Institute of Food Research. Predicting bacterial growth in reduced salt foods

Size: px

Start display at page:

Download "Institute of Food Research. Predicting bacterial growth in reduced salt foods"

Roland Cunningham
5 years ago
Views:

1 Institute of Food Research Predicting bacterial growth in reduced salt foods Dr Sandra Stringer Institute of Food Research, Norwich Nofima, Oslo, Norway 3 rd March 2011

2 Effects of salt on foods Texture Taste Colour Economics Control of yeast or starter culture Spoilage (type and shelf life) Microbiological safety

3 The preservative effect of NaCl in foods has been exploited since antiquity Salting is a very ancient method of preservation It can been used to preserve meat, fish, vegetables and dairy products

4 Available water is critical for bacterial growth Water must be available for microorganisms to remain metabolically active Water is freely permeable across a cells cytoplasmic membrane whereas many solutes are not In environments with reduced water availability cells can rapidly lose water. Loss of water means lose the ability to function Cells counteract osmotic stress by accumulating salts, synthesis or uptake of compatible solutes or changing cell membrane composition. These activities require energy. ATP Compatible solutes H 2 0 High NaCl or sugar conc

5 The work required to counteract osmotic stress will effect growth rates Despite these adaptations, the range of water activities in which each organism can grow is limited The microbial stability of foods is therefore related to the water available for utilised by microorganisms

6 Reducing salt Altering NaCl content of a food can alter the water activity (a W ) and thus affect growth of food spoilage and pathogenic bacteria The consequences on microbiological safety must be considered when reducing salt

7 water activity Water activity is the expression of available water used by food microbiologists a w = ρ/ρ 0 Inhibitory a w depend on the solute Microorganisms active in relatively narrow range so quoted to 3 significant figures

8 Typical a w of foods aw range Foods Free water Fresh meat, milk, eggs, fruit, vegetables Ham, bacon, hard cheese, bakery goods Fruit juice concentrate, maple syrup Sweetened condensed milk, parmesan, fruit cake Salted fish, prunes, jams and conserves Dried fruit, honey, confectionary, rolled oats <0.60 Dry pasta, biscuits, flour, dried vegetables, powdered milk, instant coffee Source: Christian,J.H.B.The Microbiological safety and quality of food Vol.

9 a w for growth of micro-organisms Moulds Yeast Most gram +ve bacteria Most gram -ve bacteria Staph. aureus L. monocytogenes B. cereus C. botulinum (p) Yersinia Vibrio Salmonella E. coli Shigella C. perfringens C. botulinum (np) Aeromonas Campylobacter 20% 12% 11% 10% 8% 8% 8% 8% 5% 5% 5% 5% 2%

10 How much of a food safety issue does reducing salt concentrations present?

11 It depends on the food! Reformulation can change the intrinsic properties of a food Changing the intrinsic properties will effect the foods ability to support the growth of food-borne pathogens The importance of such changes will depend on the food

12 In some foods reformulation is not a microbiological safety issue For example: Frozen foods Foods heated to the equivalent of 121 C for 2.5 min or more Acid foods (ph<3.8) Low a w foods (a w <0.86 ) where a w is not related to salt Foods with added salt treated the same as an unsalted equivalent

13 In other foods reformulation can have fatal consequences

14 Outbreak of botulism associated with hazelnut yoghurt, UK 1989 Cases: 27 people affected, 1 died Food involved: Detection: Commercially produced hazelnut yoghurt. Canned hazelnut puree added to yoghurt base Type B toxin and C. botulinum type B isolated from patients, from yoghurt and from hazelnut conserve 76 cans of puree had been sweetened using aspartame rather than sugar

15 Causes Conserve made from Roasted hazelnuts, water, starch and sugar. ph Processed using non-lethal heat treatment of cooking at 90 C for 10min followed by canning and heating at 100 C for 20min. Storage at ambient. Sugar content of original formulation had contributed to product safety Sugar substituted for aspartame changed product safety There had been reports of cans blowing and manufacturer later started adding sorbate in an attempt to control yeasts

17 What can you do to predict and prevent microbiological problems?

18 Understand your product What are the organisms of concern? What factors prevent food poisoning from the product? Is any reformulation directly related to a HACCP control point? Calculate magnitude of effect of reformulation

19 Predictive microbiology Predictive microbiology is based on the theory that growth is an intrinsic characteristic of the organism and will occur reproducibly in identical environmental conditions. It uses mathematics to describe micro-organism s response to their environment. First variations in population size against time are described by a mathematical function (primary model) Changes in primary model with conditions can be described by secondary models (T, ph, Aw, others)

20 Validation of fitted model for Non-proteolytic C. botulinum Observed rate Predicted rate

21 Validation of C. botulinum time to doubling from Growth Predictor against literature data 100 predicted time doubling (d) observed time to toxin (d) media meat fish poultry milk other

22 Predictive modeling may be used to help asses the implications of reformulation Predictive microbiology can be used to help determine the effect of changing growth conditions. It can particularly be helpful when changing NaCl concentrations as water activity (a w ) is one of the principle environmental parameters in predictive microbiology (temp, a w, ph).

23 Examples of modelling programs ComBase (data base and predictive software including Growth Predictor & Perfringens Predictor) Pathogen Modeling Program Seafood Spoilage Predictor Software, Danish Institute for Fisheries Research Sym'Previus (database and predictive software, in French)

27 ComBase Predictor (allows predictions from ComBase data) ComBase predictor is a set of models for predicting the growth survival or death of bacteria as a function of environmental factors, including temperature, ph and water activity. Some models also include an additional, fourth factor, such as the concentration of carbon dioxide or acetic acid. ComBase predictor is available for free at:

30 Time (h) Effect of a w on growth of E. coli at 20 C ph 5.6 Log concentration NaCl 0.1% aw NaCl 2.0% aw NaCl 4.0% aw NaCl 6.0% aw 0.964

31 Salt Sugar Fat UK Food Standards Agency campaigns to reduce 2006 FSA published the voluntary salt reduction targets for 2010 in 85 categories of food as guidance to the food industry New targets for 2012 were published in July 2008

32 NaCl reduction in Economy burger Reduced aqueous salt (%) Initial aqueous salt (%) FSA 2012 target salt content (g/100g food) Initial composition (mg Na /100g food) Water content (%) Economy burger Initial salt : Composition data from McCance and Widdowson's The Composition of Foods integrated dataset 2002 Reduced salt: FSA 2012 salt target

33 Log concentration (cfu ml -1 ) Effect of salt reduction on growth of pathogens in hypothetical burger ph 6.2, 20 C Time (h) A. hydrophila, 2.6% s/w B. cereus, 2.6 % s/w C. botulinum (non prot), 2.6% s/w C. Botulinum (prot), 2.6% s/w C. perfringens, 2.6% s/w E. Coli 0157:H7, 2.6% s/w L. monocytogenes, 2.6% s/w Salmonella, 2.6% s/w Staph aureus, 2.6% s/w Y. enterocolitica, 2.6% s/w A. hydrophila, 1.3% s/w B. cereus, 1.3% s/w C. botulinum (non prot), 1.3% s/w C. Botulinum (prot), 1.3% s/w C. perfringens, % s/w E. Coli 0157:H7, 1.3% s/w L. monocytogenes, 1.3% s/w Salmonella, 1.3% s/w Staph aureus, 1.3% s/w Y. enterocolitica, 1.3% s/w

34 9 8 Effect of salt reduction on growth of Salmonella in hypothetical burger ph 6.2 Log concentration (cfu ml -1 ) Time (h) Salmonella, 2.6% s/w, 20 C Salmonella, 1.3% s/w, 20 C Salmonella, 2.6% s/w, 8 C Salmonella, 1.3% s/w, 8 C

35 NaCl reduction in smoked salmon Reduced aqueous salt (%) Initial aqueous salt (%) FSA 2010 target salt content (g/100g food) Initial composition (mg Na /100g food) Water content (%) Smoked salmon

36 Effect of salt reduction on growth of pathogens in hypothetical smoked salmon Log concentration (cfu ml -1 ) ph 6.2, 8 C A. hydrophila, 7.4% s/w B. cereus, 7.4% s/w C. botulinum (non prot), 7.4% s/w L. monocytogenes, 7.4% s/w Salmonella, 7.4% s/w Staph aureus, 7.4% s/w Y. enterocolitica, 7.4% s/w A. hydrophila, 2.5% s/w B. cereus, 2.5% s/w C. botulinum (non prot), 2.5% s/w L. monocytogenes, 2.5% s/w Salmonella, 2.5% s/w Staph aureus, 2.5% s/w Y. enterocolitica, 2.5% s/w Time (d)

37 NaCl reduction in Cottage cheese Reduced aqueous salt (%) Initial aqueous salt (%) FSA 2012 target salt content (g/100g food) Initial composition (mg Na /100g food) Water content (%) Cottage cheese

38 Effect of salt reduction on growth of pathogens in hypothetical Cottage cheese Log concentration (cfu ml -1 ) ph 5.1, 8 C Time (d) A. hydrophila, 1.0% s/w B. cereus, 1.0% s/w C. botulinum (non prot), 1.0% s/w L. monocytogenes, 1.0% s/w Salmonella, 1.0% s/w Staph aureus, 1.0% s/w Y. enterocolitica, 1.0% s/w A. hydrophila, 0.7% s/w B. cereus, 0.7% s/w C. botulinum (non prot), 0.7% s/w L. monocytogenes, 0.7% s/w Salmonella, 0.7% s/w Staph aureus, 0.7% s/w Y. enterocolitica, 0.7% s/w

39 NaCl reduction in Bacon Reduced aqueous salt (%) Initial aqueous salt (%) FSA 2012 target salt content (g/100g food) Initial composition (mg Na /100g food) Water content (%) Bacon - back Bacon streaky

40 Log concentration (cfu ml -1 ) Effect of salt reduction on growth of pathogens in hypothetical back bacon ph 5.5, 8 C Time (h) A. hydrophila, 6.1% s/w B. cereus, 6.1 % s/w C. botulinum (non prot), 6.1% s/w L. monocytogenes, 6.1% s/w Salmonella, 6.1% s/w Staph aureus, 6.1% s/w Y. enterocolitica, 6.1% s/w A. hydrophila, 4.5% s/w B. cereus, 4.5% s/w C. botulinum (non prot), 4.5% s/w L. monocytogenes, 4.5% s/w Salmonella, 4.5% s/w Staph aureus, 4.5% s/w Y. enterocolitica, 4.5% s/w

41 Effect of salt reduction on growth of pathogens in hypothetical streaky bacon 9 8 ph 5.5, 8 C Log concentration (cfu ml -1 ) B. cereus, 5.6 % s/w L. monocytogenes, 5.6% s/w Staph aureus, 5.6% s/w Y. enterocolitica, 5.6% s/w B. cereus, 5.0% s/w L. monocytogenes, 5.0% s/w Staph aureus, 5.0% s/w Y. enterocolitica, 5.0% s/w Time (h)

42 NaCl reduction in cooked meats Reduced aqueous salt (%) Initial aqueous salt (%) FSA 2012 target salt content (g/100g food) Initial composition (mg Na /100g food) Water content (%) Cooked sliced beef (not reformed) Chicken roll (chopped reformed meat) Cured pork shoulder (chopped reformed meat)

43 Log concentration (cfu ml -1 ) Effect of salt reduction on growth of pathogens in hypothetical cooked beef ph 6.0, 8 C A. hydrophila,2.3% s/w B. cereus, 2.3% s/w C. botulinum (non prot), 2.3% s/w L. monocytogenes, 2.3% s/w Salmonella, 2.3% s/w Staph aureus, 2.3% s/w Y. enterocolitica, 2.3% s/w A. hydrophila, 1.1% s/w B. cereus, 1.1% s/w C. botulinum (non prot), 1.1% s/w L. monocytogenes, 1.1% s/w Salmonella, 1.1% s/w Staph aureus, 1.1% s/w Y. enterocolitica, 1.1% s/w Time (d)

44 Log concentration (cfu ml -1 ) Effect of salt reduction on growth of pathogens in hypothetical chicken roll ph 6.3, 8 C A. hydrophila, 2.4% s/w B. cereus, 2.4% s/w C. botulinum (non prot), 2.4% s/w L. monocytogenes, 2.4% s/w Salmonella, 2.4% s/w Staph aureus, 2.4% s/w Y. enterocolitica, 2.4% s/w A. hydrophila, 2.1% s/w B. cereus, 2.1% s/w C. botulinum (non prot), 2.1% s/w L. monocytogenes, 2.1% s/w Salmonella, 2.1% s/w Staph aureus, 2.1% s/w Y. enterocolitica, 2.1% s/w Time (d)

45 Log concentration (cfu ml -1 ) Effect of salt reduction on growth of pathogens in hypothetical cured pork ph 6.0, 8 C A. hydrophila, 3.4% s/w B. cereus, 3.4% s/w C. botulinum (non prot), 3.4% s/w L. monocytogenes, 3.4% s/w Salmonella, 3.4% s/w Staph aureus, 3.4% s/w Y. enterocolitica, 3.4% s/w A. hydrophila, 2.2% s/w B. cereus, 2.2% s/w C. botulinum (non prot), 2.2% s/w L. monocytogenes, 2.2% s/w Salmonella, 2.2% s/w Staph aureus, 2.2% s/w Y. enterocolitica, 2.2% s/w Time (d)

46 Additional pressures to alter food formulations, such as the desires to reduce nitrites, lower fat content and reduce additives must be considered along with salt reduction as they interact to effect product safety

47 Log concentration (cfu ml -1 ) Effect of salt and nitrite reduction on Listeria in hypothetical cured pork ph 6.0, 8 C Time (d) L. monocytogenes, 4.2%s/w 150ppm nitrite L. monocytogenes, 2.3 s/w 150ppm nitrite L. monocytogenes, 4.2%s/w 0ppm nitrite L. monocytogenes, 2.3%s/w 0ppm nitrite

48 Calculation of equivalent conditions For a product that is historically safe you can try and calculate alternative conditions that give the same safety margin Models can be generated to determine alternative combinations of conditions that result in an equivalent growth rates in a reformulated product What would be a good target for reformulation?

49 L. monocytogenes equivalent growth rates in hypothetical chicken roll

50 Blue boxes: input conditions of interest Red text: Highlights changed factors Conditions that produce a growth rate the same as the same as the original condition Model Limits for each factor

53 Manipulation of a w affects Probability of growth growth rate lag phase yield survival (remember dried foods are not sterile!) death rate Reduced aw may also interact with other preservative factors

54 Effect of a w on the survival of Listeria monocytogenes at 60 C Log concentration NaCl 1.8% aw NaCl 3.5% aw NaCl 5.1% aw NaCl 6.6% aw Time (h)

55 Effect of NaCl concentration on probability of growth of non-proteolytic C. botulinum Riemann 1967

56 Growth domain for non-proteolytic C. botulinum (90 days) 5 4 NaCl (%) ph 10 C 8 C 5 C 4 C

57 The inherent variability of a product should be considered when salt levels are reduced. Safety margins must take this variability into account

58 The inherent variability of a product can effect the margin for salt reduction Frequency SD=0.87 Frequency SD= Mean=2.8 Mean= Mean=2.8 Mean= Aqueous salt (%s/w) Aqueous salt (%s/w)

59 Summary Microorganisms require available water Manipulating available water can alter probability of growth, growth rate, lag, mass, death or survival of microorganisms The inhibitory effects of salt vary with the microorganism of concern, the environmental conditions, the presence of other preservative factors, processing and storage conditions aw often combined with other inhibitory factors to optimise product quality and safety Predictive modelling can be used to help identify safe combinations of inhibitory factors.

60 Conclusions There is scope to reduce salt in foods. However, as salt concentrations influence bacterial growth, its reduction may have consequences for food safety that must be considered in addition to organoleptic and textural considerations Any change in formulation, processing or storage conditions means product safety and shelf-life must be re-evaluated and action must be taken if new hazards are identified It may be necessary to change multiple factors factors or reduce shelf-life to maintain product safety Modelling may be a useful tool in this process

Guidance on the safety and shelf-life of vacuum and modified atmosphere packed chilled foods. January 2004 (DRAFT)

Guidance on the safety and shelf-life of vacuum and modified atmosphere packed chilled foods January 2004 (DRAFT) Introduction This document provides advice on vacuum and modified atmosphere packaged (VP/MAP)