## 5 Limitation of Vibrational Spectroscopy for Non-Invasive Food Authentication  
Despite the growing popularity and success of vibrational spectroscopy techniques, such as NIR, FTIR, and Raman spectroscopy, for food authentication [113; 114], several significant limitations must be acknowledged to ensure the development of reliable and scalable analytical protocols. First, spectral complexity and overlapping signals represent a persistent challenge. Many food matrices are chemically complex and heterogeneous, resulting in overlapping spectral bands that complicate data interpretation. This is particularly problematic when trying to identify low-concentration adulterants or contaminants within samples containing dominant matrix signals, such as fats or water. For example, water exhibits strong absorption in the IR region, which can mask the subtle spectral features of other components, especially in high-moisture products like fruits, dairy, or beverages. Second, fluorescence interference in Raman spectroscopy is a well-known limitation, especially in colored or processed food samples. The presence of pigments or phenolic compounds can induce strong background fluorescence, which overwhelms the weak Raman signal and reduces detection sensitivity. Although strategies such as shifted excitation Raman difference spectroscopy, SERS, and the use of near-infrared lasers have been explored to mitigate this effect, fluorescence remains a critical obstacle in practical applications. Third, instrumental and methodological limitations hinder widespread industrial deployment. For instance, while portable spectrometers have improved accessibility and field use, they often exhibit lower resolution, sensitivity, and reproducibility compared to benchtop systems. Calibration transfer between instruments or laboratories is non-trivial due to differences in optical configurations, detector types, and spectral response functions. These variations necessitate robust standardization protocols and cross-platform calibration models, which are currently underdeveloped. Fourth, sample heterogeneity and surface variability pose challenges for reproducibility and model generalization. In solid or semi-solid samples, differences in particle size, texture, temperature, and surface morphology can introduce significant scattering effects and baseline shifts, affecting model accuracy. Although spectral preprocessing techniques such as multiplicative scatter correction, standard normal variate, and derivatives can mitigate some of these effects, they cannot completely eliminate variability caused by inconsistent sample presentation or environmental factors. Fifth, the limited availability of reference databases and authentic standards restricts the development of robust classification and calibration models. Most models are built using narrowly defined sample sets that may not capture the natural variation present in real-world scenarios, including regional, seasonal, and process-related variability. Without comprehensive and curated databases of authenticated reference samples, models are prone to overfitting and may fail to generalize across diverse production conditions. Sixth, challenges in chemometric modeling and data fusion continue to limit the practical implementation of vibrational spectroscopy. The complexity of high-dimensional spectral data requires expertise in multivariate statistics, machine learning, and model validation. However, many studies rely on relatively simple chemometric approaches and -lack external validation using independent datasets. Moreover, data fusion--although promising in combining multiple sensors or modalities--is computationally intensive and demands the careful synchronization of acquisition protocols, preprocessing strategies, and model architectures. Lastly, while AI and deep learning models (e.g., CNNs and RNNs) have shown promise in enhancing classification accuracy and reducing dependence on feature engineering, they require large, diverse, and well-annotated datasets to avoid overfitting.