PREDICTIVE SHELF LIFE MODEL.pdf

8/11/2019 PREDICTIVE SHELF LIFE MODEL.pdf

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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


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


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


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


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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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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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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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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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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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


19/88


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.


20/88


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CHAPTER2

CHARACTERISATIONANDCOMPARISONOF

SPOILAGEPROCESSESINFRESHPORKAND

POULTRY


27/88

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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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


29/88


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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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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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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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


33/88


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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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


35/88


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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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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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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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.


39/88


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


40/88


(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


41/88


42/88


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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.,

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