and “Practicing sampling and sample preparation,” which is a simplified practical exercise.
THEORETICAL ASPECTS
This section covers the theoretical principles of sampling and sample‐size calculations. Additionally, techniques that may be followed during sampling and sample preparation of food or processing environment are covered.
Sampling Principles
Introduction
In the simplest sense, a “sample” may be defined as a small and manageable quantity intended to represent the whole. The whole is commonly referred to as the “population,” and in relevance to the subject matter of this book, the population is the food lot. Foods vary considerably in physical, chemical, and microbiological characteristics. These variations dictate the way a food is sampled and analyzed. Physically, food could be in a solid, gel, or liquid state, at different degrees of hydrophobicity, with ingredients in homogeneous or heterogeneous distribution. Compositionally, foods vary in water content, pH, presence of antimicrobial ingredients, and many other attributes. Foods differ in microbial burden and profile; this depends on whether the food is raw or processed, and the type of processing it received. The goal of the analysis also varies. Some foods are analyzed for the enumeration of indicator microorganisms (e.g., coliforms), whereas others are tested for the presence of pathogens (e.g., Listeria monocytogenes). These factors must be taken into account to determine appropriate sampling, sample preparation, and microbial recovery methodology.
Figure 2.1 Food sampling.
Foods subject to analysis are usually found in sizable quantities located in a storage facility, ship container, tanker, retail display case, vendor stand in open market, etc. With consideration of the size of the food lot and expected variations among multiple samples from the same lot, a number of samples are collected for analysis on the hope that they accurately represent the entire lot. If samples taken are not a good representation of the whole, whether it is the fault of the sample collector or due to an error inherent in the sampling plan, the laboratory results will be misleading. Therefore, sampling operations should be planned well. Additionally, every effort should be made to avoid mishandling or contamination of the collected sample. Correctly withdrawn, handled, and analyzed samples may serve as evidence of the quality or the safety of the whole.
Although food is emphasized in this book, sampling and analysis of water and processing environment will also be addressed. A sample of water from a stream is described as a “specimen.” Similarly, samples from circulating cleaning or rinsing solution or swabs from a moving conveyer belt are also considered specimens. In these situations, the population sampled is not static and thus getting a representative portion is a challenging task.
Preparing A Sampling Plan
Sampling is an essential step in any procedure for assessing the microbiological quality or safety of food. Sampling is an integral part of food inspection, which is practiced for commercial or legal reasons. Researchers experimenting with food need sampling schemes that lead to statistically meaningful results. Regardless of the ultimate goal of the analysis, sampling should be planned and executed properly. The following are steps used in preparing a sound sampling plan.
1 Identify the Reasons for SamplingSampling is a key and critical step in microbiological analyses that are done for many reasons including: (i) assessing the general microbiological quality of a raw product or an ingredient; (ii) validating a food processing operation; (iii) assuring the safety of the processed food; and (iv) evaluating the sanitary condition of a food processing environment. Each of these cases require a carefully considered sampling plan.
2 Assess the Size, Nature, and Uniformity of the Lot to be SampledThe population (i.e., the food lot) from which the samples are to be taken could be made of discrete units or bulk in a container. For example, a food lot of half‐and‐half coffee cream could be a stack of wholesale boxes, each containing multiple smaller retail boxes, and the latter containing multiple single‐serve (0.4 oz.) units. Alternatively, the lot could be bulk flour in a store bin or sack, milk in a tanker, or loose grains in a silo.Sampling starts by taking a number of units from the stacks of the lot or a portion of the bulk; these are described as gross (or primary) samples (Figure 2.1). Subsets of gross samples constitute the laboratory samples. The analyst who receives laboratory samples should further reduce them to test samples. For example, a laboratory sample could be a 10 lb cheese block, from which a 25 g test sample is withdrawn. In this particular example, it is desirable to collect several test samples to overcome the lack of uniformity from the edge to the center of the cheese block. Once the test sample is subjected to laboratory analysis, it is no longer described as a “sample.” Instead, the analyst should use descriptive words such as food homogenate, test solution, cell suspension, cell pellet, culture supernatant, isolate, etc. Note that the number of samples to be withdrawn from the lot is determined as described later.
3 Determine the Acceptable Quality Level or the Tolerable Safety RiskThe acceptable quality or safety level should be identified before sample size is determined. A supermarket chain importing strawberries may accept only a truckload that produces less than 1% moldy samples among all the samples analyzed. Determination of “moldiness” may be done subjectively (e.g., visual inspection) or analytically (e.g., fungi count on microbiological media). Note that the latter approach is time consuming, and the product could suffer significant quality damage while waiting for results to be obtained.Sampling for assessing food safety risks should be planned carefully. In addition to the factors discussed earlier, this sampling plan also should consider food status within the supply chain (e.g., raw or ready‐to‐eat), degree of processing (e.g., minimally processed or retorted), intended consumer population (e.g., infants or adults), and other factors. Analysts, for example, may be asked to determine the prevalence of Salmonella enterica on the surface of raw shell eggs produced in a cluster of farms that switched from a caged to a cage‐free or free‐range hen system. In a different scenario, analysts may be sampling for S. enterica in pasteurized shell egg from a company that introduced pasteurization as a new technology in egg processing and compare that with normal incidence of S. enterica in raw shell eggs. Although the food product and the targeted pathogen is the same in both examples, some pathogen‐positive samples are allowed in the first example (on‐farm), but none is allowed in the second example. Zero tolerance is common for infectious pathogens in ready‐to‐eat foods, whereas some degree of contamination is allowed in raw foods that are supposed to be cooked or processed before consumption.
4 Determine the Number of Samples to be WithdrawnOnce acceptable quality level or tolerable safety risk is considered, sample size should be determined. Sample size is determined by a statistical approach called “power analysis.” For explanation, let us consider the first of the two examples presented earlier: the contamination of raw shell eggs surfaces with S. enterica. The analysis can be viewed as a comparison between two groups: caged versus cage‐free products. To estimate the appropriate sample size for this experiment, a systematic approach needs to be followed.Define the hypothesis to be testedNull hypothesis: The rate of incidence of Salmonella in the two groups do not differ.Alternative hypothesis: The rate of incidence of Salmonella in the two groups is different.Determine the statistical parameters needed to calculate sample sizeSignificance level (α). This parameter is commonly set at 0.05 (i.e., 5%). It is the possibility of falsely rejecting the null hypothesis (Type‐I error), i.e., the “false positive” rate. In other words, with this α value, there is a 5% chance to conclude that there is a difference, while in fact there is no difference. The smaller the value of α, the larger the sample size needed to produce
