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Predicting bacterial spore inactivation by novel processing technologies

October, 2008

Christopher J. Doona, Florence E. Feeherry,

Edward W. Ross,Kenneth Kustin

DoD Combat Feeding DirectorateUS Army Natick RD&E Center

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IntroductionIntroduction

• High Pressure Processing (HPP) is an emerging technology that reduces losses in food quality compared to thermal processing, leaving foods with

• Improved sensory attributes• Higher consumer acceptance• More fresh-like character• Higher nutrient retention

• HPP eliminates pathogenic vegetative cells (e.g., Escherichia coli and Listeria monocytogenes), prions, spoilage microorganisms, viruses, and parasites

• Dormant bacterial spores (e.g., Clostridium botulinum) have unique structural features that impart resistance and present special challenges for the production of commercially sterile foodstuffs.

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• The inactivation of bacterial spores requires combinations of high pressure and high temperature, including sterilization temperatures that would kill spores at ambient pressure (T = 121 °C).

• Some reports (Gäenzle et al., 2007; Rajan, et al., 2006) found higher lethality at ambient pressure than at 800 MPa at some temperatures using spores.

• This result was initially thought to occur because of inherent complex mechanisms of spores, demonstrating the importance of temperature control at high pressure.

BackgroundBackground

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Background –

Bacterial spores are dormant and resistant with an alert sensor system.

Germination – receptors sense stimuli and re-awaken to form vegetative cells.

References - Setlow, 2007; Black et al., 2007; Black et al 2005).

Spore structure -i.Exosporium (not shown)ii.Coat – protein, blocks chemicalsiii.Outer membrane (not shown)iv.Cortex - peptidoglycanv.Germ cell wallvi.Inner membrane – receptorsvii.Core – CaDPA, low water content

Inner membraneis target of HPP

Bacterial spore architectureBacterial spore architecture

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Predictive Microbial Modeling Predictive Microbial Modeling

• Predictive models are essential tools for assessing microbial inactivation kinetics and ensuring food safety of commercial products, and saving time, money, and labor.

• Microbial inactivation kinetics by HPP exhibit nonlinearities such as “shoulders” and/or “tailing.” The linear model used in thermal processing is not always the best tool for HPP.

• Comparisons of predictive models helps determine their suitability for evaluating inactivation kinetics. Published comparisons (Chen, 2007; Koseki, 2007; Peleg, 2007) cite Natick’s own Quasi-chemical (QC) model with increasing frequency (see Feeherry, 2003; Taub, 2003; Doona, 2005; Ross, 2005).

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• Presently, we explore Predictive Models to examine nonlinear inactivation kinetics by HPP that show “tailing,” a phenomenon typical of spores.

• Compare performance of QC vs.established Weibull model for the inactivation of E. coli by HPP.

• Adapt QC model to account for tailing, and compare QC, Weibull, and Polylog models for L. monocytogenes inactivation by HPP showing tailing.

• Apply this framework to the inactivation kinetics of spores of B. amyloliquefaciens, and combine biophysical methods to evaluate mechanism of inactivation.

ApproachApproach

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High PressureInoculate

Plate on agar Incubatepressurize

Methodology

Whey solution

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*MM AMM ** 2

DAM *

DM *

Mechanism

1. quorum sensing2. fermentative LAB3. nutrient depletion

The Quasi-Chemical (QC) ModelThe Quasi-Chemical (QC) Model

Growth-Death Kinetics for S. aureus in BreadpH range = 5.4 - 4.9 (aw = 0.86)

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log

(CF

U/m

L)

Quasi-chemical model

30 kpsi200 MPa

50 kpsi345 MPa Time (min)

Lo

g S

(t)

0 2 4 6 8 10 12 14

0

-1

-2

-3

-4

-5

-6

-7

-8

49 kpsi

54 kpsi64 kpsi

E. coli inactivation by HPPVary P, T = 50 °C

Comparison of Models forHPP inactivation kineticsComparison of Models forHPP inactivation kinetics

Weibull model

4 param.

5 param.

*MM AMM ** 2

DAM* DM *

*MD

L. monocytogenes shows tailing – mechanism needs 5th step

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QC “Tailing” QC QC “lite” Weibull

1. M M* M M* M M* log10S(t) = -btn

2. M* 2M* + A M* 2M* + A M* 2M* 3. M* + A D M* + A D 4. M* D M* D M* D5. D M*

Comparison of models for the inactivation of L. monocytogenes by HPP

Comparison of models for the inactivation of L. monocytogenes by HPP

log(CFU/mL)

40 C, 30 kpsi 50 C, 50 kpsi

log(CFU/mL)

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QC model is best for evaluating tailing data of L. monocytogenes by HPPQC model is best for evaluating tailing data of L. monocytogenes by HPP

Log

(N/N

o)

Time (min)Time (min)

Log

(N/N

o)

Time (min)

Log

(N/N

o)

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Food pH

E. coli60,000 psi and 50 ºC

0 min 2 min 4 min

B. amyloliquefaciens80,000 psi and 65ºC

0 min 2 min 40 min

Beef 5.86 4.1 107 1.4 102 2.5 101

Chicken 6.13 4.1 107 2.3 103 7.1 102 1.8 108 5.9 106 9.8 104

Lamb – 4.1 107 2.0 102 5.0 102

Turkey – 4.1 107 2.5 102 8.5 101

Surrogate food systems influence inactivation ratesof B. amyloliquefaciens by HPP

Food matrix and spore inactivationFood matrix and spore inactivation

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1. Inactivation kinetics of B. amyloliquefaciens spores by HPP at P = 80,000 psi (552 MPa) and T = 65 °C.

2. 5-parameter QC model works great for tailing kinetics.

3. Improved temperature control of PT-1 unit (left) enhances lethality vs. EPSI unit (right) – does this explain results from slide 3?

SUCCESS!SUCCESS!

Log

(N/N

o)

Log

(N/N

o)

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HPP-treated BA sporesHPP-treated BA spores

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SEM of HPP-treated sporesSEM of HPP-treated spores

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Average FTIR of HPP-treated spores DPA in core depleted by HPPAverage FTIR of HPP-treated spores DPA in core depleted by HPP

C=O in DPA

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ConclusionsConclusions

• This research contributes foundational information on bacterial spore inactivation using the mechanistic-based QC model and biophysical measures.

• The QC, Weibull, and Polylog models each have advantages and disadvantages for evaluating nonlinear inactivation kinetics of E. coli, L. monocytogenes, and B. amyloliquefaciens spores by HPP.

• Used SEM microscopy to show deformation of spores by HPP, and FTIR showed loss of DPA.

• Frequent requests to collaborate using QC model (Fernandez-Sevilla, Leguérinel-Quimper, Luo – Clemson, etc). Since QC model is not commercial software, patent drafted to promote software development for use throughout international Predictive Modeling community.

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Patents1. Patent submitted for the Quasi-chemical model patent (2008)International and other invited talks1. Invited Symposium presentation (Doona, Ross, Feeherry) at Model-It

2008 (Madrid, Spain)2. Invited Symposium presentation (Feeherry Doona, Ross) 2007

International Conference on Predictive Modeling (Athens, Greece).3. Invited Symposium presentation (Doona, Feeherry, Ross) 2007 IFT

Annual Meeting, Chicago, IL.Publications, Books, Chapters, and posters1. Ross, Doona, Feeherry. 2008 Int J Food Microbiol.2. Chatakanonda, Doona, Chinachoti. 2008 J Food Science.3. Doona, Ross, Feeherry. 2008 Acta Horticulturae.4. High Pressure Processing of Foods (Doona and Feeherry, Eds)5. (book chapter) Doona, Feeherry, Ross, Corradini, Peleg, 2007.6. 2006 US Army Research and Development Achievement Award7. Feeherry, Ross, Doona 2006 Acta Horticulturae 674, 245-2518. Doona, Feeherry, Baik 2006 J Ag Food Chem

Recent Publications and ScholarshipRecent Publications and Scholarship