About controlling the growth of *Listeria* monocytogenes

**What is ***Listeria monocytogenes* and what is Listeriosis?
*Listeria monocytogenes* is a

Gram-positive, facultative anaerobic, rod-shaped bacterium, which can survive the effects of freezing and drying.

*Listeria* can be isolated from soil, silage, and other environmental sources.

*Listeria monocytogenes* is the causative agent of Listeriosis, a bacterial infection causing fever, muscle aches, vomiting and even Meningitis. The symptoms of Listeriosis usually last 7-10 days. Meningitis, and infection of the covering of the brain and spinal cord, develops when infection spreads to the nervous system. This can occur when the person infected has weakened immune system.

According to the American Center for Disease Control, about one-third of Listeriosis cases develop during pregnancy. Listeriosis can be transmitted to the fetus through the placenta, even if the mother is not showing signs of illness. This can lead to premature delivery, miscarriage, stillbirth, or serious health problems for her newborn.¹

^{1} *http://www.extension.iastate.edu/foodsafety/pathogens/index.cfm *
**Why do I need to control the growth of ***Listeria*?
It is important to prevent and control

*Listeria* in food items, as it is one of the most virulent food borne pathogens known to man. Infection with

*Listeria monocytogenes* can cause Listeriosis, with 20% to 30% of clinical infections resulting in death. In the USA,

*Listeria monocytogenes* is responsible for approximately 1,500 illnesses and 250 deaths annually.²

^{2} *Batz et al.; Ranking the Risks: The 10 pathogen-food combinations with the greatest burden on public health; 2011.*
**Which factors influence the growth of ***Listeria*?
For this model the gamma concept is used to represent multiple inhibitory factors. This includes low temperature, pH, water activity and the addition of Corbion solutions, which all combine independently to affect the growth rate. See question:

"How many times can I use the model?" for the ranges of the input values for these inhibitory factors. However, keep in mind that certain (processing) conditions can also influence

*Listeria* growth. Food producers who feel that an important aspect of their product or processing conditions is not integrated in the model are invited to contact us to see how these may influence the model projections.

**What is the effect of temperature on ***Listeria* growth?
*Listeria monocytogenes* grows in a specific range of temperatures.

*Listeria* has an optimal growth temperature of 30-37°C/86-99°F, but is able to survive and grow in refrigeration temperatures. Altering storage temperature to below optimal temperature will slow down growth rate. Freezing does not kill

*Listeria*; it only inhibits its growth.

**What is water activity and what is the difference with moisture content?**
Water activity is the relative availability of water in a food product. Pure water has a water activity value of 1.0. This is one of the major influencers on

*Listeria's* growth rate.

*Listeria* grows faster when water activity is high, and slows down when water activity is lower. Moisture content simply shows what weight percentage of a product consists of water and is not indicative of the water's availability to aid the growth of

*Listeria*.

**Exactly how much Corbion product is required to sufficiently inhibit ***Listeria* growth?
The model predicts the effect of a Corbion product on the growth of

*Listeria*. How much Corbion product is required exactly depends on the desired safety level of a product.

About the model

**How can the Corbion**^{®} *Listeria* Control Model help me?
The Corbion

^{®} *Listeria* Control Model predicts the growth of

*Listeria* for specific environment parameters. The Model can help processors in several ways, but most importantly it enables processors to adapt product formulation to a desired safety level. Before starting application testing, it gives a detailed estimation of the required use level of Corbion product. It can also help determine which solution is most suitable, or efficient, in a food product. This can decrease time-to-market and save costs during product development.

**What is the benefit of using a model above carrying out our own experimental work?**
Application testing is always vital for food processors to verify the efficiency of

*Listeria* control measures. The Corbion

^{®} *Listeria* Control Model has not been developed as a replacement to application work, but as a valuable tool which includes the data and experience of over a decade of

*Listeria* screening. One important goal of the Model is the aid in food product design during challenge tests.

**What kind of data is provided by the detailed results?**
Online the model shows summarized results of the projected shelf life of a specific food matrix. If you are a registered user, a detailed report is one of the model's advantages. The detailed report is a downloadable PDF which provides a data set of extensive results, including lag phase, doubling time and a graph depicting the

*Listeria* growth at different concentrations of Corbion product.

**Can this model be used for all kinds of food products?**
Corbion's

*Listeria* Control Model calculates a predicted growth curve for a set of specified environmental conditions i.e. salt, storage temperature, pH, moisture, cure, water activity and presence of Corbion solutions. This prediction is based on the modeled influence of those conditions. As such it gives a good prediction for of a food product, if no other preservatives or major components are formulated. Ingredients such as ascorbic acid, and certain (processing) conditions can also influence

*Listeria* growth.
Food producers who feel that an important aspect of their product or processing conditions is not integrated in the model are invited to

contact us to see how these may influence the model projections.

**How reliable are the predicted values?**
The output of the model is based on trend analysis of a large set of experimental data, obtained in a variety of applications. Application testing performed independently by scientist and industry were used to validate the calculations used in the model.

Using the model

**How many times can I use the model?**
The online

*Listeria* Control Model can be accessed at any time, with no usage restrictions, except for those you agreed to in the terms & conditions when registering. However, the trial version is limited in the detail of its results. To fully utilize the model, registration is recommended.

**What are the ranges of input values for the food characteristics?**
To use the model certain characteristics of a food product are required. Before starting a calculation the model first checks the specified characteristics. This is to protect a user from false input to the model, and to make sure that his values are in agreement with the situations for which we have modeling data.

The table below gives an overview of the valid ranges.

**Product characteristic** |
**Meat** |
**Refrigerated foods** |

Moisture content |
40-100% |
40-100% |

Temperature |
3.3-15 °C/ 38-59 °F |
3.3-15 °C/ 38-59 °F |

pH |
5.8-7.2 |
4.2-7.2 |

Sodium nitrite |
0-200 ppm |
Not applicable |

Water activity |
≥ 0.95 |
≥ 0.95 |

NaCl |
0-6% |
0-6% |

KCl |
0-6% |
0-6% |

**How do I select the most suitable product for my formulation?**
All Corbion solutions are designed to fulfill a certain set of needs for processors.

**What should be entered as water activity?**
When entering the food characteristics, a value can be specified for the

water activity. When the water activity of a finished product has been measured without the addition of Corbion products, this value can be filled in. This will give most accurate results.

If the water activity is unknown, a default value will be used to calculate an estimated water activity according to the specific formulation. When choosing a Corbion solution the water activity can be specified for a second time to compare the control with the Corbion solution. If unknown, the water activity will be calculated automatically.

If this water activity is known, please use this. The water activity of the control formulation will be adapted according to this value.

**What is default level for the water activity?**
The default water activity is 0.993. This is level represents meat products without the addition of any water activity reducing ingredients or additives.

**What nitrite level should be entered?**
Please do not use:

1. Residual nitrite.

2. Nitrite based on meat block weight.

The nitrite level that should be used is expressed in parts per million (ppm) ingoing (added) sodium nitrite, based on the total formulation. The model facilitates a level up to 200 ppm.

This level is calculated as follows:

weight sodium nitrite x 1 million
ppm =

weight of total product formulation

**I do not know the pH of my product, can I still use the model?**
Yes you can. A default value will be used to calculate

*Listeria* growth. The default value depends on the selected food type.

**What should be entered as initial count and maximum allowed outgrowth for ***Listeria*?
The initial count is a projected presence of

*Listeria*, when introduced into a product after cooking. Often the model is used to measure the growth rate if 1 log CFU

*Listeria*/g is assumed. The maximum allowed outgrowth specifies the increase in

*Listeria* count as compared to the initial level. The modeled shelf life of the product, with or without Corbion solution, is the shelf life until the

*Listeria* amount exceeds the maximum allowed outgrowth.

**How does filling in a 'graph scaling' influence my model output?**
The 'graph scaling' determines the horizontal axis (days) of the graph. Filling in a desired shelf life of 20 days gives a scale of 20 days on this axis. This will not affect the predicted growth.

**How can I change the scaling of the graph?**
The online model will automatically scale the horizontal and vertical axis.

**What is lag phase correction?**
The model offers possibilities to employ specific knowledge of a processor in how the lag time is estimated. Lag time of

*Listeria* growth can be highly variable, dependent on producer specific situations.

Under the 'advanced settings' button lag time can be corrected by:

- A correction factor.
- A specific lag time in days.
- Turning off the lag time estimation.

The correction factor can be calculated by dividing the model's predicted lag time by lag time generally observed in a processor's own

*Listeria* data. With this option, a consequent over- or underestimation of the lag time can be compensated. This increases the applicability of the model. A specific lag time is often used when a processor's own

*Listeria* data indicate a reoccurring static lag time.

If this data is not available the lag time as calculated by our model can be turned off.

About the results

**What is the best fit?**
The model's result is depicted as a graph with four growth lines (red and blue; dotted and solid), and a grey area around the blue solid line (see question:

"Why does this model not show confidence levels?"). The solid growth curves represent the so called 'best fit' line. The best fit line is calculated, based on an extensive data set obtained from specifically designed and validated Listeria challenge studies.

At each point in time for a specific set of food characteristics, the data set shows a measure of spread. The model represents the distribution of this data in a

normal distribution. When shown as a function, the normal distribution forms a bell shaped curve with single mean value at the peak. The mean value of a normal distribution represents the point where probability of occurrence is the highest. The

*Listeria* Control Model projects the best fit line by connecting the mean value of every point in time. The best fit line therefore represents the most probable

*Listeria* growth for the specific food characteristics entered into the model.

**What is the 95% line?**
The model's result is depicted as a graph with four growth lines (red and blue; dotted and solid), and a grey area around the blue solid line (see question:

"Why does this model not show confidence levels?").
The dotted lines represent the 95% lines. The best fit line is calculated, based on an extensive data set obtained from specifically designed and validated

*Listeria* challenge studies.

At each point in time for a specific set of food characteristics, the data set shows a measure of spread. The model represents the distribution of this data in a

normal distribution. When shown as a function, the normal distribution forms a bell shaped curve with single mean value at the peak. The 95% line represents the point where 95% of

*Listeria* growth is expected to be slower according to the data set. The

*Listeria* Control Model projects the best fit line by connecting these points on the time axis. You can use this line during product development to increase the safety margin, compared to the best-fit line.

**Why does this model not show confidence levels?**
A confidence level, for example 95%, suggests a 95% certainty for processor specific

*Listeria* growth. However some processors specific conditions (e.g.

*Listeria* strains) cannot be taken into account in the model. Therefore a '95% guarantee' cannot be given, and, strictly speaking, the term '95% confidence level' does not apply. To prevent confusion it is therefore not used. Instead, the variation in the extensive data set under the Corbion

*Listeria* Control Model is taken as a measure for variation and the result is given as a '95% line'.

**What does the grey area mean in my graph?**
The model's result is depicted as a graph with four growth lines (red and blue; dotted and solid) (see question: 'What is the best fit?'), and a grey area around the blue solid line. The growth curves represent the so called 'best fit' line. The best fit line is calculated, based on an extensive data set obtained from specifically designed and validated

*Listeria* challenge studies.

At each point in time for a specific set of food characteristics, the data set shows a measure of spread. The grey area represents 95% of all our experimental data points. The likelihood of growth is highest on the predicted growth curve and decreases when deviating from that line. Chances of growth occurring outside the grey area are lowest.

**The graph displays flat line, why?**
The graph displays a flat line when one or more of the food product's parameters allows for no, or very little,

*Listeria* growth.

These product parameters can be:

- Temperature
- pH
- Moisture content
- Water activity
- Salt concentration (NaCl or KCl)
- Nitrate concentration
- Corbion product concentration

**How can I best use the results of the CLCM?**
By benchmarking

*Listeria* screening tests and the models prediction, users can amend product formulations and optimize

*Listeria* challenge studies. This reduces the number of trials required to validate safety levels, speed up time-to-market and reduce cost.

To support users in this process a PDF document with

'Best practices guidelines' is free available online.

**Why are my predictions different from the 2007 and the 2012 model?**
The output of the model is based on trend analysis of a large set of experimental data, obtained in a variety of applications. Application testing performed independently by scientist and industry were used to validate the calculations used in the model. The 2012 model is based on a larger set of data based o a wider variety of applications. This may cause some differences in the predictions.