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The microbiological monitoring of bivalve mollusk harvesting areas in the Marche region is based on the parameters of Escherichia coli and Salmonella spp. However, Regulation EU/2019/627 stated criteria based on E. coli only to determine the health status of these areas. Therefore, the reason for Salmonella spp. monitoring, as provided in the Marche region, could be aimed at reducing the risk of placing on the market contaminated bivalve mollusks. This study, using the results of microbiological monitoring carried out in the Marche region from 2015 to 2022 and the methods based on Bayes' theorem and Poisson's distribution, evaluated the effectiveness and efficiency of Salmonella spp. monitoring in reducing the risk to the consumer. The results show that i) the use of a single sample unit significantly reduced the possibility of detecting non-compliance with the microbiological safety criterion; ii) the time taken to report positive results (average of approximately 10 days) did not allow the timely implementation of control measures; iii) the prevalence of positive outcomes was quite sporadic: a random trend of positivity is recognizable on a geographical and monthly basis for mussels and a geographical basis for striped clams; iv) considering the predictive value of E. coli against Salmonella spp., the specificity is very high and the negative predictive value versus Salmonella spp. would be >80%. In conclusion, the study shows that the monitoring of Salmonella spp. has a limited effect on reducing the risk to the consumer; however, in the cost/benefit assessment, other aspects not covered by this study should be considered., Competing Interests: Conflict of interest: the authors declare no potential conflict of interest., (Copyright © 2024, the Author(s).)

Acceptance sampling is important for food safety and is a relevant tool at production and official control levels, as it helps decision-making processes and verifies quality and food safety management. Generally, sampling plans are hypothesis tests of products that have been submitted for official appraisal and subsequent acceptance or rejection. The sample size is related to the set level of risk, the acceptable precision, and the tolerable misstatement size; therefore, sample size determination has a crucial role in setting up the accepted level of non-compliance and level of error. Using a simple predictive model based on combinatorics, this study showcases how sample size management can change the probability of rejecting good lots and/or accepting bad lots when the acceptance number is 0 (c=0). We showed that when c=0, a very high level of significance of the test corresponds to the high probability of rejecting a lot with an acceptable prevalence of defective items (type II error). We produced tables about the minimum sample size at different significance levels, which can be useful in the field. A paradigmatic example of the role of sample size in the acceptance-sampling plan is represented by the visual inspection for the detection of Anisakid larvae in fishery products. This study investigated this aspect and mainly referred to studies on the prevalence of larvae in farmed fish. We showed that, for lots ≥1000 items, the sample size is not strictly related to the lot size, but to draw a consistent control plan and reduce the variability in the clinical judgment, control authorities require a standardized approach. Because of this, the results on the prevalence of Anisakid larvae in farmed fish, if only based on sampling control plans, do not support a negligible risk statement, despite the claims reported in the EFSA opinion and several other studies., Competing Interests: Conflict of interest: the authors declare no potential conflict of interest., (©Copyright: the Author(s).)

The European Union (EU) established the criteria for the classification of shellfish harvesting areas, based on the results of monitoring E. coli in shellfish. The EU also defined E. coli as a microbiological criterion for end product safety, based on a three-class sampling plan. Both criteria are based on the MPN method, a test with a statistical approach and in which different factors contribute to the variability of the outcomes. This theoretical study, based on combinatorics and Bayes' theorem for conditional probability, investigates the consistency between these two criteria and aims to determine the probability of obtaining false compliant and non-compliant results when applying the safety criterion test to shellfish placed on the market. The results show that in the second case, the probability of non-compliant outcomes does not appear negligible within a range between 10% and 50% in the different hypothesized scenarios, with a probability of false noncompliant outcomes over 10%. In addition, the Bayes' Theorem shows that Class A, or Class B areas (as allowed), could be the origin of non-compliant shellfish, with a not negligible frequency. Therefore, within the limits of the assumed working hypotheses, the safety criterion for E. coli , as described in Regulation EC/2073/2005, does not appear to be consistent and coordinated with the classification criteria stated in the Regulation EU/2019/627 and it is not closely related to the sanitary status of shellfish harvesting area., Competing Interests: Conflict of interest: the authors declare no potential conflict of interest., (©Copyright: the Author(s).)

In the European Union, the classification of shellfish harvesting areas depends on levels of Escherichia coli checked in shellfish flesh and determines whether post-harvest treatment required before shellfish can be sold for human consumption. Nevertheless, intermittent sources of contamination, such as rainfall and runoff from agricultural and urban lands, may give rise to seasonal variations of E. coli concentration, hence an annual classification could not be correct. In this study, we investigated the microbial trend in clams ( Chamelea gallina ) harvested from the district of San Benedetto del Tronto, Italy. The Algaeadria database, a monitoring network for the whole Adriatic area, provided results from 2005 to 2012. E. coli values compliant and non-compliant with food safety criteria were evaluated by graphical data analysis tools and one-tailed Fisher's exact test. The results showed a clear general seasonal trend and, in one of the considered areas, the non-compliant values from July to February were significantly lower than those from March to June (P<0.05). These findings may scientifically support a seasonal classification.