Statistical Aspects of the Microbiological Examination of Foods

Basil Jarvis

• Publisher: Academic Press
• Year: 2016
• ISBN:
  9780128039748
• Edition: 3
• Topic: Technologie et ingénierie
• Subtopic: Sciences de l'alimentation

Statistical Aspects of the Microbiological Examination of Foods, Third Edition, updates some important statistical procedures following intensive collaborative work by many experts in microbiology and statistics, and corrects typographic and other errors present in the previous edition. Following a brief introduction to the subject, basic statistical concepts and procedures are described including both theoretical and actual frequency distributions that are associated with the occurrence of microorganisms in foods. This leads into a discussion of the methods for examination of foods and the sources of statistical and practical errors associated with the methods. Such errors are important in understanding the principles of measurement uncertainty as applied to microbiological data and the approaches to determination of uncertainty.

The ways in which the concept of statistical process control developed many years ago to improve commercial manufacturing processes can be applied to microbiological examination in the laboratory. This is important in ensuring that laboratory results reflect, as precisely as possible, the microbiological status of manufactured products through the concept and practice of laboratory accreditation and proficiency testing. The use of properly validated standard methods of testing and the verification of 'in house' methods against internationally validated methods is of increasing importance in ensuring that laboratory results are meaningful in relation to development of and compliance with established microbiological criteria for foods.

The final chapter of the book reviews the uses of such criteria in relation to the development of and compliance with food safety objectives. Throughout the book the theoretical concepts are illustrated in worked examples using real data obtained in the examination of foods and in research studies concerned with food safety.

• Includes additional figures and tables together with many worked examples to illustrate the use of specific procedures in the analysis of data obtained in the microbiological examination of foods
• Offers completely updated chapters and six new chapters
• Brings the reader up to date and allows easy access to individual topics in one place
• Corrects typographic and other errors present in the previous edition

Chapter 1

Introduction

Abstract

This chapter provides a brief introduction to the need for understanding and use of statistics in food microbiology. The objective is merely to encourage the reader to give serious consideration to the material provided in subsequent chapters.

Keywords

what are statistics?

use and misuse of statistics

statistical models

use of microbiological data

One morning a professor of statistics 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, saying, ‘If 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!’ Possibly a trite example, but statistics are relevant in most areas of life.

The word ‘statistics’ means different things to different people. According to Mark Twain, Benjamin Disraeli was the originator of the statement ‘There are lies, damned lies and statistics!’ from which one is supposed to conclude that the objective of much statistical work is to put a positive ‘spin’ onto ‘bad news’. Whilst there may be some political truth in the statement, it is generally not true in science provided that correct statistical procedures have been used. Herein lies the rub! To many people, the term ‘statistics’ 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 numerical 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 make better use of the information available.

Much statistical analysis is used to decide whether one set of data differs from another and it is essential to recognise that sometimes a difference that is statistically significant may not be of practical significance, and vice versa. Always bear in mind that statistical methods serve merely as a tool to aid interpretation of data and to enable inferences to be drawn. It is essential to understand that data should be tested for goodness of fit by seeking to fit an appropriate statistical model to the experimental data.

Microbiological testing is used in industrial process verification and sometimes to provide an index of quality for ‘payment by quality’ schemes. Examination of food, water, process plant swabs, etc., for microorganisms is used frequently in the retrospective verification of the microbiological ‘safety’ 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 ‘misguided’ because no amount of testing of finished products will improve the quality or safety of a product once it has been manufactured. Different forms of microbiological criteria have been devised for particular purposes; it is not the purpose of this book to review the advantages and disadvantages of microbiological criteria – although some statistical matters relevant to criteria will be discussed in 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 recognise 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 (eg, colony count) and quantal methods [eg, presence/absence and most probable number (MPN) methods]; the meaning of, and ways to estimate, microbiological uncertainty; the validation of microbiological methods; and the implications of statistical variation in relation to food safety and use of 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.

Chapter 2

Some basic statistical concepts

Abstract

Starting from the premise that the reader has little experience of statistics, the chapter reviews the concept of populations and their estimation, and defines the meaning of ‘lots’ and ‘samples’. This leads to a consideration of parameters and statistics that are used to describe populations and samples, such as estimates of ranges, median and mean values, and errors (variance, standard deviations, standard error of the mean). The meaning and use of confidence intervals and confidence limits to describe populations is described and illustrated in relation to the use of hypotheses in statistical science. A worked example provides instruction on estimation of the basic parameters described earlier. The references include both citations and suggestions for general reading.

Keywords

populations

lots

samples

parameters

statistics

hypotheses

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 a product. Whilst it is possible, at least theoretically, to determine the human population in a non-destructive manner by undertaking a population census, 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, gender, 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 count...