<p>Ensuring food safety and quality has become a critical priority in response to increasing global demand and regulatory requirements. To address these challenges, the food industry is demanding sensitive, selective, and robust analytical strategies. Among these, non-destructive spectroscopic sensors (NDSS), particularly those based on vibrational spectroscopy such as near infrared (NIR) and Raman spectroscopy, have demonstrated significant potential for rapid, in situ analysis of food matrices without compromising sample integrity. Current research efforts are focused on the miniaturization and cost-effective design of spectroscopic instrumentation, enabling the development of portable devices suitable for real-time food monitoring across the supply chain. In parallel, advanced chemometric techniques and machine learning algorithms are revolutionizing spectral data interpretation, enhancing model calibration, transferability, and predictive reliability. Artificial intelligence approaches in spectroscopic workflows facilitate the extraction of meaningful patterns from large and complex spectral datasets. Together, these advanced vibrational proposals are converging toward the realization of intelligent, real-time decision-making systems that support sustainable, efficient, and safe food production and distribution, redefining the standards of food quality control in modern agri-food systems.</p> Graphical abstract <p></p>