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INTRODUCTION
One morning a professor sat alone in a bar at a conference. When his colleagues joined him at the lunch break they asked why he had not attended the lecture sessions. He replied by saying, âIf I attend one session I miss nine others; and if I stay in the bar I miss all ten sessions. The probability is that there will be no statistically significant difference in the benefit that I obtain!â Possibly a trite example, but statistics are relevant in most areas of life.
The word âstatisticsâ means different things to different people. According to Mark Twain, Benjamin Disraeli was the originator of the statement âThere are lies, damned lies and statistics!â from which one is supposed to conclude that the objective of much statistical work is to put a positive âspinâ onto âbad newsâ. Whilst there may be some political truth in the statement, it is generally not true in science provided that correct statistical procedures are used. Herein lies the rub! To many people, the term âstatisticsâ implies the manipulation of data to draw conclusions that may not be immediately obvious. To others, especially many biologists, the need to use statistics implies a need to apply numeric concepts that they hoped they had left behind at school. But to a few, use of statistics offers a real opportunity to extend their understanding of bioscience data in order to increase the information available.
Microbiological testing is used in industrial process verification and sometimes to provide an index of quality for âpayment by qualityâ schemes. Examination of food, water, process plant swabs, etc. for microorganisms is used frequently in the retrospective verification of the microbiological âsafetyâ of foods and food process operations. Such examinations include assessments for levels and types of microorganisms, including tests for the presence of specific bacteria of public health significance, including pathogens, index and indicator organisms.
During recent years, increased attention has focused, both nationally and internationally, on the establishment of numerical microbiological criteria for foods. All too often such criteria have been devised on the misguided belief that testing of foods for compliance with numerical, or other, microbiological criteria will enhance consumer protection by improving food quality and safety. I say âmisguidedâ because no amount of testing of finished products will improve the quality or safety of a product once it has been manufactured. There are various forms of microbiological criteria that are set for different purposes; it is not the purpose of this book to review the advantages and disadvantages of microbiological criteria â although statistical matters relevant to criteria will be discussed (Chapter 14).
Rather, the objective is to provide an introduction to statistical matters that are important in assessing and understanding the quality of microbiological data generated in practical situations. Examples, chosen from appropriate areas of food microbiology, are used to illustrate factors that affect the overall variability of microbiological data and to offer guidance on the selection of statistical procedures for specific purposes. In the area of microbiological methodology it is essential to recognize the diverse factors that affect the results obtained by both traditional methods and modern developments in rapid and automated methods.
The book considers: the distribution of microbes in foods and other matrices; statistical aspects of sampling; factors that affect results obtained by both quantitative (e.g. colony count and most probable number (MPN) methods) and quantal methods; the meaning of, and ways to estimate, microbiological uncertainty; the validation of microbiological methods; and the implications of statistical variation in relation to microbiological criteria for foods. Consideration is given also to quality monitoring of microbiological practices and the use of Statistical Process Control for trend analysis of data both in the laboratory and in manufacturing industry.
The book is intended as an aid for practising food microbiologists. It assumes a minimal knowledge of statistics and references to standard works on statistics are cited whenever appropriate.
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SOME BASIC STATISTICAL CONCEPTS
POPULATIONS
The true population of a particular ecosystem can be determined only by carrying out a census of all living organisms within that ecosystem. This applies equally whether one is concerned with numbers of people in a town, state or country or with numbers of microbes in a batch of a food commodity or product. Whilst, in the former case, it is possible at least theoretically to determine the human population in a non-destructive manner, the same does not apply to estimates of microbial populations.
When a survey is carried out on people living for instance, in a single town or village, it would not be unexpected that the number of residents differs between different houses; nor that there are differences in ethnicity, age, sex, health and well-being, personal likes and dislikes, etc. Similarly, there will be both quantitative and qualitative differences in population statistics between different towns and villages, different parts of a country and different countries.
A similar situation pertains when one looks at the microbial populations of a food. The microbial association of foodstuffs differs according to diverse intrinsic and extrinsic factors, especially the acidity and water activity, and the extent of any processing effects. Thus the primary microbial population of acid foods will generally consist of yeasts and moulds, whereas the primary population of raw meat and other protein-rich foodstuffs will consist largely of Gram negative non-fermentative bacteria, with smaller populations of other organisms (Mossel, 1982). In enumerating microbes, it is essential first to define the population to be counted. For instance, does one need to assess the total population, that is living and dead organisms, or only the viable population; if the latter, is one concerned only with specific groups of organisms, for example aerobes, anaerobes, psychrotrophs and psychrophiles, mesophiles or thermophiles? Even when such questions have been answered, it would still be impossible to determine the true ecological population of a particular âlotâ of food, since to do so would require testing of all the food. Such a task would be both technically and economically impossible.
LOTS AND SAMPLES
An individual âlotâ or âbatchâ consists of a bulk quantity of food that has been processed under essentially identical conditions on a single occasion. The food may be stored and distributed in bulk or as pre-packaged units each containing one or more individual units of product (e.g. a single meat pie or a pack of frozen peas). Assuming that the processing has been carried out under uniform conditions, then, theoretically, the microbial population of each unit should be typical of the population of the whole lot. In practice, this will not always be the case. For instance, high levels of microbial contamination may be associated only with specific parts of a lot due to some processing defect. In addition, estimates of microbial populations will be affected by the choice of test regime that is used.
It is not feasible to determine the levels and types of aerobic and anaerobic organisms, or of acidophilic and non-acidophilic organisms, or other distinct classes of microorganism using a single test. Thus when a microbiological examination is carried out, the types of microorganisms that are detected will be defined in part by the test protocol. All such constraints therefore provide a biased estimate of the microbial population of the âlotâ. Hence, sampling of either bulk or pre-packaged units of product merely provides a sample of the types and numbers of microorganisms that make up the population of the âlotâ and those population samples will themselves be fur...