niques of analysis will challenge the ingenuity of analysts and instrument companies to come up with formats for data storage, data processing, and data retrieval. Such areas as fingerprinting samples based upon their chemical content, in situ methods, principal component analysis, and transformation of data will be particularly important to a future of data-rich characterization of samples, using both existing techniques as well as new ones which are certain to be developed.

Figure 1. Development of chemical analytical methods over time. Adapted from Seiber(1).

REFERENCES

1. Seiber, J. N., 1999. Chapter 3: Determination Meth-ods. In: "Pesticide Residues in Foods: Methods, Techniques and Regulations". pp. 64. Fong, W. G., Moye, H. A., Seiber, J. N. and Toth, J. P., eds. Wiley, New York.

2. Fagerquist, C. K., Garbus, B. R., Miller, W. G., Wil-liams, K. E., Yee, E., Bates, A. H., Boyle, S., Harden, L. A., Cooley, M. B. and Mandrell, R. E. 2010. Rapid Identification of Protein Biomarkers of Escherichia coli O157:H7 by Matrix-Assisted Laser Desorption Ionization-Time-of-Flight−Time-of-Flight Mass Spectrometry and Top-Down Proteomics. Anal. Chem. 82: 2717-2725.

3. Parker, C. T., Huynh, S., Quiñones, B., Harris, L. J. and Mandrell, R. E. 2010. Comparison of Genotypes of Salmonella enterica Serovar Enteritidis Phage Type 30 and 9c Strains Isolated during Three Out-breaks Associated with Raw Almonds. Appl. Envi-ron. Microbiol. 76: 3723-3731.

4. Knight, A. L., Light, D. M. 2001. Attractants from Bartlett pear for codling moth, Cydia pomonella (L.), larvae. Naturwissenshaften 88: 339-342.

5. Campbell, B. C., Molyneux, R. J. and Schatzki, T. F. 2003. Current Research on Reducing Pre- and Post-harvest Aflatoxin Contamination of U.S. Al-mond, Pistachio, and Walnut. J. Toxicol. Toxin Rev. 22: 225-266.

6. Filigenzi, M. S. Puschner, B., Aston, L. S. and Poppenga, R. H. 2008. Diagnostic Determination of Melamine and Related Compounds in Kidney Tissue by Liquid Chromatography/Tandem Mass Spec-trometry. J. Agric. Food Chem. 56: 7593-7599.

7. Tareke, E., Rydberg, P., Karlsson, P., Eriksson, S. and Törnqvist, M. 2002. Analysis of Acrylamide, a Car-cinogen Formed in Heated Foodstuffs. J. Agric. Food Chem. 50: 4998-5006.

8. Everts, S. 2009. Chemicals Leach From Packaging. Chem. Eng. News 87: 11-15.

9. Huang, X. and Mazza, G. 2011. Application of LC and LC-MS to the Analysis of Melatonin and Sero-tonin in Edible Plants. Crit. Rev. Food Sci. Nutr. 51: 269-284.

10. Paredes, S. D., Korkmaz, A., Manchester, L. C., Tan, D. X. and Reiter, R. J. 2009. Phytomelatonin: a re-view. J. Experimental Botany. 60: 57-69.

11. Wishart, D. 2008. Applications of Metabolomics in Nutritional Science. University of North Carolina at Chapel Hill and Nutrition Research Institute at Kan-napollis. http://www.metabolomics.ca/News/lectures/UNC-metabolomics-final.pdf.

12. Erdman Jr., J. W. Balentine, D., Arab, L., Beecher, G., Dwyer, J. T., Folts, J., Harnly, J., Hollman, P., Keen, C. L., Mazza, G., Messina, M., Scalbert, A., Vita, J., Williamson, G. and Burrowes, J. 2007. Flavonoids and Heart Health: proceedings of the ILSI North America Flavonoids Workshop, May 31-June 1, 2005, Washington, DC. J. Nutr. 137: 718S-737S.

13. Froehlich, J. W., Dodds, E. D., Barboza, M., McJimpsey, E. L., Seipert, R. R., Francis, J., An, H. J., Freeman, S., German, J. B. and Lebrilla, C. B. 2010. Glycoprotein Expression in Human Milk dur-ing Lactation. J. Agric. Food Chem. 58: 6440-6448,

14. Dallas, D. C., Martin, W. F., Strum, J. S., Zivkovic, A. M., Smilowitz, J. T., Underwood, M. A., Affolter, M.; Lebrilla, C. B. and Berman, J. B. 2011. N-Linked Glycan Profiling of Mature Human Milk by High-Performance Microfluidic Chip Liquid Chro-matography Time-of-Flight Tandem Mass Spec-trometry. J. Agric. Food Chem. 59: 4255-4263.

15. Selma, M. V., Espín, J. C. and Tomás-Barberán, F. A. 2009. Interaction between Phenolics and Gut Micro-biota: Role in Human Health. J. Agric. Food Chem. 57: 6485-6501.

16. Lehotay, S. J., Anastassiades, M. and Majors, R. E. 2010. QuEChERS, a sample preparation technique that is “catching on”: an up-to-date interview with its inventors. LCGC N. Am. 28: 504-516.

Contamination and Changes of Food Factors during Processing with Modeling Applications - Safety Related Issues

SHIOWSHUH SHEEN*,#

Eastern Regional Research Center, Agricultural Research Service, United States Department of Agriculture, PA, U.S.A.

ABSTRACT

Chemical and microbiological contaminations of food during processing and preservation can result in foodborne illness outbreaks and/or poisoning. Chemical contaminations can occur through exposure of foods to illegal additives, pesticides and fertilizer residues, toxic compounds formed by chemical reactions, and are easier to control than illnesses caused by microorganisms. In general, chemical reactions in food can be described using the first-order kinetic models. Food quality factors related to nutrition, color, texture, etc. are typically affected by temperature, pH, moisture content, as well as microbial growth. However, modeling the growth and inactivation of microorganisms is much more difficult and complex than that for chemical reactions. If harmful microbes are not eliminated during processing, their survival and growth may cause spoilage and foodborne illness. Food safety intervention technologies such as microwave heating, and modified packaging were discussed, as well as issues related to cross-contamination of foods during processing. Microbial food safety can be enhanced by the development of growth and inactivation modeling tools, world-wide data sharing, and collaboration in which the Pathogen Modeling Program (PMP) and ComBase can be utilized.

Key words: Foodborne pathogens, antimicrobial agents, MAP, microwave, modeling

                                                       *Author for correspondence. Tel: +215-836-3774; Fax: +215-233-6406; E-mail: Shiowshuh.sheen@ars.usda.gov

#Mention of trade names or commercial products in this publication is solely for the purpose of providing specific infor mation and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer

INTRODUCTION

Food processing is common in the food supply chain to ensure safety, preserve food qualities and extend shelf life. Food property changes and contaminations may occur during the process operations and storage. Important food factors for the safety concerns may include microbial and chemical aspects. Microbes can be further divided into two categories, i.e. the good and bad bugs. The good microorganisms have been applied in the fermentation, for example, to produce flavors and spirits for centuries. The bad microorganisms including foodborne pathogens, e.g. Listeria monocytogenes, Escherichia coli O157 : H7, Salmonella, Hepatitis A, Norovirus and etc., are still causing human illnesses in recent years(1). Contamination of harmful chemicals during processing usually can be predicted or prevented. Sources of potential chemical contamination sources are ingredients, additives (illegal, pesticide, or fertilizer residuals) and etc. (water contamination). Chemical reaction may generate carcinogenic compounds, e.g. acrylamide(2) during high-temperature baking or frying or heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs)(3) from charred or grilled foods in the open air. Microbial contamination is more complicated than chemical which involves living microorganisms in a nutrient-rich medium (foods). In this

paper, the foodborne pathogens will be discussed further. Three notorious foodborne pathogens caused major

human illness are L. monocytogenes, E. coli O157 : H7, and Salmonella spp., in which L. monocytogenes is at the ‘zero tolerance’ status for ready-to-eat foods in USA. When microbes grow in foods, some toxic compounds are produced to make human sick, and the severity is individual dependent. In order to reduce the foodborne pathogen hazard, it is important to eliminate those bugs (factors) during food processing operations including packaging. The keys are to prevent the contamination and then, to kill all the harmful bugs in the food supply system. Since it is almost impossible to achieve this ideal goal, except the aseptic/retort process, the modeling technique may be used to predict the growth/survival potentials and to assess the risk.

Mathematical modeling is a scientific and systematic approach to study and describe the recurrent events or phenomena with successful application track for decades. When models are properly developed and validated, their applications save costs and time. For the microbial food safety concerns, models are developed based on the facts that most bacterial behaviors are reproducible, and can be quantified by characterizing the environmental factors affecting growth, survival, and inactivation. For food safety management, control and monitor, models may serve an effective and useful tool for risk assessment.

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MATERIALS AND METHODS

I. Microbial Strains

Experiments were carried out using a cocktail mixture of pathogens of 4 - 6 strains depending on the availability in Lab to cover a wide range of potential microbial hazard. The selected strains are typical from the food outbreaks and collected by the United States Department of Agriculture (USDA) or Food and Drug Administration (FDA). A loopful of each strain was transferred from a stock culture (stored at -80◦C) into 10 mL of brain heart infusion broth (BHI, Becton, Dickinson and Co., Sparks, Md., USA) and incubated at 37◦C for 6 h. A loopful of cell suspension of each strain was then separately transferred to 10 mL of BHI broth and incubated at 37◦C for 24 h. A cocktail was formed by mixing each individual strain which has been properly diluted to have similar microbial counts. A certain amount of the cocktail (e.g. 3 log CFU/g, CFU: colony forming unit) was then inoculated onto the selected food. Food samples were taken at certain time intervals for plate counts. Selective medium agar plates were used, e.g. L. monocytogenes – MOX, Salmonella – Rappaport, and E. coli O157 : H7 – CT-SMAC.

II. Temperature and Other Parameters

Microbial growth is largely affected by temperature and other physical parameters. Most food items, except the aseptically processed ones, may subject to spoilage and cross-contaminations. For frozen and refrigerated foods, abused temperature around 6 - 10ºC were selected for E. coli O157 : H7 and Salmonella and 2 - 10ºC for L. monocytogenes. The pH and moisture content at 7.0 and > 90%, respectively, were applied to these three common pathogens. Modified Atmosphere Packaging (MAP) was applied at N2/CO2 ratio of 50/50% vs. no MAP (ambient). Two antimicrobial agents, i.e. chitosan and allyl isothiocyanate (AIT), were investigated to examine their functions in film and MAP, respectively.

III. Mathematical Model

There are several simplified growth models (primary) available in the literature which may be used for data/curve fitting when a completed data set with lag time, exponential grow phase and maximum growth population was collected. Those models were derived from the observation data with first-order kinetic reaction theory to achieve the empirical models, e.g. the Gompertz model(4); Baranyi and Roberts model(5). Those models show microbial growth as a function of time at constant temperature. If other parameters (e.g. pH, water activity, etc.) are included in the growth models, the secondary models are needed. Secondary models may include the Square-Root-Type (Ratkowsky et al.)(6), polynomial-type

and other models which can be developed by using experimental designs and regression procedures (v9.1, SAS). Factorial design and linear or non-linear regression were used in this report. Microbial surface transfer models during meat slicing were developed by the non-linear regression procedures.

IV. Microscopic Instrumentation for Microbe Testing

In order to observe the dead/live cells of microbes, it is necessary to use high resolution microscopy instrument. Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) may provide the details in cell structure to distinguish the dead/live cells under different stress. For Confocal Laser Scanning Microscope (CLSM) application, 5 μL aliquots of stain/dye (Live/Dead BacLight Bacterial Viability Kits – Invitrogen, Carlsbad, Calif., USA) was added, mixed with the microbial sample, and incubated for 15 min to identify the dead/live cells by the stained red/green color, respectively.

V. “Smart” Microwave Oven

A household microwave oven with an inverter was modified to include the infrared (IR) temperature sensor, feedback control device and software. The ‘smart’ microwave oven can monitor the food surface temperatures and regulate the inverter to deliver microwave power as required(7) which further improve cooking uniformity to achieve the thermal killing effect (lethality) in the food domain and to maintain food texture and qualities.

RESULTS AND DISCUSSION

I. Contamination and Inactivation during Processing

An example of microbial cross-contamination is the surface transfer of pathogens during slicing of ready-to-eat (RTE) meats. L. monocytogenes, E. coli O157 : H7 and Salmonella may be transferred from one surface to another contact surface with different potentials. The transfer potential also depends on other factors, e.g. foods, process parameters, etc. There is a 2 - 3 log CFU/slice gap between the inoculated (on blade surface) and transferred counts at the initial slicing stage. The discrepancy of cell counts could be due to the surface sheer stress impact. Figure 1 shows the live cells (green on blade surface before slicing) and dead cells (red on blade surface after 10 slices of agar slicing) under CLSM. Agar was used to make the CLSM images possible since meat contains proteins and phosphates which cause interference with the CLSM observation. The rpm of blade in the range between 100 and 300 was found not significant impact on cell dead. Figure 1 also demonstrates that the cells can be trapped into the rough blade surface. If cells remained alive, cross-contamination may occur anytime during

slicing to cause food safety risk. The surface transfer models(8,9) are all empirical

based on curve fitting to attain the simple and easy-to-use equations. Salmonella transfer models are shown as the following:

Transfer model for direct blade inoculation case: )061.0051.0(446.1 )301.0( nXnY (1)

For the contaminated meat (e.g. ham) to a clean blade to another clean meat (e.g. ham) case:

)151.0(713.0119.1 XnY (2)

Where Y is the microbial counts in log CFU/sliced meat or log CFU/g; n: total initial cell counts of surface inoculation in log CFU; X: sliced meat index, e.g. 1st, 5th, and etc. If meat slicing rate is m per min, X can be replaced by m·t (t: time in min). However, X in Equation (1) and (2) is an integral to be satisfied by selecting the process time. Figure 2 shows the surface transfer pattern of meat slicing at n = 5.0 of three pathogens. Without the models, the surface transfer prediction may become challenge especially at the low microbial contamination level.

II. Effects of Antimicrobial Agents and MAP on Microbial Survival

There are many reported food-grade chemicals or ingredients which may be used to reduce or inhibit the microbial growth including chitosan(10) and natural essential oils. The effects of modified atmosphere packaging (MAP) and antimicrobial agent on fish fillet (results not yet published) indicated that using a proper combination of allyl isothiocyanate (AIT) and modified atmosphere (MA) may achieve the shelf life extension. Current study showed that lowering storage temperature as well as increasing AIT concentration (up to 36 ppm)

can extend lag phase (LP) and reduce the growth rate (GR) of Pseudomonas aeruginosa. In addition, AIT alone reduced the P. aeruginosa counts within the first several hours. Results also showed that application of MA had a similar antimicrobial effect as lowering temperature. The synergy effect of AIT and MA combination extended the shelf life of fresh catfish fillet from 4 days (control) to 13 days at 8°C.

The shelf life is largely associated with the lag phase of the key microbe(s) (either spoilage or foodborne pathogens) in foods. A study to collect the survival data of certain microbe, P. aeruginosa, in a specified food system can assist in establishing the LP and/or GR using the available primary growth models (e.g. Baranyi). Thereafter, the secondary models to estimate the LP and GR of P. aeruginosa with impact of antimicrobial and/or MAP under abuse temperature conditions will be developed using the linear regression procedures (SAS) to attain polynomial equations. The results may assist food industry to predict the shelf life of catfish products which may be further optimized with proper AIC concentration in a MA package.

Chitosan has been reported an effective compound to

inhibit the growth of foodborne pathogens including L. monocytogenes, E. coli O157 : H7 and Salmonella spp. The impact may be obvious in a liquid phase where the microbe has intimate contact with chitosan. However, in an in-mobilized structure the efficacy may be significantly reduced. In current study, an edible thin film was constructed by 90% of chitosan and 10% of other polysaccharide (e.g. cellulose), followed by inoculating microbes on film surface to examine the microbial survival. There is almost no impact on the microbe survival as shown in Figure 3, where the TEM image was taken after 24 h of contact. All cells remained intact on film surface. There is not damage of cells observed with longer contact time.

 

 Figure 1. CLSM images of Listeria monocytogenes sur-vival after surface stress impact due to slicing operation (12” round blade @ 300 rpm). Alive (green, before slicing) and dead (red, after slicing) on blade surface.

Figure 2. Transfer predictions using models with inoculation level at 5 log CFU for L. monocytogenes (Lm), E. coli O157 : H7 (Ec) and Salmonella (Sal) (surface-transfer from inoculated RTE meat to blade to clean RTE meat).

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MATERIALS AND METHODS

I. Microbial Strains

Experiments were carried out using a cocktail mixture of pathogens of 4 - 6 strains depending on the availability in Lab to cover a wide range of potential microbial hazard. The selected strains are typical from the food outbreaks and collected by the United States Department of Agriculture (USDA) or Food and Drug Administration (FDA). A loopful of each strain was transferred from a stock culture (stored at -80◦C) into 10 mL of brain heart infusion broth (BHI, Becton, Dickinson and Co., Sparks, Md., USA) and incubated at 37◦C for 6 h. A loopful of cell suspension of each strain was then separately transferred to 10 mL of BHI broth and incubated at 37◦C for 24 h. A cocktail was formed by mixing each individual strain which has been properly diluted to have similar microbial counts. A certain amount of the cocktail (e.g. 3 log CFU/g, CFU: colony forming unit) was then inoculated onto the selected food. Food samples were taken at certain time intervals for plate counts. Selective medium agar plates were used, e.g. L. monocytogenes – MOX, Salmonella – Rappaport, and E. coli O157 : H7 – CT-SMAC.

II. Temperature and Other Parameters

Microbial growth is largely affected by temperature and other physical parameters. Most food items, except the aseptically processed ones, may subject to spoilage and cross-contaminations. For frozen and refrigerated foods, abused temperature around 6 - 10ºC were selected for E. coli O157 : H7 and Salmonella and 2 - 10ºC for L. monocytogenes. The pH and moisture content at 7.0 and > 90%, respectively, were applied to these three common pathogens. Modified Atmosphere Packaging (MAP) was applied at N2/CO2 ratio of 50/50% vs. no MAP (ambient). Two antimicrobial agents, i.e. chitosan and allyl isothiocyanate (AIT), were investigated to examine their functions in film and MAP, respectively.

III. Mathematical Model

There are several simplified growth models (primary) available in the literature which may be used for data/curve fitting when a completed data set with lag time, exponential grow phase and maximum growth population was collected. Those models were derived from the observation data with first-order kinetic reaction theory to achieve the empirical models, e.g. the Gompertz model(4); Baranyi and Roberts model(5). Those models show microbial growth as a function of time at constant temperature. If other parameters (e.g. pH, water activity, etc.) are included in the growth models, the secondary models are needed. Secondary models may include the Square-Root-Type (Ratkowsky et al.)(6), polynomial-type

and other models which can be developed by using experimental designs and regression procedures (v9.1, SAS). Factorial design and linear or non-linear regression were used in this report. Microbial surface transfer models during meat slicing were developed by the non-linear regression procedures.

IV. Microscopic Instrumentation for Microbe Testing

In order to observe the dead/live cells of microbes, it is necessary to use high resolution microscopy instrument. Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) may provide the details in cell structure to distinguish the dead/live cells under different stress. For Confocal Laser Scanning Microscope (CLSM) application, 5 μL aliquots of stain/dye (Live/Dead BacLight Bacterial Viability Kits – Invitrogen, Carlsbad, Calif., USA) was added, mixed with the microbial sample, and incubated for 15 min to identify the dead/live cells by the stained red/green color, respectively.

V. “Smart” Microwave Oven

A household microwave oven with an inverter was modified to include the infrared (IR) temperature sensor, feedback control device and software. The ‘smart’ microwave oven can monitor the food surface temperatures and regulate the inverter to deliver microwave power as required(7) which further improve cooking uniformity to achieve the thermal killing effect (lethality) in the food domain and to maintain food texture and qualities.

RESULTS AND DISCUSSION

I. Contamination and Inactivation during Processing

An example of microbial cross-contamination is the surface transfer of pathogens during slicing of ready-to-eat (RTE) meats. L. monocytogenes, E. coli O157 : H7 and Salmonella may be transferred from one surface to another contact surface with different potentials. The transfer potential also depends on other factors, e.g. foods, process parameters, etc. There is a 2 - 3 log CFU/slice gap between the inoculated (on blade surface) and transferred counts at the initial slicing stage. The discrepancy of cell counts could be due to the surface sheer stress impact. Figure 1 shows the live cells (green on blade surface before slicing) and dead cells (red on blade surface after 10 slices of agar slicing) under CLSM. Agar was used to make the CLSM images possible since meat contains proteins and phosphates which cause interference with the CLSM observation. The rpm of blade in the range between 100 and 300 was found not significant impact on cell dead. Figure 1 also demonstrates that the cells can be trapped into the rough blade surface. If cells remained alive, cross-contamination may occur anytime during

slicing to cause food safety risk. The surface transfer models(8,9) are all empirical

based on curve fitting to attain the simple and easy-to-use equations. Salmonella transfer models are shown as the following:

Transfer model for direct blade inoculation case: )061.0051.0(446.1 )301.0( nXnY (1)

For the contaminated meat (e.g. ham) to a clean blade to another clean meat (e.g. ham) case:

)151.0(713.0119.1 XnY (2)

Where Y is the microbial counts in log CFU/sliced meat or log CFU/g; n: total initial cell counts of surface inoculation in log CFU; X: sliced meat index, e.g. 1st, 5th, and etc. If meat slicing rate is m per min, X can be replaced by m·t (t: time in min). However, X in Equation (1) and (2) is an integral to be satisfied by selecting the process time. Figure 2 shows the surface transfer pattern of meat slicing at n = 5.0 of three pathogens. Without the models, the surface transfer prediction may become challenge especially at the low microbial contamination level.

II. Effects of Antimicrobial Agents and MAP on Microbial Survival

There are many reported food-grade chemicals or ingredients which may be used to reduce or inhibit the microbial growth including chitosan(10) and natural essential oils. The effects of modified atmosphere packaging (MAP) and antimicrobial agent on fish fillet (results not yet published) indicated that using a proper combination of allyl isothiocyanate (AIT) and modified atmosphere (MA) may achieve the shelf life extension. Current study showed that lowering storage temperature as well as increasing AIT concentration (up to 36 ppm)

can extend lag phase (LP) and reduce the growth rate (GR) of Pseudomonas aeruginosa. In addition, AIT alone reduced the P. aeruginosa counts within the first several hours. Results also showed that application of MA had a similar antimicrobial effect as lowering temperature. The synergy effect of AIT and MA combination extended the shelf life of fresh catfish fillet from 4 days (control) to 13 days at 8°C.

The shelf life is largely associated with the lag phase of the key microbe(s) (either spoilage or foodborne pathogens) in foods. A study to collect the survival data of certain microbe, P. aeruginosa, in a specified food system can assist in establishing the LP and/or GR using the available primary growth models (e.g. Baranyi). Thereafter, the secondary models to estimate the LP and GR of P. aeruginosa with impact of antimicrobial and/or MAP under abuse temperature conditions will be developed using the linear regression procedures (SAS) to attain polynomial equations. The results may assist food industry to predict the shelf life of catfish products which may be further optimized with proper AIC concentration in a MA package.

Chitosan has been reported an effective compound to

inhibit the growth of foodborne pathogens including L. monocytogenes, E. coli O157 : H7 and Salmonella spp. The impact may be obvious in a liquid phase where the microbe has intimate contact with chitosan. However, in an in-mobilized structure the efficacy may be significantly reduced. In current study, an edible thin film was constructed by 90% of chitosan and 10% of other polysaccharide (e.g. cellulose), followed by inoculating microbes on film surface to examine the microbial survival. There is almost no impact on the microbe survival as shown in Figure 3, where the TEM image was taken after 24 h of contact. All cells remained intact on film surface. There is not damage of cells observed with longer contact time.

 

 Figure 1. CLSM images of Listeria monocytogenes sur-vival after surface stress impact due to slicing operation (12” round blade @ 300 rpm). Alive (green, before slicing) and dead (red, after slicing) on blade surface.

Figure 2. Transfer predictions using models with inoculation level at 5 log CFU for L. monocytogenes (Lm), E. coli O157 : H7 (Ec) and Salmonella (Sal) (surface-transfer from inoculated RTE meat to blade to clean RTE meat).

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III. Microwave Heating Process – A ‘New’ Approach

Microwave is well-known for its un-even heating in food cooking process resulting in microbial risk, especially, for the partially-cooked not ready-to-eat food items (NRTE). However, microwave oven may be better designed with IR temperature sensor and feedback control device to improve the cooking uniformity. The modified household oven may provide a reliable cooking tool to ensure consumer food products of foodborne pathogen free. Although there are some hurdles to be overcome, preliminary results showed that products, with best sensory quality, are attainable when combined with food ingredients (formulation) and packaging (microwave susceptor) technology. Fish fillet treated in phosphate solution may be well-cooked in microwave oven which has inverter and feedback control design. The products are juicy, tender and have a nice appearance which achieved by eliminating the ‘bumping’ during microwave cooking.

CONCLUSIONS

Many food factors are affected by processing and other preservation methods. Those factors include the nutrients, texture, appearance, and safety. Among them, safety is the most important concern for government, manufacturers and consumers. Model development and applications may enhance the food safety concerns with risk assessment. The Pathogen Modeling Program (PMP) and ComBase are two useful tools (a collection of models and data) to predict foodborne pathogen growth and inactivation in different foods. Using antimicrobial agents (chemicals and MAP) to enhance food safety may be valuable, but their effectiveness should be carefully evaluated in different food application systems. Microwave heating process may become a viable operation to achieve microbial food safety goal with build-in the proper control devices and design.

ACKNOWLEDGMENTS

The author would like to thank Brittany Mills, Peggy Williams, and Sonya Costa with the USDA/ARS/ ERRC, Wyndmoor, PA., U.S.A., for their dedicated data and image collection work.

REFERENCES

1. Foodborne illness outbreaks. 2011. http://www.foodpoisonjournal.com/foodborne-illness-outbreaks/. Accessed on Oct. 7, 2011.

2. Becalski, A., Lau, B. P. Y., Lewis, D. and Seaman, S. W. 2003. Acrylamide in foods: occurrence, sources, and modeling. J. Agric. Food Chem. 51(3): 802-808.

3. National Cancer Institute. 2010. Chemicals in meat cooked at high temperatures and cancer risk. http://www.cancer.gov/cancertopics/factsheet/Risk/cooked-meats. Accessed on Oct. 3, 2011.

4. McKellar, R. C. and Lu, X. 2004. Primary model. In "Modeling Microbial Responses in Food". pp. 21-62. McKellar, R. C. and Lu, X. eds. CRC Press LLC. Boca Raton, FL, U.S.A.

5. Baranyi, J. and Roberts, T. A. 1994. A dynamic ap-proach to predicting bacterial growth in food. Int. J. Food Microbiol. 23: 277-294.

6. Ratkowsky, D. A., Lowry, R. K., McMeekin, T. A., Stokes, A. N. and Chandler, R. E., 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J. Bacteriol. 154: 1222-1226.

7. Huang, L. and Sites, J. 2010. New automated microwave heating process for cooking and pas-teurization of microwaveable foods containing raw meats. J. Food Sci. 75: E110-E115.

8. Sheen, S. 2008. Modeling surface transfer of Lis-teria monocytogenes on salami during slicing. J. Food Sci. 73(6): E304-E311.

9. Sheen, S. and Hwang, C. A. 2011. Modeling the surface cross-contamination of Salmonella spp. on Ready-To-Eat meat via slicing operation. Food Nutr. Sci. in press.

10. Raafat, D., Bargen, K. V., Haas, A. and Sahl, H. G. 2008. Insights into the mode of action of chitosan as an antibacterial compound. Appl. Environ. Microbiol. 74(12): 3764-3773.

 Figure 3. TEM image of E. coli O157:H7 on thin film (chitosan 90%: cellulose 10%, w/w, Left-hand side) sur-face after 24 h of contact.

Food Safety Research of the Agricultural Research Service, USDA

SHU-I TU*

U.S. Department of Agriculture, Agricultural Research Service, North Atlantic Area, U.S.A.

ABSTRACT

The Agricultural Research Service (ARS) is the in-house research institute of the United States Department of Agriculture (USDA). One of the key missions of ARS is to provide, through scientific research, the means to ensure that the food supply is safe and secure for consumers and that food and feed meet foreign and domestic regulatory requirements. Food safety research seeks ways to assess, control or eliminate potentially harmful food contaminants, including both introduced and naturally occurring pathogenic bacteria, viruses and parasites, toxins and non-biological-based chemical contaminants, mycotoxins and plant toxins. Food safety is a global issue; thus, the research program involves both national and international collaborations through formal and informal partnerships. To meet those responsibilities, ARS has engaged more than 180 Ph.D. research scientists in 25 laboratories to work on 62 research projects. Using the involvement in post-harvest food safety research of an Eastern Regional Research Center laboratory in the North Atlantic Area of ARS as an example, I will describe the processes and procedures for project development, research accountability, and technology transfer to stakeholders. In addition, specific examples on significant research accomplishments in pathogen detection involving collaboration with domestic and international entities will also be described.

Key words: food Safety, pathogen detection, biosensors, immuo-magnetic separation, time resolved fluorescence

                                                       *Author for correspondence. Tel: 215-233-6683;

Fax: 215-233-6668; E-mail: ShuI.Tu@ARS.USDA.GOV  

INTRODUCTION

As the in-house research arm of the United States Department of Agriculture, the Agricultural Research Service (ARS) has the responsibility to conduct research to ensure high-quality, safe food, and other agricultural product; to assess the nutritional needs of Americans; to sustain a competitive agricultural economy; to enhance the natural resource base and the environment, and to provide economic opportunities for rural citizens, com-munities, and society as a whole. To meet those missions, ARS has 2,500 scientists together with other 6,000 other program and administrative supports working on about 1,000 projects within 20 National Programs at 100 re-search locations throughout the nation.

As mentioned, to ensure the food safety is one of the key missions of ARS. According to the U.S. Centers for Disease Control and Prevention estimated that contami-nated foods account for approximately 48 million ill-nesses; 128,000 hospitalizations; and 3,000 deaths yearly in the United States alone(1). To address the problem of this magnitude, ARS has assembled more than 180 Ph.D. research scientists in 25 laboratories to work on 62 re-search projects that are designed to assess, control or eliminate potentially harmful food contaminants, includ-ing both introduced and naturally occurring pathogenic

bacteria, viruses and parasites, toxins and non-biological- based chemical contaminants, mycotoxins and plant tox-ins.

In this manuscript, we described the procedure, a peer-reviewed process, used to develop and evaluate ARS food safety research projects. An example of how this procedure (OSQR, Office of Scientific Quality Review) has been applied to the development and execution of a real laboratory project, pathogen detection by integrated biosensor processes, will be described. It is hoped that the information will be of value to the efforts of other inter-national institutes needing to engage in food safety re-search.

TOPICS I. OSQR Process for Developing Food Safety Projects

The ARS Peer Review Process is an essential part of

the 5-year ARS research program cycle (Figure 1). Review was mandated by the Agricultural Research Extension, and Education Reform Act of 1998, which requires successful completion of peer review as a prerequisite to performance of research. For food safety projects, the cycle starts with the Office and National Program (ONP) seeking inputs mainly from other federal

Journal of Food and Drug Analysis, Vol. 20, Suppl. 1, 2012414

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