Periodic Reporting for period 3 - SAFFI (Safe Food for Infants in the EU and China)
Período documentado: 2023-09-01 hasta 2024-08-31
SAFFI targets the safety of foods for EU’s 15 million and China’s 45 million children under the age of 3. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of safety risks raised by microbial and chemical hazards all along EU and China infant food chains. SAFFI focuses on infant food as this is a sensitive population and product group, whose cross-cutting nature will allow project outcomes to be transferred to most of the other food sectors. SAFFI is benchmarking the main safety risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It also develops procedures to improve hazard control by combining management options, i) with a range of innovative mild processing technologies aimed at mitigating priority risks while preserving other dimensions of product quality and, ii) with a panel of high-throughput detection techniques dedicated to improve the monitoring of priority chemical hazards. SAFFI also aims at discovering unexpected/unknown chemical hazards by non-targeted approaches combining bio-, chemo-analytics and bioinformatics and improve risk-based food safety management of biohazards by omics. SAFFI has developed a decision-support system (DSS) to enhance safety control all along the food chain. This DSS integrates the databases, predictive models, procedures and methods described above and is a framework for a generic DSS dedicated to other foods. This overall methodology was implemented in two complementary EU and Chinese sides of the project and exemplified for each, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. SAFFI also set up training and knowledge transfer activities to foster EU-China harmonization of good practices, regulations, standards and technologies, and has clustered with the DiTECT project (GA N°861915) for continuous upgrade of food safety control.
In order to enhance hazard identification and risk ranking most relevant hazards for infant food were identified: 34 microbiological hazards and 101 chemical hazards. Specific characteristics and knowledge rules for these hazards have been collected and stored in a data collection repository. This information can be used to further identify the hazards relevant in specific food products. Knowledge rules and a decision tree to classify the hazards into different empirical rankings was devised. Criteria for microbiological and chemical hazard risk ranking were defined. Data collected on the microbiological and chemical hazard databases have been used to develop the risk ranking prototypes for both types of hazards.
In order to improve hazard control and mitigation, SAFFI focused on mild processes and home practices. In addition to experiments showing that high pressure processing (HPP) mitigate chemical hazards in fruit puree, predictive models were used to define HPP conditions to achieve the performance criteria of non-thermal pasteurization. SAFFI also showed that pasteurization could be achieved with thermal radiofrequency (RF) and developed a predictive tool to simulate the thermal profile and associated microbial inactivation during infant food RF processing. Finally, high pressure thermal processing (HPTP) of vegetable-based infant foods enabled to inactivate spore-forming bacteria while limiting the neo-generation of chemicals like furans. At home, post-reheating stirring was shown to be particularly efficient for mitigating furans in vegetable based infant foods.
In order to enhance the detection and discovery of chemical hazards, several bio- and chemoanalytical methods have been developed and validated to ensure high throughput, cost-effective and robust monitoring of a wide range of priority chemicals. The sample pooling approach was also shown to drastically improve the efficiency of regulatory surveillance and industrial self-monitoring of priority hazards. For the discovery of unsuspected/unknown hazards, a 5-step action plan was proposed allowing safety assessment of infant foods from CALUX bioassay measurements while non-targeted methods coupling high-resolution mass spectrometry and bioinformatics were developed for the possible identification of suspect chemicals.
In order to enhance the detection of microbial hazards and the prediction of their behaviour, the presence, distribution and prevalence of target foodborne was analysed in samples collected over an extended period to capture seasonal fluctuations. Targeting infant cereals, samples were collected from raw materials, intermediate products, finished products, production environment to determine the spatial distribution and sources of contamination and to understand the overall structure of the microbiota in the substrates. In parallel, in order to promote prediction of microbial behaviour, Listeria monocytogenes was employed as a model organism and the mild acid adaptation and subsequent increased robustness to lethal acid pH were tested both in vitro and in situ in order to identify transcriptome and volatolome derived biomarkers.
An integrated, upgradeable Decision Support System (DSS) has been developed for assessing hazards in infant food chains. Initial stages focused on gathering data requirements and developing model prototypes across SAFFI activities. Data profiling mapped key variables, created metadata, and identified dataset commonalities, facilitating seamless integration of diverse models. Conceptual and logical data models were then built using entity relationships and data flow diagrams, and workshops and surveys provided feedback for refinement. The resulting beta DSS enables hazard identification, control, and detection through a structured SQL database and visualization tools. Designed for scalability, the DSS supports data uploads via templates, allowing future expansion beyond infant food chains to address broader food safety hazards.
In addition to the DSS and 7 key exploitable results, SAFFI results have been described in over 70 peer-reviewed open access scientific articles and in conference presentations, webinars, workshops, courses, datasets and quizz.
In order to improve hazard control and mitigation, SAFFI focused on mild processes and home practices. In addition to experiments showing that high pressure processing (HPP) mitigate chemical hazards in fruit puree, predictive models were used to define HPP conditions to achieve the performance criteria of non-thermal pasteurization. SAFFI also showed that pasteurization could be achieved with thermal radiofrequency (RF) and developed a predictive tool to simulate the thermal profile and associated microbial inactivation during infant food RF processing. Finally, high pressure thermal processing (HPTP) of vegetable-based infant foods enabled to inactivate spore-forming bacteria while limiting the neo-generation of chemicals like furans. At home, post-reheating stirring was shown to be particularly efficient for mitigating furans in vegetable based infant foods.
In order to enhance the detection and discovery of chemical hazards, several bio- and chemoanalytical methods have been developed and validated to ensure high throughput, cost-effective and robust monitoring of a wide range of priority chemicals. The sample pooling approach was also shown to drastically improve the efficiency of regulatory surveillance and industrial self-monitoring of priority hazards. For the discovery of unsuspected/unknown hazards, a 5-step action plan was proposed allowing safety assessment of infant foods from CALUX bioassay measurements while non-targeted methods coupling high-resolution mass spectrometry and bioinformatics were developed for the possible identification of suspect chemicals.
In order to enhance the detection of microbial hazards and the prediction of their behaviour, the presence, distribution and prevalence of target foodborne was analysed in samples collected over an extended period to capture seasonal fluctuations. Targeting infant cereals, samples were collected from raw materials, intermediate products, finished products, production environment to determine the spatial distribution and sources of contamination and to understand the overall structure of the microbiota in the substrates. In parallel, in order to promote prediction of microbial behaviour, Listeria monocytogenes was employed as a model organism and the mild acid adaptation and subsequent increased robustness to lethal acid pH were tested both in vitro and in situ in order to identify transcriptome and volatolome derived biomarkers.
An integrated, upgradeable Decision Support System (DSS) has been developed for assessing hazards in infant food chains. Initial stages focused on gathering data requirements and developing model prototypes across SAFFI activities. Data profiling mapped key variables, created metadata, and identified dataset commonalities, facilitating seamless integration of diverse models. Conceptual and logical data models were then built using entity relationships and data flow diagrams, and workshops and surveys provided feedback for refinement. The resulting beta DSS enables hazard identification, control, and detection through a structured SQL database and visualization tools. Designed for scalability, the DSS supports data uploads via templates, allowing future expansion beyond infant food chains to address broader food safety hazards.
In addition to the DSS and 7 key exploitable results, SAFFI results have been described in over 70 peer-reviewed open access scientific articles and in conference presentations, webinars, workshops, courses, datasets and quizz.
SAFFI has developed generic risk assessment tools enabling a prioritization of food hazards on which to focus. Regarding hazard control, SAFFI confirms that both the implementation of mild industrial processes and certain home practices might significantly enhance food safety. In terms of detection of chemical hazards, SAFFI shows that sample pooling might improve drastically both regulatory surveillance and self-monitoring. SAFFI also paves the way for real paradigm shifts i) for the discovery of the numerous unexpected/unknown chemical hazards ii) for better predicting the behaviour of pathogens and better assessing their related risk. Finally, the user-friendly and scalable DSS will be a useful integrated tool for food chain stakeholders to guide them in the main food safety issues and their management.
SAFFI will contribute to ensure and enhance the transparency and reliability of food safety along the food chain with regard to international trade. SAFFI will also i) enhance the capacity of operators along the chain to detect, assess and mitigate food safety risks; ii) improve the efficiency of the official controls and iii) contribute to standard setting and regulatory cooperation in the EU and China.
SAFFI will contribute to ensure and enhance the transparency and reliability of food safety along the food chain with regard to international trade. SAFFI will also i) enhance the capacity of operators along the chain to detect, assess and mitigate food safety risks; ii) improve the efficiency of the official controls and iii) contribute to standard setting and regulatory cooperation in the EU and China.