The CDTI and the European Union have approved funding for SavIA
The CDTI Innovation and the European Union, through the INNTERCONECTA-STEP program, have officially approved the funding for SavIA.
This is an ambitious cooperative Research and Development (R&D&i) project in which we are actively participating alongside a consortium of leading companies. The goal is to completely transform the way we guarantee the quality and safety of what we eat, joining forces between cutting-edge sensor technology, biotechnology, and, of course, Artificial Intelligence.
What is the SavIA project?
Under the full name of “New sensor technology to predict food quality and safety”, SavIA is a strategic initiative born to overcome the limitations of traditional control systems in the food industry.
Today, conventional laboratory analytics are rigorous, but they are often slow, expensive, and destructive. Furthermore, they are performed on small samples, which can allow hidden defects in the rest of the batch to pass through, causing economic losses or the dreaded food waste.
SavIA changes the rules of the game. The project seeks to transition from those retrospective control models (which analyze what has already happened) towards proactive and predictive real-time systems. How? By creating advanced and modular technological solutions that will integrate directly into production lines to evaluate complex food matrices, focusing specifically on smoked fish products and cured meat products.
Who is funding this initiative?
A project of this magnitude requires solid backing. SavIA is co-funded by:
- CDTI Innovation (Center for the Development of Industrial Technology), dependent on the Ministry of Science, Innovation and Universities of the Government of Spain.
- The European Union, through European Funds within the framework of the INNTERCONECTA-STEP program.
This aid aligns with the Strategic Technologies for Europe Platform (STEP), boosting industrial development in the continent’s digital and biotechnological sectors.
SavIA’s Objectives: Towards predictive food
The main purpose of the project is to research and prototype a new operational logic in food plants to guarantee food safety and increase the shelf life of products. To achieve this, the work is divided into several key objectives:
- Advanced characterization
- Non-invasive intelligent sensor technology
- Predictive modeling with AI
- Sustainable biotechnological solutions
The SavIA Consortium: A multidisciplinary team
Transforming a traditional industry requires the perfect combination of food expertise and frontier technological knowledge. For this reason, SavIA has been structured as a complementary consortium of 5 organizations from four Autonomous Communities:
- EMENASA Industria y Automatismo, S.A.: Project leader, experts with a solid track record in process engineering, industrial automation, and custom prototype development.
- UBAGO Group Mare, S.L.: A national and international benchmark in the production of smoked fish products, contributing their plant and deep sector knowledge.
- PERNISAL, S.L.: Top-tier specialists in the processing of cured meat products (especially ham), focused on solving the challenges of cross-contamination in deboning.
- Digital Food Quality, S.L. (CHEMOMETRIC BRAIN): Specialists in advanced chemometric SaaS software and AI for rapid food quality control.
- Luce Innovative Technologies, S.L.: From our team, we lead the design, development, and validation of portable IoT sensors, as well as the pipelines for ingestion, preprocessing, and fusion of multimodal data.
In addition, this great team has the scientific support of two Research Organizations (OPIs) of recognized prestige in Spain: ANFACO-CECOPESCA and the CARTIF Technology Center.
We are very proud to be part of this technological journey that will not only improve the competitiveness of companies but will also lay the foundations for a food future that is much safer, smarter, more efficient, and environmentally responsible. We will keep you informed of our progress!
At Luce IT, we are passionate about connecting data and technology to solve real-world problems through advanced tools like our Data Platform. If you want to know how we can help you drive digital transformation, optimize your processes, or implement artificial intelligence in your organization, get in contact with us!
Frequently asked questions about SavIA
What is the SavIA project?
SavIA is a strategic R&D&i project co-funded by the CDTI and the European Union that seeks to revolutionize quality control and food safety in real-time. The project focuses on creating advanced, non-destructive technological solutions to evaluate smoked fish and cured meat products. Within this multidisciplinary consortium, Luce IT leads the design and development of low-cost portable IoT sensors (focused on measuring volatiles and environmental variables), in addition to researching the digital architecture necessary for the efficient transmission and sharing of this data.
How is Artificial Intelligence used within SavIA?
The main goal of SavIA is to transition towards predictive management in food plants using Artificial Intelligence and Machine Learning models. To achieve this, Luce IT is responsible for designing and implementing robust algorithms and analytical tools aimed at the acquisition, ingestion, preprocessing, alignment, and fusion of multimodal data (coming from both spectral and environmental sensors). These pipelines structure and normalize the massive information collected, laying the ideal foundations for the proper training and subsequent commissioning of AI prediction systems.
What goals are pursued in the SavIA project?
In the final stages of the project, Luce IT is responsible for developing and integrating the IoT architecture of the final prototype to ensure its connectivity and on-site operability within the production plant.
The purpose of Luce IT, in collaboration with technology centers, is to validate the response of the sensor technology under real working conditions and evaluate its behavior against operational variability and different levels of contamination in the meat matrix.
