Establishing shelf life: Use of Predictive modelling Linda Everis Linda.Everis@campdenbri.co.uk Tel 01386 842063

What is Predictive microbiology? Use of mathematical equations to describe microbiological behaviour e.g. how bacterial populations change with time how this change is affected by intrinsic and extrinsic parameters such as pH and temperature This information is used to predict likely responses in new previously untested situations

Types of predictions available Kinetic growth models - lag time, growth rate, time to reach target level of numbers Kinetic death models - time to reach a target decrease in numbers Growth/no-growth or growth boundary models - likelihood of growth occurring within a defined time Time to growth models - likely time to reach visible growth

Which variables can be changed in models? Models are usually limited to 3 or 4 variables e.g. Temperature of storage pH Water activity (salt/sugar etc) Preservatives/MAP/nitrite etc.

How are models produced? Choose organisms  Collect growth data from laboratory experiments under a range of conditions  Apply appropriate mathematical equations to produce model Verify the predicted values against data produced in real foods

Kinetic growth models •

Fitted growth curve

Curves from different conditions

Curves from different conditions

Primary model Primary model One equation LHS = result, RHS = time (+parameters) environment is a “given” Fitted separately at each temperature So each fitted primary model only works at temperature it was fitted - given environment Primary model

Example of a modelling interface

Example of a modelling interface

Modeling systems Pathogen models Spoilage models Combase predictor (CP) http://browser.combase.cc/ComBase_Predictor.aspx?model=1 USDA Pathogen Modelling Programme (PMP) http://pmp.errc.ars.usda.gov/PMPOnline.aspx?ModelID=2&Aerobic=True systems combined in a joint initiative(COMBASE) to make microbial response data freely available Spoilage models FORECAST Specific food types Seafood Spoilage and Safety Predictor

Pathogen growth models Aeromonas hydrophila (anaerobic, aerobic) Aeromonas hydrophila anaerobic Bacillus cereus (anaerobic, aerobic) Clostridium botulinum (non-proteolytic) Clostridium botulinum (proteolytic) Clostridium perfringens Escherichia coli O157:H7 (anaerobic, aerobic) Listeria monocytogenes (NaCl/aw) (anaerobic, aerobic) Salmonella (aerobic) Staphylococcus aureus (anaerobic, aerobic) Yersinia enterocolitica (aerobic) Shigella flexneri (anaerobic, aerobic)

Thermal death /inactivation models Clostridium botulinum (non-proteolytic) Escherichia coli O157:H7 Listeria monocytogenes Saccharomyces cerevisiae Salmonella (NaCl) Salmonella (glucose) Yersinia enterocolitica

Campden BRI FORECAST SYSTEM Exclusively for Spoilage organisms Describes kinetic growth time to turbidity for individual spoilage organisms groups of organisms product specific spoilage flora

Models in FORECAST Model Temperature (C) NaCl (% aq) Equivalent Aw pH Other Conditions Pseudomonas 0 - 15 0.0 - 4.0 1.00 - 0.977 5.5 - 7.0 Fluctuating temperature, pH, salt Bacillus spp. 5 - 25 0.5 - 10 0.997 - 0.935 4.0 - 7.0 Enterobacteriaceae 0 - 27 Yeasts (chilled foods) 0 - 22 2.6 - 6.0 Yeasts (fruit/drinks) (time to growth) - 2.0 - 7.0 0 - 60% Sucrose (w/v) 0 - 20% Ethanol (v/v) Potassium sorbate 0 - 1000(ppm) Lactic acid bacteria 2 - 30 3.0 - 6.0 Fluctuating temperature Meat spoilage 2 - 22 0 - 6 1.00 - 0.964 4.6 - 7.0 0 - 240 KNO2 (ppm) Fluctuating temperature, pH, salt Fish spoilage 4.5 - 8.0 Fresh produce TVC 2 - 25 Fresh produce Enterobacteriaceae Fresh produce lactic acid bacteria Fresh produce Pseudomonas Enterobacteriaceae death model 52 to 64 0 - 8 1.00 - 0.95 Predicts D value Bacillus (time to growth) 8 - 45 5 - 45 1.33-17.5 0.845-0.988 3.48-5.03

Model development: Fresh Produce Washed/shredded iceberg lettuce Gas mixes: 1= 5% O2: 5% CO2: 90% N2 2= 10% O2: 5% CO2: 85% N2 3= 5% O2: 95% N2 Storage temperatures : 2,5,8,10,15,25oC TVC, Enterobacteriaceae, Lactic acid bacteria and Pseudomonas enumerated Data suggests MAP has no effect on levels

Prediction of fresh produce shelf-life at 8°C

Application of predictive models to the food industry Combase Predictor Pathogen Modelling Program Campden BRI Forecast Campden BRI Acid club models

What can be predicted? Lag times Growth rate Time to reach a target level e.g. 105 Numbers reached in X hours e.g. 96 Likelihood of spoilage i.e. stability, risk, shelf life, best formulations and processes

Industrial applications of modeling systems New product development define final product formulation evaluate recipe changes rapid assessment of shelf-life affect of storage temperature help focus resources for microbial tests

Industrial applications of modeling systems Trouble shooting to assess process deviations decrease in process temperature increased pH value Setting microbiological specifications determine likely levels of bacteria present at end of shelf-life set GMP levels to ensure specifications are met

EVALUATION OF DIFFERENT SHELF LIFE PROTOCOLS Aim Use Predictive Microbiological Models in order to determine the likely differences between the six shelf life testing protocols. Conditions Organisms Time 10 day period Enterobacteriaceae pH 6.5 Pseudomonas Salt 1.0% Meat spoilage organisms End-of-life levels 106 cfu/g The above conditions have been used for illustrative purposes only and are not intended to represent actual food products or associated microorganisms

PROTOCOLS SUPPLIED DAY A B C D E F 0 4* 4 4 4 4 0 4* 4 4 4 4 1 4 2 4h@22 4 2h@22 4h@22 2 4 4 8 4 6 8 3 4 4 8 2h@22 6 8 4 4 6 8 8 6 8 5 4 6 8 8 6 8 6 4 6 8 8 6 8 7 4 6 8 8 6 8 8 4 6 8 8 6 8 9 10 6 8 8 6 8 10 6 8 8 6 8

Which will spoil fastest? or Product development Which will spoil fastest? or  12C 6C  pH5 pH7 ? ? 12C&pH5 6C&pH7

Models in product formulation 2 different conditions - which will spoil fastest ? from Forecast

Predicted growth of meat spoilage organisms as affected by water activity (other conditions held constant at 8ºC, 100ppm nitrite and pH 6.0)

Pathogen predictions Product Organism Initial level pH Salt (%) or aw Temperature (°C) Time (h) for 0.5 log cfu/g increase Corrected C.bot incl safety factor (1.5) Time (h) to 100 cfu/g Cooked beef C.botulinum 10 cfu/g 6.16 0.978 5°C for 96h 8°C for 264h (15d total) 503 (20d) 335 (13d) NA Listeria 115 (4d) 139 (5d) 6.10 0.985 296 (12d) 197 (8d) 109 (4d) 127 (5d) 121(5d) 145(6d) 5.92 0.98 503(20d) 335(13d)

Use of predictive models to demonstrate the effect of frankfurter aw on microbial growth Data assumes a constant pH of 6.0, and a constant temperature of 8ºC

Pathogen predictions C.botulinum Listeria Time 0.5 log increase (apply safety factor 1.5) Listeria Time 0.5 log increase Time to 100 cfu/g

How reliable are models Need to ensure the model shows a good fit to the original data Need to ensure that the model was validated in foods Need to back up model predictions by laboratory studies in your own foods but models can produce reliable estimations of microbial responses to environmental conditions

Advantages of using models Rapid assessment of the likely shelf-life of new product formulations Trouble shooting to predict effects of process deviations e.g. increase in temperature Focus resources for microbial tests models will not replace microbial testing but will indicate which formulations should be tested in the laboratory

Limitations of using models Predictions are ONLY predictions and need to be backed up with challenge tests/shelf life studies Models only usually take into account 3 or 4 factors Input parameters tend to be aw/salt, pH, temperature Models do not account for all factors that maybe present in a food product (e.g. natural antimicrobials or organic acids)

Limitations of using models Most models are food validated but they don’t take into account food matrix Models may tend to be fail safe i.e. predict growth when growth wouldn’t be observed in a real foodstuff