P174: Enumeration of Salmonella with the Polymerase Chain Reaction BAX System and Simulation Modeling Thomas P. Oscar, Agricultural Research Service, USDA,

Slides:



Advertisements
Similar presentations
Detection and Enumeration of Food Pathogens with the BAX® PCR System Thomas P. Oscar, Ph.D. Research Food Technologist Welcome……thank you for coming!
Advertisements

Validation of a Salmonella Survival and Growth Model for Extrapolation to a Different Previous History: Frozen Storage Thomas P. Oscar, Agricultural Research.
Predictive Microbiology Approach for Enumeration of Salmonella on Chicken Parts Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center.
Predictive Model for Survival and Growth of Salmonella on Chicken during Cold Storage Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111,
Overarching Goal: Understand that computer models require the merging of mathematics and science. 1.Understand how computational reasoning can be infused.
Validation of Microbiological Methods for Use in the Food Industry Brazilian Association for Food Protection 6 th International Symposium Sao Paulo, Brazil.
Chapter 28 Design of Experiments (DOE). Objectives Define basic design of experiments (DOE) terminology. Apply DOE principles. Plan, organize, and evaluate.
An Enumeration Method and Sampling Plan for Mapping the Number and Distribution of Salmonella on the Chicken Carcass Thomas P. Oscar, Agricultural Research.
A Simulation Model for Predicting the Potential Growth of Salmonella as a Function of Time, Temperature and Type of Chicken Thomas P. Oscar, Agricultural.
Methods in Microbial Ecology
Introduction to Lab Ex. 20: Enumeration of Bacteria - Most Probable Number method Membrane Filter method.
APPLICATION OF MICROTESTER FOR DETECTION OF LOW MICROBIAL CONTAMINATION Oliver Reichart Katalin Szakmár.
Fitness Problems in Escherichia coli K-12 Transformed with a High Copy Plasmid Encoding the Green Fluorescent Protein Thomas P. Oscar a, Kalpana Dulal.
Introduction to Lab Ex. 19: Enumeration of Bacteria
 Bacterial Enumeration Gloria Phuong Le Microbiology Lab Dr. Fran Norflus.
Results Bacteria were detected at 10 3 cells/g in un-injected controls, but none were Salmonella sp. Salmonella recovery from injected controls exceeded.
Thomas P. Oscar, Ph.D. USDA, ARS
Post-Harvest Predictive Microbiology & Process Risk Models Microbial Food Safety Research Unit Eastern Regional Research Center North Atlantic Area.
Study of microorganisms in foods by conventional methods
Mapping the Distribution of Salmonella Contamination on the Chicken Carcass Thomas P. Oscar 1, Geoffrey K. Rutto 2, Jacquelyn B. Ludwig 1 and Salina Parveen.
Variation among Batches of Freshly Ground Chicken Breast Meat Complicates the Modeling of Salmonella Growth Kinetics Thomas P. Oscar USDA, ARS Microbial.
Validating the Micro PRO™ Technology. Overview of Today’s Presentation Validation Resources Micro PRO™ Applications and Corresponding Validation Parameters.
Graphs of Functions Graphs of Functions In addition to level 3.0 and beyond what was taught in class, the student may: Make connection with other.
Modeling the Survival and Growth of Salmonella on Chicken Skin Stored at 4 to 12  C Thomas P. Oscar, Ph.D. U.S. Department of Agriculture Agricultural.
The Examination of Residuals. Examination of Residuals The fitting of models to data is done using an iterative approach. The first step is to fit a simple.
Modeling Salmonella Growth from a Low Initial Density on Chicken Products with Native Microflora Thomas P. Oscar, Agricultural Research Service, USDA,
T4-04 Predictive Model for Growth of Salmonella Typhimurium DT104 on Ground Chicken Breast Meat Thomas P. Oscar, Ph.D. USDA-ARS, Microbial Food Safety.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
Practical Part Microscopic Examination of Microorganisms Experiments Identification of MOs Different Staining Techniques.
Comparison of the Bacterial Community Naturally Occurring on Spinach Seeds and Seedlings Phyllis Carder 1,2, Gabriela Lopez-Velasco 1, Monica Ponder 1.
Performance of Growth Models for Salmonella and Other Pathogens Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center for Food Science.
Populations Group of individuals that belong to the same species and live in the same area Chapter 5 California Biology Science Standards B1 6.b. Students.
P2-78: Generic Modeling Approach for Quantitative Microbial Risk Assessment Thomas P. Oscar, USDA, ARS/1890 Center of Excellence in Poultry Food Safety.
Modeling for Quantitative Microbial Risk Assessment
USDA, ARS Workshop Poultry Food Assess Risk Model (Poultry FARM)
Improving Food Safety by Poultry Carcass Mapping
Growth Kinetics of Parent and Green Fluorescent Protein-Producing Strains of Salmonella Thomas P. Oscar, Agricultural Research Service, USDA, 1124 Trigg.
Figure 1 P176: A Quantitative Risk Assessment Model for Salmonella and Whole Chickens at Retail Thomas P. Oscar, Agricultural Research Service, USDA, 1124.
Response Surface Models for Effects of Previous Sodium Chloride and Temperature on Growth Kinetics of Salmonella Typhimurium on Cooked Chicken Breast Thomas.
Lab 8: Most Probable Number Method (MPN). Most Probable Number Method (MPN) What is the MPN method? How to determine the amount of bacteria from the MPN.
© 2010 Pearson Prentice Hall. All rights reserved 7-1.
WORKPACKAGE 2.?: DELIVERABLE 2.?.?: PILLAR 2: Control and intervention strategies along the fork-to-farm chain to ensure beef safety Laboratory of Microbiology.
min 30 min 45 min 60 min KDa Figure S1. SDS-PAGE of supernatant after incubation in digestion buffer. L. monocytogenes.
STANDARD PLATE COUNT PCA or NA growth of wide range of microorganisms not exacting in their nutrition requirements fastidious organisms may not grow colonies.
Single and Multiple Substrate Kinetics A culture grown in a simple medium including 0.3% wt/vol of glucose; at time t=0 , it is inoculated into.
Innovative Modeling Approaches Applicable to Risk Assessments Thomas P. Oscar, PhD USDA, ARS Princess Anne, MD, USA.
HMP Simulation - Introduction Deterministic vs. Stochastic Models Risk Analysis Random Variables Best Case/Worst Case Analysis What-If Analysis.
Enumeration (determine the numbers of bacteria in a sample) Direct Measurement of Microbial Growth  Microscopic count - the microbes in a measured volume.
Microbial kinetics of growth and substrate utilization. Batch culture and Kinetics of Microbial growth in batch culture After inoculation the growth rate.
Abstract In addition to continued testing for E. coli O157:H7, new regulations in the United States require industry to begin monitoring for six non-O157.
General Regression Neural Network Model for Growth of Salmonella Serotypes on Chicken Skin for Use in Risk Assessment Thomas P. Oscar, Ph.D. USDA, ARS.
Survival of pathogenic microorganisms in spices and herbs Ioanna Stratakou, Ilias Apostolakos, Heidy MW Den Besten and Marcel H Zwietering Introduction.
Microbial Kinetics and Substrate utilization in Fermentation
A very common question that gets asked is: “Why does my ATP test tell me that I have substantial microbial contamination when my culture-based tests tell.
Bacterial Cultures *Bacteria grow best in warm, moist, dark areas that contain a lot of food. -When we culture bacteria, we provide them with this environment.
Clinical Laboratory Analysis of Immunoglobulin Heavy Chain Variable Region Genes for Chronic Lymphocytic Leukemia Prognosis  Philippe Szankasi, David.
Restriction Enzyme Digestion of Phage DNA
Result Introduction Methods
Evaluation of chemical immersion treatments to reduce microbial counts in fresh beef Ahmed Kassem1, Joseph Meade1, Kevina McGill1, James Gibbons1, James.
Claim 1 Smarter Balanced Sample Items Grade 8 - Target E
Fungal Assessment by culture and RT-PCR assay in a Waste Sorting Plant
Variability and Its Impact
النمو والعد البكتيري Microbial growth النمو الجرثومي.
Quantitative detection of Toxoplasma gondii DNA in human body fluids by TaqMan polymerase chain reaction  O. Kupferschmidt, D. Krüger, T.K. Held, H. Ellerhrok,
Marianne Bénard, Chrystelle Maric, Gérard Pierron  Molecular Cell 
Clinical Laboratory Analysis of Immunoglobulin Heavy Chain Variable Region Genes for Chronic Lymphocytic Leukemia Prognosis  Philippe Szankasi, David.
Today, we will review for the module 5 end of module assessment.
Claim 1 Smarter Balanced Sample Items Grade 8 - Target E
IL-12 affects Dermatophagoides farinae–induced IL-4 production by T cells from pediatric patients with mite-sensitive asthma  Takeshi Noma, MD, PhD, Izumi.
Marianne Bénard, Chrystelle Maric, Gérard Pierron  Molecular Cell 
Presentation transcript:

P174: Enumeration of Salmonella with the Polymerase Chain Reaction BAX System and Simulation Modeling Thomas P. Oscar, Agricultural Research Service, USDA, 1124 Trigg Hall, University of Maryland Eastern Shore, Princess Anne, MD ; (fax); INTRODUCTION With the advent of molecular methods such as polymerase chain reaction (PCR) detection that have high specificity for pathogens, it is now possible to develop enumeration methods for pathogens that only require pre-enrichment of the food sample and thus, are more rapid than traditional most probable number (MPN) enumeration methods. In 1998, Bailey evaluated a commercial PCR system (BAX, Qualicon, Inc., Wilmington, DE) for its ability to detect Salmonella in poultry samples relative to the conventional culture method. Using serial dilutions, he demonstrated that the size of the PCR band in the electrophoresis gel was related to the density of Salmonella in the pre-enrichment sample. In fact, the PCR band increased from a faint band at 10 2 CFU per ml to a full band at 10 7 CFU per ml. A visual scoring system based on PCR band size was developed for semi-quantitative enumeration of Salmonella in pre-enrichment samples. In the current study, a modified version of the PCR band size scoring system of Bailey (1998) was used to develop a simulation model for predicting the initial contamination of chicken with Salmonella as a function of PCR detection time score (PCR DTS ) and sample size. The method combines concepts of microbial growth kinetics, PCR detection of pathogens and simulation modeling to form a new method of enumeration for risk assessment. MATERIALS AND METHODS Challenge studies. Salmonella Typhimurium from ATCC and Salmonella Worthington from broiler ceca were used to develop the model. Stationary phase cultures grown at 37°C for 23 h were used to inoculate chicken homogenates consisting of 25 g of sterile chicken or 25 g of naturally contaminated chicken and 225 ml of sterile buffered peptone water. The initial density of Salmonella ranged from 10 0 to 10 6 CFU per 25 g of chicken. At 0, 2, 4, 6, 8, 10, 12 and 24 h of incubation at 37°C, a one ml subsample was collected for PCR analysis using the Qualicon BAX system. PCR analysis. One gel was run per chicken homogenate sample. For the eight lanes in the gel corresponding to the eight subsamples, a score of zero for no band, one for a faint band, two for a less than full band, and three for a full band was assigned. Thus, each chicken homogenate sample received a PCR DTS from zero to 24 by summing the scores for the eight subsample lanes in the gel. Standard curve. The PCR DTS for six or 12 chicken homogenate samples per experiment were graphed as a function of initial CFU of inoculated Salmonella per 25 g of chicken and the resulting curve was fit to a first or second order polynomial using GraphPad Prism (Figure 1). Simulation modeling. A simulation model for predicting the incidence and distribution of Salmonella among contaminated chicken samples as a function of PCR DTS and sample size was created in an Excel spreadsheet and was simulated (Figure 2). The PCR DTS of 12 naturally contaminated 25 g samples of chicken were used to define the frequency of occurrence of PCR DTS in the simulation model. The scenario depicted in Figure 2 was simulated for chicken samples that ranged in size from 25 to 500 g to determine the effect of sample size on the distribution of Salmonella contamination (Table 1). RESULTS AND DISCUSSION A linear relationship between PCR DTS and the initial density of Salmonella inoculated was observed for sterile chicken homogenates (results not shown). In contrast, a non-linear relationship was observed for non-sterile chicken homogenates (Figure 1) and could be explained by inhibition of Salmonella growth by competing microorganisms at low but not at high initial density of inoculated Salmonella. Type of sterile chicken meat and serotype did not affect the shape of the standard curve. The simulation model (Figure 2) was created from the standard curve for non-sterile chicken homogenates (Figure 1) and was simulated for sample sizes from 25 to 500 g (Table 1). Results of the simulation demonstrated that the incidence and number of Salmonella among contaminated samples of chicken increased in a non-linear manner (Table 1). Thus, linear extrapolation of enumeration results, a common practice in risk assessment, is not appropriate. The outputs of the model can serve as inputs in a risk assessment model developed using the method of Oscar (1997) or other similar methods. REFERENCES Bailey, J. S Detection of Salmonella cells within 24 to 26 hours in poultry samples with the polymerase chain reaction BAX system. J. Food Prot. 61: Oscar, T. P., Predictive modeling for risk assessment of microbial hazards, Reciprocal Meat Conference Proceedings, 50: Figure 2 Figure 1