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UNCLASSIFIED Predicting bacterial spore inactivation by novel processing technologies October, 2008 Christopher J. Doona, Florence E. Feeherry, Edward.

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Presentation on theme: "UNCLASSIFIED Predicting bacterial spore inactivation by novel processing technologies October, 2008 Christopher J. Doona, Florence E. Feeherry, Edward."— Presentation transcript:

1 UNCLASSIFIED Predicting bacterial spore inactivation by novel processing technologies October, 2008 Christopher J. Doona, Florence E. Feeherry, Edward W. Ross, Kenneth Kustin DoD Combat Feeding Directorate US Army Natick RD&E Center

2 UNCLASSIFIED Introduction 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.

3 UNCLASSIFIED 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. Background

4 UNCLASSIFIED 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 chemicals iii.Outer membrane (not shown) iv.Cortex - peptidoglycan v.Germ cell wall vi.Inner membrane – receptors vii.Core – CaDPA, low water content Inner membrane is target of HPP Bacterial spore architecture

5 UNCLASSIFIED 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).

6 UNCLASSIFIED 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. Approach

7 UNCLASSIFIED High Pressure Inoculate Plate on agarIncubate pressurize Methodology Whey solution

8 UNCLASSIFIED Mechanism 1. quorum sensing 2. fermentative LAB 3. nutrient depletion The Quasi-Chemical (QC) Model Growth-Death Kinetics for S. aureus in Bread pH range = 5.4 - 4.9 (a w = 0.86)

9 UNCLASSIFIED log(CFU/mL) Quasi-chemical model 30 kpsi 200 MPa 50 kpsi 345 MPa Time (min) Log S(t) 49 kpsi 54 kpsi 64 kpsi E. coli inactivation by HPP Vary P, T = 50 °C Comparison of Models for HPP inactivation kinetics Comparison of Models for HPP inactivation kinetics Weibull model 4 param. 5 param. L. monocytogenes shows tailing – mechanism needs 5 th step

10 UNCLASSIFIED QC “Tailing” QC QC “lite”Weibull 1. M  M* M  M* M  M*log 10 S(t) = -b  t n 2. M*  2M* + A M*  2M* + A M*  2M* 3. M* + A  D M* + A  D 4. M*  D M*  D M*  D 5. D  M* Comparison of models for the inactivation of L. monocytogenes by HPP log(CFU/mL) 40  C, 30 kpsi50  C, 50 kpsi log(CFU/mL)

11 UNCLASSIFIED QC model is best for evaluating tailing data of L. monocytogenes by HPP Log(N/N o ) Time (min) Log(N/N o ) Time (min) Log(N/N o )

12 UNCLASSIFIED FoodpH E. coli 60,000 psi and 50 º C 0 min 2 min 4 min B. amyloliquefaciens 80,000 psi and 65 º C 0 min 2 min 40 min Beef5.86 4.1  10 7 1.4  10 2 2.5  10 1 Chicken6.13 4.1  10 7 2.3  10 3 7.1  10 2 1.8  10 8 5.9  10 6 9.8  10 4 Lamb – 4.1  10 7 2.0  10 2 5.0  10 2 Turkey – 4.1  10 7 2.5  10 2 8.5  10 1 Surrogate food systems influence inactivation rates of B. amyloliquefaciens by HPP Food matrix and spore inactivation

13 UNCLASSIFIED 13 Date: 01OCT08 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! Log(N/N o )

14 UNCLASSIFIED HPP-treated BA spores

15 UNCLASSIFIED SEM of HPP-treated spores

16 UNCLASSIFIED Average FTIR of HPP-treated spores DPA in core depleted by HPP C=O in DPA

17 UNCLASSIFIED Conclusions 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.

18 UNCLASSIFIED Patents 1. Patent submitted for the Quasi-chemical model patent (2008) International and other invited talks 1. 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 posters 1. 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 Award 7. Feeherry, Ross, Doona 2006 Acta Horticulturae 674, 245-251 8. Doona, Feeherry, Baik 2006 J Ag Food Chem Recent Publications and Scholarship

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