S.M.A.R.T 360

The main aim of this project is the set-up of green, smart sensors for visual monitoring food quality and reducing the waste of high perishable foods. Eco-friendly colorimetric sensors array will be designed, optimized and tested for quality monitoring on a model food. Three food categories will be fully characterized, during the whole shelf-life, in terms of microbial population, volatile molecule profiles, colour and textural changes in order to establish a link between the change of sensor’s colors and variations in microbial populations and metabolites. The sensor prototypes will be validated on the target foods at optimal/non-optimal storage conditions by using a multidisciplinary approach.

Results achieved

The project successfully achieved its main objectives through a coordinated multidisciplinary approach. Sustainable colorimetric sensor arrays were developed and optimized using biodegradable starch/glycerol/carboxymethylcellulose-based films as supporting materials and selected pH-sensitive dyes as sensing components. Different formulations and indicator concentrations were evaluated in order to obtain responsive, stable and reproducible sensing units. The most promising sensor configurations were then tested on real food systems during refrigerated shelf-life.

A comprehensive characterization of the selected food matrices was carried out. Stracchino cheese, chicken burgers and salmon were monitored during storage under optimal refrigeration conditions and under simulated temperature abuse conditions. Microbiological analyses allowed to monitor the evolution of spoilage-related microbial groups, including total aerobic bacteria, lactic acid bacteria, Enterobacteriaceae, coliforms, Pseudomonas spp., yeasts and moulds. In parallel, physico-chemical analyses were performed, including colour, texture, rheological properties, water holding capacity, weight loss and proton molecular mobility by low-field NMR, depending on the specific matrix. Volatile organic compounds were also analysed by SPME-GC-MS, and the resulting datasets were processed using multivariate statistical tools such as Principal Component Analysis and heat maps.

The integration of microbiological, chemical and physico-chemical data enabled the identification of key spoilage markers associated with quality changes during refrigerated storage. In dairy products, increases in volatile metabolites such as ethanol, 3-methyl-1-butanol, phenylethyl alcohol, acetic acid and 3-methyl-butanoic acid were associated with product spoilage. These changes were consistent with microbial growth dynamics and with physico-chemical modifications occurring during shelf-life. In meat and fish products, relevant spoilage-related changes were also observed, including increases in ketones, alcohols, organic acids, esters and aldehydes, confirming the progression of microbial and biochemical degradation processes during storage.

The developed sensor system showed its best performance in dairy matrices. In stracchino cheese, the selected sensor array produced clear colour changes that reflected the progression of spoilage processes and were consistent with the evolution of volatile profiles, microbial populations and physico-chemical quality indicators. The most reliable response was obtained using the sensor array with the highest indicator concentration, which allowed a clearer colour variation detectable by naked eye. Under temperature abuse conditions, the colour change of the sensor occurred earlier than under optimal refrigeration, reflecting the accelerated spoilage dynamics caused by non-optimal storage. These results confirmed the potential of the developed sensor array as a practical tool for monitoring quality changes and supporting shelf-life prediction in perishable dairy products.

The application of the sensors to meat and fish matrices highlighted some limitations. Although microbiological, chemical and physico-chemical analyses clearly described spoilage evolution in these products, no significant sensor colour variations were observed during refrigerated shelf-life. This behaviour was mainly related to the different composition of the headspace generated by these matrices and to the instability of the biofilm support under the acidic conditions required to obtain the appropriate reactive form of some indicators. These findings provided important knowledge for future optimization of sensing materials, indicating that alternative supports or printed ink-based sensor systems may be more suitable for meat and fish applications.

The potential of the sensor system for early spoilage detection was also investigated. The main microbial species naturally present in the tested food matrices were isolated and genetically identified. Since the most promising results were obtained in dairy products, further experiments focused on microorganisms isolated from stracchino cheese, including Lacticaseibacillus casei, Lactobacillus delbrueckii, Pseudomonas spp., Kluyvera cryocrescens, Kluyvera intermedia and Leclercia adecarboxylata. Controlled growth experiments in sterile skim milk showed that specific microbial strains, particularly Pseudomonas spp., were able to induce colour changes in the sensing units. The analysis of volatile compounds produced by selected isolates allowed the sensor response to be linked to microbial metabolism and to the production of specific spoilage-related metabolites. These results confirmed the potential of the sensor array as an early indicator of microbial spoilage under controlled conditions.

The project generated scientific outputs and dissemination activities addressed to both academic and non-academic audiences. Results were shared through scientific publications, oral communications and poster presentations at national and international conferences. Dissemination activities also involved stakeholders and the general public, including participation in events such as CIBUS and the European Researchers’ Night. Overall, the project strengthened interdisciplinary collaboration and produced valuable knowledge for the future development of smart, sustainable and consumer-friendly food packaging systems.

In conclusion, the project demonstrated the feasibility of developing biodegradable colorimetric sensors for intelligent packaging applications, especially for dairy products and other food matrices characterized by suitable volatile spoilage profiles. The results obtained contribute to the advancement of sustainable food quality monitoring strategies, support more informed consumer decisions, and may help reduce avoidable food waste, in line with European priorities for circular economy, environmental sustainability and responsible food systems.

Project details

• Programma di finanziamento: PRIN 2022
• D.D. del MUR : 104 del 02/02/2022
• Codice progetto MUR: 2022XSFEY4
• CUP: J53D23010550006
• Contributo al DISTAL: € 66.286,00
• Durata: 12/10/2023 - 28/02/2026
• Coordinatore: Università degli Studi di Parma
• Ruolo DISTAL: RL
• Responsabile Scientifico: Lorenzo Siroli