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Minimising the between-sample variance in colony counts on foods
Food Microbiology 27 (2010) 598e603
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Food Microbiology journal homepage: www.elsevier.com/locate/fm
Minimising the between-sample variance in colony counts on foods Janet E.L. Corry a, *, Basil Jarvis b, Alan J. Hedges c a
University of Bristol, Department of Clinical Veterinary Science, Langford, Bristol BS40 5DU, UK Ross Biosciences Ltd, Upton Bishop, Ross-on-Wye, HR9 7UR, UK c University of Bristol, Department of Cellular and Molecular Medicine, University Walk, Bristol BS8 1TD, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 December 2009 Received in revised form 4 February 2010 Accepted 9 February 2010 Available online 16 February 2010
The objective of this study was to assess whether it is possible to minimise the variance in colony counts on replicate target samples of foods by aseptic compositing of the samples or by increasing the quantity of sample examined. The results show that compositing reduces the overall variance, and hence the standard deviation, to very low levels, although in some cases the overall variance remains relatively high, reflecting the heterogeneous distribution of microorganisms in the foods. Increasing the weight of target sample examined (e.g. from 10 g to 100 g) had a pronounced effect on the mean log10 colony count and significantly reduced the variance of the mean. The results are discussed in relation to the quantity of sample that is recommended for examination in international and other standards. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Colony counts Foods Variance Standard deviation Sample size
1. Introduction Colony count procedures are used widely in the examination of foods and other matrices, but the repeatability and reproducibility of the procedures is often poor, especially in comparison with chemical and physical methods of analysis. Measurement uncertainty is recognised to be important in any form of analysis (EURACHEMeCITAC, 2000; EURACHEM, 2006; ISO, 2004, 2006). It comprises two parts: analytical uncertainty and sampling uncertainty, both aspects being increasingly well characterized in the physical and chemical sciences, although there is still much debate on the way in which the combined uncertainty should be used (Codex, 2009). The importance of analytical uncertainty in microbiology is now recognised widely (Augustin and Carlier, 2006; Corry et al., 2007; Jarvis et al., 2007a,b). However, although the heterogeneous distribution of microorganisms in foods and other matrices is acknowledged (Jarvis, 2008), little attention has been paid to assessment of the extent, and hence the potential significance, of microbial distributions in relation to sampling uncertainty. Analytical measurements are made on target samples of a food matrix that are drawn randomly, yet are intended to be representative of the ‘lot’ of raw or processed food (ICMSF, 1986). For a heterogeneous matrix, it is dubious whether a random sample
* Corresponding author. Tel.: þ44 (1)17 928 9409; fax: þ44 (1)17 928 9324. E-mail address: [email protected] (J.E.L. Corry). 0740-0020/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.fm.2010.02.002
can ever be considered to provide a truly representative sample of the matrix, even if samples are composited, i.e. are made up of numerous sub-samples taken from different parts of a target sample. Analytical measurement uncertainty provides an estimate of the repeatability and reproducibility of a result determined by a specific method carried out within one or more laboratories and by one or more analysts (Corry et al., 2007). By contrast, sampling uncertainty reflects the impact on the analytical result of the variability between target samples, and any sub-samples, drawn from a ‘lot’ of material (EURACHEM, 2006; CODEX, 2007). It is recognised that microorganisms in foods do not conform to a ‘normal’ distribution. Whilst the distribution of microorganisms in simple suspension often conforms reasonably to Poisson, there are many cases where this is not so. Clumping of microbial cells is well known to give rise to heterogeneous distributions, such that log-normal, negative binomial or other even more complex distributions that may be required to describe properly their distribution (Jarvis, 2008). In natural matrices such as soil, the distribution of microorganisms is very complex (Jones et al., 1948) and a similar situation undoubtedly pertains to the distribution of microorganisms within a food matrix. Even in a liquid food matrix, such as milk, the distribution of microorganisms is both spatially and numerically complex (Wilson, 1935). Ramsay et al. (2001) and Lyn et al. (2002, 2003, 2007) demonstrated that the estimate of sampling uncertainty is at least as large as, and often larger than, the estimate of analytical measurement uncertainty. It is likely that the same situation will apply in the
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microbiological examination of foods, but there are few published data. Jarvis et al. (2007a) observed that two thirds of the total measurement uncertainty calculated from data on replicate aerobic colony counts on two ‘lots’ of prawns was due to the variation between target samples. This suggests that for microbiological examination of foods, the estimate of sampling uncertainty included in any overall uncertainty estimate is probably at least as great as the estimate of analytical uncertainty. As with chemical analyses, such underestimates may be critical to the assessment for compliance of foods with legislative and commercial microbiological criteria. The overall objective of our work is to investigate whether the concept of sampling uncertainty as now described in the chemistry sector, can be extended to the microbiology sector. The work described here was concerned with evaluating the effects of sample size and treatment in order to minimise the analytical measurement uncertainty of microbial colony counts. 2. Materials and methods 2.1. Food samples For any one experiment, all food samples had the same use-by date and were taken randomly from different parts of the retail display or from the manufacturing batch when sampled at Bristol University, Langford, meat processing facility. The same technician did all examinations and colony counts on all the products to minimise this source of uncertainty. The following foods were purchased from retail outlets: 1-pint plastic bottles of semi-skimmed and skimmed homogenised pasteurised milk; 500 g packs of low-fat fresh minced beef; 500 g packs of fresh pork sausages; 100 g packs of ground coriander. Five hundred g packs of pork sausages and of minced beef were also sampled in the meat processing facility at Langford. 2.2. Bacteriological colony count methods All counts were made after diluting the products in MRD (maximum recovery diluent, Oxoid CM733) by surface plating onto
599
PCA (plate count agar, Oxoid CM325). For minced meat and sausages the MRD was supplemented with 0.5% Tween 80 (Sigma). The diluent was sterilized in 1 L quantities, chilled to 4 2 C, dispensed aseptically in appropriate volumes and stored at 4 2 C for not more than 24 h before use. The diluent used for the initial homogenate was prechilled, but the diluents used for the subsequent dilutions, and the pre-poured surface dried agar plates, were equilibrated to room temperature before use. Following inoculation, PCA plates were incubated inverted at 30 C for 48 h. Colonies were counted manually. 2.3. Homogenisation procedures and protocols 2.3.1. Preparation of primary 1 in 10 homogenates Initially various approaches were used to prepare the primary 1 in 10 homogenates (see Results). Subsequently, a standardised double mixing procedure was used with minor variations for specific food samples. The standardised protocol (Fig. 1) was based on five target samples. For the first stage of mixing, five approximately equal 20 g sub-samples from different parts of a target sample were composited in a sterile Stomacher 400 plastic bag to give a total recorded weight of 100 g (99.5e100.5 g). One hundred ml of diluent, tempered to 2 1 C, were added and the composite sample was homogenised either in a Stomacher 400Ô (Seward, London) for 1 min or in a Pulsifier (Microgen Bioproducts Ltd, Camberley, UK) for 30s to obtain a primary 1 in 2 dilution. This procedure was repeated with the other target samples to provide five sets of primary 1 in 2 dilutions. Sets of ‘combined’ samples were prepared by aseptically combining 20 g (19.5e20.5 g) from each of the five primary 1 in 2 homogenates in a sterile filter bag of a Stomacher 400 plastic bag (Grade Packaging Ltd., Leicester, UK). This was repeated five times. . Four hundred ml of diluent was then added to each bag and the mixture was further homogenised to give 101 homogenates. Sets of ‘separate’ samples were prepared by aseptically weighing 20 g (19.5e20.5 g) from each of the 1 in 2 homogenates individually into a sterile filter bag within a Stomacher 400 bag. Eighty ml of diluent was added to each bag and the mixture was again homogenised to prepare the 101 homegenates. In some experiments, ‘individual’ samples were also prepared using separate quantities taken directly
Fig. 1. Schematic of the double maceration protocol for sample preparation. Five portions of 20 g from each primary target sample are composited with 100 ml of diluent to give a 1 in 2 dilution. A combined sample is prepared by combining 20 g of each of the 5 primary target sub-samples (homogenates) and mixing again with 400 ml of diluent to give a 101 dilution; this is repeated four times. For separate samples, 20 g of each of the 5 target sub-samples (1 in 2 suspensions) is separately mixed with 4 volumes of diluent.
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from each target sample (typically 25, 50 or 100 g) and homogenised with an equal volume of diluent before addition of a further four volumes of diluent and then repeating the homogenisation. Milk was handled similarly, except that the samples were shaken (ISO, 2005) before removing a sub-sample. A primary 1 in 2 dilution was prepared by first diluting 100 ml from each of the five well-mixed target samples with an equal volume (100 ml) of diluent. After thorough mixing, 20 ml from each of these primary dilutions were combined and further diluted with 400 ml of diluent to provide the 101 dilution (combined samples). Separate samples were prepared by diluting 20 ml of each separate 1 in 2 primary dilution with 80 ml diluent to produce the 101 dilution. Further serial dilutions to 104 were prepared in duplicate from each 101 dilution of the target sample(s) by adding 1e9 ml of diluent. For each set of sample dilutions examined, 100 ml volumes of the dilutions were plated in duplicate (two plates per sample per dilution). 2.4. Statistical methods For each set of analyses, the five ‘combined’ and five ‘separate’ test samples were prepared as described above and illustrated schematically in Fig. 1; in addition, five ‘independent’ samples were analyzed where stated in the text. The combined and separate samples therefore comprised a nested design with two sub-samples (combined and separate) nested within each of the five primary target samples and duplicate sets of colony counts on each analytical sample. The numbers of colony forming units (cfu) per plate were recorded and converted to cfu g1 or ml1, allowance being made for the quantity and dilutions of sample examined. All colony counts were log10-transformed before statistical analysis. The underlying statistical parameters (mean, median, variance and standard error of the mean) were determined for each data set. The transformed data were examined also by a standard one-way ANOVA for a 2factor fully nested design (ISO, 1994), using the F-ratio to determine the significance of differences in the variances. Where appropriate, Tukey's ‘Wholly Significant Difference’ test was used to assess the 5% level of significance of differences between test systems. In addition, values for the ‘log10 average count’ (log m) of samples were derived from the moments of the log-normal distribution, using the equation of Rahman (1968) _
log m ¼ m þ ln 10$s2 =2 _
where m ¼ estimate of the mean log10 colony count and s2 ¼ the variance of the log10 colony count. 3. Results 3.1. Experimental design Preliminary tests on a diverse range of target samples suggested that thorough shaking, either in the original container or after transfer to a large sterile plastic bag, ensured reasonable mixing of non-viscous liquid samples (e.g. milk) and of dried foods (e.g. milk powder, egg powder, spices). Unsurprisingly, however, manual and mechanical mixing techniques such as squeezing and pummelling were unable to ensure effective mixing of minced meat, shellfish and raw vegetable products, although such procedures were suitable for raw fruits (e.g. raw mixed fruit salad). Consequently, we devised a procedure for multiple homogenisation (Fig. 1) that was very efficient at minimising the overall variance of colony counts. We compared the effects of stomaching or pulsifying the primary sample with an equal volume of diluent in order to produce a 50:50 suspension and found no statistically
significant differences in microbial counts or variance, although as reported by Kang et al. (2001) and Wu et al. (2003) the Pulsifier produced less physical breakdown of the samples. A quantity of the primary homogenate was then weighed aseptically into the filter bag inside a plastic stomacher bag together with an additional four volumes of diluent, prior to further maceration to give the initial 1 in 10 dilution of the target sample. This procedure minimised the risk of transfer of food particles into the subsequent serial dilutions as well as avoiding blockage of pipettes. An additional benefit of the double homogenisation procedure is that it permitted the examination of both combined (i.e. composited) and separate target samples. The results of all the trials are summarised in Table 1. 3.2. Liquid pasteurised milk Two trials were carried out, the first with half-fat milk and the second with skimmed milk. On each occasion, five plastic bottles of each having the same date code, purchased from a local retailer, were tested after 2 days storage at 10 C followed by 18 h at 1 C (a preliminary investigation had found very low numbers of organisms in a similar sample). In the first trial, the median and mean colony counts (as log cfu/ml) of the combined and separate samples were of the same order of magnitude but the variance of the combined target samples was only 30% of that of the separate target samples, each of which was representative of a different bottle of milk. This shows that the bacterial load in each of the five target samples differed slightly, suggesting either that the containers into which the pasteurised milk had been filled were not ‘commercially sterile’ or that the distribution of organisms was very heterogeneous. By combining the samples, these between-target sample differences were reduced. In the second trial the colony counts were higher than those in the first milk trial, probably because the milk was nearer its use-by date. It was notable that in this trial the variances of the combined and separate samples were closer, possibly as a result of the higher levels of organisms. As the distribution of organisms in a food often approximates to log-normal, it can be expected that the counts obtained will be distributed about the geometric average (i.e. the back-transformed mean log count), which is also the median for a true log-normal distribution. It has been shown previously (Kilsby and Pugh, 1981) that the mean log count underestimates the true average level of organisms in the food. The greater the variation in the distribution of organisms, the more the mean log count will underestimate the log average count. Calculated log average colony counts on the milk samples were generally similar to the median counts, indicating that the distribution of colony forming units in the milk conformed to a log-normal distribution. The distribution of colony counts on the plates was tested using the Index of Distribution (Fisher et al., 1943). In almost all cases it was not possible to reject the hypothesis that the colonies were distributed randomly (P > 0.05). This shows that the dilutions of milk samples used in the colony count procedures were done efficiently and that there was no evidence of over-dispersion in the dilutions tested. However, the results also show that even in wellmixed liquid samples the actual levels and distribution of organisms differ between apparently replicate samples taken from the same production lot (cf. Wilson, 1935). 3.3. Minced meat In the first trial, five packs of low-fat minced meat, each bearing the same date code, were purchased from a supermarket; the second experiment used five packs of low-fat minced meat taken from a single batch prepared in the Langford meat processing facility from cold-stored vacuum-packed beef.
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601
Table 1 The median, mean, variance and standard deviations of the mean log10 cfu/g or /ml and the log10 average cfu/g or ml on different food matrices determined on five combined, five separate and five individual samples by the procedures described in the text. Food matrix
Pasteurised milk
Trial
1 2
Minced meat
1 2
Sausages
1 2
Coriander
1
Sample type
Weight/volume (g or ml)
Colony count as log10 cfu/g or /ml Median
Mean
Variance**
Standard deviation
Log10 average count (cfu/g or ml)
Combined Separate Combined Separate
100 100 100 100
4.680 4.663 7.113 7.072
4.683 4.689 7.126 7.066
0.0040 0.0122 0.0027a 0.0034a
0.064 0.110 0.052 0.058
4.69 4.70 7.13 7.07
Combined Separate Combined Separate Individual* Individual Individual
100 100 100 100 10 25 100
7.049 7.188 9.15 9.18 8.72 8.80 9.04
7.038 7.145 9.16 9.18 8.55 8.77 9.04
0.0013b 0.0045b 0.0002c 0.0005c 0.1432 0.0101 0.0025
0.036 0.067 0.014 0.022 0.378 0.100 0.049
7.08 7.15 9.16 9.18 8.71 8.78 9.04
Combined Separate Combined Separate Individual Individual Individual
100 100 100 100 10 25 100
4.283 4.330 5.629 5.702 5.352 5.411 5.665
4.279 4.285 5.629 5.698 5.393 5.408 5.670
0.0130d 0.0193d 0.0004e 0.0006ef 0.0105g 0.0007g 0.0032ef
0.114 0.139 0.021 0.024 0.102 0.027 0.057
4.29 4.31 5.63 5.70 5.41 5.41 5.67
Combined Separate* Individual Individual
25 25 5 50
5.100 5.045 4.924 4.903
5.069 5.114 4.932 4.939
0.0172h 0.0368 0.0213hi 0.0063hi
0.130 0.192 0.146 0.080
5.09 5.16 4.96 4.95
* Distribution of the log-transformed colony counts was not ‘normal’ e data excluded from ANOVA. ** Values within a group followed by the same superscript are not significantly different at P ¼ 0.05.
The mean log colony counts on both sets of combined and separate samples were not statistically different even though the variances of the separate samples were more than three times higher than those of the combined samples. However, the variance ratios were not statistically significant at P ¼ 0.05, and there was no significant effect on the mean log-counts. These results confirmed that the double stage mixing procedure was able to produce replicate counts of high precision. In the second trial we also tested three sets of five individual target samples of differing sample weights, each set being drawn directly from the same primary target samples. The individual samples were also subjected to double mixing in order to ensure comparability in the overall procedure. As the sample size was increased from 10 g to 100 g the mean log counts increased and the variance generally decreased (cf. Kilsby and Pugh, 1981). For the smallest sample size, the median log count differed considerably from the mean log count, and tests for normality showed that the log-transformed data did not conform to a normal distribution, although the calculated log-mean colony count was essentially identical to the median log count. Analysis of variance, followed by Tukey's test for Wholly Significant Differences, showed that the log
colony counts on the 10 g and 50 g individual samples did not differ significantly from each other (P > 0.05) but were significantly different (P ¼ 0.01) to those on any of the 100 g samples, which did not differ significantly from each other. The colony counts on both samples of minced meat were high. Target sample 1 was within 2 days of its stated shelf life when examined. The difference in the level of colony count on the minced meats used in the two trials probably reflects the difference in the source of the meats prior to mincing. The meat used to make the minced meat in the second trial had been dissected from carcasses as part of a research project, vacuum packed and stored chilled for some time before mincing. Although the colony counts were in the order of 109 per g the minced meat was organoleptically acceptable and most of the organisms were lactic acid bacteria. 3.4. Sausages For the first trial, five 250 g packs of raw pork sausages, all bearing the same date code, were tested on the day of purchase. For the second trial, five randomly selected packs of 500 g of sausages were obtained from production in the Langford meat processing
Table 2 Colony counts on replicate analytical samples of sausages (trial 2) prepared using the protocol for combined, separate and individual samples. Mean Log10 cfu/g in replicate test sample #
Overall mean
Overall median
Variance
Standard deviation
5.645 5.643
5.629
5.630
0.0004
0.020744
5.737 5.716
5.713 5.711
5.698
5.702
0.0006
0.025133
5.384 5.346
5.373 5.330
5.330 5.318
5.393
5.354
0.0105
0.102317
5.428 5.408
5.415 5.462
5.408 5.365
5.387 5.384
5.408
5.408
0.0007
0.027318
5.659 5.670
5.737 5.718
5.633 5.619
5.732 5.731
5.670
5.665
0.0032
0.056526
Sample type
Replicate test
R1
R2
R3
R4
R5
Combined
C-1 C-1
5.626 5.628
5.631 5.627
5.629 5.575
5.645 5.643
Separate
S-1 S-2
5.704 5.701
5.659 5.657
5.699 5.683
Individual 10 g
I10-1 I10-2
5.587 5.577
5.362 5.318
Individual 25 g
I25-1 I25-2
5.427 5.396
Individual 100 g
I100-1 I100-2
5.600 5.596
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facility. Target samples (100 g) of sausages, including skin, were examined after the double mixing process to produce separate and combined samples. In addition, five sets of individual random samples, each of 10, 25 and 100 g, were taken from the same packs for analysis in the second trial. As anticipated, the individual samples gave more variable results (Table 2). The mean and median colony counts on the combined and separate samples were essentially identical in each trial but the variance of the log colony counts on the separate samples was greater than on the combined samples. Because the variances were low, the calculated log average counts differed only slightly from the average log counts. No statistically significant differences were observed between the mean counts on the combined and separate samples, or the 100 g individual samples. As with the minced meat, the mean log counts on the individual samples increased with increasing sample size and the variance on the individual 100 g samples was somewhat greater than on the 25 g samples, although less than on the 10 g samples. 3.5. Ground coriander We examined samples of coriander, as an example of a dried food product, following preliminary tests that showed very low bacterial colony counts in many other herbs and spices. Only one trial was undertaken: five samples, all having the same date code, were purchased from an ethnic food retailer. The samples were shaken well to minimise the within sample variation and weighed samples were again prepared for analysis using the double homogenisation procedure. The variance of the combined samples was less than half of that on the separate samples although the mean log counts were similar, thereby giving a slightly greater log average count on the separate samples. As anticipated, the smallest (5 g) individual samples had the greatest variance and the lowest mean log count, whilst the variance on the larger individual sample (50 g) was smaller than that on either the combined or separate 25 g samples using both colony count procedures. However, none of the mean log counts differed statistically (P > 0.05). 4. Discussion and conclusions Premixing of target samples, by repeated inversion for liquids, shaking for powders and kneading/pummelling for semi-solid foods reduces, but does not eradicate, the difference in colony counts between target samples. The double homogenisation procedure used for this work allowed the comparative analysis of both combined and separate samples. Although in most of the trials the mean log counts on combined and separate samples were similar, in almost every case the variances of the separate samples were greater than those of the combined samples implying heterogeneous distribution in the lot, although the differences were not always statistically significant. The two stage protocol of homogenising composite sub-samples of each target sample with an equal quantity of diluent to produce a primary 1 in 2 dilution, which could then be further composited with primary dilutions from other target samples, provided a most effective means to minimise the analytical variance of colony counts between target samples. In many ways this protocol is similar to the procedure used in sampling for many chemical analyses. Similarly, dilution and homogenisation of composited sub-samples from separate replicate target samples was quite effective and was better in most cases than merely taking a single quantity from each individual target sample, especially if such individual samples were of low weight.
There is no doubt that the quantity of sample taken for analysis is a critical factor in minimising the variance between target samples. Kilsby and Pugh (1981) showed that if the distribution of organisms is log-normal then, since the ‘true’ level of organisms (i.e. the log ‘average’ count) in a sample is ‘constant’ at any one time, any reduction in the variance will be accompanied by an apparent increase in the mean log colony count. As the weight of the target sample examined is increased, the variance of the count per gram decreases and the mean log colony count increases. This was confirmed in our experiments and leads us to conclude that the weight of sample to be processed for examination should be greater than that often specified in standard microbiological methods and should be specified in legislation such as EU (2005). In this respect we consider that the minimum individual or composite sub-sample taken from a target sample for examination should not be less than 100 g. The USDA FSIS and the APHA standard methods specify that a sample should be of 50 0.1 g (Lattuada et al., 1998; Midura and Bryant, 2001). Other standard methods such as those published by ISO recommend different quantities for different foods. For instance ISO (1999) makes a general recommendation to use 10 g or 10 ml, of a representative test sample, unless otherwise stated in specific standards, yet there is a lack of consistency in such standards. ISO standards for examination of specific food products (ISO 2003a, b, c) make the following recommendations: for meat products, the entire sample mass if 50 g; for carcass neck skin from poultry, 5e10 g; for cereal products, 50 g; for gelatine, 20 g; for margarine and spreads, 40 g. For fish and fish products, dehydrated products, and egg products no recommendations are made. IDF/ISO recommend examination of 10 ml of liquid milk or 10 g of milk powder from each target sample (ISO, 2005). Whilst such diverse recommendations probably reflect traditional ‘custom and practice’ in food examination it is our opinion that there should be greater consistency in recommending a quantity of sample for examination in order to minimise overall measurement uncertainty. In this work we have assumed that the objective of examining replicate target samples and sub-samples is to obtain a precise estimate of the overall microbial density in a lot. It might be argued that it is more important to assess the maximum, or peak, levels of microbial density rather than an overall estimate. But such a task would require the examination of many parallel samples with little overall benefit. After all, the consumer eats portions of food rather than small individual samples and in assessing both organoleptic quality and microbiological safety we are concerned with typical microbial densities. Above all else, this work has demonstrated that it is possible to determine microbial colony counts on diverse food matrices with a higher degree of analytical precision than might be expected from most reports of microbiological measurement uncertainty (e.g. Jarvis et al., 2007a,b). However, in a busy industrial microbiology laboratory it is doubtful whether such a high level of precision could be achieved because of the need for high-level sample throughput. Acknowledgements This work was supported by the Food Standards Agency as part of a research project concerned with microbiological sampling uncertainty. We acknowledge with thanks the technical assistance of Miss Raquel Pinho in this work. AJH thanks Prof. Anthony Hollander, Head of Department, for providing facilities. References Augustin, J.-C., Carlier, V., 2006. Lessons from the organization of a proficiency testing program in food microbiology by interlaboratory comparison: analytical
J.E.L. Corry et al. / Food Microbiology 27 (2010) 598e603 methods in use, impact of methods on bacterial counts and measurement uncertainty of bacterial counts. Food Microbiol. 23, 1e38. CODEX, 2007. Uncertainty of sampling. In: CODEX Committee on Methods of Analysis and Sampling. 28th Session, March 2007, Budapest. CX/MAS 07/28/11. Codex, 2009. Proposed Draft Guidelines on Measurement Uncertainty e Government Comments at Step 3 (New Zealand). CODEX Document CRD 14. CODEX Alimentarius Commission, FAO/WHO, Rome, Italy. Corry, J.E.L., Jarvis, B., Passmore, S., Hedges, A.J., 2007. A critical review of measurement uncertainty in the enumeration of food microorganisms. Food Microbiol. 24, 230e253. EU, 2005. Commission regulation (EC) No 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. Official J. L338, 1e26. EURACHEM, April 2006. Estimation of measurement uncertainty arising from sampling. 6th Committee Draft of the Eurachem/EUROLAB/CITAC/Nordtest Guide. EURACHEMeCITAC, 2000. Guide to Estimating Uncertainty, second ed. EMPA, St. Gallen, Switzerland. Fisher, R.A., Corbett, A.S., Williams, C.B., 1943. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Animal Ecol. 2, 42e58. ICMSF, 1986. Microorganisms in Foods: 2. Sampling for Microbiological Analysis: Principles and Specific Applications, second ed. University of Toronto Press, Toronto, Canada. ISO, 1994. Accuracy (Trueness and Precision) of Measurement Methods and Results: Intermediate Measures of the Precision of a Standard Measurement Method. ISO5727-3:1994. International Organisation for Standardisation, Geneva. ISO, 1999. Microbiology of Food and Animal Feeding Stuffs e Preparation of Test Samples, Initial Suspension and Decimal Dilutions for Microbiological Examination. Part 1: General rules For the Preparation of the Initial Suspension and Decimal Dilutions. ISO 6887-1:1999. International Organisation for Standardisation, Geneva. ISO, 2003a. Microbiology of Food and Animal Feeding Stuffs e Preparation of Test Samples, Initial Suspension and Decimal Dilutions for Microbiological Examination. Part 2: Specific Rules for the Preparation of Meat and Meat Products. ISO 5887-2:2003. International Organisation for Standardisation, Geneva. ISO, 2003b. Microbiology of Food and Animal Feeding Stuffs e Preparation of Test Samples, Initial Suspension and Decimal Dilutions for Microbiological Examination. Part 3: Specific Rules for the Preparation of Fish and Fishery Products. ISO 6887-3:2003. International Organisation for Standardisation, Geneva. ISO, 2003c. Microbiology of Food and Animal Feeding Stuffs e Preparation of Test Samples, Initial Suspension and Decimal Dilutions for Microbiological Examination. Part 4: Specific Rules for the Preparation of Products Other than Milk and Milk Products, Meat and Meat Products and Fish and Fishery Products. ISO 6887-4:2003. International Organisation for Standardisation, Geneva. ISO, 2004. Guidance for the Use of Repeatability, Reproducibility and Trueness Estimates in Measurement Uncertainty Estimation. ISO/TS 21478:2004. International Organisation for Standardisation, Geneva.
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ISO, 2005. Milk and Milk Products e Quality Control in the Microbiological Laboratory e Part 1. Analyst Performance Assessment for Colony Counts. ISO 144611:2005. International Organisation for Standardisation, Geneva. ISO, 2006. Microbiology of Food and Animal Feedingstuffs e Guidelines For the Estimation of Measurement Uncertainty for Quantitative Determinations. Rev 1. ISO TS 19036:2006. International Organisation for Standardisation, Geneva. Jarvis, B., 2008. Statistical Aspects of the Microbiological Examination of Foods, second ed. Academic Press, London. Jarvis, B., Corry, J.E.L., Hedges, A.J., 2007a. Estimates of measurement uncertainty from proficiency testing schemes, internal laboratory quality monitoring and during routine enforcement examination of foods. J. Appl. Microbiol. 103, 462e467. Jarvis, B., Hedges, A.J., Corry, J.E.L., 2007b. Assessment of measurement uncertainty for quantitative methods of analysis: comparative assessment of the precision (uncertainty) of bacterial colony counts. Int. J. Food Microbiol. 116, 44e51. Jones, P.C.T., Mollinson, J.E., Quenouille, M.H., 1948. A technique for the quantitative estimation of soil microorganisms. Statistical note. J. General Microbiol. 2, 54e69. Kang, D.H., Dougherty, R.H., Fung, D.Y.C., 2001. Comparison of pulsifier and stomacher to detach microorganisms from lean meat tissues. J. Rapid Methods Automat. Microbiol. 9, 27e32. Kilsby, D.C., Pugh, M.E., 1981. The relevance of the distribution of microorganisms within batches of food to the control of microbiological hazards from foods. J. Appl. Microbiol. 51, 345e354. Lattuada, C.P., Dillard, L.H., Rose, B.E., 1998. Examination of fresh, refrigerated and frozen prepared meat, poultry and pasteurized egg products. In: Microbiology Laboratory Guidebook, third ed. USDA/FSIS, Washington, USA (Chapter 3). Lyn, J.H., Ramsey, M.H., Wood, R., 2002. Optimised uncertainty in food analysis: application and comparison between four contrasting ‘analyte-commodity’ combinations. Analyst 127, 1252e1260. Lyn, J.H., Ramsey, M.H., Wood, R., 2003. Multi-analyte optimisation of uncertainty in infant food analysis. Analyst 128, 379e388. Lyn, J.A., Ramsay, M.H., Coad, D.S., Damant, A.P., Wood, R., Boon, K.A., 2007. The duplicate method of uncertainty estimation: are 8 targets enough? Analyst 132, 1147e1152. Midura, T.F., Bryant, R.G., 2001. Sampling plans, sample collection, shipment and preparation for analysis. In: Downes, F.P., Ito, K. (Eds.), Compendium of Methods For the Microbiological Examination of Foods, fourth ed. APHA, Washington, USA (Chapter 3). Rahman, N.A., 1968. A Course in Theoretical Statistics. Griffin, London, pp. 298e299. Ramsay, M.H., Lyn, J.H., Wood, R., 2001. Optimised uncertainty at minimum overall cost to achieve fitness-for-purpose in food analysis. Analyst 126, 1777e1783. Wilson, G.S., 1935. The Bacteriological Grading of Milk. Special Report to the Medical Research Council No. 206. HMSO, London, p. 150. Wu, V.C.H., Jitareerat, P., Fung, D.Y.C., 2003. Comparison of the pulsifier and the stomacher for recovering microorganisms in vegetables. J. Rapid Methods Automat. Microbiol. 11, 145e152.
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Food Microbiology journal homepage: www.elsevier.com/locate/fm
Minimising the between-sample variance in colony counts on foods Janet E.L. Corry a, *, Basil Jarvis b, Alan J. Hedges c a
University of Bristol, Department of Clinical Veterinary Science, Langford, Bristol BS40 5DU, UK Ross Biosciences Ltd, Upton Bishop, Ross-on-Wye, HR9 7UR, UK c University of Bristol, Department of Cellular and Molecular Medicine, University Walk, Bristol BS8 1TD, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 December 2009 Received in revised form 4 February 2010 Accepted 9 February 2010 Available online 16 February 2010
The objective of this study was to assess whether it is possible to minimise the variance in colony counts on replicate target samples of foods by aseptic compositing of the samples or by increasing the quantity of sample examined. The results show that compositing reduces the overall variance, and hence the standard deviation, to very low levels, although in some cases the overall variance remains relatively high, reflecting the heterogeneous distribution of microorganisms in the foods. Increasing the weight of target sample examined (e.g. from 10 g to 100 g) had a pronounced effect on the mean log10 colony count and significantly reduced the variance of the mean. The results are discussed in relation to the quantity of sample that is recommended for examination in international and other standards. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Colony counts Foods Variance Standard deviation Sample size
1. Introduction Colony count procedures are used widely in the examination of foods and other matrices, but the repeatability and reproducibility of the procedures is often poor, especially in comparison with chemical and physical methods of analysis. Measurement uncertainty is recognised to be important in any form of analysis (EURACHEMeCITAC, 2000; EURACHEM, 2006; ISO, 2004, 2006). It comprises two parts: analytical uncertainty and sampling uncertainty, both aspects being increasingly well characterized in the physical and chemical sciences, although there is still much debate on the way in which the combined uncertainty should be used (Codex, 2009). The importance of analytical uncertainty in microbiology is now recognised widely (Augustin and Carlier, 2006; Corry et al., 2007; Jarvis et al., 2007a,b). However, although the heterogeneous distribution of microorganisms in foods and other matrices is acknowledged (Jarvis, 2008), little attention has been paid to assessment of the extent, and hence the potential significance, of microbial distributions in relation to sampling uncertainty. Analytical measurements are made on target samples of a food matrix that are drawn randomly, yet are intended to be representative of the ‘lot’ of raw or processed food (ICMSF, 1986). For a heterogeneous matrix, it is dubious whether a random sample
* Corresponding author. Tel.: þ44 (1)17 928 9409; fax: þ44 (1)17 928 9324. E-mail address: [email protected] (J.E.L. Corry). 0740-0020/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.fm.2010.02.002
can ever be considered to provide a truly representative sample of the matrix, even if samples are composited, i.e. are made up of numerous sub-samples taken from different parts of a target sample. Analytical measurement uncertainty provides an estimate of the repeatability and reproducibility of a result determined by a specific method carried out within one or more laboratories and by one or more analysts (Corry et al., 2007). By contrast, sampling uncertainty reflects the impact on the analytical result of the variability between target samples, and any sub-samples, drawn from a ‘lot’ of material (EURACHEM, 2006; CODEX, 2007). It is recognised that microorganisms in foods do not conform to a ‘normal’ distribution. Whilst the distribution of microorganisms in simple suspension often conforms reasonably to Poisson, there are many cases where this is not so. Clumping of microbial cells is well known to give rise to heterogeneous distributions, such that log-normal, negative binomial or other even more complex distributions that may be required to describe properly their distribution (Jarvis, 2008). In natural matrices such as soil, the distribution of microorganisms is very complex (Jones et al., 1948) and a similar situation undoubtedly pertains to the distribution of microorganisms within a food matrix. Even in a liquid food matrix, such as milk, the distribution of microorganisms is both spatially and numerically complex (Wilson, 1935). Ramsay et al. (2001) and Lyn et al. (2002, 2003, 2007) demonstrated that the estimate of sampling uncertainty is at least as large as, and often larger than, the estimate of analytical measurement uncertainty. It is likely that the same situation will apply in the
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microbiological examination of foods, but there are few published data. Jarvis et al. (2007a) observed that two thirds of the total measurement uncertainty calculated from data on replicate aerobic colony counts on two ‘lots’ of prawns was due to the variation between target samples. This suggests that for microbiological examination of foods, the estimate of sampling uncertainty included in any overall uncertainty estimate is probably at least as great as the estimate of analytical uncertainty. As with chemical analyses, such underestimates may be critical to the assessment for compliance of foods with legislative and commercial microbiological criteria. The overall objective of our work is to investigate whether the concept of sampling uncertainty as now described in the chemistry sector, can be extended to the microbiology sector. The work described here was concerned with evaluating the effects of sample size and treatment in order to minimise the analytical measurement uncertainty of microbial colony counts. 2. Materials and methods 2.1. Food samples For any one experiment, all food samples had the same use-by date and were taken randomly from different parts of the retail display or from the manufacturing batch when sampled at Bristol University, Langford, meat processing facility. The same technician did all examinations and colony counts on all the products to minimise this source of uncertainty. The following foods were purchased from retail outlets: 1-pint plastic bottles of semi-skimmed and skimmed homogenised pasteurised milk; 500 g packs of low-fat fresh minced beef; 500 g packs of fresh pork sausages; 100 g packs of ground coriander. Five hundred g packs of pork sausages and of minced beef were also sampled in the meat processing facility at Langford. 2.2. Bacteriological colony count methods All counts were made after diluting the products in MRD (maximum recovery diluent, Oxoid CM733) by surface plating onto
599
PCA (plate count agar, Oxoid CM325). For minced meat and sausages the MRD was supplemented with 0.5% Tween 80 (Sigma). The diluent was sterilized in 1 L quantities, chilled to 4 2 C, dispensed aseptically in appropriate volumes and stored at 4 2 C for not more than 24 h before use. The diluent used for the initial homogenate was prechilled, but the diluents used for the subsequent dilutions, and the pre-poured surface dried agar plates, were equilibrated to room temperature before use. Following inoculation, PCA plates were incubated inverted at 30 C for 48 h. Colonies were counted manually. 2.3. Homogenisation procedures and protocols 2.3.1. Preparation of primary 1 in 10 homogenates Initially various approaches were used to prepare the primary 1 in 10 homogenates (see Results). Subsequently, a standardised double mixing procedure was used with minor variations for specific food samples. The standardised protocol (Fig. 1) was based on five target samples. For the first stage of mixing, five approximately equal 20 g sub-samples from different parts of a target sample were composited in a sterile Stomacher 400 plastic bag to give a total recorded weight of 100 g (99.5e100.5 g). One hundred ml of diluent, tempered to 2 1 C, were added and the composite sample was homogenised either in a Stomacher 400Ô (Seward, London) for 1 min or in a Pulsifier (Microgen Bioproducts Ltd, Camberley, UK) for 30s to obtain a primary 1 in 2 dilution. This procedure was repeated with the other target samples to provide five sets of primary 1 in 2 dilutions. Sets of ‘combined’ samples were prepared by aseptically combining 20 g (19.5e20.5 g) from each of the five primary 1 in 2 homogenates in a sterile filter bag of a Stomacher 400 plastic bag (Grade Packaging Ltd., Leicester, UK). This was repeated five times. . Four hundred ml of diluent was then added to each bag and the mixture was further homogenised to give 101 homogenates. Sets of ‘separate’ samples were prepared by aseptically weighing 20 g (19.5e20.5 g) from each of the 1 in 2 homogenates individually into a sterile filter bag within a Stomacher 400 bag. Eighty ml of diluent was added to each bag and the mixture was again homogenised to prepare the 101 homegenates. In some experiments, ‘individual’ samples were also prepared using separate quantities taken directly
Fig. 1. Schematic of the double maceration protocol for sample preparation. Five portions of 20 g from each primary target sample are composited with 100 ml of diluent to give a 1 in 2 dilution. A combined sample is prepared by combining 20 g of each of the 5 primary target sub-samples (homogenates) and mixing again with 400 ml of diluent to give a 101 dilution; this is repeated four times. For separate samples, 20 g of each of the 5 target sub-samples (1 in 2 suspensions) is separately mixed with 4 volumes of diluent.
600
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from each target sample (typically 25, 50 or 100 g) and homogenised with an equal volume of diluent before addition of a further four volumes of diluent and then repeating the homogenisation. Milk was handled similarly, except that the samples were shaken (ISO, 2005) before removing a sub-sample. A primary 1 in 2 dilution was prepared by first diluting 100 ml from each of the five well-mixed target samples with an equal volume (100 ml) of diluent. After thorough mixing, 20 ml from each of these primary dilutions were combined and further diluted with 400 ml of diluent to provide the 101 dilution (combined samples). Separate samples were prepared by diluting 20 ml of each separate 1 in 2 primary dilution with 80 ml diluent to produce the 101 dilution. Further serial dilutions to 104 were prepared in duplicate from each 101 dilution of the target sample(s) by adding 1e9 ml of diluent. For each set of sample dilutions examined, 100 ml volumes of the dilutions were plated in duplicate (two plates per sample per dilution). 2.4. Statistical methods For each set of analyses, the five ‘combined’ and five ‘separate’ test samples were prepared as described above and illustrated schematically in Fig. 1; in addition, five ‘independent’ samples were analyzed where stated in the text. The combined and separate samples therefore comprised a nested design with two sub-samples (combined and separate) nested within each of the five primary target samples and duplicate sets of colony counts on each analytical sample. The numbers of colony forming units (cfu) per plate were recorded and converted to cfu g1 or ml1, allowance being made for the quantity and dilutions of sample examined. All colony counts were log10-transformed before statistical analysis. The underlying statistical parameters (mean, median, variance and standard error of the mean) were determined for each data set. The transformed data were examined also by a standard one-way ANOVA for a 2factor fully nested design (ISO, 1994), using the F-ratio to determine the significance of differences in the variances. Where appropriate, Tukey's ‘Wholly Significant Difference’ test was used to assess the 5% level of significance of differences between test systems. In addition, values for the ‘log10 average count’ (log m) of samples were derived from the moments of the log-normal distribution, using the equation of Rahman (1968) _
log m ¼ m þ ln 10$s2 =2 _
where m ¼ estimate of the mean log10 colony count and s2 ¼ the variance of the log10 colony count. 3. Results 3.1. Experimental design Preliminary tests on a diverse range of target samples suggested that thorough shaking, either in the original container or after transfer to a large sterile plastic bag, ensured reasonable mixing of non-viscous liquid samples (e.g. milk) and of dried foods (e.g. milk powder, egg powder, spices). Unsurprisingly, however, manual and mechanical mixing techniques such as squeezing and pummelling were unable to ensure effective mixing of minced meat, shellfish and raw vegetable products, although such procedures were suitable for raw fruits (e.g. raw mixed fruit salad). Consequently, we devised a procedure for multiple homogenisation (Fig. 1) that was very efficient at minimising the overall variance of colony counts. We compared the effects of stomaching or pulsifying the primary sample with an equal volume of diluent in order to produce a 50:50 suspension and found no statistically
significant differences in microbial counts or variance, although as reported by Kang et al. (2001) and Wu et al. (2003) the Pulsifier produced less physical breakdown of the samples. A quantity of the primary homogenate was then weighed aseptically into the filter bag inside a plastic stomacher bag together with an additional four volumes of diluent, prior to further maceration to give the initial 1 in 10 dilution of the target sample. This procedure minimised the risk of transfer of food particles into the subsequent serial dilutions as well as avoiding blockage of pipettes. An additional benefit of the double homogenisation procedure is that it permitted the examination of both combined (i.e. composited) and separate target samples. The results of all the trials are summarised in Table 1. 3.2. Liquid pasteurised milk Two trials were carried out, the first with half-fat milk and the second with skimmed milk. On each occasion, five plastic bottles of each having the same date code, purchased from a local retailer, were tested after 2 days storage at 10 C followed by 18 h at 1 C (a preliminary investigation had found very low numbers of organisms in a similar sample). In the first trial, the median and mean colony counts (as log cfu/ml) of the combined and separate samples were of the same order of magnitude but the variance of the combined target samples was only 30% of that of the separate target samples, each of which was representative of a different bottle of milk. This shows that the bacterial load in each of the five target samples differed slightly, suggesting either that the containers into which the pasteurised milk had been filled were not ‘commercially sterile’ or that the distribution of organisms was very heterogeneous. By combining the samples, these between-target sample differences were reduced. In the second trial the colony counts were higher than those in the first milk trial, probably because the milk was nearer its use-by date. It was notable that in this trial the variances of the combined and separate samples were closer, possibly as a result of the higher levels of organisms. As the distribution of organisms in a food often approximates to log-normal, it can be expected that the counts obtained will be distributed about the geometric average (i.e. the back-transformed mean log count), which is also the median for a true log-normal distribution. It has been shown previously (Kilsby and Pugh, 1981) that the mean log count underestimates the true average level of organisms in the food. The greater the variation in the distribution of organisms, the more the mean log count will underestimate the log average count. Calculated log average colony counts on the milk samples were generally similar to the median counts, indicating that the distribution of colony forming units in the milk conformed to a log-normal distribution. The distribution of colony counts on the plates was tested using the Index of Distribution (Fisher et al., 1943). In almost all cases it was not possible to reject the hypothesis that the colonies were distributed randomly (P > 0.05). This shows that the dilutions of milk samples used in the colony count procedures were done efficiently and that there was no evidence of over-dispersion in the dilutions tested. However, the results also show that even in wellmixed liquid samples the actual levels and distribution of organisms differ between apparently replicate samples taken from the same production lot (cf. Wilson, 1935). 3.3. Minced meat In the first trial, five packs of low-fat minced meat, each bearing the same date code, were purchased from a supermarket; the second experiment used five packs of low-fat minced meat taken from a single batch prepared in the Langford meat processing facility from cold-stored vacuum-packed beef.
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601
Table 1 The median, mean, variance and standard deviations of the mean log10 cfu/g or /ml and the log10 average cfu/g or ml on different food matrices determined on five combined, five separate and five individual samples by the procedures described in the text. Food matrix
Pasteurised milk
Trial
1 2
Minced meat
1 2
Sausages
1 2
Coriander
1
Sample type
Weight/volume (g or ml)
Colony count as log10 cfu/g or /ml Median
Mean
Variance**
Standard deviation
Log10 average count (cfu/g or ml)
Combined Separate Combined Separate
100 100 100 100
4.680 4.663 7.113 7.072
4.683 4.689 7.126 7.066
0.0040 0.0122 0.0027a 0.0034a
0.064 0.110 0.052 0.058
4.69 4.70 7.13 7.07
Combined Separate Combined Separate Individual* Individual Individual
100 100 100 100 10 25 100
7.049 7.188 9.15 9.18 8.72 8.80 9.04
7.038 7.145 9.16 9.18 8.55 8.77 9.04
0.0013b 0.0045b 0.0002c 0.0005c 0.1432 0.0101 0.0025
0.036 0.067 0.014 0.022 0.378 0.100 0.049
7.08 7.15 9.16 9.18 8.71 8.78 9.04
Combined Separate Combined Separate Individual Individual Individual
100 100 100 100 10 25 100
4.283 4.330 5.629 5.702 5.352 5.411 5.665
4.279 4.285 5.629 5.698 5.393 5.408 5.670
0.0130d 0.0193d 0.0004e 0.0006ef 0.0105g 0.0007g 0.0032ef
0.114 0.139 0.021 0.024 0.102 0.027 0.057
4.29 4.31 5.63 5.70 5.41 5.41 5.67
Combined Separate* Individual Individual
25 25 5 50
5.100 5.045 4.924 4.903
5.069 5.114 4.932 4.939
0.0172h 0.0368 0.0213hi 0.0063hi
0.130 0.192 0.146 0.080
5.09 5.16 4.96 4.95
* Distribution of the log-transformed colony counts was not ‘normal’ e data excluded from ANOVA. ** Values within a group followed by the same superscript are not significantly different at P ¼ 0.05.
The mean log colony counts on both sets of combined and separate samples were not statistically different even though the variances of the separate samples were more than three times higher than those of the combined samples. However, the variance ratios were not statistically significant at P ¼ 0.05, and there was no significant effect on the mean log-counts. These results confirmed that the double stage mixing procedure was able to produce replicate counts of high precision. In the second trial we also tested three sets of five individual target samples of differing sample weights, each set being drawn directly from the same primary target samples. The individual samples were also subjected to double mixing in order to ensure comparability in the overall procedure. As the sample size was increased from 10 g to 100 g the mean log counts increased and the variance generally decreased (cf. Kilsby and Pugh, 1981). For the smallest sample size, the median log count differed considerably from the mean log count, and tests for normality showed that the log-transformed data did not conform to a normal distribution, although the calculated log-mean colony count was essentially identical to the median log count. Analysis of variance, followed by Tukey's test for Wholly Significant Differences, showed that the log
colony counts on the 10 g and 50 g individual samples did not differ significantly from each other (P > 0.05) but were significantly different (P ¼ 0.01) to those on any of the 100 g samples, which did not differ significantly from each other. The colony counts on both samples of minced meat were high. Target sample 1 was within 2 days of its stated shelf life when examined. The difference in the level of colony count on the minced meats used in the two trials probably reflects the difference in the source of the meats prior to mincing. The meat used to make the minced meat in the second trial had been dissected from carcasses as part of a research project, vacuum packed and stored chilled for some time before mincing. Although the colony counts were in the order of 109 per g the minced meat was organoleptically acceptable and most of the organisms were lactic acid bacteria. 3.4. Sausages For the first trial, five 250 g packs of raw pork sausages, all bearing the same date code, were tested on the day of purchase. For the second trial, five randomly selected packs of 500 g of sausages were obtained from production in the Langford meat processing
Table 2 Colony counts on replicate analytical samples of sausages (trial 2) prepared using the protocol for combined, separate and individual samples. Mean Log10 cfu/g in replicate test sample #
Overall mean
Overall median
Variance
Standard deviation
5.645 5.643
5.629
5.630
0.0004
0.020744
5.737 5.716
5.713 5.711
5.698
5.702
0.0006
0.025133
5.384 5.346
5.373 5.330
5.330 5.318
5.393
5.354
0.0105
0.102317
5.428 5.408
5.415 5.462
5.408 5.365
5.387 5.384
5.408
5.408
0.0007
0.027318
5.659 5.670
5.737 5.718
5.633 5.619
5.732 5.731
5.670
5.665
0.0032
0.056526
Sample type
Replicate test
R1
R2
R3
R4
R5
Combined
C-1 C-1
5.626 5.628
5.631 5.627
5.629 5.575
5.645 5.643
Separate
S-1 S-2
5.704 5.701
5.659 5.657
5.699 5.683
Individual 10 g
I10-1 I10-2
5.587 5.577
5.362 5.318
Individual 25 g
I25-1 I25-2
5.427 5.396
Individual 100 g
I100-1 I100-2
5.600 5.596
602
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facility. Target samples (100 g) of sausages, including skin, were examined after the double mixing process to produce separate and combined samples. In addition, five sets of individual random samples, each of 10, 25 and 100 g, were taken from the same packs for analysis in the second trial. As anticipated, the individual samples gave more variable results (Table 2). The mean and median colony counts on the combined and separate samples were essentially identical in each trial but the variance of the log colony counts on the separate samples was greater than on the combined samples. Because the variances were low, the calculated log average counts differed only slightly from the average log counts. No statistically significant differences were observed between the mean counts on the combined and separate samples, or the 100 g individual samples. As with the minced meat, the mean log counts on the individual samples increased with increasing sample size and the variance on the individual 100 g samples was somewhat greater than on the 25 g samples, although less than on the 10 g samples. 3.5. Ground coriander We examined samples of coriander, as an example of a dried food product, following preliminary tests that showed very low bacterial colony counts in many other herbs and spices. Only one trial was undertaken: five samples, all having the same date code, were purchased from an ethnic food retailer. The samples were shaken well to minimise the within sample variation and weighed samples were again prepared for analysis using the double homogenisation procedure. The variance of the combined samples was less than half of that on the separate samples although the mean log counts were similar, thereby giving a slightly greater log average count on the separate samples. As anticipated, the smallest (5 g) individual samples had the greatest variance and the lowest mean log count, whilst the variance on the larger individual sample (50 g) was smaller than that on either the combined or separate 25 g samples using both colony count procedures. However, none of the mean log counts differed statistically (P > 0.05). 4. Discussion and conclusions Premixing of target samples, by repeated inversion for liquids, shaking for powders and kneading/pummelling for semi-solid foods reduces, but does not eradicate, the difference in colony counts between target samples. The double homogenisation procedure used for this work allowed the comparative analysis of both combined and separate samples. Although in most of the trials the mean log counts on combined and separate samples were similar, in almost every case the variances of the separate samples were greater than those of the combined samples implying heterogeneous distribution in the lot, although the differences were not always statistically significant. The two stage protocol of homogenising composite sub-samples of each target sample with an equal quantity of diluent to produce a primary 1 in 2 dilution, which could then be further composited with primary dilutions from other target samples, provided a most effective means to minimise the analytical variance of colony counts between target samples. In many ways this protocol is similar to the procedure used in sampling for many chemical analyses. Similarly, dilution and homogenisation of composited sub-samples from separate replicate target samples was quite effective and was better in most cases than merely taking a single quantity from each individual target sample, especially if such individual samples were of low weight.
There is no doubt that the quantity of sample taken for analysis is a critical factor in minimising the variance between target samples. Kilsby and Pugh (1981) showed that if the distribution of organisms is log-normal then, since the ‘true’ level of organisms (i.e. the log ‘average’ count) in a sample is ‘constant’ at any one time, any reduction in the variance will be accompanied by an apparent increase in the mean log colony count. As the weight of the target sample examined is increased, the variance of the count per gram decreases and the mean log colony count increases. This was confirmed in our experiments and leads us to conclude that the weight of sample to be processed for examination should be greater than that often specified in standard microbiological methods and should be specified in legislation such as EU (2005). In this respect we consider that the minimum individual or composite sub-sample taken from a target sample for examination should not be less than 100 g. The USDA FSIS and the APHA standard methods specify that a sample should be of 50 0.1 g (Lattuada et al., 1998; Midura and Bryant, 2001). Other standard methods such as those published by ISO recommend different quantities for different foods. For instance ISO (1999) makes a general recommendation to use 10 g or 10 ml, of a representative test sample, unless otherwise stated in specific standards, yet there is a lack of consistency in such standards. ISO standards for examination of specific food products (ISO 2003a, b, c) make the following recommendations: for meat products, the entire sample mass if 50 g; for carcass neck skin from poultry, 5e10 g; for cereal products, 50 g; for gelatine, 20 g; for margarine and spreads, 40 g. For fish and fish products, dehydrated products, and egg products no recommendations are made. IDF/ISO recommend examination of 10 ml of liquid milk or 10 g of milk powder from each target sample (ISO, 2005). Whilst such diverse recommendations probably reflect traditional ‘custom and practice’ in food examination it is our opinion that there should be greater consistency in recommending a quantity of sample for examination in order to minimise overall measurement uncertainty. In this work we have assumed that the objective of examining replicate target samples and sub-samples is to obtain a precise estimate of the overall microbial density in a lot. It might be argued that it is more important to assess the maximum, or peak, levels of microbial density rather than an overall estimate. But such a task would require the examination of many parallel samples with little overall benefit. After all, the consumer eats portions of food rather than small individual samples and in assessing both organoleptic quality and microbiological safety we are concerned with typical microbial densities. Above all else, this work has demonstrated that it is possible to determine microbial colony counts on diverse food matrices with a higher degree of analytical precision than might be expected from most reports of microbiological measurement uncertainty (e.g. Jarvis et al., 2007a,b). However, in a busy industrial microbiology laboratory it is doubtful whether such a high level of precision could be achieved because of the need for high-level sample throughput. Acknowledgements This work was supported by the Food Standards Agency as part of a research project concerned with microbiological sampling uncertainty. We acknowledge with thanks the technical assistance of Miss Raquel Pinho in this work. AJH thanks Prof. Anthony Hollander, Head of Department, for providing facilities. References Augustin, J.-C., Carlier, V., 2006. Lessons from the organization of a proficiency testing program in food microbiology by interlaboratory comparison: analytical
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