The IsoFoodTrack database is a comprehensive, scalable, and flexible platform designed to manage isotopic and elemental composition data for a wide range of food commodities. It supports research in food authenticity and fraud detection by integrating isotopic data with rich metadata, including geographical, production, and methodological details. The database is built for scalability, allowing the addition of new commodities, analytical methods, and metadata fields, while ensuring interoperability with external databases through standardized formats and API integration. Based on the data collected in IsoFoodTrack using statistical, chemometric and machine learning approaches it has a capability to identify and classify the origin of food commodities. IsoFoodTrack also supports isotope mapping (isoscapes), providing spatially continuous predictions that enhance the detection of food fraud. Rigorous quality control measures ensure high data reliability, and the user-friendly web interface facilitates easy access and visualization. Openly accessible through platforms like National Center for Biomedical Ontology (NCBO) BioPortal, IsoFoodTrack is positioned for future expansion and integration of open-access data, making it a vital tool for researchers and regulatory agencies in ensuring food authenticity and traceability.

The authentication of high-value spices such as paprika and cinnamon is critical due to increasing food fraud. This study explored the potential of a multi-analytical approach, combined with chemometric tools, to differentiate 45 paprika and 46 cinnamon samples from the Slovenian market based on their geographic origin, production methods, and possible adulteration. The applied techniques included stable isotope ratio analysis (δ¹³C, δ¹⁵N, δ³⁴S), multi-elemental profiling, FTIR, and antioxidant compound analysis. Distinct isotopic and elemental markers (e.g., δ¹³C, δ³⁴S, Rb, Cs, V, Fe, Al) contributed to classification by geographic origin, with preliminary classification accuracies of 90% for paprika (Hungary, Serbia, Spain) and 89% for cinnamon (Sri Lanka, Madagascar, Indonesia). Organic paprika samples showed higher values of δ¹⁵N, δ³⁴S, and Zn, whereas conventional ones had more Na, Al, V, and Cr. For cinnamon, a 95% discrimination accuracy was achieved between production practice using δ³⁴S and Ba, as well as As, Rb, Na, δ¹³C, S, Mg, Fe, V, Al, and Cu. FTIR differentiated Ceylon from cassia cinnamon and suggested possible paprika adulteration, as indicated by spectral features consistent with oleoresin removal or azo dye addition, although further verification is required. Antioxidant profiling supported quality assessment, although the high antioxidant activity in cassia cinnamon may reflect non-phenolic contributors. Overall, the results demonstrate the promising potential of the applied analytical techniques to support spice authentication. However, further studies on larger, more balanced datasets are essential to validate and generalize these findings.

This paper describes an approach to implementing an IoT system for fish farmers, with open integration and support to the end-to-end sea to fork digital platform for transparency, seafood traceability and consumer engagement. The system is implemented as a Cloud SaaS solution providing digital tools for easier fish farm management, easily accessible via Internet using desktop/laptop or mobile devices. The paper discusses the overall sea to fork platform, and then focuses on the specifics related to the IoT sensors and systems requirements. A discussion on initial evaluation of the pilot system implemented according to the requirements is given at the end. The solution is open for adding different IOT sensors that can be installed in the other stages of the seafood value chain, such as transport, packing/processing facilities, and retail. The system is open to utilization of QR codes to uniquely identify items in different phases of the chain, e.g., trade units or final products, to allow interaction with different stakeholders and motivate seafood consumers to engage with the overall platform.

Saffron origin authentication was investigated using a combined approach of stable isotope and multi-elemental analysis coupled with statistical modeling. A total of 201 saffron samples were analyzed, including 163 authentic ones, with commercial varieties excluded. Among them, 75 samples were recently sourced from Morocco, Greece, Slovenia, Spain, Iran, and the United Kingdom. Statistical analysis of the combined dataset enabled effective saffron origin discrimination, achieving 94% accuracy. Key discriminatory markers identified were δ²H, δ¹⁵N, K, Ca, δ¹³C, Sr, Mn, Co, and Zn. Regional predictability was confirmed through pairwise comparisons, particularly for Moroccan, Iranian, and Italian saffron. The integration of elemental ratios (Rb/Sr, K/Rb, Ca/Sr) and δ¹⁸O further enhanced accuracy and precision for geographical differentiation. Verification of saffron labeling using a DD-SIMCA classification model indicated potential mislabeling in up to 60% of samples. These findings underscore the efficacy of stable isotope and multi-elemental analysis for improving saffron traceability and combating fraud.