Research project "LZ SiS"

Monitoring food safety and authenticity: ground meat case study

Developing an AI-based IR spectroscopy application to assess the safety and authenticity of ground meat

Product safety for minced meat through AI
© Ilia Nesolenyi/

Ensuring the authenticity of food and monitoring its quality are very time- and cost-intensive processes. Companies face many challenges in this regard, which differ greatly depending on the product. Particularly in the case of fresh and/or raw food, as well as products with certain ingredient ratios and those especially prone to microbial contamination, it is important to use a method whereby process chains, incoming goods and final product quality can be precisely monitored in real time.

Food safety and authenticity: ground meat case study

By developing technologies for new detection methods, damaged, spoiled or contaminated products can be identified more quickly, safely and efficiently across the entire process chain. This enables accurate predictions to be made concerning their quality.

With the aim of being able to analyze the quality and authenticity of a packaged product — without destroying or damaging it — at any point in the production and storage chain, our researchers set out to study meat using infrared spectroscopy as part of the LZSiS project. In doing so, they discovered a correlation between the freshness of meat and its chemical composition. In the next stage of the research, the data will be modeled using an intelligent algorithm. Doing so will allow product quality and authenticity to be assessed and continually monitored in real time.

The project team focused on developing infrared (IR) spectroscopy applications using artificial intelligence (AI) with a view to providing cost-effective and fast analysis tools for monitoring the safety of meat, not only during production but also up until its expiration date. Various safety aspects were investigated, including differentiating between different meat compositions, detecting spoilage during storage and packaging safety. To this end, the practicality of various IR technologies in combination with AI was investigated in terms of data collection, analysis and prediction modeling.

Chemical imaging systems and AI applications in product safety

Using chemical imaging systems as well as algorithms and artificial intelligence that are capable of segmenting images and recognizing patterns, companies can now detect packaging defects, accurately predict ingredient ratios and ensure product freshness — all non-destructively, non-invasively and from any location. Our researchers are also working on the transferability of the models and algorithms developed in the project to applications for other product groups, particularly in the recycling, packaging, cosmetics, food and drink sectors. This technology can be implemented in all areas where process monitoring is required and chemical imaging systems can be used.

Other projects from the research field of food spoilage indicators



Sustainable consumer protection with the help of artificial intelligence (AI) and the use of new sensor technologies.

Joint project "SHIELD"

Safe (organic) food and less losses through sensory detection methods.