Food Sampling & Analysis - II

Food Sampling Methods 

With a single grain of rice, an Asian housewife tests if all the rice in the pot has boiled; from a cup of tea, a tea-taster determines the quality of the brand of tea; and a sample of moon rocks provides scientists with information on the origin of the moon. This process of testing some data based on a small sample is called sampling. “Sampling is the process by which inference is made to the whole by examining a part”. In general terms, Food Sampling is a scientific method used to confirm the safety and wholesomeness of food. As such it is a useful support tool for officers inspecting food businesses and to food law enforcement generally.

Samples are basically submitted for two different types of tests: - 

Microbiological examination to determine both the general level of microbes and the presence of specific pathogens (e.g. Salmonella, E.coli O157)

Analysis for non-microbiological contamination (e.g. glass pieces in manufactured beverages)

In addition to sampling food, other techniques are available that assist in determining food safety, e.g. swab testing of equipment and work surfaces.   

Sampling Priorities

The focus of food sampling activity mostly based on:

The investigation of food contamination and food poisoning incidents 

Complaints concerning the sale or supply of contaminated foodstuffs 

National and European (EU) coordinated sampling programmes 

Locally manufactured products; local events and initiatives 

Local high risk premises (EU approved or licensed food producers) 

However, there has to be sufficient flexibility to allow for emergency responses or where other particular issues of concern arise. 

Methods of Sampling

It can be seen that there is a dichotomy - probability and non probability sampling methods. Two major principles underlie all sample design: the desire to avoid bias in the selection procedure and to achieve the maximum precision for a given outlay of resources. Sampling bias arises when selection is consciously or unconsciously influenced by human choice, the sampling frame inadequately covers the target population or some sections of the population cannot be found or refuse to co-operate.

Random, or probability sampling, gives each member of the target population a known and equal probability of selection. Systematic sampling is a modification of random sampling. To arrive at a systematic sample we simply calculate the desired sampling fraction and take every nth case. Stratification increases precision without increasing sample size. There is no departure from the principles of randomness. It merely denotes that before any selection takes place, the population is divided into a number of strata, then a random sample is taken within each stratum. It is only possible to stratify if the distribution of the population with respect to a particular factor is known, and if it is also known to which stratum each member of the population belongs. Random stratified sampling is more precise and more convenient than simple random sampling.

Random Sampling

Random, or probability sampling, gives each member of the target population a known and equal probability of selection. 
The two basic procedures are:
The lottery method, e.g. picking numbers out of a hat or bag
The use of a table of random numbers

Systematic Sampling

Systematic sampling is a modification of random sampling. To arrive at a systematic sample we simply calculate the desired sampling fraction, e.g. if there are 100 distributors of a particular product in which we are interested and our budget allows us to sample say 20 of them then we divide 100 by 20 and get the sampling fraction 5. Thereafter we go through our sampling frame selecting every 5th distributor. In the purest sense this does not give rise to a true random sample since some systematic arrangement is used in listing and not every distributor has a chance of being selected once the sampling fraction is calculated. However, all but the most pedantic of practitioners would treat a systematic sample as though it were a true random sample, because there is no conscious control of precisely which distributors are selected.

Stratified Samples

Stratification increases precision without increasing sample size. Stratification does not imply any departure from the principles of randomness it merely denotes that before any selection takes place, the population is divided into a number of strata, then random samples taken within each stratum. It is only possible to do this if the distribution of the population with respect to a particular factor is known, and if it is also known to which stratum each member of the population belongs. Examples of characteristics which could be used in marketing to stratify a population include: income, age, sex, race, geographical region, possession of a particular commodity.

Stratification can occur after selection of individuals, e.g. if one wanted to stratify a sample of individuals in a town by age, one could easily get figures of the age distribution, but if there is no general population list showing the age distribution, prior stratification would not be possible. What might have to be done in this case at the analysis stage is to correct proportional representation. Weighting can easily destroy the assumptions one is able to make when interpreting data gathered from a random sample and so stratification prior to selection is advisable. Random stratified sampling is more precise and more convenient than simple random sampling.

When stratified sampling designs are to be employed, there are 3 key questions which have to be immediately addressed:

The bases of stratification, i.e. what characteristics should be used to subdivide the universe/population into strata?

The number of strata, i.e. how many strata should be constructed and what stratum boundaries should be used?

Sample sizes within strata, i.e. how many observations should be taken in each stratum?

Bases of Stratification

Intuitively, it seems clear that the best basis would be the frequency distribution of the principal variable being studied. For example, in a study of coffee consumption we may believe that behavioural patterns will vary according to whether a particular respondent drinks a lot of coffee, only a moderate amount of coffee or drinks coffee very occasionally. Thus we may consider that to stratify according to "heavy users", "moderate users" and "light users" would provide an optimum stratification.

In general, it is desirable to make up strata in such a way that the sampling units within strata are as similar as possible. In this way a relatively limited sample within each stratum will provide a generally precise estimate of the mean of that stratum. Similarly it is important to maximize differences in stratum means for the key survey variables of interest. This is desirable since stratification has the effect of removing differences between stratum means from the sampling error.

Number of Strata

The next question is that of the number of strata and the construction of stratum boundaries. As regards number of strata, as many as possible should be used. If each stratum could be made as homogeneous as possible, its mean could be estimated with high reliability and, in turn, the population mean could be estimated with high precision. However, some practical problems limit the desirability of a large number of strata:

No stratification scheme will completely "explain" the variability among a set of observations. Past a certain point, the "residual" or "unexplained" variation will dominate, and little improvement will be effected by creating more strata.

Depending on the costs of stratification, a point may be reached quickly where creation of additional strata is economically unproductive.

If a single overall estimate is to be made (e.g. the average per capita consumption of coffee) we would normally use no more than about 6 strata. If estimates are required for population subgroups (e.g. by region and/or age group), then more strata may be justified.

Quota Sampling

Quota sampling is a method of stratified sampling in which the selection within strata is non-random. Therefore, it is not possible to estimate sampling errors. A quota interview on average costs only half or a third as much as a random interview, the labour of random selection is avoided, and so are the headaches off non-contact and call-backs, and if fieldwork has to be quick, perhaps to reduce memory errors, quota sampling may be the only possibility. Quota sampling is independent of the existence of sampling frames.

Cluster Sampling

The process of sampling complete groups or units is called cluster sampling, situations where there is any sub-sampling within the clusters chosen at the first stage are covered by the term multistage sampling. For example, suppose that a survey is to be done in a large town and that the unit of inquiry (i.e. the unit from which data are to be gathered) is the individual household.

A large number of small clusters is better, all other things being equal, than a small number of large clusters. Whether single stage cluster sampling proves to be as statistically efficient as a simple random sampling depends upon the degree of homogeneity within clusters. If respondents within clusters are homogeneous with respect to such things as income, socio-economic class etc., they do not fully represent the population and will, therefore, provide larger standard errors. On the other hand, the lower cost of cluster sampling often outweighs the disadvantages of statistical inefficiency. In short, cluster sampling tends to offer greater reliability for a given cost rather than greater reliability for a given sample size.

Multistage Sampling

The population is regarded as being composed of a number of first stage or primary sampling units (PSU's) each of them being made up of a number of second stage units in each selected PSU and so the procedure continues down to the final sampling unit, with the sampling ideally being random at each stage. The necessity of multistage sampling is easily established. PSU's for national surveys are often administrative districts, urban districts or parliamentary constituencies. Within the selected PSU one may go direct to the final sampling units, such as individuals, households or addresses, in which case we have a two-stage sample. It would be more usual to introduce intermediate sampling stages, i.e. administrative districts are sub-divided into wards, then polling districts.

Area Sampling

Area sampling is basically multistage sampling in which maps, rather than lists or registers, serve as the sampling frame. This is the main method of sampling in developing countries where adequate population lists are rare. The area to be covered is divided into a number of smaller sub-areas from which a sample is selected at random within these areas; either a complete enumeration is taken or a further sub-sample.