Evaluation of universal preenrichment broth and comparison of rapid molecular methods for the detection of Salmonella from spent sprout irrigation water (SSIW).

Sprouts and spent sprout irrigation water (SSIW) present unique challenges for the development of a Salmonella detection method in food matrices. This study aimed to compare universal preenrichment broth (UPB) and lactose broth (LB) as preenrichment media for cultural and rapid screening methods and to compare their abilities to recover Salmonella in SSIW samples from different sprout varieties (i.e., alfalfa, broccoli, and mung bean sprouts). The associated co-enriched microbiota with different sprout varieties using different preenrichment media were also examined using a quasi-metagenomic approach. The performance of media and detection methods was compared using the relative level of detection (RLOD) value, as recommended by ISO 16140-2:2016. The level of detection (LOD) for Salmonella culture method with UPB was similar to that with LB in low aerobic plate count (APC) background samples (the relative LOD, i.e., RLOD, was nearly 1 after adjusting for the effects of SSIW variety and serovar), but significantly lower than that with LB in high APC background samples (RLOD = 0.32). The LOD for Salmonella with selected rapid methods was comparable to each other (RLOD from 0.97 to 1.50) and to the culture method (RLOD from 0.69 to 1.03), and no significant difference was detected between preenrichment broths in low APC background samples with RLOD values between 0.76 and 1.04. In samples with a high APC background, however, a drastic difference in LOD was observed between methods and between preenrichment broths for each method. The RLOD ranged from 0.03 to 0.32 when UPB was compared to LB as preenrichment broth. The composition and relative abundance (RA) of co-enriched microbiota was affected by multiple factors including food matrices, preenrichment media and Salmonella contamination. Altogether, this study validated UPB as a better preenrichment broth than LB for the detection of Salmonella enterica from SSIW. This study also suggested UPB may also be an optimal preenrichment medium for rapid screening methods when APC level is high. The observation of potential exclusion of Salmonella in preenrichment through the overgrowth of competitive microflora from the quasi-metagenomic study provided novel information that may be used to further optimize preenrichment formulations.

Sample Size Determination for Food Sampling.

Industry and public health agencies sample and test food products for various purposes related to food safety and quality. Methods of sample selection and sample size determination are important in designing an optimal sampling plan. The appropriate sample size of a sampling plan depends on the objective. We examine the methods of sample size calculation for the following four objectives commonly associated with food sampling: (1) estimate prevalence (e.g., of contaminated products), (2) detect presence (e.g., of contaminated products), (3) estimate maximum prevalence, and (4) compare estimated prevalence with a specified value (e.g., a previous estimate or a threshold value). We illustrate these methods using examples and provide a web-based application (https://simple-sample.galaxytrakr.org/) written in R, using the shiny package, to help users with the application of each method.

Transient and resident pathogens: Intra-facility genetic diversity of Listeria monocytogenes and Salmonella from food production environments.

Food production facilities are often routinely tested over time for the presence of foodborne pathogens (e.g., Listeria monocytogenes or Salmonella enterica subsp. enterica). Strains detected in a single sampling event can be classified as transient; positive findings of the same strain across multiple sampling events can be classified as resident pathogens. We analyzed whole-genome sequence (WGS) data from 4,758 isolates (L. monocytogenes = 3,685; Salmonella = 1,073) from environmental samples taken by FDA from 536 U.S. facilities. Our primary objective was to determine the frequency of transient or resident pathogens within food production facilities. Strains were defined as isolates from the same facility that are less than 50 SNP (single-nucleotide polymorphisms) different from one another. Resident pathogens were defined as strains that had more than one isolate collected >59 days apart and from the same facility. We found 1,076 strains (median = 1 and maximum = 21 strains per facility); 180 were resident pathogens, 659 were transient, and 237 came from facilities that had only been sampled once. As a result, 21% of strains (180/ 839) from facilities with positive findings and that were sampled multiple times were found to be resident pathogens; nearly 1 in 4 (23%) of L. monocytogenes strains were found to be resident pathogens compared to 1 in 6 (16%) of Salmonella strains. Our results emphasize the critical importance of preventing the colonization of food production environments by foodborne pathogens, since when colonization does occur, there is an appreciable chance it will become a resident pathogen that presents an ongoing potential to contaminate product.

On the Estimation of POD and LOD of Qualitative Microbiological Assays from a Multi-Laboratory Validation Study.

BACKGROUND: Jarvis et al. in 2019 (J. AOAC Int. 102: 1617-1623) estimated the mean laboratory effect (µ), standard deviation of laboratory effects (σ), probability of detection (POD), and level of detection (LOD) from a multi-laboratory validation study of qualitative microbiological assays using a random intercept complementary log-log model. Their approach estimated σ based on a Laplace approximation to the likelihood function of the model, but estimated µ from a fixed effectmodel due to a limitation in the MS Excel spreadsheet which was used by the authors to develop a calculation tool. OBJECTIVE: We compared the estimates of µ and σ from three approaches (the Laplace approximation that estimates µ and σ simultaneously from the random intercept model, adaptive Gauss-Hermite quadrature (AGHQ), and the method of Jarvis et al.) and introduced an R Shiny app to implement the AGHQ using the widely used "lme4" R package. METHODS: We conducted a simulation study to compare the accuracy of the estimates of µ and σ from the three approaches and compared the estimates of µ, σ, LOD, etc. between the R Shiny app and the spreadsheet calculation tool developed by Jarvis et al. for an example dataset. RESULTS: Our simulation study shows that, while the three approaches produce similar estimates of σ, the AGHQ has generally the best performance for estimating µ (and hence mean POD and LOD). The differences in the estimates between the R Shiny app and the spreadsheet were demonstrated using the example dataset. CONCLUSION: The AGHQ is the best method for estimating µ, POD, and LOD. HIGHLIGHTS: The user-friendly R Shiny app provides a better alternative to the spreadsheet.

On the Statistical Testing Methods for Single Laboratory Validation of Qualitative Microbiological Assays with an Unpaired Design.

BACKGROUND: There exists several statistical methods for detecting a difference of detection rates between alternative and reference qualitative microbiological assays in a single laboratory validation study with an unpaired design. OBJECTIVE: We compared performance of eight methods including Fisher's exact test, unequal variance two-sample t-test, Wilcoxon rank-sum test, z-test, and methods based on Wilson confidence intervals, complementary log-log regression, Firth's logistic regression, and ordinary logistic regression. METHOD: We first compared the minimum detectable difference in the proportion of detections between the alternative and reference methods among these statistical methods for a varied number of test portions. We then compared power and size of test of these methods using simulated data. RESULTS: Firth's logistic regression and the unequal variance two-sample t-test had the lowest minimum detectable difference and highest power. None of these statistical methods had an estimated size of test always within a 95% confidence interval of the nominal value 0.05 with small numbers of test portions (n = 12, 20, 30). Fisher's exact test, the Wilcoxon rank-sum test, and the z-test were conservative even with a moderately large number of test portions (n = 40), while Firth's logistic regression and the unequal variance two-sample t-test had a size of test closer to 0.05 than other methods. CONCLUSIONS: Firth's logistic regression and the unequal variance two-sample t-test are better choices than other competing methods. HIGHLIGHTS: We recommend the unequal variance two-sample t-test over Firth's logistic regression because the unequal variance two-sample t-test is better known and easier to use. We provide an example using real data.

On the Statistical Testing Methods for Single Laboratory Validation of Qualitative Microbiological Assays with a Paired Design.

BACKGROUND: There are several statistical methods for detecting a difference of detection rates between alternative and reference qualitative microbiological assays in a single laboratory validation study with a paired design. OBJECTIVE: We compared performance of eight methods including McNemar's test, sign test, Wilcoxon signed-rank test, paired t-test, and the regression methods based on conditional logistic (CLOGIT), mixed effects complementary log-log (MCLOGLOG), mixed effects logistic (MLOGIT) models, and a linear mixed effects model (LMM). METHODS: We first compared the minimum detectable difference in the proportion of detections between the alternative and reference detection methods among these statistical methods for a varied number of test portions. We then compared power and type 1 error rates of these methods using simulated data. RESULTS: The MCLOGLOG and MLOGIT models had the lowest minimum detectable difference, followed by the LMM and paired t-test. The MCLOGLOG and MLOGIT models had the highest average power but were anticonservative when correlation between the pairs of outcome values of the alternative and reference methods was high. The LMM and paired t-test had mostly the highest average power when the correlation was low and the second highest average power when the correlation was high. Type 1 error rates of these last two methods approached the nominal value of significance level when the number of test portions was moderately large (n > 20). HIGHLIGHTS: The LMM and paired t-test are better choices than other competing methods, and we provide an example using real data.

Multi-Laboratory Validation of a Loop-Mediated Isothermal Amplification Method for Screening Salmonella in Animal Food.

Loop-mediated isothermal amplification (LAMP) has gained wide popularity in the detection of Salmonella in foods owing to its simplicity, rapidity, and robustness. This multi-laboratory validation (MLV) study aimed to validate a Salmonella LAMP-based method against the United States Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) Chapter 5 Salmonella reference method in a representative animal food matrix (dry dog food). Fourteen independent collaborators from seven laboratories in the United States and Canada participated in the study. Each collaborator received two sets of 24 blind-coded dry dog food samples (eight uninoculated; eight inoculated at a low level, 0.65 MPN/25 g; and eight inoculated at a high level, 3.01 MPN/25 g) and initiated the testing on the same day. The MLV study used an unpaired design where different test portions were analyzed by the LAMP and BAM methods using different preenrichment protocols (buffered peptone water for LAMP and lactose broth for BAM). All LAMP samples were confirmed by culture using the BAM method. BAM samples were also tested by LAMP following lactose broth preenrichment (paired samples). Statistical analysis was carried out by the probability of detection (POD) per AOAC guidelines and by a random intercept logistic regression model. Overall, no significant differences in POD between the Salmonella LAMP and BAM methods were observed with either unpaired or paired samples, indicating the methods were comparable. LAMP testing following preenrichment in buffered peptone water or lactose broth also resulted in insignificant POD differences (P > 0.05). The MLV study strongly supports the utility and applicability of this rapid and reliable LAMP method in routine regulatory screening of Salmonella in animal food.