Meat and milk from cloned animals 'unlikely to present any food safety risk'

2010 ◽  
Vol 167 (23) ◽  
pp. 886-886
Keyword(s):  
Food Safety ◽  
Safety Risk ◽  
Food Safety Risk

Comparing the Microbiological Status of Pre- and Postharvest Produce from Small Organic Production

2015 ◽  
Vol 78 (6) ◽  
pp. 1072-1080 ◽  
Author(s):  
AIXIA XU ◽  
DONNA M. PAHL ◽  
ROBERT L. BUCHANAN ◽  
SHIRLEY A. MICALLEF
Keyword(s):  
Food Safety ◽  
Organic Production ◽  
Wash Water ◽  
Small Scale ◽  
Organic Farms ◽  
Production Scale ◽  
Safety Risk ◽  
Leafy Greens ◽  
Postharvest Handling ◽  
Food Safety Risk

Consumption of locally, organically grown produce is increasing in popularity. Organic farms typically produce on a small scale, have limited resources, and adopt low technology harvest and postharvest handling practices. Data on the food safety risk associated with hand harvesting, field packing, and packing-house handling with minimal treatment, at this production scale, are lacking. We followed produce from small organic farms from the field through postharvest handling and packing. Pre- and postharvest produce (177 samples) and water (29 samples) were collected and analyzed quantitatively for Escherichia coli, total coliforms (TC), aerobic bacteria (APC), yeasts, molds (M), and enteric pathogens. No pathogens were recovered. E. coli was detected in 3 (3.6%) of 83 preharvest produce samples, 2 (6.3%) of 32 unwashed and 0 of 42 washed postharvest produce samples, and 10 (34.5%) of 29 water samples. No correlation was found between bacterial levels in irrigation water and those on produce. Postharvest handling without washing was a factor for APC and M counts on tomatoes, with lower frequencies postharvest. Postharvest handling with washing was a factor for leafy greens for TC counts, with higher frequencies postharvest. APC (P = 0.03) and yeast (P = 0.05) counts were higher in preharvest than in unwashed postharvest tomatoes. Washed postharvest leafy greens had higher M counts (P = 0.03) and other washed produce had higher TC counts (P = 0.01) than did their preharvest counterparts. Barriers were found to the use of sanitizer in wash water for leafy greens among small farms using organic practices. Hand harvesting and dry handling did not appear to be associated with a significant food safety risk, but washed leafy greens carried higher levels of some microbial indicators, possibly because of the lack of sanitizer in the wash water. The development of resources and materials customized for this sector of growers could enhance dissemination of information on best practices for handling of leafy greens.

Microbiological Safety of Commercial Prime Rib Preparation Methods: Thermal Inactivation of Salmonella in Mechanically Tenderized Rib Eye†

2015 ◽  
Vol 78 (12) ◽  
pp. 2126-2135 ◽  
Author(s):  
ALEXANDRA CALLE ◽  
ANNA C. S. PORTO-FETT ◽  
BRADLEY A. SHOYER ◽  
JOHN B. LUCHANSKY ◽  
HARSHAVARDHAN THIPPAREDDI
Keyword(s):  
Food Safety ◽  
Low Temperatures ◽  
Thermal Inactivation ◽  
Medium Size ◽  
Preparation Methods ◽  
Safety Risk ◽  
Microbiological Safety ◽  
Pathogen Populations ◽  
Food Safety Risk ◽  
Midwestern City

Boneless beef rib eye roasts were surface inoculated on the fat side with ca. 5.7 log CFU/g of a five-strain cocktail of Salmonella for subsequent searing, cooking, and warm holding using preparation methods practiced by restaurants surveyed in a medium-size Midwestern city. A portion of the inoculated roasts was then passed once through a mechanical blade tenderizer. For both intact and nonintact roasts, searing for 15 min at 260°C resulted in reductions in Salmonella populations of ca. 0.3 to 1.3 log CFU/g. For intact (nontenderized) rib eye roasts, cooking to internal temperatures of 37.8 or 48.9°C resulted in additional reductions of ca. 3.4 log CFU/g. For tenderized (nonintact) rib eye roasts, cooking to internal temperatures of 37.8 or 48.9°C resulted in additional reductions of ca. 3.1 or 3.4 log CFU/g, respectively. Pathogen populations remained relatively unchanged for intact roasts cooked to 37.8 or 48.9°C and for nonintact roasts cooked to 48.9°C when held at 60.0°C for up to 8 h. In contrast, pathogen populations increased ca. 2.0 log CFU/g in nonintact rib eye cooked to 37.8°C when held at 60.0°C for 8 h. Thus, cooking at low temperatures and extended holding at relatively low temperatures as evaluated herein may pose a food safety risk to consumers in terms of inadequate lethality and/or subsequent outgrowth of Salmonella, especially if nonintact rib eye is used in the preparation of prime rib, if on occasion appreciable populations of Salmonella are present in or on the meat, and/or if the meat is not cooked adequately throughout.

Salmonella enterica in Superficial Cervical (Prescapular) and Ileocecal Lymph Nodes of Slaughtered Pigs

2006 ◽  
Vol 69 (4) ◽  
pp. 925-927 ◽  
Author(s):  
PETER B. BAHNSON ◽  
CLAUDIA SNYDER ◽  
LATIFA M. OMRAN
Keyword(s):  
Food Safety ◽  
Confidence Interval ◽  
Lymph Nodes ◽  
Salmonella Enterica ◽  
Food Products ◽  
Safety Risk ◽  
Bacterial Cultures ◽  
Limited Food ◽  
Food Safety Risk ◽  
Slaughtered Pigs

Because certain lymph nodes may be incorporated in food products, the presence of Salmonella enterica in these tissues could pose a food safety risk. We designed this two-part study to assess the prevalence of Salmonella in prescapular lymph nodes from normal slaughtered swine. Prescapular lymph nodes were collected from 300 systematically selected pigs in study 1 and, in study 2, from 75 pigs distributed among 10 herds. For study 2, pooled bacterial cultures were also completed on ileocecal lymph nodes, combining tissue from five pigs per pool (n = 60 pools). No Salmonella was detected in study 1 among prescapular lymph nodes (95% confidence interval, 0.0 to 1.16%). Salmonella was not detected in 75 prescapular lymph nodes from study 2, although Salmonella was detected in 5 of 10 herds in ileocecal lymph nodes. We conclude that prescapular lymph nodes posed a limited food safety risk in this population of pigs.

scholarly journals Food safety risk analysis from the producers' perspective: prioritisation of production process stages by HACCP and TOPSIS

2018 ◽  
Vol 17 (4) ◽  
pp. 396 ◽  
Author(s):  
Antonella Certa ◽  
Mario Enea ◽  
Giacomo Maria Galante ◽  
Joaquín Izquierdo ◽  
Concetta Manuela La Fata
Keyword(s):  
Food Safety ◽  
Risk Analysis ◽  
Production Process ◽  
Safety Risk ◽  
Food Safety Risk

Food Safety Risk Identified in a Population of Elderly Home-Delivered Meal Participants

2001 ◽  
Vol 101 (9) ◽  
pp. 1055-1057 ◽  
Author(s):  
NANCY FEY-YENSAN ◽  
CATHERINE ENGLISH ◽  
SUSAN ASH ◽  
CYNTHIA WALLACE ◽  
HEATHER MUSELER
Keyword(s):  
Food Safety ◽  
Safety Risk ◽  
Elderly Home ◽  
Food Safety Risk

scholarly journals Enlisting Supervised Machine Learning in Mapping Scientific Uncertainty Expressed in Food Risk Analysis

2017 ◽  
Vol 48 (3) ◽  
pp. 608-641 ◽  
Author(s):  
Akos Rona-Tas ◽  
Antoine Cornuéjols ◽  
Sandrine Blanchemanche ◽  
Antonin Duroy ◽  
Christine Martin
Keyword(s):  
Machine Learning ◽  
Food Safety ◽  
Supervised Machine Learning ◽  
Risk Assessments ◽  
Scientific Uncertainty ◽  
Empirical Measure ◽  
Taxonomic Structure ◽  
Safety Risk ◽  
Food Safety Risk

Recently, both sociology of science and policy research have shown increased interest in scientific uncertainty. To contribute to these debates and create an empirical measure of scientific uncertainty, we inductively devised two systems of classification or ontologies to describe scientific uncertainty in a large corpus of food safety risk assessments with the help of machine learning (ML). We ask three questions: (1) Can we use ML to assist with coding complex documents such as food safety risk assessments on a difficult topic like scientific uncertainty? (2) Can we assess using ML the quality of the ontologies we devised? (3) And, finally, does the quality of our ontologies depend on social factors? We found that ML can do surprisingly well in its simplest form identifying complex meanings, and it does not benefit from adding certain types of complexity to the analysis. Our ML experiments show that in one ontology which is a simple typology, against expectations, semantic opposites attract each other and support the taxonomic structure of the other. And finally, we found some evidence that institutional factors do influence how well our taxonomy of uncertainty performs, but its ability to capture meaning does not vary greatly across the time, institutional context, and cultures we investigated.

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