Yam, Kit L. (ed.). The Wiley encyclopedia of packaging technology

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migration limit (44). The plastic packaging material

should also have met overall migration limit, typically

60 mg per kg food. In 2004, active and intelligent packa-

ging including antimicrobial food packaging has been

allowed in the EC Framework Regulation 1935/2004.

Further detailed requirements and speciﬁcation on adopt-

ing antimicrobial packaging will soon come out, to include

positive lists of authorized substances and/or materials

and articles.

General food packaging regulations in Japan made

allowances for active packaging emitting ethanol and

wasabi extract volatiles to preserve the bakery products

and prepared foods. Australia and New Zealand legisla-

tion permits silicon dioxide sachets containing ethanol

and ﬂavors speciﬁcally (45), which may act to inhibit

microbial growth.

BIBLIOGRAPHY

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7. J. Han, M. E. Castell-Perez, and R. G. Moreira, ‘‘The Inﬂuence

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9. J. H. Han and J. D. Floros, ‘‘Simulating Diffusion Model and

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10. J. H. Choi, W. Y. Choi, D. S. Cha, M. J. Chinnan, H. J. Park,

D. S. Lee, and J. M. Park, ‘‘Diffusivity of Potassium Sorbate in

Kappa-Carrageenan Based Antimicrobial Film,’’ LWT-Food

Sci. Technol. 38, 417–423 (2005).

11. P. Appendini and J. H. Hotchkiss, ‘‘Review of Antimicrobial

Food Packaging,’’ Innov. Food Sci. Emerg. Technol. 3, 113–126

(2002).

12. T. Tashiro, ‘‘Antibacterial and Bacterium Adsorbing Macro-

molecules,’’ Macromol. Mater. Eng. 286, 63–87 (2001).

13. T. J. Franklin and G. A. Snow, Biochemistry of Antimicrobial

Action, Chapman and Hall, New York, 1989, pp. 55–72.

14. S. P. Denyer and G. S. A. B. Stewart, ‘‘Mechanisms of Action of

Disinfectants,’’ Int. Biodeterior. Biodegrad. 41, 261–268

(1998).

15. Y. M. Weng, M. J. Chen, and W. Chen, ‘‘Antimicrobial Food

Packaging Materials from Poly(ethylene-co-methacrylic

Acid),’’ LWT-Food Sci. Technol. 32, 191–195 (1999).

16. S. Min and J. M. Krochta, ‘‘Edible Coating Containing Bioac-

tive Antimicrobial Agents’’ in J. H. Han, ed., Packaging for

Nonthermal Processing of Food, Blackwell Publishing, Ames,

IA, 2007, pp. 29–52.

17. H. Pehlivan, D. Balkose, S. Ulku and F. Tihminlioglu, ‘‘Char-

acterization of Pure and Silver Exchanged Natural Zeolite

Filled Polypropylene Composite Films,’’ Compos. Sci. Technol.

65, 2049–2058 (2005).

18. A. L. Brody, E. R. Strupinsky, and L. R. Kline, Active Packa-

ging for Food Applications, CRC Press, Boca Raton, FL, 2001,

pp. 131–194.

19. FDA/CFSAN web information, ‘‘Summary of Color Additives

Listed for Use in the US,’’ Available at http://www.foodsafe-

ty.gov/Bdms/opa-col2.html, Accessed October 9, 2008.

20. Y. Kikuchi, K. Sunada, T. Iyoda, K. Hashimoto, and A.

Fujishim

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Bactericidal Effect of TiO

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51–56 (1997).

21. C. Chawengkijwanich and Y. Hayata, ‘‘Development of TiO2

Powder-Coated Food Packaging Film and Its Ability to In-

activate Escherichia coli in Vitro and in Actual Tests,’’ Int. J.

Food Microbiol. 123, 288–292 (2008).

22. M. Cho, Y. Choi, H. Park, K. Kim, G. J. Woo, and J. Park,

‘‘Titanium Dioxide/UV Photocatalytic Disinfection in Fresh

Carrots,’’ J. Food Prot., 70, 97–101 (2007).

23. A. S. Naidu, ‘‘Overview’’ in A. S. Naidu, ed., Natural

Food Antimicrobial Systems, CRC Press, New York, 2000,

pp. 1–16.

24. L. V. Thomas, M. R. Clarkson and J. Delves-Broughton,

‘‘Nisin’’ in A.S. Naidu, ed., Natural Food Antimicrobial Sys-

tems, CRC Press, New York, 2000, pp. 463–525.

25. K. Cooksey, ‘‘Antimicrobial food packaging materials,’’ Addi-

tives for Polymers 2001, 6–10 (2001).

26. P. Blackburn, J. Polak, S.-A. Gusik, and S. D. Rubino, ‘‘U.S.

Patent 5,753,614, Nisin compositions for Use as Enhanced,

Broad Range Bactericides,’’ 1998.

27. J. L. Grower, K. Cooksey, and K. J. K. Getty, ‘‘Development

and Characterization of an Antimicrobial Packaging Film

Coating Containing Nisin for Inhibition of Listeria monocy-

togenes,’’ J. Food Prot., 67, 475–479 (2004).

28. T. Padgett, I. Y. Han, and P. L. Dawson, ‘‘Incorporation of

Food-Grade Antimicrobial Compounds into Biodegradable

Packaging Films,’’ J. Food Prot. 61, 1330–1335 (1998).

29. V. Coma, ‘‘Bioactive Packaging Technologies for Extended

Shelf Life of Meat-Based Products,’’ Meat Sci. 78, 90–103

(2008).

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Naidu, ed., Natural Food Antimicrobial systems, CRC Press,

New York, 2000, pp. 185–210.

31. J. H. Han, ‘‘Antimicrobial Packaging Systems’’ in J. H. Han,

ed., Innovations in Food Packaging, Elsevier Ltd., Amster-

dam, 2005, pp. 80–106.

32. C- .M.Gu

c- bilmez, A. Yemeniciog

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microbial and Antioxidant Activity of Edible Zein Films

Incorporated with Lysozyme, Albumin Proteins and Diso-

dium EDTA,’’ Food Res. Int. 40, 80–91 (2007).

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Nobile, ‘‘Development of Immobilized Lysozyme Based Active

Film,’’ J. Food Eng. 78, 741–745 (2007).

58 ANTIMICROBIAL PACKAGING

34. A. Smith-Palmer, J. Stewart, and L. Fyfe, ‘‘Antimicrobial

Properties of Plant Essential Oils and Essences Against

Five Important Food-Borne Pathogens,’’ Lett. Appl. Microbiol.

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ism of Allyl Isothiocyanate, J. Food Prot. 63, 727–734 (2000).

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cyanate and Its Transport in PVDC/PVC Copolymer Packa-

ging Film,’’ J. Food Sci. 62, 1061–1066 (1997).

37. R. D. Joerger, Antimicrobial Films for Food Applications: A

Quantitative Analysis of Their Effectiveness,’’ Packag. Tech-

nol. Sci. 20, 231–273 (2007).

38. J. Vartiainen, E. Skytta, J. Enqvist, and R. Ahvenainen,

‘‘Properties of Antimicrobial Plastics Containing Traditional

Food Preservatives,’’ Packag. Technol. Sci. 16, 223–229 (2003).

39. K. Cooksey, ‘‘Effectiveness of Antimicrobial Food Packaging

Materials,’’ Food Addit. Contam. 22, 980–987 (2005).

40. A. Jofre, T. Aymerich, and M. Garriga, ‘‘Assessment of the

Effectiveness of Antimicrobial Packaging Combined with

High Pressure to Control Salmonella sp in Cooked Ham,’’

Food Control 19, 634–638 (2008).

41. C. H. Lee, H. J. Park, and D. S. Lee, ‘‘Inﬂuence of Antimicro-

bial Packaging on Kinetics of Spoilage Microbial Growth in

Milk and Orange Juice,’’ J. Food Eng. 65, 527–531 (2004).

42. L. Vermeiren, F. Devlieghere, and J. Debevere, ‘‘Effectiveness

of Some Recent Antimicrobial Packaging Concepts,’’ Food

Addit. Contam. 19, 163–171 (2002).

43. S. Quintavalla and L. Vicini, ‘‘Antimicrobial Food Packaging

in Meat Industry,’’ Meat Sci. 62, 373–380 (2002).

44. N. D. de Kruijf and R. Rijk, ‘‘Legislative Issues Relating to

Active and Intelligent Packaging’’ in R. Ahvenainen, ed.,

Novel Food Packaging Techniques, CRC Press LLC, Boca

Raton, FL, 2003, pp. 459–496.

45. N. Waite, Active Packaging, Pira International, Surrey, Uni-

ted Kingdom, 2003, pp. 1–110.

APPLICATIONS OF PREDICTIVE

MICROBIOLOGY TO FOOD PACKAGING

SHIOWSHUH SHEEN

Eastern Regional Research

Center, Microbial Food Safety

Research Unit, Agricultural

Research Service, U.S.

Department of Agriculture,

Wyndmoor, Pennsylvania

INTRODUCTION

This section is to introduce the currently available pre-

dictive microbiology models, such as microbial growth,

inactivation, survival, and others, to food applications

including packaging. Because packaging may be the last

step in food-processing operations, it may inherit the

consequences of microbial transfer, growth, inactivation,

and survival cascaded down during the food manufactur-

ing. Packaging also functions as a protection barrier to

environmental changes and potential abuses, which may

cause food spoilage. In general, food-processing operations

should have eliminated or reduced the harmful microbial

counts to a safety level before the ﬁnal packaging step,

which provides a means to control the food qualities,

including foodborne hazards. The microbiological shelf

life can be estimated or predicted using mathematical

models, if available and properly selected.

Microbial safety is one of the major quality control

attributes for packaged food. Pathogen contamination in

consumer food products happens every year. The develop-

ment of effective methods to reduce and eliminate the

potential microbial food hazards is the primary goal of

food scientists, producers, and government at different

levels. Several food pathogens have potentials to cause

public foodborne hazards including Listeria monocyto-

genes, Escherichia coli O157:H7, Salmonella spp. and so

on. The Centers for Disease Control and Prevention (CDC)

estimated that about 2500 cases of listeriosis occurr each

year, which results in 500 deaths in the United States. A

survey in eight categories of ready-to-eat foods, collected

over 14–23 months, from retail markets in Maryland and

northern California FoodNet sites showed that 1.82%

samples were positive with L. monocytogenes contamina-

tion (1). The E. coli O157:H7 outbreaks in spinaches and

ground beef stress the needs of effective means to monitor

and to ensure food safety, even more. The most recent

Salmonella outbreaks in peanut butter indicated that

microbial cross-contamination could occur during food

production. Pathogenic public health hazard is always

on the top of safety list for food producers and inspection

agencies. Predictive microbiology is a useful tool to de-

scribe, predict, and assess the potential hazard in pro-

cessed and packaged consumer food products.

The Food and Drug Administration (FDA) and the

United States of Department of Agriculture (USDA)

have established food-safety regulations that govern the

manufacture and distribution of domestic and imported

foods to ensure the safety of food supply chain. These

regulations are often difﬁcult to locate and/or complicated

to comply even for food companies that have resources to

employ food safety experts and to interpret the regula-

tions. To promote food safety and reduce the ﬁnancial

burden, especially for small companies, the Predictive

Microbiology and Bioinformatics for Food Safety and

Security research group of USDA/Agricultural Research

Service (ARS)/Eastern Regional Research Center (ERRC)

has implemented a 3-year project to create the Predictive

Microbiology Information Portal (PMIP) available on the

Internet to help food producers and safety researchers

better access food-safety information, regulations, and

tools at no cost to the end users. The PMIP provides a

wide range of food-safety-related information such as

regulations, microbiological models, and microbiological

data. The portal has been available to the public since

September 2007, and it can be accessed at http://portal.

arserrc.gov/. The portal was designed to make food reg-

ulations and tens of thousands of microbiological data

accessible to the public.

The PMIP has been accessed million times from tens of

thousands unique Internet Protocol (IP) addresses world-

wide since its launch. The microbial modeling component

in the portal, the USDA Pathogen Modeling Program

APPLICATIONS OF PREDICTIVE MICROBIOLOGY TO FOOD PACKAGING 59

(PMP), was developed and is maintained and regularly

improved by the USDA/ARS/ERRC staff. The PMP is a

user-friendly software that contain a set of mathematical

models, which predict the behavior of major human

pathogens in foods under selected environmental condi-

tions commonly used in the food industry. The PMP is

used throughout the world to assist food companies com-

ply with food-safety regulations and to reduce the human

illness risk through better food process and product de-

signs. The PMP was downloaded about 5000 times an-

nually and is routinely used by 30% of the food industry.

The number of downloads of the PMP has increased 40%

to an estimated 7000 times/yr since the PMIP launch,

which indicates the portal is reaching out a wider custo-

mer base including the food packaging and shelf-life

applications.

PREDICTIVE MICROBIOLOGY AND FOOD PACKAGING

The predictive microbiology (microbial predictive models)

include growth, inactivation, surface transfer (or cross

contamination), and survival models, which play impor-

tant roles in the microbial food safety while tied in the food

packaging design to reduce the microbial hazard. The

transfer model may predict the pathogen transferred

among process equipment or surfaces. The growth models

show the potential growth of a speciﬁed pathogen under

different conditions, e.g., temperature, pH, water activity,

added preservative, and so on. The growth models may

take into account of other environmental factors, which

include modiﬁed atmosphere packaging (MAP) conditions,

transportation, distribution, and consumer abuses if those

factors are built in. Thermal or nonthermal process to

reduce or eliminate microbial counts may be evaluated

using the inactivation models. For the entire microbial

safety assessment in a packaged product, a microbial

transfer model can be applied to estimate the quantity of

contamination, followed by the growth and/or inactivation

models with designed process conditions to predict the

potential pathogenic health hazards. With all information

collected and available in the models, the users may select

the parameters to match the packaging conditions and to

predict the shelf life, for example, the fresh-cut packaged

ready-to-eat vegetables. Other packaging-related models

may apply to the model construction step, which describes

the entire production processes to achieve the microbial

shelf-life assessment.

Users also can utilize the models and database in the

portal (PMIP) to reduce food-safety challenge studies for

their new products. The PMP and Combase, which are

accessible through PMIP, may provide the useful tools for

microbial-safety-related shelf-life optimization and packa-

ging design.

PREDICTIVE MICROBIOLOGY MODELS

For microbial growth, the sigmoid functions have been the

most popular empirical models used to describe the micro-

bial growth; one of such models is the modiﬁed Gompertz

model, which is shown below (2):

log xðtÞ¼A þ C expfexp½Bðt  MÞg ð1Þ

where x(t) is the number of cells at time t; A is the

asymptotic count as t decreases to zero; C is the difference

in value of upper and lower asymptote; B is the relative

growth rate at M; and M is the time at which the absolute

growth rate is maximum or the inﬂexion point of the

curve. Using the parameters in equation (1), the following

terms can be derived to characterize the microbial growth:

Lag time ¼ M ð1=BÞþ

log Nð0ÞA

BC=2:718

ð2Þ

Exponential growth time ¼ BC=2:718 ð3Þ

Generation time ¼ logð2Þ2:718=BC ¼ 0:8183=BC ð4Þ

The lag phase is the time for microbial to adjust to a new

environment, followed by the exponential phase with a

maximum growth rate until the available medium de-

prived or limited by other factors, then to the stationary

phase. Baranyi and coworkers (3–5) introduced a mechan-

istic model, which includes the lag phase, the exponential

growth phase, and the stationary phase. The explicit

Baranyi model is expressed as the following:

yðtÞ¼y

þ m

max

AðtÞ

ln 1 þ

expðmm

max

AðtÞÞ 1

expðmðy

max

 y

ÞÞ



ð5Þ

AðtÞ¼t þ

expðvtÞþq

1 þq



ð6Þ

where y(t)=ln x(t), y

= ln(x

), and v is the rate of increase

of the limiting substrate, which is generally assumed

equal to m

max

. Parameter m is an index of the curvature

before the stationary phase. The q

and x

represent the

initial concentration of limiting substrate and cell number,

respectively. m

max

is the maximum growth rate. Baranyi

models are more complicated than the modiﬁed Gompertz

models and may be applied to the dynamic process condi-

tions, e.g., temperature changes with time.

Other predictive models may include inactivation

(thermal and nonthermal), cooling survival, and growth

under inﬂuence of other factors, added or environmental.

The growth models are classiﬁed as the primary model.

The growth and/or inactivation models with parameters

that interact with other factors are considered as the

secondary models. Recently, the surface transfer models

were developed to describe the cross contamination of food

pathogens during slicing of foods, e.g., ready-to-eat deli

meats, smoked salmon, and so on.

The transfer models typically only consider the patho-

gens transferred from one step to another step. The

microbial counts may be performed at each individual

step or at the end of one series of steps. Because it is a

continuous and in a relative short time period, no growth

or inactivation factors were built in this kind of modeling.

The important factors will be the processing parameters

used in the process ﬂow, and therefore, empirical models

60 APPLICATIONS OF PREDICTIVE MICROBIOLOGY TO FOOD PACKAGING

were considered. Typically, a microbial count as a function

of several operation parameters was presented. The sur-

face transfer models shown below were the recently

developed by Sheen (6) for L. monocytogenes transfer

during slicing in two cross-contamination routes.

I: Slicing blade to meat product,

Y ¼ 0:461 Expð0:255  nÞExp

X

0:0215 n^4:962



ð7Þ

II: Meat to blade to meat product,

Y ¼ 0:495 Expð0:244  nÞExp

X

23:98 Expð0:413  nÞ



ð8Þ

where Y is the log colony forming unit (CFU) per slice; X is

the slice number index; and n is the initial microbial count

in log CFU.

The general microbial inactivation model developed

following the ﬁrst-order kinetic chemical reaction de-

scribed the microbial death reasonably well. However,

the microbial inactivation could become complicated be-

cause of the microbial itself, environment conditions, and

treatment applied to kill the micro-organism, especially

for food pathogens. Some important parameters may

be temperature dependent. The D and Z values, which

are typically used in the thermal process to evaluate the

thermal lethality and also can be adopted to other inacti-

vation study. To simulate the inactivation, some studies

used the curve-ﬁtting method simply ﬁt the experimental

data. A nonlinear approach using the power law function

that best represented the inactivation is shown as:

Log ðN=NoÞ¼ðt

=DÞð9Þ

where p is the power. A concave or convex curve is

represented by po1orpW1, respectively.

If shoulder or tailing appears in the inactivation curve,

then the model will become even more complicated. Re-

searchers have demonstrated that the asymmetric Gom-

pertz function or the mirror image of Baranyi growth

model may ﬁt certain nonlinear survival or inactivation

curves well.

PMIP AND PMP SAMPLE CASE DEMONSTRATIONS

The homepage of PMIP shown below serves as the gate-

way to access other components available to the users. It is

highly beneﬁcial for the users to navigate the website and

become familiar with all options and available data. Two

examples are demonstrated.

EXAMPLE 1: THERMAL INACTIVATION OF PATHOGENS

IN FOODS

The inactivation on microbial growth due to thermal (tem-

perature) effect can be estimated by using the PMP models,

APPLICATIONS OF PREDICTIVE MICROBIOLOGY TO FOOD PACKAGING 61

62 APPLICATIONS OF PREDICTIVE MICROBIOLOGY TO FOOD PACKAGING

which are available to certain food items. The user may

select the heat inactivation (under the ‘‘ModelsBacte-

rium’’ headline, online option), then the ‘‘food pathogen’’ to

select the microbe, which leads the user to key in other

parameters, like pH, salt content, sodium pyrophosphate,

and targeted log reduction. The temperature also needs to

be speciﬁed for the time-required calculation. The following

ﬁgure shows the heat inactivation of 8 log L. monocytogenes

reduction in ground beef requires 3.87 min (D-value = 0.48

min at 651C). If a lower temperature at 551Cisselected

with other factors remained the same, the process time

becomes 159.93 min (D-value = 19.94 min at 551C). There-

fore, to achieve the desired thermal inactivation, the PMP

model may provide useful information to select the proper

process with parameters ﬁt to the product.

EXAMPLE II: SHELF LIFE STUDY AND PREDICTION

The PMP models can be used to predict the shelf life of

packaged foods. The user may ﬁnd the parameters to

match or closely ﬁt the conditions of a food item and

make the reasonable shelf-life prediction. For example,

to predict the shelf life of vacuum-packaged seafood salad

with the targeted pathogen like L. monocytogenes o100

CFU/g, the user may apply the following steps: (a) bacter-

iamodel; (b) L. monocytogenes; (c) growth anaerobic

(shrimp and imitation crab salad); (d) select temperature,

pH, time duration, time interval, initial level of L mono-

cytogenes and level of concern; and (e) calculate growth

data. Results will appear on the screen as the following,

which indicates the shelf life is about 10 days. For

comparison, one may increase the temperature to 81C

and pH to 5.0, and the shelf life becomes 5 days.

The table also shows some useful information, us such

as growth rate, generation time, lag phase duration, and

so on. When the users apply a low level of L. monocyto-

genes (e.g., o0.1 log CFU), it is recommended that the

worst-case situation used for food safety. This example is

for demonstration only; the user should visit the regula-

tory component available on the same website and acquire

the food regulations in different country and products. The

L monocytogenes detection level in the United States is

zero (negative) per 25 g and other countries may impose

different tolerance levels.

BIBLIOGRAPHY

1. D. E. Gombas, Y. Chen, R. S. Clavero, and V. N. Scott, ‘‘Survey

of Listeria monocytogenes in Ready-to-Eat Foods,’’ J. Food Prot.

66, 559–569 (2003).

2. R. C. McKellar and X. Lu, ‘‘Primary Models’’ in R. C. McKellar,

and X. Lu, ‘‘Modeling Microbial Responses in Food,’’ CRC

Series in Contemporary Food Science, CRC Press, New York,

2004.

3. J. Baranyi and T. A. Roberts, ‘‘Mathematics of Predictive

Food Microbiology,’’ Int. J. Food Microbiol., 26 , 199–218

(1995).

4. J. Baranyi and T. A. Roberts, ‘‘A Dynamic Approach to Pre-

dicting Bacterial Growth in Food,’’ Int. J. Food Microbiol., 23,

277–294 (1994).

5. J. Baranyi T. A. Roberts, and P. McClure, ‘‘A Non-Autonomous

Differential Equation to Model Bacterial Growth,’’ Food Micro-

biol. 10, 43–59 (1993).

6. S. Sheen ‘‘Modeling Surface Transfer of Listeria monocytogenes

on Salami During Slicing,’’ J. Food Science 73(6), E304–E311

(2008).

General References

C. A. Hwang and M. L. Tamplin, ‘‘The Inﬂuence of Mayonnaise

pH and Storage Temperature on the Growth of Listeria

monocytogenes in Seafood Salad,’’ Int. J. Food Microbiol.

102, 277–285 (2004).

P. S. Mead, L. Slutsker, V. Dietz, L. F. McCaig, J. S. Bresee, C.

Shapiro, P. M. Grifﬁn, and R. V. Tauxe, ‘‘Food-Related Illness

and Death in the United States,’’ Emerg. Infect. Dis. 5 (1999).

Available at: http://www.cdc.gov/ncidod/eid/vol5no5/mead.htm.

USDA, http://pmp.arserrc.gov.

USDA, http://portal.arserrc.gov.

AROMA BARRIER TESTING

ANNE-MARIE SEUVRE

FRE

RIC DEBEAUFORT

IUT Ge

nie Biologique,

Universite

de Bourgogne, Dijon

Cedex, France; and ENSBANA,

Laboratoire EMMA, Universite

de Bourgogne, Dijon, France

ANDRE

E VOILLEY

ENSBANA, Laboratoire

EMMA, Universite

Bourgogne, Dijon, France

The aroma of a food product is the whole of the volatile

compounds that may be perceived by the olfactory system

at extremely low levels, which implies that a reduced loss

or sorption (adsorption and/or absorption) will be detected

by the consumer (1). Losses of aroma compounds can be

selective (affecting one or few components in a complex

mixture) and result in changes of the aromatic proﬁle. The

food industry has long depended upon reliable, imperme-

able packaging materials such as glass and metal. Both

suppliers and food manufacturers focus research efforts

into lighter-weight, ﬂexible and semirigid packages, which

are typical qualities of plastics. While parameters such as

functionality, recyclability, and cost are critical character-

istics, the lack of complete impermeability and inertness

in these polymer materials can have important effects.

Due to their size (molecular weight o400 g mol

1

) and

nature (very little polar until apolar and hydrophobic), the

aroma compounds will interact with packaging materials

often consisting of lipophilic hydrocarbons (2). Aroma

compounds are able to interact with the polymer matrix,

leading to polymer structural changes (3). Plastic

packages are made up of polymers that form a matrix of

crystalline and amorphous regions (which contain

AROMA BARRIER TESTING 63

submicroscopic voids). Aromas permeate through packa-

ging by ﬁrst being adsorbed onto the package’s surfaces,

diffusing through the voids (absorption), and, without a

barrier material, desorbed to the package’s exterior. A

sorption–diffusion mechanism is thus applied (4, 5). The

mass transfer phenomenon, commonly described by the

sorption, the migration, and the permeation can be deter-

mined by three parameters: S, the solubility coefﬁcient; D,

the diffusion coefﬁcient; and P, the permeability coefﬁcient

(6). When diffusion is Fickian and sorption follows Henry’s

low, the relationship P = D S can be used. Literature and

knowledge on mass transfer of aroma compounds are few

and no standard procedure is recommended. Methods

developed for aroma compounds permeability measure-

ments are commonly approached by isostatic or quasi-

isostatic methods and depend on the physical state (vapor

or liquid) of the aroma compounds (6, 7).

The issue is how to obtain results of aroma permeabil-

ity of packaging ﬁlms in a reasonable time-frame. Depend-

ing on the static or dynamic conditions of permeation

measurement, the detection systems and the environmen-

tal conditions (temperature, pressure, ﬂows, physical state

of the aromas, etc.), numerous apparatus have been

designed, to different degrees of success, to obtain this

information (4, 7):

Systems can be classiﬁed as a function of:

. the vapor or liquid state of the ﬂavored medium in

contact with the packaging ﬁlm;

. the static, quasi-isostatic or dynamic methods;

. or the detection or analysis systems.

The aroma compound in contact with the ﬁlm can be

pure or dilute in a simple solution or in the food, in a vapor

state, or in a liquid state at the inner surface of the ﬁlm.

When the ﬂavored medium or pure aroma compound are

static (no stirring, no sweeping), the technique is qualiﬁed

as static and employs an accumulation process. These

methods correspond to integral permeation processes (8).

In dynamic (or quasi-isostatic) systems, the ﬂavored med-

ium and/or medium collected for analysis are stirred or

swept, and the method deals with a differential permea-

tion in which the instantaneous ﬂow rate through the

ﬁlms is measured. When the two sides of the ﬁlm are

exposed to the same total pressure, but with different

partial vapor pressures of the aroma, the technique is

qualiﬁed as isobaric.

AROMA VAPOR PERMEABILITY MEASUREMENT

One primary concern when dealing with aroma barrier

testing is generating an accurate test vapor of aroma.

Accurate data require precise control of the test permeant

concentration in the vapor phase. Since aromas are a

complex mixture, the use of an aroma as a test permeant

is typically difﬁcult. Aroma generation is usually obtained

by bubbling the carrier gas through pure liquid aroma and

diluting the aroma saturated carrier gas with another ﬂow

of aroma-free carrier gas or with a second ﬂow containing

an other aroma compound.

The Isostatic and Isobaric Methods

The gravimetric method is probably the most simple but

one of the less accurate. It requires us to work with pure

aroma compounds and then at saturated concentration of

aroma in the vapor phase in contact with the ﬁlm. Only a

partial vapor pressure differential of the ﬂavor compound is

applied between the two faces of the ﬁlm; that is, the same

total pressure is applied on both faces of the ﬁlm. The

method consists in storing pure aroma compound in a

permeation cell, usually in glass or metal, sealed by the

ﬁlm to study which is ﬁxed between two Teﬂon O-rings. The

permeation cell is stored in a ventilated room where the air

is permanently renewed to maintain the lower concentra-

tion of ﬂavor in the outside environment of the cell. The cell

is periodically weighted and the cumulative mass of aroma

loss as a function of time is plotted. This method is also

called the integral permeation method (8). Figure 1 gives

an example of the permeation cell and the kinetic obtained.

This method, even when coupled with a very sensitive

microbalance or a DVS system (Dynamic Vapor Sorption,

Surface Measurement Systems Ltd, UK), such as done by

Zhou et al. (9), is one of the less accurate (except when

coupled with gas chromatography analysis) and permits us

to measure permeability to only one pure compound.

The sensory method is sometimes more accurate than

the gravimetric ones, and it only needs selected and

trained panelists and does not required any speciﬁc

equipment. First, the concentration level (threshold) at

which the panelist perceived the ﬂavor has to be measured

and quantiﬁed by sensory experiments (or has to be found

in the literature). Second, permeation cells having two

compartments, one containing the ﬂavor solution or ﬂa-

vored food and the other empty, separated by the tested

ﬁlm were stored in standard conditions [Figure 2 (10)].

The panelists smelt at different times. When the ﬂavor is

detected in the empty compartment, permeability could be

calculated. The results obtained by this method were

comparable to those obtained by the analytical method

which used gas chromatography quantiﬁcation. The ad-

vantage

of this method

is also that it uses either (a) low

ﬂavor concentrations and aroma compound mixtures be-

cause the nose is very accurate and is able to detect

several ﬂavor compounds at concentrations lower than

parts per billion or (b) real food products instead of pure

aroma compounds. Indeed, the nose is very accurate and is

able to detect simultaneously several ﬂavor compounds at

Cell top

PTFE compression washer

Test film

O-ring

Cell base

Figure 1. Permeation cell for gravimetric method and cumula-

tive amount of aroma transferred through a ﬁlm.

64 AROMA BARRIER TESTING

concentrations lower than the ppb. Numerous studies

demonstrate the performances (detection level and iden-

tiﬁcation of aroma) of the panelists and concluded that the

technical instrument’s (electronic nose) capability for de-

tecting the aroma differences was fairly comparable with

the sensory human detector.

The Gas Chromatography method uses the same type

of cell that is used for the sensory method. The cell is made

either of glass or stainless steel, but the two chambers of

the permeation cell are equipped with a sampling port

having PTFE septa. The gas phase is periodically collected

with a gas-tight syringe in both chambers and injected in

gas chromatograph for analysis (11).

Quasi-isostatic and Isobaric Method

In this method, the cell consists of an upper and lower

chamber separated by the test packaging ﬁlm (Figure 3).

Complete separation and closure of each chamber were

accomplished with Viton or PTFE O-rings between which

the ﬁlm is placed. The cell is accurately maintained at the

testing temperature. The pure aroma compound, or a

dilute aroma solution or a real ﬂavored product, was

placed in a glass dish at the bottom of the lower cell as

used by Rubino et al. (12). The RH conditions could also be

controlled. A smaller dish ﬁlled with a saturated salt

solution controlling the water activity was placed in the

center of the aroma dish. Aroma vapor that diffused

through the ﬁlm was purged by a stream of carrier gas

(nitrogen, helium, or argon) that ﬂowed through the upper

cell at a constant rate and that carried the permeated

vapor to the gas chromatograph for measurement as

displayed in Figure 3.

Isostatic with Dynamic (or Continuous) Flow and Isobaric

Methods

The isostatic technique is very similar to that of quasi-

isostatic and is the most used technique for measuring

aroma vapor permeabilities. The material sample was also

mounted in a two-chamber permeability cell, but both

chambers of the permeation cell are continuously swept

by the ﬂow of gas: on one side the aroma-enriched carrier

gas and on the other side the permeated low aroma

concentration carrier gas (Figure 4a). As the permeant

diffuses across the membrane, headspace samples are

automatically collected and quantiﬁed using gas chroma-

tography. This principle corresponds to the automatic

apparatus sold for aroma permeability measurement,

such as the Aromatran

(Mocon, Minneapolis, MN, USA)

or the MAS2000

(Mas Technologies Inc., Zumbrota, MN,

USA). Instead of FID-equipped gas chromatography ana-

lysis or detection, some authors used more speciﬁc detec-

tors such as PDS or mass spectrometer coupled with the

gas chromatograph, or UV–VIS spectrometer, FTIR or

ATR–FTIR spectrometer directly coupled at the vent of

the permeation cell (13).

Film

Flavored

solution

PTFE dis

Cork

Septum

PTFE O-rings

Septum

Figure 2. Permeation cell for measuring ﬁlm permeability from

sensory analysis. [From reference (10).]

0.2

0.4

0.6

Relative amount permeated

0.8

50000

Time (sec)

100000

gas intake

Film

Water

Aroma compound

Carrier gas exit to GC

Circulating water bath

(a)

(b)

Figure 3. (a) Quasi-isostatic permeation cell

(adapted from reference 12) and (b) an example

of kinetic of 1-hexanol permeation through poly-

propylene ﬁlm. [Adapted from Gavara et al. (6).]

AROMA BARRIER TESTING 65

In this method, permeant ﬂow values as a function of

time is recorded during the experiment. Initially the

permeant ﬂow is zero. After some time, permeant ﬂow

starts to increase during the transition state until it

reaches a constant value. At this time, the system is in a

stationary state and the experiment can be stopped. From

the ﬂow F

at the stationary state, the permeability can

be calculated according differential permeation equations

(6, 8, 14). Figure 4b shows the data obtained for 1-hexanol

through a polypropylene ﬁlm (6).

To improve the sensitivity and accuracy of the isostatic

method, a cold trap (liquid nitrogen) or adsorbent trap

(tenax, active charcoal, etc.) can be placed at the vent of

the cell to concentrate the permeated aroma compounds.

After deﬁnite times, the trap is desorbed by heating and

injected in a gas chromatograph as shown in Figure 5 (15–

17). This is necessary for obtaining a suitable method

allowing the permeability measurement of high-barrier

polymer ﬁlms at low permeant vapor pressures.

Static and Manometric Methods

Basically, the manometric methods measure the quantity of

aroma vapor that has permeated through a test specimen

in a given time, as a change in pressure and volume (18).

The test specimen forms a barrier between two chambers in

a permeation cell. A constant pressure of the aroma vapor

is maintained in one chamber and a low pressure, usually

vacuum, is initially established in the other chamber. A

manometer is coupled to the low-pressure chamber and is

used to measure the change in pressure and volume over a

speciﬁed length of time. In order that the quantity of vapor

measured be equal to that entering the polymer ﬁlm,

steady-state conditions must exist. This requires a period

of time to lapse so that a constant concentration gradient is

obtained across the ﬁlm.

In the permeation device developed by Okuno et al. (19)

and described in Figure 6, the permeability is determined

in static conditions and for a pressure differential lower

than 1 atm. This method allows us to strongly improve the

sensitivity of the Lomax (18) manometric method pre-

viously described. First, the liquid (aroma compound or

volatile organic compound) in the vessel was degassed as

follows: After closing the valve V

, the liquid in the liquid

vessel was frozen at liquid nitrogen temperature

(1961C). Then the liquid vessel was evacuated by open-

ing the valve V

, and the frozen liquid was melted at

ambient temperature. This procedure was repeated sev-

eral times. The valves V

, and V

were closed,

followed by heating the liquid vessel at a certain tempera-

ture. The vapor pressure supplied to the membrane was

controlled by the liquid temperature in the vessel. The

permeation measurement started by opening the valve V

Film

+ Aroma compound

Purge

High concentration

compartment

Low concentration

compartment

6-way

valve

to GC

1/4

1/2

3/4

Permeant flow (kg/s)

max

10 20 30

Time (h)

40 50 60 70

(a)

(b)

Figure 4. (a) Isostatic permeation de-

vice and (b) an example of typical kinetic

of aroma permeation through polypropy-

lene ﬁlm.

Polymer film

aroma vapors

Vent

Upstream side

Downstream side

Absorption tube He

Figure 5. Improved device for the isostatic method using adsor-

bent trap to concentrate permeated compounds. [Adapted from

reference (15).]

66 AROMA BARRIER TESTING

The vapor permeated through the membrane was col-

lected in one the U-tube at liquid nitrogen temperature.

The collected permeants were vaporized in the U-tube

disconnected from the system by closing the valve V

. The

valve V

was opened and then the pressure of the per-

meated aroma vapor in a known volume is measured by

the pressure gauge 5. During the above pressure measure-

ment, another U-tube was used to collect the permeated

vapors. The steady state of permeability of the vapor was

determined by repeating this procedure. If several com-

pounds and mixed and studied simultaneously, the com-

position of the permeate has to be analyzed by gas

chromatography. The main limit of this method is that

the permeability value is strongly dependent of the vapor

pressure applied at the inner surface of the membrane,

and of the efﬁciency of the cold trap, as shown in Figure 7

where the permeability of a PVC sheet to ethanol varies

twice according the ethanol vapor pressure used.

AROMA LIQUID PERMEABILITY MEASUREMENT

Quasi-isostatic Isobaric Method

The permeability of packaging ﬁlms in liquid medium is

simply determined using a two-compartment glass diffu-

sion cell (20). The two compartments were separated by

the ﬁlm and were continuously stirred with a magnetic

stirrer to ensure homogeneity of the solutions on both

sides of the ﬁlm. The permeate concentration can be

measured by gas chromatography analysis or, in the

case of Figure 8, by ﬂuorescence spectrometry.

This method was also adapted for measuring the

permeability of a complete package, taking into account

the sealing zone, and using a continuous measurement of

the permeated aroma concentration by UV–VIS spectro-

photometer such as done by Gotz and Weisser (21) and

shown in Figure 9.

Static High-Pressure Method

Gotz and and Weisser (21) designed a permeation device

for measuring permeation of packaging in liquid submitted

Figure 6. Permeation device for measuring permeability of

organic vapors (1, permeation cell; 2, liquid vessel; 3, U-tube;

4, pressure gauge; 5, Pirani low-pressure gauge; 6, cold trap;

7, vacuum pump; 8, thermostat; V, valves). [Adapted from Okuno

et al. (9).]

0.2

0.4

Relative permeability

0.6

0.8

2468

Pressure (cm Hg)

10 12 14

Figure 7. Effect of vapor pressure on permeability of ethanol

vapor through a PVC membrane. [Adapted from reference (18).]

Aroma or dye

Water

Film

Figure 8. Experimental design for measuring permeability of

aroma compounds (or dye compound) in packaging ﬁlms.

Solvent

Aroma

compounds

Solvent

UV / VIS

Film

Figure 9. In situ permeability measurement of aroma in liquid

medium. [From reference (21).]

AROMA BARRIER TESTING 67