The development of innovative identifying markers integrated into various fibre-based packaging (FBP) components can enhance effective sorting and identification, paving the way for advanced intelligent packaging solutions in the food and beverage packaging sector.
According to the Redysign project, it has successfully achieved the second key milestone in its initiative, which is focused on the integration of identification markers and digital sorting technologies. Led by FNMT, TECNALIA, and PACKBENEFIT, this accomplishment aims to enhance the traceability of smart fresh meat packaging by optimizing the detection and quantification of both embedded traceability markers and contaminants, such as blood, oils, and fats, found in contaminated packaging waste.
One of Redysign’s core technologies involves incorporating specific identification markers into each component of fresh meat packaging, including the tray, absorbent pad, and lidding film. This strategic approach aims to facilitate accurate sorting of biocontaminated products and optimize targeted treatments designed to sanitize those materials.
During the first 18 months of the project, the participating partners concentrated on developing detection systems for traceability markers and pollutants. This research follows two primary lines:
- the development, integration, and detection of specific markers using advanced sensors
- the identification of organic contaminants within absorbent pads. RAMAN, NIR, and RGB imaging technologies are employed to achieve these objectives.
A significant advancement includes the development of a RAMAN marker by the Fábrica Nacional de la Moneda y Timbre-Real Casa de la Moneda (FNMT), which has been successfully integrated into trays thermoformed by PACKBENEFIT. Tests conducted by TECNALIA under both static and dynamic conditions demonstrated the marker’s detection efficiency in industrial settings, validating its potential for effective sorting and recycling of FBP.
RAMAN spectroscopy has proven particularly effective, employing inelastic scattering of light from a laser on the sample, revealing information about the vibrational and rotational modes of the molecules. This method allows for the identification and characterization of the substances.
NIR (point spectral near-infrared) technology evaluates organic components such as fats and proteins, as well as sample moisture. Matrix point measurements are overlapped with RGB image captures from the same sample. This provides compositional information through spectroscopy and visual data through imaging. Consequently, it enables the development of AI models capable of visually delineating contaminated surfaces and correlating visual data with chemical composition.
The project team emphasized that the findings highlight the effectiveness of spectroscopic sensors in detecting contaminants on food tray pads under various controlled and real-world conditions. Furthermore, the combination of machine vision and spectroscopy has led to the creation of predictive models linking visual segmentation with chemical identification, thereby improving pollutant detection accuracy.
The successful application of the RAMAN marker is recognized as a pivotal advancement in Redysign’s mission to enhance sustainable packaging within the food and beverage packaging industry.
As the project progresses, the next steps will involve adjusting the maximum movement speed at which the sample travels along the surface.