Food Identification in PAT through Classical and Novel Raman Sensing Strategies

Lipids contain rich chemical information valuable in food analysis, providing insight into composition, processing history and chemical stability, and are therefore widely used for food identification and authentication. Within the framework of Process Analytical Technology (PAT), rapid, non-destructive and in-line capable analytical methods are essential to support quality control and decision-making across food production chains. Raman spectroscopy is particularly attractive for PAT applications due to its minimal sample preparation, chemical specificity and compatibility with portable sensing platforms.

In this work, Raman-based analytical strategies for food identification are explored using meat as a representative and economically relevant product category. In meat products, authenticity and labeling are of high importance, particularly for differentiating organically produced meat from conventionally farmed alternatives with significant price differences. Using portable, fiber-optic-coupled Raman spectroscopy, pork adipose tissue samples from different production origins were analyzed. Multivariate analysis of the Raman spectra demonstrated reliable classification performance, confirming the suitability of classical Raman spectroscopy for PAToriented meat identification and authentication tasks.

Moreover, the analysis also revealed limitations of conventional Raman spectroscopy when applied to complex lipid systems. Fatty acids occur as mixtures of structurally similar and isomeric species, with vibrational signatures that often overlap due to limited spectral accuracy and peak broadening. This constraint becomes particularly relevant when monitoring subtle chemical changes related to lipid oxidation, which is a critical parameter for food quality, shelf life and process control. To address these challenges, the ongoing development of a novel Raman sensing approach is presented, focusing on ultra-high spectral accuracy rather than conventional spectral resolution. By targeting sub–wavenumber band position changes, the sensor aims to enable improved discrimination of fatty acid isomers and oxidation related transformations that are inaccessible with standard Raman systems.