Food and Industrial Microbiology

Module 2. Microorganisms and food materials

Lesson 5
ROLE OF PREDICTIVE MICROBIOLOGY

5.1 Introduction

Every year thousands of new food products are added to the previous list world over. The initial microbial population of these products generally includes spoilage as well as pathogenic organisms which may grow to a higher number and may thus make the food unsafe as well as spoil it. In order to ensure their longer shelf life & safety it is important that adequate control measures are adopted to prevent the growth of microorganisms in the entire food chain i.e. from production to consumption. It is very challenging to produce these new products that are healthy (low fat, low salt), minimally processed fresh to taste, do not contain chemical preservatives and are safe.

To produce new types of foods with desirable safety, stability and acceptance quality we need to determine the influence of several factors on microbial growth such as, concentration of different ingredients, processing and storage conditions, water activity, pH under the intrinsic and extrinsic factors. For determining the safety and stability of the processed foods it may not be always possible to do the microbiological analysis of each product by the traditional methods. Thus there is a need to develop mathematical models and put into application the computers for determining the influence of a vast array of different parameters that affect the microbial growth.

Microbiologists and hygienists would like to improve their approach by using new tools for modeling and simulation. Some models have been proposed and used with the aim to describe the effect of temperature & heat treatments on microbial destruction but it is only at the end of 1980s that the first mathematical tools have been built to stimulate the complete behavior of microflora in food products under processed conditions. However, predictive microbiology started as a purely empirical (though quantitative) science. In 1922 Esty and Meyer described the thermal death of Clostridium botulinum type A spores by a log-linear model, which is still used to estimate the necessary heat processing of low-acid canned foods. This model simply says that, at a given temperature, the relative (or specific) death rate of the bacteria is constant with time. In other words, the percentage of the cell population inactivated in a unit time is constant.

5.2 General Principle

As food safety is a growing concern in modern society, the scientific discipline of predictive food microbiology gains more and more interest worldwide. The term ‘Predictive Microbiology’ describes the scientific discipline of predicting microbiological growth or decline as a function of environmental factors. Such methods may not allow us to arrive at a very definite and accurate conclusion but certainly they can be an effective tool in obtaining hostile information quite rapidly. They can be a forerunner for conducting traditional studies that are relatively feasible experimentally as well as economically.

Predictive Modeling is the detailed knowledge of the behavior (growth, survival, and inactivation) of microorganisms in food products condensed into a mathematical model that enables an objective evaluation of the microbiological safety and quality of foods.

Food processing may be described as a succession of steps, from the input of raw materials to the distribution and consumption of the processed food. Different steps can be identified as critical from the microbiological point of view. At these steps microbial contamination or multiplication can occur.. If we consider that a contamination occurs at a given initial level during one of these critical steps, predictive microbiology consists of simulation of the behavior of these contaminations, from the starting point to a given time, taking into account variation in processing conditions. This approach allows the microbial hazard to be predicted and therefore, helps to prevent and control the risk, allows quantification of the risk, optimize experimental designs and thus reduces delay and cost. Further this approach is supportive of debate, and thus helps to improve communication between the experts under managers.

The objective of the predictive microbiology is to be a good forward microbial dynamics using mathematical models. But before that, it is important to consider the classical behavior of the microbial population.

i. The lag phase : This is considered as an adaptation phase between an initial physiological state and growth state wherein the cell number remains constant. This phase may get longer under unfavorable conditions.

ii. Logarithmic growth phase : This is characterized by a linear portion if the logarithm of the variable (biomass, number or concentration of cells) is represented versus time.

iii. The stationary phase : This is linked to lack of nutrients, acidity etc. that leads to stable number of cells in the growth medium.

iv. Death phase : The decrease in cell number takes place when the medium and the conditions become too unfavorable. This phase is usually close to an exponential decrease of the cell density.

5.3 Conventional Methods for Predicting Shelf life of Food Products

5.3.1 Spiking studies

In this method a food is inoculated with the microorganisms that are expected to be the major causes of loss of shelf life or safety under storage conditions. A definite but realistic number of microbial cells or spores is used as inoculum and stored under conditions at which the food would normally be placed. Afterwards it is examined for microbial growth or toxin production. However a large number of variables (extrinsic and intrinsic factors) need to be studied. This is quite time consuming, costly and cumbersome.

5.3.2 Storage studies

A food product is stored under normal storage conditions and microbiological analysis is done at regular intervals and extent of spoilage and the growth of pathogenic organisms/ production of toxin is assessed. The data thus generated can be useful for predicting the expected shelf life and safety of the product. However, there are certain limitations to the use of this method as it would not take into consideration the temperature of use for a short time and other analytical problems.

5.3.3 Accelerated tests

In this procedure the product is held at relatively higher temperature (near ambient) so as to increase the rate of growth of the organism and thereby accelerate the spoilage. This process is used particularly for foods that have relatively longer shelf life. However, there are also limitations to arriving at a logical conclusion as different organisms in the mixed flora of the food behave differently at different temperatures.

5.4 Predictive Modeling

A number of mathematical models have been developed to predict the growth of pathogenic and spoilage microorganisms in foods from the data generated by studying growth rate at different pH, water activity, temperature and preservative conditions in the laboratory media, with suitable computers help in the rapid analysis of the huge data. Two kinetic based models that take into consideration the effect of culture parameters on the growth rate of microorganisms are:

5.4.1 Square root model

This model is based on the linear relationship between the square root of the growth rate and temperature. This model is quite effective when one or two parameters are used. The effectivity of this model decreases if several parameters in combination are used to control the microbial growth.

5.4.2 Sigmoidal model

This model has been developed by the US department of Agriculture (USDA) to predict microbial growth in a food system that is controlled by several parameters. It has been tested in the laboratory media to determine the growth rate of several pathogenic microorganisms under different physical and chemical parameters. This model is extensively used because of its simplicity and effectiveness.

Predictive microbiology is quite interesting and important. With the advent of computers and subsequent developments, the processing of data has become easier and quicker. Most of the studies so far on this aspect have been carried out in laboratory media and a very limited number of studies have been carried out in food systems. Therefore, there is a limit to the effectiveness of this modeling system. The information obtained through this modeling system, therefore, needs to be used with caution.