Estimating a risk exposure on biogenic amines trough surveys on the population and contents in foods and beverages
Bibliographic record
Abstract
As part of its mission, ISEKI-Food Association establishes and maintains a network among universities, research institutions, and companies in the food chain in addition to working to ensure that food studies are of high quality.However, we must also begin planning how to gear science, education, and the food industry to meet the needs of future generations as well as how to contribute to the sustainability of our planet by these food actors.In light of this, the 7 th International ISEKI-Food Conference, which had as main theme "NEXT-GENERATION OF FOOD RESEARCH, EDUCATION AND INDUSTRY", focused on future challenges in education on food science and technology, in research activities related to processing, quality and safety, packaging of foods and in societal engagements in the field divided in three main sections: EDUCATION: CHALLENGES OF EDUCATION IN A CHANGING WORLD; RESEARCH: NEXT GENERATION OF FOODS; and SOCIETY ENGAGEMENT: SOCIETY AND FOOD INDUSTRY.The conference was dedicated to all food actors, creating bridges among them.The delegates had the opportunity to exchange new ideas and experiences face to face, to establish business or research relations, and find global partners for future collaborations.We were privileged to host the following keynote speakers: HUGO DE VRIES from INRAE, France, talked about "FOODPathS towards sustainable outcomes"; JESPER TÆKKE from Aarhus University, Denmark whose talk was about "Digitalization of education -the theory of the three waves"; LAURENT GUILLIER from ANSES, France informed us on "Challenges and perspectives in food safety"; GILLES TRYSTRAM from AgroParisTech, France, presented "Some research questions in food and biotechnological process engineering"; HORST-CHRISTIAN LANGOWSKI from Fraunhofer Institute, Germany, talked about "Circular Economy for Plastic Food Packaging -Options and Challenges"; CATHERINE BAYARD from Givaudan Naturals, France, questioned us "Are you ready for the future of dairy alternatives?";MAURO SERAFINI from Teramo University, Italy, spoke about "Functional food and health: essentiality of human evidence"; STELLA CHILD from GFI Europe, Belgium informed us on "The 3 pillars of future protein production: Plant-based, cultivated, and fermentation-made meat, eggs and dairy" and FERRUH ERDOGDU from Ankara University, Türkiye presented the "Future of Food Engineering in the Digitalization Era -Preparing for Industry X.0".There were 244 abstracts submitted and accepted!Among them, 80 were selected to be oral presentations and all the others poster presentations.The abstracts of the keynote speakers together with the abstracts of the oral and poster presentations can be found in this Book of Abstracts.The authors of the best oral and poster presentations have the possibility to publish their works in the Food and Bioproducts Processing Journal, in the International Journal of Food Studies, and in a book published by Springer entitled NEXT-GENERATION OF FOOD RESEARCH, EDUCATION AND INDUSTRY.We would like to express our sincere appreciation to all participants, speakers, and contributors for their valuable inputs to the success of the 7 th International ISEKI-Food Conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".