International standards for Olive Oil (OO) analysis face challenges, especially with the rampant adulteration of Extra Virgin Olive Oil (EVOO). As demands grow for innovative methods beyond conventional techniques, VIS + NIRS spectroscopy emerges prominently. This study postulates enhanced efficacy through integrating VIS + NIRS with Fluorescence spectroscopy. Addressing challenges like instrument optimization and data security is paramount. Our research evaluates a hand-held multi-mode spectroscopy system combining fluorescence and reflectance. Employing three prototypes, we analyzed EVOO, VOO, and LOO categories. Results, compared against a benchtop instrument, provide insights into tackling EVOO adulteration through advanced spectral sensing.
The food service industry must keep premises clean and free of foodborne pathogens that can be harbored in biofilms and organic residues. These may cause foodborne infections, endangering consumers and service providers. New fluorescence technology with advanced artificial intelligence algorithms can be a solution for detecting invisible contamination problems. However, improving algorithms requires access to data, raising concerns about data privacy and potential leaks of sensitive data. We present federated learning, a decentralized privacy-preserving method, to train algorithms for precisely detecting contamination in food preparation facilities and improving cleanliness while providing data privacy assurance for clients in the food-service industry.
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