Periodic Reporting for period 1 - DETECTIVE (Detection of NGT products to promote innovation in Europe)
Okres sprawozdawczy: 2024-01-01 do 2025-06-30
DETECTIVE is at the forefront of advanced methods for detecting, identifying and quantifying plant and animal NGT-derived products. DETECTIVE is exploring cutting-edge amplification- and sequencing-based approaches on a wide range of samples covering different criteria. The project is also exploring machine learning-based screening to discover traces in the genetic material that may reveal its origin. DETECTIVE is moreover delving into non-technical identity preservation strategies to enable traceability and authenticity, recognising the potential limitations of analytical detection alone. The project´s holistic approach also considers economic and legal implications of different detection and traceability strategies. DETECTIVE actively engages stakeholders at every stage of the project, fostering a robust Community of Practice that allows enforcement authorities, public and private laboratories, developers, and agri-food operators to co-create an empowerment plan and share knowledge and expertise.
DETECTIVE has made significant progress in mapping the legal and economic landscape for NGT-derived product traceability. Legal research has assessed current regulatory shortcomings for NGT-derived product traceability and explored how blockchain or probabilistic models can aid traceability, although these do not meet current authorisation requirements. Recommendations for legal framework adaptation are presented. An economic analysis, simulating how prices, quantities, and welfare indicators shift in response to adoption under different regulatory frameworks, shows that NGTs offer benefits in terms of productivity and sustainability while fragmented regulatory framework can act as a barrier. An Austrian case study grounds the work in practical production realities.
DETECTIVE aligns with stakeholder needs through the Systems Mapping Approach and the RRI Roadmap©™. Key stakeholders (e.g. enforcement laboratories and developers), have been engaged to co-define empowerment priorities, laying the foundation for a practical and stakeholder-driven Empowerment Programme. The Community of Practice (CoP), a science-based, multi-actor platform for trust-building and mutual learning, already includes over 40 members without formal promotion. An important and unique EU-wide survey on empowerment needs engages three stakeholder groups: 1) laboratories, 2) agri-food and feed value chain operators, and 3) competent authorities.
DETECTIVE has collected samples from canola, potato, rice, soybean, sugar beet, wheat, cow, goat and pig, representing commercially important plant and animal products of various complexity. In addition to samples derived from targeted mutagenesis, a cisgenic sample (potato) has also been collected. Partners are currently working on these samples to test a range of analytical detection methods for both single and multiple known mutations. In an innovative approach, the project explores machine-learning to analyse patterns to estimate the probability of artificially generated edits.
DETECTIVE develops a knowledge-based detection tool which applies machine-learning to large datasets to detect unknown NGT-derived products by identifying patterns or anomalies across the value chains. The criteria for the corresponding data space have been defined and the data space has been structured around multiple data silos: traits, genetic research, GMO research, NGT research, breeders, seed producers, regions and cultivation data. Seed potato, maize and sugar beet have been selected as proof-of-concept cases. An improved GenEdit database is developed, serving as the most comprehensive and up-to-date repository available. A machine-learning approach is applied to build a knowledge graph that will capture the complexity of the agri-food value chains and serve as a detection strategy targeting NGT-derived products.
DETECTIVE bridges the gap from research and development of detection methods to their implementation in enforcement laboratories. Tightly linked to WP3, a decision support system covering different attributes has been developed to evaluate the analytical methods. The development of standard operating procedures and validation protocols is still pending further progress in WP3.
DETECTIVE aligns with stakeholder needs through the Systems Mapping Approach and the RRI Roadmap©™. Key stakeholders (e.g. enforcement laboratories and developers), have been engaged to co-define empowerment priorities, laying the foundation for a practical and stakeholder-driven Empowerment Programme. The Community of Practice (CoP), a science-based, multi-actor platform for trust-building and mutual learning, already includes over 40 members without formal promotion. An important and unique EU-wide survey on empowerment needs engages three stakeholder groups: 1) laboratories, 2) agri-food and feed value chain operators, and 3) competent authorities.
DETECTIVE has collected samples from canola, potato, rice, soybean, sugar beet, wheat, cow, goat and pig, representing commercially important plant and animal products of various complexity. In addition to samples derived from targeted mutagenesis, a cisgenic sample (potato) has also been collected. Partners are currently working on these samples to test a range of analytical detection methods for both single and multiple known mutations. In an innovative approach, the project explores machine-learning to analyse patterns to estimate the probability of artificially generated edits.
DETECTIVE develops a knowledge-based detection tool which applies machine-learning to large datasets to detect unknown NGT-derived products by identifying patterns or anomalies across the value chains. The criteria for the corresponding data space have been defined and the data space has been structured around multiple data silos: traits, genetic research, GMO research, NGT research, breeders, seed producers, regions and cultivation data. Seed potato, maize and sugar beet have been selected as proof-of-concept cases. An improved GenEdit database is developed, serving as the most comprehensive and up-to-date repository available. A machine-learning approach is applied to build a knowledge graph that will capture the complexity of the agri-food value chains and serve as a detection strategy targeting NGT-derived products.
DETECTIVE bridges the gap from research and development of detection methods to their implementation in enforcement laboratories. Tightly linked to WP3, a decision support system covering different attributes has been developed to evaluate the analytical methods. The development of standard operating procedures and validation protocols is still pending further progress in WP3.
What makes this project unique is its comprehensive and holistic approach and its solid anchoring in the reality of the stakeholders. While developing detection methods from a technical point of view, we are also looking at the regulatory requirements and the socio-economic implications of these methods. We are looking at the practicalities at the enforcement laboratories, the cost and the validation issues, and we are taking the perspectives of the agri-food value chain operators into account.
For analytical detection methods, we are testing cutting-edge technology on a large variety of samples covering several different criteria; not only genomic complexity but also relevance as market commodities.
For unknown mutations, we are exploring machine learning-based detection through a novel approach looking at patterns in the genetic material.
We explore a novel knowledge-based approach with a federated data space containing a multitude of information in which AI may discover “items of interest/concern” for further technical analysis.
Our legal analysis on the requirements for detection, identification and quantification methods is unique in its approach and will be of great importance for future policy and regulatory developments in the EU.
The socio-economic impact analysis of various regulatory options has never been carried out before and will serve as an important basis for future EU decision-making.
The EU-wide surveys will provide information on stakeholder needs that have never been comprehensively assembled before.
The empowerment programme and Community of Practice represent a novel and unique feature bringing together the various stakeholders in the field of NGT-derived products.
For analytical detection methods, we are testing cutting-edge technology on a large variety of samples covering several different criteria; not only genomic complexity but also relevance as market commodities.
For unknown mutations, we are exploring machine learning-based detection through a novel approach looking at patterns in the genetic material.
We explore a novel knowledge-based approach with a federated data space containing a multitude of information in which AI may discover “items of interest/concern” for further technical analysis.
Our legal analysis on the requirements for detection, identification and quantification methods is unique in its approach and will be of great importance for future policy and regulatory developments in the EU.
The socio-economic impact analysis of various regulatory options has never been carried out before and will serve as an important basis for future EU decision-making.
The EU-wide surveys will provide information on stakeholder needs that have never been comprehensively assembled before.
The empowerment programme and Community of Practice represent a novel and unique feature bringing together the various stakeholders in the field of NGT-derived products.