Sampling and Sample Handling PDF

SAMPLING AND SAMPLE HANDLING D. Julian McClements University of Massachusetts 1. Introduction Analysis of the properties of a food material depends on the successful completion of a number of differ...

SAMPLING AND SAMPLE HANDLING D. Julian McClements University of Massachusetts 1. Introduction Analysis of the properties of a food material depends on the successful completion of a number of different steps: planning (identifying the most appropriate analytical procedure), sample selection, sample preparation, performance of analytical procedure, statistical analysis of measurements, and data reporting. 2. Sample Selection and Sampling Plans A food analyst often has to determine the characteristics of a large quantity of food material, such as the contents of a truck arriving at a factory, a day’s worth of production, or the products stored in a warehouse. Ideally, the analyst would like to analyze every part of the material to obtain an accurate measure of the property of interest, but in most cases this is practically impossible. Many analytical techniques destroy the food and so there would be nothing left to sell if it were all analyzed. Another problem is that many analytical techniques are time consuming, expensive or labor intensive and so it is not economically feasible to analyze large amounts of material. It is therefore normal practice to select a fraction of the whole material for analysis, and to assume that its properties are representative of the whole material. Selection of an appropriate fraction of the whole material is one of the most important stages of food analysis procedures, and can lead to large errors when not carried out correctly. Populations, Samples and Laboratory Samples. It is convenient to define some terms used to describe the characteristics of a material whose properties are going to be analyzed. Population. The whole of the material whose properties we are trying to obtain an estimate of is usually referred to as the population. Sample. Only a fraction of the population is usually selected for analysis, which is referred to as the sample. The sample may be comprised of one or more sub-samples selected from different regions within the population. Laboratory Sample. The sample may be too large to conveniently analyze using a laboratory procedure and so only a fraction of it is actually used in the final laboratory analysis. This fraction is usually referred to as the laboratory sample. The primary objective of sample selection is to ensure that the properties of the laboratory sample are representative of the properties of the population; otherwise, erroneous results will be obtained. Selection of a limited number of samples for analysis is of great benefit because it allows a reduction in time, expense and personnel required to carry out the analytical procedure, while still providing useful information about the properties of the population. Nevertheless, one must always be aware that analysis of a limited number of samples can only give an estimate of the true value of the whole population. 1  Sampling Plan. To ensure that the estimated value obtained from the laboratory sample is a good representation of the true value of the population, it is necessary to develop a sampling plan. A sampling plan should be a clearly written document that contains precise details that an analyst uses to decide the sample size, the locations from which the sample should be selected, the method used to collect the sample, and the method used to preserve them prior to analysis. It should also stipulate the required documentation of procedures carried out during the sampling process. The choice of a particular sampling plan depends on the purpose of the analysis, the property to be measured, the nature of the total population and of the individual samples, and the type of analytical technique used to characterize the samples. For certain products and types of populations sampling plans have already been developed and documented by various organizations which authorize official methods, e.g., the Association of Official Analytical Chemists (AOAC). Some of the most important considerations when developing or selecting an appropriate sampling plan are discussed below. 2.1 Purpose of Analysis The first thing to decide on when choosing a suitable sampling plan is the purpose of the analysis. Samples are analyzed for a number of different reasons in the food industry and this affects the type of sampling plan used: Official samples. Samples may be selected for official or legal requirements by government laboratories. These samples are analyzed to ensure that manufacturers are supplying safe foods that meet legal and labeling requirements. An officially sanctioned sampling plan and analytical protocol is often required for this type of analysis. Raw materials. Raw materials are often analyzed before acceptance by a factory, or before use in a particular manufacturing process, to ensure that they are of an appropriate quality. Process control samples. A food is often analyzed during processing to ensure that the process is operating in an efficient manner. Thus if a problem develops during processing it can be quickly detected and the process adjusted so that the properties of the sample are not adversely effected. Techniques used to monitor process control must be capable of producing precise results in a short time. Manufacturers can either use analytical techniques that measure the properties of foods on-line, or they can select and remove samples and test them in a quality assurance laboratory. Finished products. Samples of the final product are usually selected and tested to ensure that the food is safe, meets legal and labeling requirements, and is of a high and consistent quality. Officially sanctioned methods are often used for determining nutritional labeling. Research and Development. Samples are analyzed by food scientists involved in fundamental research or in product development. In many situations it is not necessary to use a sampling plan in R&D because only small amounts of materials with well-defined properties are analyzed. 2  2.2 Nature of Measured Property Once the reason for carrying out the analysis has been established it is necessary to clearly specify the particular property that is going to be measured, e.g., color, weight, presence of extraneous matter, fat content or microbial count. The properties of foods can usually be classified as either attributes or variables. An attribute is something that a product either does or does not have, e.g., it does or does not contain a piece of glass, or it is or is not spoilt. On the other hand, a variable is some property that can be measured on a continuous scale, such as the weight, fat content or moisture content of a material. Variable sampling usually requires less samples than attribute sampling. The type of property measured also determines the seriousness of the outcome if the properties of the laboratory sample do not represent those of the population. For example, if the property measured is the presence of a harmful substance (such as bacteria, glass or toxic chemicals), then the seriousness of the outcome if a mistake is made in the sampling is much greater than if the property measured is a quality parameter (such as color or texture). Consequently, the sampling plan has to be much more rigorous for detection of potentially harmful substances than for quantification of quality parameters. 2.3 Nature of Population It is extremely important to clearly define the nature of the population from which samples are to be selected when deciding which type of sampling plan to use. Some of the important points to consider are listed below: A population may be either finite or infinite. A finite population is one that has a definite size, e.g., a truckload of apples, a tanker full of milk, or a vat full of oil. An infinite population is one that has no definite size, e.g., a conveyor belt that operates continuously, from which foods are selected periodically. Analysis of a finite population usually provides information about the properties of the population, whereas analysis of an infinite population usually provides information about the properties of the process. To facilitate the development of a sampling plan it is usually convenient to divide an “infinite” population into a number of finite populations, e.g., all the products produced by one shift of workers, or all the samples produced in one day. A population may be either continuous or compartmentalized. A continuous population is one in which there is no physical separation between the different parts of the sample, e.g., liquid milk or oil stored in a tanker. A compartmentalized population is one that is split into a number of separate sub-units, e.g., boxes of potato chips in a truck, or bottles of tomato ketchup moving along a conveyor belt. The number and size of the individual sub-units determines the choice of a particular sampling plan.  A population may be either homogenous or heterogeneous. A homogeneous population is one in which the properties of the individual samples are the same at every location within the material (e.g. a tanker of well stirred liquid oil), whereas a heterogeneous population is one in which the properties of the individual samples vary with location (e.g. a truck full of potatoes, some of which are bad). If the properties of a population were homogeneous then there would be no problem in selecting a sampling plan because every individual sample would be representative of the whole population. In practice, most populations are heterogeneous and so we must carefully select a number of individual samples from different locations within the population to obtain an indication of the properties of the total population. 3 2.4 Nature of Test Procedure The nature of the procedure used to analyze the food may also determine the choice of a particular sampling plan, e.g., the speed, precision, accuracy and cost per analysis, or whether the technique is destructive or non-destructive. Obviously, it is more convenient to analyze the properties of many samples if the analytical technique used is capable of rapid, low cost, nondestructive and accurate measurements. 3. Developing a Sampling Plan After considering the above factors one should be able to select or develop a sampling plan which is most suitable for a particular application. Different sampling plans have been designed to take into account differences in the types of samples and populations encountered, the information required and the analytical techniques used. Some of the features that are commonly specified in official sampling plans are listed below. Sample size. The size of the sample selected for analysis largely depends on the expected variations in properties within a population, the seriousness of the outcome if a bad sample is not detected, the cost of analysis, and the type of analytical technique used. Given this information it is often possible to use statistical techniques to design a sampling plan that specifies the minimum number of sub-samples that need to be analyzed to obtain an accurate representation of the population. Often the size of the sample is impractically large, and so a process known as sequential sampling is used. Here sub-samples selected from the population are examined sequentially until the results are sufficiently definite from a statistical viewpoint. For example, sub-samples are analyzed until the ratio of good ones to bad ones falls within some statistically predefined value that enables one to confidently reject or accept the population. Sample location. In homogeneous populations it does not matter where the sample is taken from because all the sub-samples have the same properties. In heterogeneous populations the location from which the sub-samples are selected is extremely important. In random sampling the sub-samples are chosen randomly from any location within the material being tested. Random sampling is often preferred because it avoids human bias in selecting samples and because it facilitates the application of statistics. In systematic sampling the samples are drawn systematically with location or time, e.g., every 10th box in a truck may be analyzed, or a sample may be chosen from a conveyor belt every 1 minute. This type of sampling is often easy to implement, but it is important to be sure that there is not a correlation between the sampling rate and the sub-sample properties. In judgment sampling the sub-samples are drawn from the whole population using the judgment and experience of the analyst. This could be the easiest sub- sample to get to, such as the boxes of product nearest the door of a truck. Alternatively, the person who selects the sub-samples may have some experience about where the worst sub- samples are usually found, e.g., near the doors of a warehouse where the temperature control is not so good. It is not usually possible to apply proper statistical analysis to this type of sampling, since the sub-samples selected are not usually a good representation of the population. Sample collection. Sample selection may either be carried out manually by a human being or by specialized mechanical sampling devices. Manual sampling may involve simply picking a sample from a conveyor belt or a truck, or using special cups or containers to collect samples from a tank or sack. The manner in which samples are selected is usually specified in sampling plans. 4 4. Preparation of Laboratory Samples Once we have selected a sample that represents the properties of the whole population, we must prepare it for analysis in the laboratory. The preparation of a sample for analysis must be done very carefully in order to make accurate and precise measurements. 4.1 Making Samples Homogeneous The food material within the sample selected from the population is usually heterogeneous, i.e., its properties vary from one location to another. Sample heterogeneity may either be caused by variations in the properties of different units within the sample (inter-unit variation) and/or it may be caused by variations within the individual units in the sample (intra-unit variation). The units in the sample could be apples, potatoes, bottles of ketchup, containers of milk etc. An example of inter-unit variation would be a box of oranges, some of good quality and some of bad quality. An example of intra-unit variation would be an individual orange, whose skin has different properties than its flesh. For this reason it is usually necessary to make samples homogeneous before they are analyzed, otherwise it would be difficult to select a representative laboratory sample from the sample. A number of mechanical devices have been developed for homogenizing foods, and the type used depends on the properties of the food being analyzed (e.g., solid, semi-solid, liquid). Homogenization can be achieved using mechanical devices (e.g., grinders, mixers, slicers, blenders), enzymatic methods (e.g., proteases, cellulases, lipases) or chemical methods (e.g., strong acids, strong bases, detergents). 4.2 Reducing Sample Size Once the sample has been made homogeneous, a small more manageable portion is selected for analysis. This is usually referred to as a laboratory sample, and ideally it will have properties which are representative of the population from which it was originally selected. Sampling plans often define the method for reducing the size of a sample in order to obtain reliable and repeatable results. 5. Preventing Changes in Sample Once we have selected our sample we have to ensure that it does not undergo any significant changes in its properties from the moment of sampling to the time when the actual analysis is carried out, e.g., enzymatic, chemical, microbial or physical changes. There are a number of ways these changes can be prevented. Enzymatic Inactivation. Many foods contain active enzymes they can cause changes in the properties of the food prior to analysis, e.g., proteases, cellulases, lipases, etc. If the action of one of these enzymes alters the characteristics of the compound being analyzed then it will lead to erroneous data and it should therefore be inactivated or eliminated. Freezing, drying, heat treatment and chemical preservatives (or a combination) are often used to control enzyme activity, with the method used depending on the type of food being analyzed and the purpose of the analysis. Lipid Protection. Unsaturated lipids may be altered by various oxidation reactions. Exposure to light, elevated temperatures, oxygen or pro-oxidants can increase the rate at which these reactions proceed. Consequently, it is usually necessary to store samples that have high unsaturated lipid contents under nitrogen or some other inert gas, in dark rooms or covered 5 bottles and in refrigerated temperatures. Providing that they do not interfere with the analysis antioxidants may be added to retard oxidation. Microbial Growth and Contamination. Microorganisms are present naturally in many foods and if they are not controlled they can alter the composition of the sample to be analyzed. Freezing, drying, heat treatment and chemical preservatives (or a combination) are often used to control the growth of microbes in foods. Physical Changes. A number of physical changes may occur in a sample, e.g., water may be lost due to evaporation or gained due to condensation; fat or ice may melt or crystallize; structural properties may be disturbed. Physical changes can be minimized by controlling the temperature of the sample, and the forces that it experiences. 6. Sample Identification Laboratory samples should always be labeled carefully so that if any problem develops its origin can easily be identified. The information used to identify a sample includes: a) Sample description, b) Time sample was taken, c) Location sample was taken from, d) Person who took the sample, and, e) Method used to select the sample. The analyst should always keep a detailed notebook clearly documenting the sample selection and preparation procedures performed and recording the results of any analytical procedures carried out on each sample. Each sample should be marked with a code on its label that can be correlated to the notebook. Thus if any problem arises, it can easily be identified. Source: Procter, A. and Meullenet, J.F. (1998). Sampling and Sample Preparation. In: Nielsen S.S. (ed.) Food Analysis, 2nd Edition. Gaithersberg, Maryland: Aspen Publication. 6