Rainfall is generally accepted as one of the most important factors associated with an increased level of E. coli in bivalve molluscs. Performing microbiological risk assessment is relevant to official control authorities to determine the sanitary status of harvesting areas and, therefore, develop monitoring strategies and identify management practices that could be used to improve the quality and safety of the final product. The present study aimed to investigate the impact of rainfall on the content of E. coli in bivalve molluscs farmed in Sardinia (Italy). Enumeration of E. coli was performed according to the Most Probable Number (MPN) method (ISO 16649-3) on 1,920 bivalve samples collected from 7 regional counties between 2018 and 2020. Bivalve molluscs samples included 955 mussels (Mytilus galloprovincialis), 500 oysters (Crassostrea gigas), 325 clams (Ruditapes decussatus), 94 warty venus (Venus verrucosa), and 46 lagoon cockles (Cerastoderma glaucum). Rainfall data were obtained by the Department of Meteorology of the ARPA Sardegna. For each sampling site, GPS coordinates were used to identify gauge stations within catchment areas. Cumulative rain (mm) was recorded 1, 3, 5, 7, and 15 days before sampling, among which the 7-day cumulative rain was the strongest predictor of E. coli counts. Several thresholds of 7-day cumulative rain (from <10 mm up to >300 mm) before sampling were used to estimate the chances of a non-compliant sample (E. coli levels above the limit for sanitary class A; 230 MPN/100 g). The 7-day cumulative rain was positively associated with the chances of non-compliance. When the 7-day cumulative rain before sampling was >300 mm, 80.5 % of the samples were non-compliant, and the odds of a non-compliant sample were 23.6 times higher, as compared to samples harvested when the 7-day cumulative rainfall was <10 mm. Precipitation data could be a useful tool for interpreting anomalous results from official control authorities and reduce the costs that originate from closure of production areas.
Association between rainfall and Escherichia coli in live bivalve molluscs harvested in Sardinia, Italy / Mudadu, A. G.; Spanu, C.; Salza, S.; Piras, G.; Uda, M. T.; Giagnoni, L.; Fois, G.; Pereira, J. G.; Pantoja, J. C. F.; Virgilio, S.; Tedde, T.. - In: FOOD RESEARCH INTERNATIONAL. - ISSN 0963-9969. - 174:Pt 1(2023), p. 113563. [10.1016/j.foodres.2023.113563]
Association between rainfall and Escherichia coli in live bivalve molluscs harvested in Sardinia, Italy
Spanu C.
;Giagnoni L.;
2023-01-01
Abstract
Rainfall is generally accepted as one of the most important factors associated with an increased level of E. coli in bivalve molluscs. Performing microbiological risk assessment is relevant to official control authorities to determine the sanitary status of harvesting areas and, therefore, develop monitoring strategies and identify management practices that could be used to improve the quality and safety of the final product. The present study aimed to investigate the impact of rainfall on the content of E. coli in bivalve molluscs farmed in Sardinia (Italy). Enumeration of E. coli was performed according to the Most Probable Number (MPN) method (ISO 16649-3) on 1,920 bivalve samples collected from 7 regional counties between 2018 and 2020. Bivalve molluscs samples included 955 mussels (Mytilus galloprovincialis), 500 oysters (Crassostrea gigas), 325 clams (Ruditapes decussatus), 94 warty venus (Venus verrucosa), and 46 lagoon cockles (Cerastoderma glaucum). Rainfall data were obtained by the Department of Meteorology of the ARPA Sardegna. For each sampling site, GPS coordinates were used to identify gauge stations within catchment areas. Cumulative rain (mm) was recorded 1, 3, 5, 7, and 15 days before sampling, among which the 7-day cumulative rain was the strongest predictor of E. coli counts. Several thresholds of 7-day cumulative rain (from <10 mm up to >300 mm) before sampling were used to estimate the chances of a non-compliant sample (E. coli levels above the limit for sanitary class A; 230 MPN/100 g). The 7-day cumulative rain was positively associated with the chances of non-compliance. When the 7-day cumulative rain before sampling was >300 mm, 80.5 % of the samples were non-compliant, and the odds of a non-compliant sample were 23.6 times higher, as compared to samples harvested when the 7-day cumulative rainfall was <10 mm. Precipitation data could be a useful tool for interpreting anomalous results from official control authorities and reduce the costs that originate from closure of production areas.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
