The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractUncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. It is therefore relevant in all EFSA's scientific assessments and also necessary, to ensure that the assessment conclusions provide reliable information for decisionmaking. The form and extent of uncertainty analysis, and how the conclusions should be reported, vary widely depending on the nature and context of each assessment and the degree of uncertainty that is present. This document provides concise guidance on how to identify which options for uncertainty analysis are appropriate in each assessment, and how to apply them. It is accompanied by a separate, supporting opinion that explains the key concepts and principles behind this Guidance, and describes the methods in more detail.

The qualified presumption of safety (QPS) was developed to provide a safety pre‐assessment within EFSA for microorganisms. Strains belonging to QPS taxonomic units (TUs) still require an assessment based on a specific data package, but QPS status facilitates fast track evaluation. QPS TUs are unambiguously defined biological agents assessed for the body of knowledge, their safety and their end use. Safety concerns are, where possible, to be confirmed at strain or product level, and reflected as ‘qualifications’. Qualifications need to be evaluated at strain level by the respective EFSA units. The lowest QPS TU is the species level for bacteria, yeasts and protists/algae, and the family for viruses. The QPS concept is also applicable to genetically modified microorganisms used for production purposes if the recipient strain qualifies for the QPS status, and if the genetic modification does not indicate a concern. Based on the actual body of knowledge and/or an ambiguous taxonomic position, the following TUs were excluded from the QPS assessment: filamentous fungi, oomycetes, streptomycetes, Enterococcus faecium, Escherichia coli and bacteriophages. The list of QPS‐recommended biological agents was reviewed and updated in the current opinion and therefore now becomes the valid list. For this update, reports on the safety of previously assessed microorganisms, including bacteria, yeasts and viruses (the latter only when used for plant protection purposes) were reviewed, following an Extensive Literature Search strategy. All TUs previously recommended for 2016 QPS list had their status reconfirmed as well as their qualifications. The TUs related to the new notifications received since the 2016 QPS opinion was periodically evaluated for QPS status in the Statements of the BIOHAZ Panel, and the QPS list was also periodically updated. In total, 14 new TUs received a QPS status between 2017 and 2019: three yeasts, eight bacteria and three algae/protists.

No abstract

The changes in microbial flora and sensory characteristics of fresh ground meat (beef and pork) with pH values ranging from 5.34 to 6.13 were monitored at different isothermal storage temperatures (0 to 20°C) under aerobic conditions. At all conditions tested, pseudomonads were the predominant bacteria, followed by Brochothrix thermosphacta, while the other members of the microbial association (e.g., lactic acid bacteria and Enterobacteriaceae) remained at lower levels. The results from microbiological and sensory analysis showed that changes in pseudomonad populations followed closely sensory changes during storage and could be used as a good index for spoilage of aerobically stored ground meat. The kinetic parameters (maximum specific growth rate [ max ] and the duration of lag phase []) of the spoilage bacteria were modeled by using a modified Arrhenius equation for the combined effect of temperature and pH. Meat pH affected growth of all spoilage bacteria except that of lactic acid bacteria. The "adaptation work," characterized by the product of max and ( max ؋ ) was found to be unaffected by temperature for all tested bacteria but was affected by pH for pseudomonads and B. thermosphacta. For the latter bacteria, a negative linear correlation between ln( max ؋ ) and meat pH was observed. The developed models were further validated under dynamic temperature conditions using different fluctuating temperatures. Graphical comparison between predicted and observed growth and the examination of the relative errors of predictions showed that the model predicted satisfactorily growth under dynamic conditions. Predicted shelf life based on pseudomonads growth was slightly shorter than shelf life observed by sensory analysis with a mean difference of 13.1%. The present study provides a "readyto-use," well-validated model for predicting spoilage of aerobically stored ground meat. The use of the model by the meat industry can lead to effective management systems for the optimization of meat quality.Fresh meat is a highly perishable food product and unless appropriately actions are taken, e.g., packaged, transported and stored at refrigeration temperatures, can spoil in relatively short time. Factors affecting meat spoilage include intrinsic (e.g., pH, a w , composition, type, and extent of initial contamination) and extrinsic parameters (e.g., temperature and packaging atmosphere). Among these, temperature is considered the most important factor. Although most countries have established regulations with maximum temperature limits for refrigeration storage, in practice these are often violated. Survey studies have shown that temperature conditions higher than 10°C are not unusual during transportation, retail storage, and consumer handling (13,15). Such temperature abuses during any stage of the chill chain may result in an unexpected loss of quality and a significant decrease of meat shelf life.Challenge tests are the main current method used by the meat industry and academia to evaluate product's shelf life. The disadv...

The Scientific Committee confirms that the Threshold of Toxicological Concern (TTC) is a pragmatic screening and prioritisation tool for use in food safety assessment. This Guidance provides clear step-bystep instructions for use of the TTC approach. The inclusion and exclusion criteria are defined and the use of the TTC decision tree is explained. The approach can be used when the chemical structure of the substance is known, there are limited chemical-specific toxicity data and the exposure can be estimated. The TTC approach should not be used for substances for which EU food/feed legislation requires the submission of toxicity data or when sufficient data are available for a risk assessment or if the substance under consideration falls into one of the exclusion categories. For substances that have the potential to be DNA-reactive mutagens and/or carcinogens based on the weight of evidence, the relevant TTC value is 0.0025 lg/kg body weight (bw) per day. For organophosphates or carbamates, the relevant TTC value is 0.3 lg/kg bw per day. All other substances are grouped according to the Cramer classification. The TTC values for Cramer Classes I, II and III are 30 lg/kg bw per day, 9 lg/kg bw per day and 1.5 lg/kg bw per day, respectively. For substances with exposures below the TTC values, the probability that they would cause adverse health effects is low. If the estimated exposure to a substance is higher than the relevant TTC value, a non-TTC approach is required to reach a conclusion on potential adverse health effects.