Safety Analysis of Foods of Animal Origin

SAFETYANALYSISOF FOODS OFANIMALORIGIN

SAFETYANALYSISOF FOODS OFANIMALORIGINEdited by

Leo M.L. Nollet and Fidel Toldr

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Safety analysis of foods of animal origin / editors, Leo M.L. Nollet, Fidel Toldr.p. cm.

A CRC title.Includes bibliographical references and index.ISBN 978-1-4398-4817-3 (hardcover : alk. paper)1. Meat inspection. 2. Meat--Analysis. 3. Dairy inspection. 4. Dairy products--Analysis. I. Nollet,

Leo M. L., 1948- II. Toldr, Fidel.

TS1975.S24 2011664.9072--dc22 2010026750

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vContents

Preface...................................................................................................................................ixEditors...................................................................................................................................xiContributors....................................................................................................................... xiii

Part I Meat, ProCessed Meats and Poultry

1 Methods.to.Predict.Spoilage.of.Muscle.Foods..............................................................3GEraldinE.duFFy,.anthony.dolan,.and.CathErinE.M..BurGESS

2 Microbial.Foodborne.Pathogens.................................................................................21MarioS.MataraGaS.and.ElEFthErioS.h..droSinoS

3 Parasites.......................................................................................................................59anu.nrEaho

4 Mycotoxin.analysis.in.Poultry.and.Processed.Meats..................................................77JEan-dEniS.Bailly.and.PhiliPPE.GuErrE

5 detection.of.Genetically.Modified.organisms.in.Processed.Meats.and.Poultry......125andrEa.GErMini.and.alESSandro.tonElli

6 detection.of.adulterations:.addition.of.Foreign.Proteins........................................155Mara.ConCEPCin.GarCa.lPEz.and.Mara.luiSa.Marina.alEGrE

7 detection.of.adulterations:.identification.of.animal.Species...................................187JohannES.arJEn.lEnStra

8 detection.of.irradiated.ingredients..........................................................................207EilEEn.M..StEwart

9 Growth.Promoters.....................................................................................................229MilaGro.rEiG.and.FidEl.toldr

10 antibiotic.residues.in.Muscle.tissues.of.Edible.animal.Products...........................249EriC.VErdon

vi Contents

11 determination.of.Persistent.organic.Pollutants.in.Meat..........................................349anna.laura.iaMiCEli,.iGor.FoChi,.GianFranCo.BraMBilla,.and.alESSandro.di.doMEniCo

12 Biogenic.amines........................................................................................................399M..CarMEn.Vidal-Carou,.M..luz.latorrE-Moratalla,.and.Sara.BoVEr-Cid

13 nitrosamines.............................................................................................................421SuSannE.rath.and.FElix.GuillErMo.rEyES.rEyES

14 Polycyclic.aromatic.hydrocarbons...........................................................................441PEtEr.iMko

Part II FIsh and seaFoods

15 assessment.of.Seafood.Spoilage.and.the.Microorganisms.involved.........................463roBErt.E..lEVin

16 detection.of.the.Principal.Foodborne.Pathogens.in.Seafoods.and.Seafood-related.Environments.................................................................................485daVid.rodrGuEz-lzaro.and.Marta.hErnndEz

17 Parasites.....................................................................................................................507Juan.antonio.BalBuEna.and.Juan.antonio.raGa

18 techniques.of.diagnosis.of.Fish.and.Shellfish.Virus.and.Viral.diseases..................531CarloS.PErEira.doPazo.and.iSaBEl.Bandn

19 Marine.toxins...........................................................................................................577Cara.EMPEy.CaMPora.and.yoShitSuGi.hokaMa

20 detection.of.adulterations:.addition.of.Foreign.Proteins........................................603VroniquE.VErrEz-BaGniS

21 detection.of.adulterations:.identification.of.Seafood.Species..................................615antonio.PuyEt.and.JoS.M..BautiSta

22 Spectrochemical.Methods.for.the.determination.of.Metals.in.Seafood...................641JoSEPh.SnEddon.and.Chad.a..thiBodEaux

23 Food.irradiation.and.its.detection...........................................................................663yiu.ChunG.wonG,.dElla.wai.MEi.Sin,.and.wai.yin.yao

24 Veterinary.drugs.......................................................................................................687anton.kauFMann

25 analysis.of.dioxins.in.Seafood.and.Seafood.Products..............................................707luiSa.raMoS.BordaJandi,.BEln.GMara,.and.Mara.JoS.GonzlEz

26 Environmental.Contaminants:.Persistent.organic.Pollutants..................................727Monia.PEruGini

Contents vii

27 Biogenic.amines.in.Seafood.Products.......................................................................743Claudia.ruiz-CaPillaS.and.FranCiSCo.JiMnEz-ColMEnEro

28 detection.of.GM.ingredients.in.Fish.Feed................................................................761kathy.MESSEnS,.niColaS.GrySon,.kriS.audEnaErt,.and.Mia.EECkhout

Part III MIlk and daIry Foods

29 Microbial.Flora..........................................................................................................781EFFiE.tSakalidou

30 Spoilage.detection....................................................................................................799Maria.CriStina.dantaS.VanEtti

31 PCr-Based.Methods.for.detection.of.Foodborne.Bacterial.Pathogens.in.dairy.Products....................................................................................................................811ilEx.whitinG,.niGEl.Cook,.Marta.hErnndEz,.daVid.rodrGuEz-lzaro,.and.Martin.daGoStino

32 Mycotoxins.and.toxins.............................................................................................823Carla.SolEr,.JoS.MiGuEl.Soriano,.and.Jordi.MaES

33 detection.of.adulterations:.addition.of.Foreign.lipids.and.Proteins......................851SaSkia.M..van.ruth,.Maria.G..E..G..BrEMEr,.and.roB.FrankhuizEn

34 detection.of.adulterations:.identification.of.Milk.origin.......................................865GolFo.MoatSou

35 analysis.of.antibiotics.in.Milk.and.its.Products......................................................887Jian.wanG

36 Chemical.Contaminants:.Phthalates........................................................................ 907JiPinG.zhu,.SuSan.P..PhilliPS,.and.xu-lianG.Cao

37 Environmental.Contaminants...................................................................................929Sara.BoGialli.and.antonio.di.CorCia

38 allergens....................................................................................................................951VirGiniE.trEGoat.and.arJon.J..van.hEnGEl

index..................................................................................................................................969

ix

Preface

Safety of food, in general, and safety of foods of animal origin are of great importance for consum-ers, the food industry, and health authorities. Consumers expect healthy and safe food. Producers need to meet certain standards, guidelines, or directives imposed by local, governmental, con-tinental, and global authorities. Producers and authorities have to rely on adequate methods of analysis for an accurate detection, including the choice of an adequate sample preparation method, which is also of high relevance.

Analysis and detection methods are evolving fast toward miniaturization, automation, and increased limits of detection. Contamination of foods may be of different origins, either biologi-cal (bacteria, viruses, or parasites and products of organisms, e.g., marine toxins or mycotoxins) or chemical (residues of growth promoters, antibiotics, food contact materials, persistent organic materials, environmental contaminants, and many others). The more man interferes with the pro-duction of raw materials and end products, the more the detection methods required to check the authenticity of foods, the addition of foreign compounds, the use of irradiation, and the presence of genetically modified organisms.

This book, Safety Analysis of Foods of Animal Origin, is divided into three parts: Part I deals with meat, processed meats, and poultry; Part II with fish and seafood products; and Part III with milk and dairy products.

In all three parts, selected chapters (Chapters 1 through 3, 15, 17, and 29) deal first with the safety aspects of biological agents and products of different organisms. They also deal with meth-ods to control the presence of bacteria, viruses, or parasites in food (Chapters 5, 16, 18, 30, and 31). Sometimes it is not the biological agent that is hazardous, but rather its productsthis is covered in Chapters 4, 12, 19, 28, and 32.

Authenticity is a very important factor in the food industry and for consumers. Aspects like adulteration, addition of foreign compounds, irradiation, and genetically modified organisms are discussed in depth in several chapters (Chapters 5 through 8, 20 through 23, 28, 33, and 34).

Residues in foods may be from internal or external sources. Several chapters discuss residues of growth promoters, antibiotics, persistent organic pollutants, biogenic amines, n-nitroso com-pounds, and polycyclic aromatic compounds (Chapters 9 through 14, 24 through 27, and 35 through 38). This list of compounds is not exhaustive.

In each chapter, the authors start with a discussion of the parameter in question. Sample preparation and cleanup methods are reviewed in depth. This is followed by a detailed overview of different separation and detection methods. Special attention is given to limits of detection and reliability of methods. Finally, a brief summary covers the presence of these parameters in different end products, regions, and countries.

x Preface

All the chapters have been written by renowned scientists who are experts in their fields of research. Only the most recent techniques and related literature have been dealt with. We would like to thank all the contributing authors for their great efforts in producing this excellent work.

leo.M.l..nolletFidel.toldr

There are two ways of spreading light: to be the candle or the mirror that reflects it. (Edith Wharton)

xi

editors

dr..leo.M.l..nollet is an editor and associate editor of several books. He has edited the first and second editions of Food Analysis by HPLC and the Handbook of Food Analysis (which is a three-volume book) for Marcel Dekker, New York (now CRC Press of Taylor & Francis Group). He has also edited the third edition of the book, Chromatographic Analysis of the Environment (CRC Press, Boca Raton, Florida) and the second edition of the Handbook of Water Analysis (CRC Press, Boca Raton, Florida) in 2007. He coedited two books with F. Toldr that were published in 2006: Advanced Technologies for Meat Processing (CRC Press, Boca Raton, Florida) and Advances in Food Diagnostics (Blackwell Publishing, New York). He also coedited Radionuclide Concentrations in Foods and the Environment with M. Pschl in 2006 (CRC Press, Boca Raton, Florida).

Dr. Nollet has coedited several books with Y. H. Hui and other colleagues, including the Handbook of Food Product Manufacturing (Wiley, New York, 2007); the Handbook of Food Science, Technology and Engineering (CRC Press, Boca Raton, Florida, 2005); and Food Biochemistry and Food Processing (Blackwell Publishing, New York, 2005). He has also edited the Handbook of Meat, Poultry and Seafood Quality (Blackwell Publishing, New York, 2007).

Dr. Nollet has worked on the following six books on analysis methodologies with F. Toldr for foods of animal origin, several of these books have already been published by CRC Press, Boca Raton, Florida:

Handbook of Muscle Foods AnalysisHandbook of Processed Meats and Poultry AnalysisHandbook of Seafood and Seafood Products AnalysisHandbook of Dairy Foods AnalysisHandbook of Analysis of Edible Animal By-ProductsHandbook of Analysis of Active Compounds in Functional Foods

He has also worked with Professor H. Rathore on the Handbook of Pesticides: Methods of Pesti-cides Residues Analysis, which was published by CRC Press, Boca Raton, Florida, in 2009.

dr.. Fidel. toldr is a research professor in the Department of Food Science at the Instituto de Agroqumica y Tecnologa de Alimentos (CSIC), Valencia, Spain, and serves as the European editor of Trends in Food Science & Technology, the editor in chief of Current Nutrition & Food Science, and as a member of the Flavorings and Enzymes Panel at the European Food Safety Authority. In recent years, he has served as an editor or associate editor of several books. He was the editor of Research Advances in the Quality of Meat and Meat Products (Research Signpost,

xii Editors

Trivandrum, Kerala, India, 2002) and the associate editor of the Handbook of Food and Beverage Fermentation Technology and the Handbook of Food Science, Technology and Engineering published in 2004 and 2006, respectively, by CRC Press, Boca Raton, Florida. He coedited two books with L. Nollet that were published in 2006: Advanced Technologies for Meat Processing (CRC Press, Boca Raton, Florida) and Advances in Food Diagnostics (Blackwell Publishing, New York). Both he and Nollet are also associate editors of the Handbook of Food Product Manufacturing published by John Wiley & Sons, New York, in 2007. He has also edited Meat Biotechnology and Safety of Meat and Processed Meat (Springer, Berlin, Germany, 2008 and 2009, respectively) and has authored Dry-Cured Meat Products (Food & Nutrition Pressnow Wiley-Blackwell, New York, 2002).

Dr. Toldr has worked on the following six books on analysis methodologies with L. Nollet for foods of animal origin, several of these books have already been published by CRC Press, Boca Raton, Florida:

Handbook of Muscle Foods AnalysisHandbook of Processed Meats and Poultry AnalysisHandbook of Seafood and Seafood Products AnalysisHandbook of Dairy Foods AnalysisHandbook of Analysis of Edible Animal By-ProductsHandbook of Analysis of Active Compounds in Functional Foods

Dr. Toldr was awarded the 2002 International Prize for Meat Science and Technology by the International Meat Secretariat. He was elected as a fellow of the International Academy of Food Science & Technology in 2008 and as a fellow of the Institute of Food Technologists in 2009. He recently received the 2010 Distinguished Research Award from the American Meat Science Association.

xiii

Contributors

kris.audenaertFaculty of Biosciences and Landscape

ArchitectureDepartment of Plant ProductionUniversity College GhentGhent University AssociationGhent, Belgium

Jean-denis.BaillyMycotoxicology Research UnitNational Veterinary School of TaulouseToulouse, France

Juan.antonio.BalbuenaCavanilles Institute of Biodiversity and

Evolutionary BiologyUniversity of ValenciaValencia, Spain

isabel.BandnDepartamento de Microbiologa y

ParasitologaInstituto de AcuiculturaUniversidad de Santiago de CompostelaSantiago de Compostela, Spain

Jos.M..BautistaFaculty of Veterinary SciencesDepartment of Biochemistry and Molecular

BiologyUniversidad Complutense de MadridCiudad UniversitariaMadrid, Spain

Sara.BogialliDipartimento di ChimicaUniversit La SapienzaRome, Italy

luisa.ramos.BordajandiInstrumental Analysis and Environmental

Chemistry DepartmentSpanish National Research CouncilGeneral Organic Chemistry InstituteMadrid, Spain

Sara.Bover-CidMeat Technology CenterInstitute for Food and Agricultural Research

and TechnologyGirona, Spain

Gianfranco.BrambillaIstituto Superiore di SanitRoma, Italy

Maria.G..E..G..BremerRIKILTInstitute of Food SafetyWageningen University and Research CentreWageningen, the Netherlands

Catherine.M..BurgessAshtown Food Research CentreDublin, Ireland

xiv Contributors

Cara.Empey.CamporaDepartment of PathologyJohn A. Burns School of MedicineUniversity of HawaiiHonolulu, Hawaii

xu-liang.CaoFood Research DivisionHealth CanadaOttawa, Ontario, Canada

nigel.CookFood and Environment Research AgencyYork, United Kingdom

Martin.dagostinoFood and Environment Research AgencyYork, United Kingdom

antonio.di.CorciaDipartimento di ChimicaUniversity of Roma La SapienzaRome, Italy

anthony.dolanAshtown Food Research CentreDublin, Ireland

alessandro.di.domenicoIstituto Superiore di SanitRome, Italy

Carlos.Pereira.dopazoDepartamento de Microbiologa y

ParasitologaInstituto de AcuiculturaUniversidad de Santiago de CompostelaSantiago de Compostela, Spain

Eleftherios.h..drosinosLaboratory of Food Quality Control and

HygieneDepartment of Food Science & TechnologyAgricultural University of AthensAthens, Greece

Geraldine.duffyAshtown Food Research CentreDublin, Ireland

Mia.EeckhoutFaculty of Biosciences and Landscape

ArchitectureDepartment of Food Science and TechnologyUniversity College GhentGhent University AssociationGhent, Belgium

igor.FochiIstituto Superiore di SanitRome, Italy

rob.FrankhuizenRIKILTInstitute of Food SafetyWageningen University and Research CentreWageningen, the Netherlands

andrea.GerminiFaculty of AgricultureUniversity of ParmaParma, Italy

Beln.GmaraInstrumental Analysis and Environmental


Mara.Jos.GonzlezInstrumental Analysis and Environmental


nicolas.GrysonFaculty of Biosciences and Landscape

ArchitectureDepartment of Food Science and TechnologyUniversity College GhentGhent University AssociationGhent, Belgium

Contributors xv

Philippe.GuerreMycotoxicology Research UnitNational Veterinary School of ToulouseToulouse, France

arjon.J..van.hengelEuropean CommissionJoint Research CentreInstitute for Reference Materials and

MeasurementsGeel, Belgium

Marta.hernndezLaboratory of Molecular Biology and

MicrobiologyInstituto Tecnologico Agrario de Castilla y

LenValladolid, Spain

yoshitsugi.hokamaDepartment of PathologyJohn A. Burns School of MedicineUniversity of HawaiiHonolulu, Hawaii

anna.laura.iamiceliIstituto Superiore di SanitRome, Italy

Francisco.Jimnez-ColmeneroDepartment of Meat and Fish Science and

TechnologyConsejo Superior de Investigaciones

CientficasInstituto del FroCiudad UniversitariaMadrid, Spain

anton.kaufmannOfficial Food Control Authority of the Canton

of ZurichKantonales Labor ZurichZurich, Switzerland

M..luz.latorre-MoratallaDepartment of Nutrition and BromatologyFaculty of PharmacyUniversity of BarcelonaBarcelona, Spain

Johannes.arjen.lenstraFaculty of Veterinary MedicineInstitute for Risk Assessment SciencesUtrecht UniversityUtrecht, the Netherlands

robert.E..levinDepartment of Food ScienceUniversity of MassachusettsAmherst, Massachusetts

Mara.Concepcin.Garca.lpezFaculty of ChemistryDepartment of Analytical ChemistryUniversity of AlcalMadrid, Spain

Mara.luisa.Marina.alegreFaculty of ChemistryDepartment of Analytical ChemistryUniversity of AlcalMadrid, Spain

Jordi.MaesFaculty of PharmacyLaboratory of Toxicology and Food ChemistryUniversity of ValenciaValencia, Spain

Marios.MataragasLaboratory of Food Quality Control and

HygieneDepartment of Food Science & TechnologyAgricultural University of AthensAthens, Greece

kathy.MessensFaculty of Biosciences and Landscape

ArchitectureLaboratory AgriFingDepartment of Food Science and TechnologyUniversity College of GhentGhent University AssociationGhent, Belgium

xvi Contributors

Golfo.MoatsouLaboratory of Dairy TechnologyDepartment of Food Science and TechnologyAgricultural University of AthensAthens, Greece

anu.nreahoWitold Stefanski Institute of ParasitologyPolish Academy of SciencesWarsaw, Poland

Monia.PeruginiDepartment of Food ScienceUniversity of TeramoTeramo, Italy

Susan.P..PhillipsDepartments of Family Medicine and

Community Health and EpidemiologyQueens UniversityKingston, Ontario, Canada

antonio.PuyetFaculty of Veterinary SciencesDepartment of Biochemistry and Molecular

BiologyUniversidad Complutense de MadridCiudad UniversitariaMadrid, Spain

Juan.antonio.ragaCavanilles Institute of Biodiversity and

Evolutionary BiologyUniversity of ValenciaValencia, Spain

Susanne.rathGrupo de Toxicologia de Alimentos e

FrmacosDepartamento de Qumica AnalticaInstituto de QumicaUniversidade Estadual de CampinasCampinas, Brazil

Milagro.reigInstitute of Food Engineering for

DevelopmentUniversidad Politcnica de ValenciaValencia, Spain

Felix.Guillermo.reyes.reyesDepartment of Food ScienceUniversity of CampinasCampinas, Sao Paulo, Brazil

david.rodrguez-lzaroFood Safety and Technology Research GroupInstituto Tecnologico Agrario de Castilla y

LenValladolid, Spain

Claudia.ruiz-CapillasDepartment of Meat and Fish Science and

TechnologyConsejo Superior de Investigaciones

CientficasInstituto del FroCiudad UniversitariaMadrid, Spain

Saskia.M..van.ruthRIKILTInstitute of Food SafetyWageningen, the Netherlands

Peter.imkoInstitute of Food Science and BiotechnologyFaculty of ChemistryBrno University of TechnologyBrno, Czech Republic

della.wai.Mei.SinAnalytical and Advisory Services DivisionGovernment LaboratoryHong Kong, Peoples Republic of China

Joseph.SneddonDepartment of ChemistryMcNeese State UniversityLake Charles, Louisiana

Contributors xvii

Carla.SolerFaculty of PharmacyLaboratory of Food TechnologyUniversity of ValenciaValencia, Spain

Jos.Miguel.SorianoFaculty of PharmacyLaboratory of Food Chemistry and ToxicologyUniversity of ValenciaValencia, Spain

Eileen.M..StewartAgriculture, Food and Environmental Science

DivisionAgri-Food and Bioscience InstituteBelfast, Ireland

Chad.a..ThibodeauxDepartment of ChemistryMcNeese State UniversityLake Charles, Louisiana

Fidel.toldrDepartment of Food ScienceConsejo Superior de Investigaciones

CientficasInstituto de Agroqumica y Tecnologa de

AlimentosValencia, Spain

alessandro.tonelliFaculty of AgricultureUniversity of ParmaParma, Italy

Virginie.tregoatEuropean CommissionJoint Research CentreInstitute for Reference Materials and

MeasurementsGeel, Belgium

Effie.tsakalidouLaboratory of Dairy ResearchDepartment of Food Science and TechnologyAgricultural University of AthensAthens, Greece

Maria.Cristina.dantas.VanettiDepartamento de MicrobiologiaUniversidade Federal de ViosaViosa, Brazil

Eric.VerdonE.V. Community Reference Laboratory for

Antimicrobial and Dye Residues in Food from Animal Origin

Fougres, France

Vronique.Verrez-BagnisInstitut franais de recherche pour

lexploitation de la merNantes, France

M..Carmen.Vidal-CarouDepartment of Nutrition and BromatologyFaculty of PharmacyUniversity of BarcelonaBarcelona, Spain

Jian.wangCalgary LaboratoryCanadian Food Inspection AgencyCalgary, Alberta, Canada

ilex.whitingFood and Environment Research AgencyYork, United Kingdom

yiu.Chung.wongAnalytical and Advisory Service DivisionGovernment LaboratoryHomantin Government OfficeHong Kong, Peoples Republic of China

wai.yin.yaoAnalytical and Advisory Services DivisionGovernment LaboratoryHong Kong, Peoples Republic of China

Jiping.zhuChemistry Research DivisionSafe Environments ProgrammeHealth CanadaOttawa, Ontario, Canada

IMeat, ProCessed Meats and Poultry

3Chapter 1

Methods to Predict spoilage of Muscle Foods

GeraldineDuffy,AnthonyDolan,andCatherineM.Burgess

Contents1.1 Introduction ....................................................................................................................... 41.2 Culture-Based Methods ..................................................................................................... 5

1.2.1 Agar Plate Count Methods ..................................................................................... 51.2.2 Alternative Culture Methods .................................................................................. 5

1.3 Direct Epifluorescent Filtration Technique ......................................................................... 61.4 ATP Bioluminescence Methods ......................................................................................... 81.5 Electrical Methods ............................................................................................................. 91.6 Limulus Amoebocyte Lysate Assay ..................................................................................... 91.7 Spectroscopic Methods ......................................................................................................101.8 Developmental Methods ...................................................................................................10

1.8.1 Flow Cell Cytometry .............................................................................................111.8.2 Molecular Methods ...............................................................................................11

1.8.2.1 Polymerase Chain Reaction .....................................................................111.8.2.2 Fluorescent In Situ Hybridization ............................................................13

1.9 Electronic Nose .................................................................................................................141.10 TimeTemperature Integrators ..........................................................................................141.11 Conclusion ........................................................................................................................15References ..................................................................................................................................16

4 SafetyAnalysisofFoodsofAnimalOrigin

1.1 IntroductionAll animals, birds, fish, etc. contain a host of microorganisms in their intestinal tract and on their exposed outer skins, membranes, etc. During the slaughter and processing of the live organism into food, the muscle surface can become contaminated with microorganisms. Microbial con-tamination on the food and its composition/diversity is dependent on both the microbial load of the host organism and the hygiene practices employed during slaughter, processing, and distri-bution [1]. For example, during beef slaughter cross-contamination of microbial flora from the bovine hide, feces, and gut contents are recognized as the main cause of microflora on the beef car-cass [2]. Among the principal genera of bacteria that are present on postslaughter muscle surfaces are Pseudomonas spp., Acinetobacter spp., Aeromonas spp., Brochothrix thermosphacta, members of the lactic acid bacteria (LAB) such as Lactobacillus and Leuconostoc, as well as many members of the Enterobacteriaceae including Enterobacter and Serratia spp. [37].

From a microbiological standpoint, muscle foods have a particularly unique nutritional profile, with intrinsic factors such as a neutral pH, high water content, a high protein content, and fat pro-viding an excellent platform for microbial growth. Thus, during storage of muscle foods, favorable environmental conditions (temperature, pH, aw, etc.) will allow the microflora to grow. As the micro-organisms grow they metabolize the food components into smaller biochemical constituents, many of which emit unacceptable flavors, odors, colors, or appearance [6,8]. Spoilage may be defined as the time when the microorganisms reach a critical level, usually at around log10 78 colony forming units (CFU) g1, to induce sufficient organoleptic changes to render the food unacceptable to the consumer. The particular species of bacteria that contaminate the muscle, along with the environmental condi-tions, will determine the spoilage profile of the stored muscle food [5]. Under aerobic storage condi-tions, certain species of the genus Pseudomonas are generally considered to significantly contribute to spoilage. This is due to the organisms ability to utilize amino acids and grow well at refrigeration temperatures. Although it is a facultative anaerobe, under anaerobic conditions, the bacterium B. ther-mosphacta is considered a dominant member of the spoilage flora of meat products, producing lactic acid and ethanol as by-products of glucose utilization [9]. Recently, the use of modified atmosphere packaging (MAP) has gained popularity as a method of preservation. Gas mixtures containing vari-able O2 and CO2 concentrations are used to inhibit the growth of different spoilage-related bacteria. Under certain MAP conditions, lactic acid bacteria dominate and are prolific spoilers [10].

The storage period under a particular set of environmental conditions until the spoilage micro-flora reaches a threshold level is known as shelf life. To extend the shelf life of muscle foods, a range of procedures to prevent or retard microbial growth are deployed. When storing fresh muscle foods, where only chill storage temperatures (

MethodstoPredictSpoilageofMuscleFoods 5

which will give a predicted shelf life under a defined set of storage conditions. This chapter will review a selection of commonly used and emerging technologies that are used to directly or indi-rectly enumerate the total microbial load and predict spoilage.

1.2 Culture-Based MethodsMicrobial cultural assays are generally dependent on the growth of a microbial population to form colonies on an agar plate, which are visible to the analyst. Specific conditions such as temperature, moisture content, atmosphere, and nutrient availability on solid media (agar) are used to induce this growth.

1.2.1 Agar Plate Count MethodsThe gold standard method to assess microbial numbers remains the aerobic standard plate count (SPC). This cultural method has been widely and successfully used for many years in the food, pharmaceutical, and medical sectors. Serial dilutions of the sample material are prepared, plated onto agar (plate count agar), and incubated under specific conditions. When visible colonies appear, the number of CFU per gram of food can be readily calculated. The Association of Official Analytical Chemists (AOAC) Official Method 966.23 [11] and the International Organization for Standardization (ISO) (No. 4833:2003) [12] have standardized the test protocol. All alternative methods must generally be correlated or validated against these methods.

Although gold standard indicates the method is perfect, there are in fact some drawbacks to the method. The SPC result is often referred to as total viable count implying that all viable microorganisms will be incorporated in results of the assay. This is not so, as certain microorgan-isms, referred to as viable but nonculturable (VBNC) [13], may have growth requirements not met by the incubation conditions. The failure of the assay to account for these organisms may lead to an underestimation of the true microbial load. From a practical perspective the method is also very slow and labor intensive, requiring 3 days for the colonies to form and thus, a result to be obtained. For products with a short shelf life this delay is very impractical and a product may be in retail distribution before microbial counts are obtained.

1.2.2 Alternative Culture MethodsThere are alternative agar-based methods, such as Petrifilm (3M Microbiology Products, USA), that are AOAC accredited (Method 990.12) [14] and show comparable counts to SPC for a wide variety of meat samples [15]; although some problems have been noted [16]. Another product, SimPlate (IDEXX Labs Inc., USA), has also been applied to meat muscle with relative success and is an approved AOAC method [17,18].

There is also an automated method based on a liquid mediabased most probable number (MPN) technique (TEMPO, bioMriuex, France). The system is based on wells containing a traditional culture media formula with a fluorescent indicator. Each well corresponds to an MPN dilution tube. Once the sample is distributed in the wells, the microorganisms metabolize the culture media producing a fluorescent signal. The system uses an MPN calculation to assess the number of microorganisms in the original sample. Apart from the obvious advantage that this type of automated instrumentation offers, the TEMPO system has a reduced incubation time (48 h) compared with the ISO SPC method that takes 3 days. When applied to meat samples, the technology shows a high correlation with the SPC (r = .99) [19].


1.3 direct epifluorescent Filtration techniqueAn alternative approach to culture is to directly extract the microorganisms from the muscle food by membrane filtration. When concentrated onto the membrane surface, the microorganisms can be stained using a fluorescent dye and the cells then detected and enumerated using epifluorescent microscopy.

The first step in this direct epifluorescent filtration technique (DEFT) is the use of membrane filtration to recover the bacteria from the food and this step poses some challenges in relation to muscle foods. When membrane filtration is used to recover microorganisms from muscle foods, they must be first placed in a liquid media and homogenized, stomached, or pulsified (Microgen Bioproducts, UK) [20] to remove the bacteria from the food surface or matrix into the liquid dilu-ent. A problem encountered is that food particles in the liquid have a tendency to clog the pores of the membrane during filtration. This may mean that the required volume cannot be filtered and that any food debris on the membrane can interfere with the enumeration of bacterial cells. Some approaches to improve filterability of muscle foods have been employed to physically or chemically remove as much of the food suspension as possible before filtration. These have included the use of low-speed centrifugation, appropriate surfactants such as Tween 80 and sodium dodecyl sulfate (SDS), and the proteolytic enzyme, Alcalase [21].

Once the microorganisms are concentrated onto the membrane surface, the membrane is over-laid with a fluorescent dye such as acridine orange and mounted on a glass slide. The microorgan-isms are viewed using fluorescent microscopy, and the total numbers of organisms in a defined number of fields of view are counted. The microscopic count is used to predict the gold standard plate count using a calibration curve relating the DEFT count to the aerobic plate count.

DEFT (Figure 1.1) has been applied to the estimation of microbial numbers in a range of muscle foods (Table 1.1). Although acridine orange is the most commonly used fluorescent dye, it

Figure 1.1 Flow diagram of a direct epifluorescent filtration technique (deFt) for enumeration of microorganisms from muscle foods.

Pretreatments,i.e., centrifugation,

prefiltration,chemicals,

enzymes, etc.

Filter supernatantthrough membrane(0.2 m0.8 m) and

stain cells withfluorescent dye

Slide

Sample and buffer

Count cellsmanually or by

computerimage

analysis

Membrane


table 1.1 Correlation of direct epifluorescent Filtration technique (deFt) with the standard aerobic Plate Count (sPC) for enumeration of Microorganisms in a range of Muscle Foods

MuscleFood

TreatmentofSamplebeforeFiltrationthroughMembrane(0.40.8m)

FluorescentDye

CorrelationwithSPC Reference

Freshmeat Stomached2min AcridineOrange

r=.91 [97]

Cannedhams Stomached2min,prefiltrationthroughglassmicrofiberfilter

AcridineOrange

Poor [98]

Rawgroundbeef

Stomached2min,prefilteredthroughnylonfilter,TritonX,andbactotrypsin

AcridineOrange

r=.79 [99]

Rawbeefpieces Stomached30s,prefilteredthroughglassmicrofiberfilter,TritonX

AcridineOrange

r 2=.91 [100]

Rawporkmince Stomached30s,Tween80,SDS,Alcalase0.6L

AcridineOrange

r=.97 [101]

Rawbeefmince Stomached30s,low-speedcentrifugation,Tween80,SDS,Alcalase0.6L

AcridineOrange

r=.97 [102]

Lambcarcasses Stomached30s,low-speedcentrifugation,Tween80,SDS,Alcalase2.5L

AcridineOrange

r 2=.87 [103]

Mincedbeef Stomached30s,low-speedcentrifugation,Tween80,SDS,Alcalase2.5L

AcridineOrange

r 2=.97 [21]

Processedmeat(mincedbeef,cookedham,baconrashers,frozenburgers)

Stomached30s,low-speedcentrifugation,Tween80,SDS,Alcalase2.5L

BacLight r2=(.90,.87,.82,.80)

[22]

does not distinguish between live and dead cells and so may overestimate the bacterial load in pro-cessed meat samples containing large numbers of dead cells. To overcome this problem, a viability stain BacLight (Molecular Probes Inc., The Netherlands) was reported to successfully distinguish between live and dead cells and in a DEFT gave a good correlation with the SPC for microorgan-isms in processed meat (r 2 = .87.93) [22]. The DEFT takes approximately 1520 min to analyze one sample and so at most 20 samples can be analyzed manually in a working day.

The DEFT has been successfully automated for high throughput enumeration of microorgan-isms in milk samples [23]. Commercial systems for analysis of milk include the Bactoscan (Foss,


Denmark) and Cobra systems (Biocom, France). However, the DEFT has not been automated for muscle foods. This has hugely impacted its uptake commercially by this industry sector as, apart from the small number of samples that can be analyzed manually daily, the approach is labor intensive and requires significant operator skills. Manual enumeration is particularly dif-ficult when there are very high or low numbers of microorganisms on the slide or when there is particulate debris on the slide. Future developments to make this approach commercially suit-able for muscle foods may incorporate the initial membrane filtration approach to extract the microorganisms with an automated detection system. A solid-phase cytometry method has been proposed by DHaese and Nelis [24] and could use a laser beam to detect microorganisms recov-ered onto a membrane filter. This method would potentially be very rapid and automated but a potential problem could arise from any food debris remaining on the membrane surface.

1.4 atP Bioluminescence MethodsEnzyme-mediated light production, bioluminescence, is a widespread phenomenon in nature [25]. Bioluminescent organisms are widely distributed throughout the oceans and include bacteria, sea anemones, worms, crustaceans, and fish. Fireflies and glow worms are the best-known terrestrial organisms producing light. The principles of firefly bioluminescence were discovered over 40 years ago [26]. In the firefly bioluminescence reaction, adenosine 5-triphosphate (ATP) reacts with the enzyme luciferase and the substrate luciferin producing a photon of light. ATP is a high-energy substance found only in living cells. It takes part in all metabolic pathways and therefore its con-centration in all cells including bacterial cells is strictly regulated. When luciferin and luciferase are added to a cell suspension the amount of light emitted is proportional to the amount of ATP present. The amount of light can be measured using a photometer to give an indirect indication of the microbial population density.

Luciferin ATP LuciferaseMg AMP CO pyrophosphate ox+ + + + ++2 2 yyluciferin photon+

The firefly bioluminescence reaction has been exploited as a rapid and sensitive method for mea-suring cell numbers, including microbial cells.

The ATP bioluminescent assay has been widely applied to assess hygiene, based on detection of all ATP present [27,28]. However, a major problem in the use of bioluminescence to predict the microbial SPC of foods is interference from nonmicrobial ATP. If an accurate estimation of the microbial load is to be obtained, nonmicrobial somatic ATP must be destroyed before the bioluminescence test is carried out. The most common approach is the enzymatic destruction of nonmicrobial ATP, followed by release and estimation of residual ATP from the microbial cells [29,30]. Another approach is to separate the microorganisms from the rest of the material and esti-mate the ATP in the microbial fraction. Stannard and Wood [31] used this approach to estimate bacterial numbers in minced beef. The results show a linear relationship (r = .94) between colony counts and microbial ATP content in raw beef. An ATP bioluminescence test was shown by Siragusa et al. [32] to be an adequate means to assess the microbial load of poultry carcasses. This assay utilized differential extraction and filtration to separate somatic ATP from microbial ATP in a very rapid time frame. The assay required approximately 5 min to complete: approximately 3.5 min to sample and 90 s analytical time. The correlation coefficient (r) between aerobic colony counts and the ATP test was .82. Ellerbroek and Lox [33] used an ATP bioluminescence approach


to investigate the total bacterial counts on poultry neck and carcasses. The correlation between the bioluminescence method and the total viable counts of neck skin samples was r = .85, whereas a lower correlation was reported between the bioluminescence count and the total viable counts on the carcass (r = .66).

Commercially available bioluminescent systems include Celsis (Celsis International plc., UK) and Bactofoss (Foss) but their application to date has been aimed at hygiene testing and liquid foods rather than muscle foods.

1.5 electrical MethodsElectrical methods for assessing bacterial numbers include impedance and conductance. Imped-ance is the opposition to flow of an alternating electrical current in a conducting material [34]. The conductance of a solution is the charge carrying capacity of its components and capacitance is the ability to hold a charge [34].

When monitoring the growth of microorganisms, the conducting material is a microbiologi-cal medium. As microorganisms grow they utilize nutrients in the medium, converting them into smaller more highly charged molecules, for example, fatty acids, amino acids, and various organic acids [35]. If electrodes are immersed in the medium and an alternating current is applied, the metabolic activity of the microorganisms results in detectable changes in the flow of current. Typi-cally, impedance decreases while conductivity and capacitance increase [35]. When the microbial population reaches a threshold of 106107 CFU mL1 an exponential change in impedance can be observed [34]. The elapsed time until this exponential change occurs is defined as impedance detection time and is inversely proportional to the initial microbial numbers in the sample.

The most commonly used application of impedance is shelf-life testing. This test determines whether a sample contains above or below a predetermined concentration of microorganisms. Impedance testing has been used in conjunction with a calibration curve with the SPC for a num-ber of products including raw milk (r = .96) [36], frozen vegetables (92.6% agreement between methods) [37] and meat [38]. A conductance method was used to predict microbial counts on fish [39] with a correlation of r = .92 to .97 using brain heart infusion.

Of all developed alternative methods to predict microbial load, the impedance technique has been most widely accepted within the food industry. Commercially available automated systems include the Malthus (Malthus Instruments Ltd., UK) system, which measures conductance, and the Bactometer (Bactomatic Inc., USA) system, which can measure impedance, conductance, and capacitance. Both systems can measure several hundred samples simultaneously and have detection limits of 1.0 CFU mL1. Using these systems to predict the SPC count on meat, correlations of r = .83 and r = .80 were reported for the Bactometer and Malthus machines, respectively [40]. In the muscle food sector, uptake has been in the processing sector rather than for raw foods.

1.6 Limulus amoebocyte lysate assayGram-negative bacteria are important food spoilage organisms in muscle foods [41]. They dif-fer from gram-positive bacteria in that their cell wall contains lipopolysaccharides (LPS). Based on this difference a Limulus amoebocyte lysate (LAL) assay method that targets LPS has been developed. LPS contains an endotoxin that activates a proteolytic enzyme found in the blood cells (amoebocytes) of the horseshoe crab (Limulus polyphemus). The enzyme activates a clotting reaction, which results in gel formation. The concentration of LPS is determined by making serial


dilutions of the sample and noting the greatest dilution at which a gel is formed within a given time [41]. The reaction has been used to develop a colorometric assay.

LAL has been applied to the evaluation of microbial contamination on pork carcasses [42]. Although the test correlated well with coliform numbers, it did not correlate well with total numbers of organisms, indicating its limited usefulness as a spoilage indicator. However, more recently a chromogenic LAL was reported by Siragusa et al. [43] to rapidly predict microbial contamination on beef carcasses. A high correlation (r 2 = .90) was reported with the standard aerobic plate count.

1.7 spectroscopic MethodsVarious spectroscopic methods have been proposed as rapid, noninvasive methods for the detection of microbial spoilage in muscle foods. Such methods are based on the measurement of biochemical changes that occur in the meat as a result of the decomposition and formation of metabolites caused by the growth and enzymatic activity of microorganisms, which eventually results in food spoilage.

Fourier transform infrared (FT-IR) spectroscopy involves the observation of vibrations in mol-ecules when excited by an infrared beam. An infrared absorbance spectrum gives a fingerprint-like spectral signature, which is characteristic of any chemical or biochemical substance [44]. Such a method is therefore potentially useful to measure biochemical changes in muscle foods due to microbial growth and could be used as an indicator for spoilage. FT-IR spectroscopy has been suc-cessfully employed to discriminate, classify, and identify microorganisms. Some examples include discrimination between Alicyclobacillus strains associated with spoilage in apple juice [45], the dis-crimination of Staphylococcus aureus strains from different staphylococci [46], and the setting up of a spectral database for the identification of coryneform strains [47]. Mariey et al. [48] provide a review of many other characterization methods using FT-IR. This also gives an overview of the statistical methods used to interpret spectroscopic data.

In addition to these discriminatory uses, FT-IR has shown promise for use as a spoilage detec-tion method. FT-IR has been used to predict spoilage of chicken breasts in a rapid, reagent-less, noninvasive manner [49]. The metabolic snapshot correlated well with the microbial load. Ellis et al. [50] applied FT-IR to predict microbial spoilage of beef; although the correlation with the microbial load was less accurate than for poultry.

Another spectroscopic method that has been used in recent times for detection of microbial spoil-age is short-wavelength-near-infrared (SW-NIR) diffuse reflectance spectroscopy (6001100 nm). It has the advantage over FT-IR in that it is useable through food packaging and can be used to examine bulk properties of a food due to its greater pathlength [51]. This technique was applied to predict spoilage of chicken breast muscle and the results showed that SW-NIR could be used in a partial least squares model to predict microbial load [51]. Lin et al. [52] have used this technique with success in predicting spoilage of rainbow trout.

1.8 developmental MethodsThere are a number of emerging methods and technologies that are being shown to be suitable for the rapid and specific identification or enumeration of microorganisms from clinical or liquid samples. Although most have not yet been applied to predict the total microbial flora or spoilage of muscle foods, they have the potential with further development to be applied in the future. Some of these technologies are summarized in the following sections.


1.8.1 Flow Cell CytometryFlow cell cytometry is a technique that can be used to detect and enumerate cells as they are passed on an individual cell basis, suspended in a stream of fluid, past a laser beam. A flow cytometer typically has several key components including a light or excitation source, a laser that emits light at a particular wavelength, and a liquid flow that moves liquid-suspended cells through the instru-ment past the laser and a detector, which is able to measure the brief flashes of light emitted as cells flow past the laser beam. Thus, individual cells can be detected and counted by the system. The technique has been successfully applied to the enumeration of microorganisms in raw milk [53] and milk powder [54], but it has not yet been applied to muscle foods. As described in Sec-tion 1.3, a solid-phase cytometry method could potentially be applied to muscle foods, based on the assumption that the microorganisms could be successfully extracted from the food onto a filter and the filter then scanned by a laser beam [24].

1.8.2 Molecular MethodsMajor advances in biotechnology have rapidly progressed the use of genetic tools for microbial detection. In particular, developments in the level of genomic information available for foodborne pathogens have been widely exploited in methods to detect and genetically characterize microor-ganisms. Genetic tools are now commonly used to detect specific pathogens or groups of spoilage microorganisms. However, to date the use of molecular technology to detect and enumerate, in a single assay, all microorganisms in a food sample is limited by the huge diversity of microorgan-isms likely to be present and identification of a common gene target present in all the foodborne microorganisms. The use of the 16S ribosomal RNA (rRNA) gene has been reported for this pur-pose [55]. If technological complexity can be overcome, this approach has enormous potential as a very rapid and specific test to predict spoilage in muscle foods.

1.8.2.1 PolymeraseChainReaction

Nucleic acid methods that include an amplification step for the target DNA/RNA are now rou-tinely employed in molecular biology. These amplification methods can increase the target nucleic acid material more than a billion fold and are particularly important in the arena of food micro-biology where one of the major hurdles is the recovery and detection of very low numbers of a particular species. The most popular method of amplification is the polymerase chain reaction (PCR) technique (Figure 1.2). In PCR, a nucleic acid target (DNA) is extracted from the cell and denatured into single-stranded nucleic acid. An oligonucleotide primer pair specific for the selected gene target, along with an enzyme (usually Taq polymerase, a thermostable and thermo-active enzyme originally derived from Thermus aquaticus) in the presence of free deoxynucleoside triphosphates (dNTPs), is used to amplify the gene target exponentially, resulting in a double replication of the starting target material. This reaction is carried out in an automated, program-mable block heater called a thermocycler, which provides the necessary thermal conditions needed to achieve amplification. Following amplification, the PCR products are separated by gel electro-phoresis, stained with ethidium bromide, and visualized using ultraviolet light. This type of PCR, sometimes referred to as conventional PCR, can be used for the identification of specific groups of spoilage bacteria in meat including lactic acid bacteria [56,57].

A quantitative method using electrochemiluminescence to measure the PCR product was applied to predict the spoilage bacterial load on aerobically stored meat [58]. The correlation of


this method with the SPC was r = .94. Gutierrez et al. [55] combined conventional PCR with an enzyme-linked immunosorbent assays (ELISA) to allow enumeration of microorganisms. On applying this technique to the detection of the microbial load in meat samples, a good correlation was achieved between the SPC counts and the PCR-ELISA (r = .95). The authors did express con-cerns about the complexity of the assay and about its suitability as a routine assay.

Recently, a more advanced quantitative PCR technology, in the form of real-time PCR (RT-PCR), has entered and revolutionized the area of molecular biology [59]. RT-PCR allows continuous monitoring of the amplification process through the use of fluorescent double-stranded DNA intercalating dyes or sequence-specific probes [60]. The amount of fluorescence after each amplification cycle can be measured and visualized in real time on a computer monitor attached to the RT-PCR machine. A number of dye chemistries have been reported for use in RT-PCR, from DNA-binding dyes such as SYBR Green (Molecular Probes Inc.) to more complex fluorescent probe technologies such as TaqMan (Roche Molecular Systems, UK), molecular beacons, and HybProbes (Roche Molecular Systems) [61]. Whatever signal chemistry is used, RT-PCR not only allows quick determination of the presence/absence of a particular target, but can also be used for the quantification of a target that may then be related to microbial counts.

To quantify microorganisms by RT-PCR, a set of standards of known concentration must first be analyzed. The standards may be of known CFU per milliliter or known gene copy number and then related to the CT (threshold cycle) of the reaction to generate a standard curve, which can be used to quantify unknown samples. An important factor to be considered when quantifying bacteria is that relying on a DNA-based RT-PCR will lead to a count that comprises of live, dead, and VBNC bacteria, which could potentially lead to an overestimation of the numbers present. A way to overcome this is by coupling RT-PCR with reverse transcription. This technique tran-scribes RNA (present only in viable cells) into complementary DNA (cDNA), which can then be employed in a RT-PCR reaction. Because the cDNA originates from RNA the quantification will be based on viable cells only, leading to a more accurate determination of the number of metaboli-cally active bacteria.

Figure 1.2 diagram showing the main events in a typical polymerase chain reaction (PCr).

TaqTaq

DNA double helix

Denaturation: 95C. DNAstrands separate

Annealing: 45C60C. Specificoligonucleotide primers bindto strands allowing Taqpolymerase enzyme to bind

TaqTaq

Extension: 72C. Taq enzymesynthesizes a second DNAstrand. Successive rounds ofdoubling will produce >1 109copies


To date, RT-PCR has been mainly used for the sensitive and rapid detection of a wide range of pathogens, such as Salmonella spp., Escherichia coli, and Listeria on meat [6265], and a multiplex assay that has the ability to detect more than one pathogen at the same time has been described [66]. The quantitative feature of RT-PCR has been examined for the enumeration of the spoilage organism Lactobacillus sakei in meat products, and the application of a live staining method in combination with real-time technology for quantitative analysis has been reported using model organisms [67,68]. There is huge potential for this technology to quantify total microorganisms using an RNA gene target common to all microflora likely to be present.

1.8.2.2 FluorescentInSitu Hybridization

In situ hybridization (ISH) using radiolabeled DNA was first reported by Pardue and Gall [69] and John et al. [70] for direct examination of cells. It was applied to bacteria for the first time in 1988 [71], and with the advent of fluorescent labels the technique became more widely used [72]. Fluorescent in situ hybridization (FISH) is a technique that specifically detects nucleic acid sequences in a cell using a fluorescently labeled probe that hybridizes specifically to its comple-mentary target gene within the intact cell. The target gene is the intercellular rRNA in the micro-organism as these genes are relatively stable, occur in high copy numbers, and have variable and conserved sequence domains, which allows for the design of discriminatory probes either specific to an individual species or to particular genera [73]. FISH generally involves four steps, fixa-tion of the sample, permeabilization of cells to release the nucleic acids, hybridization with the fluorescent labeled probe, and detection by fluorescent microscopy. Traditionally, FISH methods have been implemented using DNA oligonucleotide probes. A typical oligonucleotide probe is between 15 and 30 base pairs in length. Short probes have easier access to the target but also may have fewer labels [74]. There are a number of ways in which probes can be labeled. Direct labeling is most commonly used where the fluorescent molecule is directly bound to the oligonucleotide either chemically during synthesis or enzymatically using terminal transferase at the 3-end. This method is considered to be the fastest, cheapest, and most convenient [75]. Sensitivity of FISH assays can be increased using indirect labeling, where the probe is linked to a reporter molecule that is detected by a fluorescent antibody [76] or where the probe is linked to an enzyme and a fluorescent substrate can be added [77]. A more recent development in probe technology is the development of peptide nucleic acid (PNA) probes. PNAs are uncharged DNA analogs in which the negatively charged sugarphosphate backbone is replaced by an achiral sugarphosphate back-bone formed by repetitive units of N-(2-aminoethyl) glycine [78]. PNA probes can hybridize to target nucleic acids more rapidly and with higher affinity and specificity than DNA probes [79].

FISH technology has been applied to the detection of bacterial pathogens in clinical and food samples [80,81] and can allow direct identification and quantification of microbial species. A FISH assay has been developed for the Pseudomonas genus, which is important in milk spoilage, allowing for the specific detection and enumeration of this group of organisms in milk much more rapidly than a cultural method [82]. In the wine industry, lactic acid bacteria can be detrimental or beneficial depending on the species, and when they develop in the process. A FISH technique has been described as the one that utilizes probes to differentiate between different LAB genera so that it is possible to identify potential spoilage strains from the species responsible for successful fermentation [83]. FISH technology has potential as a method to enumerate all microorganisms in a food sample using a common gene target but could be limited in its uptake by its current reliance on microscope-based detection.


1.9 electronic noseIt is well known that microorganisms produce a range of volatiles as they grow on food and can be used to identify particular species of microorganisms that have a unique volatile fingerprint or potentially, to determine the total level of microbial contamination on a food and predict spoilage. Gardener and Bartlett [84] defined an electronic nose as an instrument which comprises an array of electronic, chemical sensors with partial specificity and an appropriate pattern recognition sys-tem, capable of recognising simple or complex odours. An electronic nose normally consists of a vapor-phase flow over the sensor, interaction with the sensor, and analyses of the interaction using computer software. The field of sensor development is highly active and includes a range of sensor types based on metal oxide, metal oxide silicon, piezoelectric, surface acoustic waves, optical, and electrochemical premises [85].

Blixt and Borch [86] investigated the use of an electronic nose to predict the spoilage of vacuum-packaged beef. The volatile compounds were analyzed using an electronic nose contain-ing a sensory array composed of 10 metal oxide semiconductor field-effect transistors, four Tagushi type sensors, and one CO2-sensitive sensor. Two of the Tagushi sensors performed best and cor-related well with evaluation of spoilage by a sensory panel. They did not attempt to correlate the results with microbial counts.

Du et al. [87] used an electronic nose (AromaScan) to predict spoilage of yellowfin tuna fish. The change in fish quality as determined by AromaScan (AromaScan plc., UK) followed increases in microbiological counts in tuna fillets, indicating that electronic nose devices can be used in conjunction with microbial counts and sensory panels to evaluate the degree of decomposition in tuna during storage.

1.10 timetemperature IntegratorsOne of the key contributors to the spoilage of fresh muscle foods is a breakdown in the chill chain during distribution. The prediction of spoilage and the application of an optimized qual-ity and safety assurance scheme for chilled storage and distribution of fresh meat and meat products would be greatly aided by the continuous monitoring of temperature during distribu-tion and storage.

A timetemperature integrator (TTI) is defined as a small, inexpensive device that can be incorporated into a food package to show a visible change according to the time and temperature history of the stored food [88]. TTIs are devices that contain a thermally labile substance, which can be biological (microbiological or enzymatic), chemical, or physical. Of these groups, biologi-cal TTIs are the best studied. TTIs can be used to determine both whether a heat treatment has worked effectively and whether temperature abuse has occurred during storage. Different types of TTIs have been used for determining the effectiveness of heat treatment. -Amylase from Bacillus species has been evaluated in a number studies [8891] and a recent report describes the use of amylase from the hyperthermophile Pyrococcus furiosus as a sterilization TTI [92].

TTIs can be used to monitor temperature abuse during storage, transportation, and handling and thus to monitor such abuses that may lead to a shortened shelf life and spoilage. This type of TTI must (1) be easily activated and sensitive; (2) provide a high degree of precision; (3) have tamper-evident characteristics; (4) have a response that is irreversible, reproducible, and correlated with food quality changes; (5) have determined physical and chemical characteristics; and (6) have an easily readable response [93].


Giannakourou et al. [94] demonstrated that TTI readings using a commercially available enzyme could be adequately correlated to the remaining shelf life of the product (in this case marine-cultured gilt-head seabream) at any point of its distribution. The same TTI has also shown positive results for fresh chicken storage [95]; although the TTI predictions would be inaccurate following an extreme instance of temperature abuse, with the TTI indicator changing color before the product had actually spoiled [96].

Numerous timetemperature indicators are commercially available for all types of food stuffs. However, it is necessary to validate the TTI of choice with the product and process of choice before correlations can be made between the TTI and the potential for spoilage of the product.

1.11 ConclusionMuscle foods pose considerably more challenges than other foods for development and successful application of spoilage detection methods. These include a highly complex food tissue matrix in which the microorganisms may be embedded and strongly attached, and from which they must be detached to detect and enumerate the microorganisms. This means the food must generally be placed in a liquid diluent and then physically manipulated to release the microorganisms into the liquid. This dilution effect obviously creates a need for a more sensitive detection method than a sample to which the method could be applied directly, such as a liquid food (i.e., milk). In addi-tion, the microflora is generally quite diverse and the dominant flora is very much dependent on the storage environment. At the early stage of the food process the levels of microorganisms on the raw muscle food may be as low as log 2.0 CFU g1, thus posing additional challenges for the sensitivity of the detection method.

The gold standard method to predict spoilage remains the aerobic SPC, but it is still required by the industry that alternative methods are validated against this method. However, as previ-ously described in this chapter, the SPC is far from perfect and more rapid methods to predict spoilage are urgently needed by the muscle foods sector (fish, meat, and poultry industries). These alternative spoilage methods must generally give results that are comparable and validated against gold standard cultural microbial methods. The methods must be sensitive, rapid, suited to online use, and at least semiautomated. They must be suited to routine use, without the need for highly skilled operators, as high staff turnover is often a major issue in the muscle food industry, and it is neither practical nor possible to keep retraining staff to carry out a test that is highly complex.

The muscle food sector has undoubtedly been the slowest sector in the food industry to take on board alternative technologies for spoilage prediction. However, they are now being com-pelled by their customers, regulatory authorities, and consumers to more accurately predict shelf life. This is even more pertinent with the continued market move toward chilled prepared foods with minimal preservatives and a short shelf life. Although there has been much research and development in the area of rapid spoilage detection methods, recent research on rapid microbial methods has tended to focus more on methods for identification of specific species of microor-ganisms rather than on the total microbial load. However, some of the emerging technologies developed, albeit for other applications, have enormous potential to be further developed for enumeration of the total microbial load and to predict spoilage. More research efforts should now be refocused in this direction using the newer technologies to overcome the hurdles that have to date prevented the widespread uptake of rapid methods to predict spoilage by the muscle food sector.


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