Kjeldahl

    (64LFGB,L06.007).FatcontentwasdeterminedaccordingtoWeibullStoldt,whichisalso

    specifiedamongtheofficialanalyticalmethods(64LFGB,L06.006)

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    2Characterisationandcomparisonofspoilageprocesses 20

    2.2.6 Statisticalmethods

    The microbiological growth data were transformed to log10 values and then fitted using

    nonlinear regression (LevenbergMarquardt algorithm) by the statistical software package

    Origin 8.0G (OriginLab Corporation, Northampton, USA). The Gompertz model was used to

    describethegrowthofmicroorganismswithtime(equation2.2)(Gibsonetal.,1987):

    )(

    )(MtB

    eeCAtN

    += (2.2)

    with N(t):microbialcount[log10 cfu/g]attime t,A: lowerasymptotic lineofthegrowthcurve(initial

    bacterial count), C: difference between upper asymptotic line of the growth curve (Nmax= maximum

    population level) and the lower asymptotic line, B: relative growth rate at time M [1/h], M: time at

    whichmaximumgrowthrateisobtained(reversalpoint),t:time[h].

    DataofmicrobialandintrinsicparameterswereanalysedusingSPSSstatistics17(SPSSInc.,Chicago, USA). Due to the sample size, normality was checked for using ShapiroWilkTest

    (Janssen&Laatz,2007).Basedontheresults,correlationswerecalculatedwithSpearmans

    Rho. Evaluation of these correlations was made according to the classification of Bhl

    (2008):r |0.2|=verylowcorrelation,|0.2|

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    2Characterisationandcomparisonofspoilageprocesses 21

    pork poultry

    0 48 96 144 192 240 288

    2

    4

    6

    8

    10

    2C

    4C

    7C

    10C

    15CBacterialcount

    log

    10

    [cfu/g]

    Storage time [h]

    0 48 96 144 192 240 288

    2

    4

    6

    8

    10

    2C

    4C

    7C

    10C

    15CBacterialcount

    log

    10

    [cfu/g]

    Storage time [h]

    Figure 2.1: Growth of Pseudomonas sp. on fresh pork (left) and poultry (right) at constant storage

    hesensoryindex(SI)decreasedlinearlyforfreshporkandpoultry.Withincreasingstorage

    pork poultry

    temperaturesfittedwiththeGompertzmodel

    T

    temperatureafasterdecreaseoftheSIwasobserved(Figure2.2).Asformicrobialgrowth,

    theSIdeclinedfasterforfreshpoultrythanforfreshpork.

    0 48 96 144 192 240 288

    1

    2

    3

    end of shelf life

    2C

    4C

    7C

    10C

    15C

    Sensory

    index

    Storage time [h]

    0 48 96 144 192 240 288

    1

    2

    3

    2C

    4C

    7C

    10C

    15C

    Sensory

    index

    Storage time [h]

    end of shelf life

    Figure2.2:Sensoryindexforfreshpork(left)andpoultry(right)atdifferentconstantstoragetemperatures

    (endofshelflife SI 1.8)

    all i age temperatures, very high significant correlations

    re cfu/g.

    common

    at a between

    at

    nvestigated constant storAt

    (p

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    2Characterisationandcomparisonofspoilageprocesses 22

    estimated microbial and sensory shelf lives for fresh pork and poultry is good, with a

    maximum discrepancy of 25.1 h for poultry at 2C and 24.7 h for pork at 4C (Table 2.1).

    Differencesweregreateratlowertemperatures.

    Table 2.1: Microbial and sensory determined shelf lives of fresh pork and poultry at different constant

    PoultryPorkandPoultry

    storagetemperatures

    PorkDifferencebetween

    Tempe ature

    [C]

    Microbial

    shelflifea)

    Sensory

    shelflifeb)

    Microbial

    shelflife

    Sensory

    shelflife

    Mic

    shelf

    ry

    life

    r

    [h] [h] [h] [h]

    robial Senso

    life shelf

    [h] [h]

    2 165.8 180.7 126.4 151.5 39.4 29.2

    4 1 1 1 22.2 46.9 98.6 16.4 23.6 30.5

    7 92.9 110.5 63.9 56.1 29.0 54.4

    10 75.4 69.5 41.5 42.1 33.9 27.4

    15 45.5 45.9 27.1 29.3 18.4 16.6

    Micro andsensory wasestim fromtime pointzero elaboratory igations, hichmea afterslaughtering.a)

    Evaluatedbycounto onassp of shelflife:7.5log10

    teachconstantstoragetemperaturelevel,shelflifeoffreshporkwaslongerthanshelflife

    bial shelflife ated ofth invest w ns24h

    fPseudom .:End cfu/gb)Evaluatedbysensoryindex:Endofshelflife:SI 1.8

    A

    of fresh poultry. The decay of shelf life with increasing temperature can be described

    exponentially (Figure 2.3). Differences between shelf life of fresh pork and fresh poultry

    were between 18.4 h and 39.4 h for microbial shelf life and 16.6 h and 54.4 h for sensory

    shelflife,respectively.

    0 2 4 6 8 10 12 14 16

    20

    40

    60

    80

    100

    120

    140

    160

    Shelflife[h]

    Temperature [C]

    Figure2.3:Microbialshelflifeoffreshpork()andpoultry()atdifferentconstantstoragetemperatures

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    2Characterisationandcomparisonofspoilageprocesses 23

    2.3.2 Influenceofintrinsicparameters

    Table 2.2 shows the mean values and standard deviations of the analysed intrinsic

    parametersinfreshporkandpoultryatdifferentsamplepointsduringstorage,aswellasthe

    totalmeanvalue.

    Table2.2:Intrinsicparameters(meanvaluesstandarddeviation)duringstorageinfreshporkandpoultry

    meatat4C

    Parameter Meat Samplepoints Meanvalue

    type I II III IV V

    ph pork 5.48aa)

    0.14 5.61a0.14 5.66a0.24 5.76a0.24 5.73a0.15 5.65a0,21

    poultry 6.02b0.16 6.16b0.15 6.16b0.17 6.08b0.27 6.23b0.17 6.13b0.20

    aw pork 0.990a0.001 0.990a0.001 0.990a0.001 0.992a0.002 0.990a0.002 0.990a0.002

    poultry 0.991b0.001 0.991b0.001 0.991b0.001 0.990b0.001 0.990a0.001 0.990b0.001

    Dglucose pork 0.236a0.120 0.241a0.110 0.234a0.151 0.187a0.120 0.135a0.111 0.207a0.127

    [g/100g] poultry 0.043b0.027 0.022b0.012 0.028b0.016 0.033b0.033 0.014b0.009 0.028b0.023

    Llacticacid pork 0.939a0.139 0.874a0.081 0.878a0.116 0.820a0.099 0.731a0.153 0.849a0.137

    [g/100g] poultry 0.828b0.081 0.763b0.095 0.760b0.086 0.774a0.113 0.762a0.085 0.778b0.094

    WBSF pork 3.14a0.52 3.38a0.61 3.22a0.60 3.20a0.52 3.18a0.49 3.22a0.54

    [kg] poultry 1.19b0.28 1.15b0.25 1.12b0.32 1.02b0.32 1.13b0.22 1.12b0.28

    Fatb)

    pork 116.04a84.94 116.04a84.94

    [g/kg] poultry 13.85b3.97 13.85a3.97b

    Proteinb)

    pork 208.33a24.35 208.33a24.35

    [g/kg] poultry 237.73b6.69 237.73b6.69

    Samplepoints:I=day0;II=day2;III=day5(poultry)/day6(pork);IV=day9(poultry)/day11(pork);V=day13(poultry)/day14(pork)

    WBSF=WarnerBratzlershearforcea)

    Meanswithdifferentsmalllettersinthesamecolumnrepresentsignificantdifferencesatp

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    2Characterisationandcomparisonofspoilageprocesses 24

    0 2 4 6 8 10 12 14

    4,8

    5,2

    5,6

    6,0

    6,4

    6,8

    pH

    Storage time [days]

    Figure2.4:Changesinmeanvalues(standarddeviation)forpHinfreshpork()andpoultry()during

    storageat4C.( )microbialendofshelflifepork,( )microbialendofshelflifepoultry

    InitialpHvalue(24hafterslaughtering)forpork(5.480.14)wasconsistentwithgenerally

    observedpHvaluesforfreshporkmeatintheliterature(5.45.8)(Blickstad&Molin,1983;

    Klont et al., 1999). Initial pH for poultry was higher than for pork at the beginning

    (6.020.16), as described previously (Newton & Gill, 1981) and also slightly higher than

    reported values for poultry breast fillets in other studies (Barnes, 1976; Fletcher, 1999).

    Comparisons showed significant differences (p

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    2Characterisationandcomparisonofspoilageprocesses 25

    Table2.3: Correlationsbetween intrinsic and microbiologicalparameters as wellas sensory index forfresh

    porkat4C

    TVC Pseu.sp. pH aw D

    Glucose

    LLactic

    acid

    WBSF Sensory

    index

    Fat

    content

    Pseudomonassp. 0.852

    pH 0.608 0.467

    aw 0.554 0.391 0.427

    DGlucose 0.560 0.364 0.773 0.384

    LLacticacid 0.645 0.505 0.667 0.375 0.672

    WBSF 0.094 0.021 0.239 0.066 0.147 0.155

    Sensoryindex 0.909 0.818 0.525 0.395 0.402 0.546 0.154

    Fatcontent 0.654 0.611 0.754 0.590 0.689 0.239 0.059 0.346

    Proteincontent 0.601 0.625 0.810 0.668 0.680 0.322 0.222 0.421 0.912

    Significantcorrelations(p

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    2Characterisationandcomparisonofspoilageprocesses 26

    Byun et al. (2003), Nychas & Tassou (1997) as well as Nychas (1998) reported lower initial

    and lower end values at a storage temperature of 3 4C than observed in this study. For

    example, initial values were about twice as high for pork and four times higher for poultry

    than in the mentioned studies. But the values were comparable when considering the

    standard deviation in these investigations, which was not mentioned in the other studies.

    Especially for poultry, observed standard deviations were very high compared to the mean

    value, which can be attributed to a greater variability in poultry samples as well as their

    independence from each other. For poultry, at every sample point individual breast fillets

    were analysed, so that the greater variability between animals played a role. Pork chops

    analysed at each sample point were always from the same loin, thus the same pig. So, the

    changes of glucose content in loins from several animals were tracked during storage.

    However,thehighvariationinglucosecontentinmeatiswellknown(Gill,1983;Belitzetal.,

    2009). This is easily understood as the amount of Dglucose is strongly influenced by the

    condition of the animal before slaughter e.g. age, nutritional state, stress, exercise levels

    (Nychasetal.,1988;Gill,1983;Dainty&Mackey,1992;Belitzetal.,2009).Theresultsofthe

    studyalsoshowthatglucosecontentsweresignificantly(p

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    2Characterisationandcomparisonofspoilageprocesses 27

    Incontrasttotheglucosecontent, lacticacidvalueswere inthesamerangeforbothmeat

    types at all sample points when considering the standard deviation with no significant

    difference(p>0.05)atsamplepointsIVandV(Figure2.6).

    0 2 4 6 8 10 12 14

    0,5

    0,6

    0,7

    0,8

    0,9

    1,0

    1,1

    1,2

    L-lacticacid[g/100g]

    Storage time [days]

    Figure2.6:Changesinmeanvalues(standarddeviation)forLlacticacidinfreshpork()andpoultry()

    duringstorageat4C.( )microbialendofshelflifepork,( )microbialendofshelflifepoultry

    The decrease during storage can be explained by the metabolism ofPseudomonassp.: the

    bacteria utilise lactate after the depletion of glucose during storage (Nychas et al., 2008).

    Theamountoflacticacidinthebeginningofstoragedependsontheglycogencontentatthe

    timeofslaughtering.Afterslaughter,thedegradationofglycogenleadstotheaccumulation

    of lactic acid and a decrease of the meat pH to about 5.5 (Newton & Gill, 1981). These

    relationswereconfirmedinsignificantmediumtohighcorrelationsbetweenlacticacidand

    pHvalue(pork:r= 0.667,poultry:r= 0.719)inthisstudy.However,correlationsbetween

    Pseudomonassp.countandlacticacidconcentrationweresignificant,buttheirmagnitudes

    were low (pork: r = 0.505; poultry: r= 0.302) which is why the Llactic acid concentration

    can be considered as having little relevance for the growth ofPseudomonas sp. and thus

    shelflifeoffreshporkandpoultry.

    Theawvaluewascomparableatallsamplepointsforbothmeattypes(around0.990 0.992)

    andsimilartothevalueof0.993reportedbyRdel&Krispien(1977)and0.99reportedby

    Inghametal.(2009)forfreshmeat.AsforthepHvalue,correlationswithPseudomonassp.

    were significant but low (pork: r = 0.391; poultry: r = 0.317). The minimum awvalue for

    growthofPseudomonassp.isdeterminedat0.97(Singh&Anderson,2004).Withminimum

    measured awvalues of 0.985 for pork and 0.986 for poultry, optimal growth conditions for

    Pseudomonassp.existedduringthewholestudy.Therefore,noinfluenceonshelflifeofthe

    awvaluecanbeassumedforfreshporkandpoultry.

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    2Characterisationandcomparisonofspoilageprocesses 28

    Becausesensorycharacteristicsareavaluableindicatoroftheshelflifestatusoffreshmeat

    (as also proven by high and very high significant correlations between Pseudomonas sp.

    count and sensory index in thisstudy (pork: r = 0.818; poultry: r= 0.908)), the WBSF was

    investigated as an objective measurement for the sensory characteristic texture. No clear

    trend in the development of the WBSF could be observed during storage for both meat

    types.TheaverageWBSFforporkwastwiceashighasforpoultryateverysamplepointata

    significance levelofp

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    2Characterisationandcomparisonofspoilageprocesses 29

    (r= 0.625). According to Nychas et al. (2008) Pseudomonas sp. metabolise nitrogenous

    compounds (e.g. amino acids) as energy substrates not until the exhaustion of glucose,

    lactateandotherlowmolecularsubstrates.Atthepointwhennitrogenouscompounds(e.g.

    aminoacids)areutilised,overtspoilage intheformofoffodoursandslimealreadyoccurs.

    Therefore,theproteincontentisnotrelevantfortheshelflifeoffreshmeat.

    Insummary,severalstoragetrialswithporkchopsandchickenbreastfilletswereconducted

    forthecharacterisationandcomparisonofthespoilageprocessesoffreshporkandpoultry.

    Especially the relevant factors influencing the growth of Pseudomonas sp. as specific

    spoilage organism (SSO) of fresh pork and poultry were attempted to be identified. The

    findings of this study shall provide a more elementary and better understanding of the

    spoilageprocessesthanhasbeenresearcheduptothepresent,whichgivesasolidbasisfor

    the improvementofquality managementandshelf life prediction in thefood industry. TheresultsshowedthatthegrowthofPseudomonassp.wasclearlydependentontemperature,

    with faster growth at higher temperatures as described in the literature previously.

    Pseudomonassp.grewmorerapidlyonfreshpoultrythanonfreshporkwhichledtoshorter

    shelf lives at constant temperatures from 2 15C for fresh poultry. The highly significant

    correlations (p

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    2Characterisationandcomparisonofspoilageprocesses 31

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    CHAPTER3

    INFLUENCEOFCOLDCHAININTERRUPTIONS

    ONTHESHELFLIFEOFFRESHPORKAND

    POULTRY

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    3Influenceofcoldchaininterruptions 35

    3.1 Introduction

    Temperatureisthemostimportantenvironmentalinfluencefactoronmicrobialgrowthand

    thus on shelf life of fresh meat as shown in chapter 2. The higher thetemperature during

    transportationand

    storage,

    the

    faster

    the

    rate

    of

    microbial

    growth.

    To

    prevent

    spoilage

    and

    toprolongtheshelf lifeoffreshmeat it is importanttomaintainthecoldchainduringthe

    entiresupplychainoffreshmeat.ThisisstipulatedamongstothersintheRegulationonthe

    hygieneoffoodstuffs(Regulation(EC)No852/2004),whichdemands,thatthecoldchainis

    notinterrupted.Butlimitedperiodsoutsidetemperaturecontrolarepermitted.

    Several investigations have shown that temperatures in real food supply chains often vary

    greatly from the mandatory or recommended temperature. For example, Raab &

    Kreyenschmidt

    (2008)

    recorded

    environmental

    temperatures

    in

    a

    truck

    during

    transportation in a German poultry supply chain ranging between 3C and +15C in the

    summer and between 2.5C and 7.5C in the winter. In the study of Koutsoumanis et al.

    (2010)ofaGreekmilkchain,thetemperature inthetrucksranged from+3.6Cto+10.9C

    during transportation. To estimate the influence of these temperature variations on shelf

    life,thebehaviourofPseudomonassp.asspecificspoilageorganism(SSO)offreshmeat(e.g.

    Gill & Newton 1977; Pooni & Mead, 1974; Coates et al., 1995; Raab et al., 2008) under

    dynamic temperature conditions is of substantial importance (Shimoni & Labuza 2000;

    Koutsoumaniset

    al.,

    2006;

    Nychas

    et

    al,

    2008).

    Severalstoragetrialsatfluctuatingtemperatureconditionshavebeenconductedduringthe

    last years. Koutsoumanis (2001) investigated the growth of Pseudomonas sp. on gilthead

    sea breamunder five different dynamic scenarios. In another study, the growth of several

    microorganisms (Pseudomonas sp.,