Incarlopsa is researching the development of new tecnologies to improve the hygienic quality of the sector’s facilities

  • With a budget about 1.2 million euros, VISIONMEAT will combine the use of multispectral technologies with artificial intelligence to detect posible microbial contaminations in real time
  • The project is part of Incarlopsa’s integrated innovation strategy, which aims, among other objectives, to lead the highest quality standards and innovate in food safety
  • Incarlopsa focuses its R&D policy, both directly and in collaboration with third parties, on four main lines of action: development of new products, new technologies, new materials and promotion of animal welfare

Incarlopsa, a Castilla La-Mancha leading company in the production and processing of pork products, has launched VISIONMEAT, an innovation project focused on the development of a new system for controlling the hygiene of their products and the critical surfaces of its production plants, which will allow them to detect possible microbial contamination in real time, in a non-destructive and non-invasive way, using multispectral technologies in combination with artificial intelligence.

Under the name “Development of hyperspectral vision technology for hygienic quality control in the meat industry”, the project has a budget of 1.165.694 euros, with the collaboration of the AINA Technology Center, a research center with more than 30 years of experience in the food quality and safety field, and with funding from the Center of Industrial Technological Development (CDTI), a Public Business Entity, under the Ministry of Science and Innovation, which promotes innovation and technological development of Spanish companies.

A project that reinforces Incarlopsa’s commitment to food quality and safety

The VISIONMEAT project seeks to integrate new scientific developments in hyperspectral vision to capture and manage online information related to the characteristics that define meat quality. The parameters to be monitored are water holding capacity, microbial contamination and spoilage degree, among others, ensuring that all products go to market with their optimum characteristics[1],[2].

As in other examples of innovation projects involving artificial intelligence (AI), the model is fed with hyperspectral images and laboratory results, allowing the system to learn or generate values through the hyperspectral image to classify the raw material based on the defined characters.

The incorporation of hyperspectral vision and AI will facilitate the possibility of actively exercising the function of safeguarding the final consumer, as well as optimizing processes to obtain quality improvements.

The VISIONMEAT project will run until June 2024.

More than 30 innovation projects of our own and in collaboration with third parties.

Incarlopsa’s commitment to innovation is firm. The company’s R&D strategy is based on a compehensive approach that combines both its own projects and collaborations with third parties in research projects that complement the company’s activity.

The VISIONMEAT project is part of this strategy which, among other objectives, seeks to lead the highest quality standards and innovate in food safety and includes four lines of action: new products, new technologies, new materials and promotion of animal welfare.

Currently, Incarlopsa has a specific multidisciplinary R&D&i department made of 19 qualified professionals, including 4 PhDs, these include chemists, agricultural engineers, veterinarians, food technologists and nutritionists, among others. The company has more than 30 ongoing research projects and collaborations, approximately half of which are funded by the CDTI.

[1] Kozan HI, Sariçoban C, Akyürek HA, Ünver A. HYPERSPECTRAL IMAGING TECHNIQUE AS A STATE OF ART TECHNOLOGY IN MEAT SCIENCE. Green Chem Technol Lett. 2016 Jun 26;2(3):127–37.

[2] He H-J, Sun D-W. Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products. Trends Food Sci Technol. 2015 Nov 1;46(1):99–109.