## 2 Food Fingerprinting and Vibrational Spectroscopy Techniques
NIR spectroscopy is highly adaptable and can be used to analyze a variety of samples, including those with high moisture content, powders, whole fruits, grains, individual kernels, and different plant parts. This method is applicable at various points in the food supply chain, from the farm and harvesting stages to in-line processing, transportation, storage, and even retail environments such as supermarkets. In addition to infrared spectroscopy, Raman spectroscopy has become a valuable tool in food analysis. This technique detects vibrations caused by changes in the electrical polarizability of bonds, with bonds such as C-C typically exhibiting stronger activity compared to less polarizable bonds like O-H. Raman spectroscopy is particularly sensitive to molecular functional groups exhibiting high polarizability, such as carbon-carbon double and triple bonds (C=C and C\(\equiv\)C) and carbon-nitrogen triple bonds (C\(\equiv\)N), distinguishing it from infrared spectroscopy, which more strongly absorbs highly polar bonds, like those found in hydroxyl (O-H) and carbonyl (C=O) groups [27; 31]. The spectral features observed in Raman arise from vibrational transitions analogous to those in the infrared region; however, stretching vibrations tend to be more pronounced in Raman spectra. Fundamentally, Raman analysis relies on the inelastic scattering of photons as they interact with molecular vibrations. Despite its advantages, a notable limitation is the inherently weak Raman signal, which results in relatively low sensitivity when applied to food samples [33]. To overcome the inherent limitations of weak Raman signals, multiple advanced signal enhancement strategies have been developed. These include techniques such as stimulated Raman scattering (SRS), coherent anti-Stokes Raman scattering (CARS), resonance Raman spectroscopy (RRS), and surface-enhanced Raman spectroscopy (SERS), all of which aim to amplify signal intensity and improve detection sensitivity [34; 35]. A notable benefit of Raman spectroscopy, particularly in the context of aqueous food systems, is its minimal sensitivity to water. Unlike infrared spectroscopy, where water exhibits strong absorption bands, Raman spectra are largely unaffected by water, thus allowing clearer detection of solute-specific molecular features. Nevertheless, interferences from fluorescence and other instrumentation-related issues remain major limitations of Raman spectroscopy. Recently, there has been a rise in portable instruments aimed at mitigating some of these challenges, positioning Raman spectroscopy as a complementary technique to both NIR and MIR spectroscopy. While both NIR and MIR spectroscopy provide point-based spectral data, they lack spatial information during analysis. In response to this limitation, hyperspectral imaging has emerged as a technique that combines point-based spectral analysis with spatial data, allowing for the simultaneous collection of digital images alongside molecular information from a sample. Hyperspectral imaging integrates the strengths of spectral analysis with spatial resolution, making it an effective tool for evaluating food quality and safety [36; 37; 38; 39]. The primary advantage of hyperspectral imaging is its ability to provide both spatial and spectral information from the sample. This technique utilizes various wavelengths within the electromagnetic spectrum, including the visible and NIR ranges, and may also incorporate Raman spectroscopy [40]. Hyperspectral imaging has emerged as a cutting-edge analytical approach with strong potential for real-time surveillance and control within food production environments. However, despite its technical merits, such as the ability to simultaneously capture spatial and spectral data, its broader implementation in industrial settings faces notable limitations.