## 5 Challenges, Limitations, and Future Prospects  
Application development in hardware and software for hyperspectral imaging in recent years has brought about a promising perspective on food quality and safety assessment. However, there are still some key issues that need to be solved in further studies. Those are mainly connected with the establishment of more efficient, reliable, fast, and low-cost modality that could be traditionally applied both in research functions and practical industrial applications. The large volume of spectral-spatial data is often characterized by a number of irrelevant or noisy signals that may seriously challenge the processing of the data and the extraction of meaningful information. In general, models are built with limited datasets, acquired under controlled conditions in a laboratory, which may not work well for new samples that will normally be measured in natural environments. Indeed, in this respect, it is of crucial importance to provide these models first with extensive calibration and validation against a wide variability of samples and conditions; this research needs to be carried out within the industry for robust and effective models that will work well in practical situations. The construction of an effective and robust model should be based on serious insight into light-tissue interactions and also the relationships between spectral-spatial features and the quality attributes under investigation. However, few of them offer in-depth data analysis during the development of the calibration model, a factor that often results in limited generalization among different application scenarios. Future studies should, therefore, be supported with investigations of the mechanisms of light-tissue interaction in hyperspectral imaging, and secondly in the development of more sophisticated methods of data mining to the full advantage of all the spectral-spatial information provided by the technology. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Product** & **Objective** & **Reason of Application** & **Model Topology Preprocessing** & **Feature Selection** & **ML Model** & **Reference** \\ \hline Egg pancake & distinguishing green vegetables & Rapid, more accurate through feature vision & - & CARS & LDA & [158] \\ \hline Dairy powders & Quality assessment & Rapid, Non-invasive, high accuracy & - & - & PLRR & [159] \\ \hline Cheese & Maturation detection & potential to optimize ripening logistics & SNV & - & PLS & [160] \\ Chicken eggs & Residual dirt or breakage detection & Real-time inspection, rapid processing time & - & - & CNN & [161] \\ \hline Egg & & Accurate detection of egg freshness and high identification accuracy for scattered yolk and eggshell cracks using non-destructive methods & - & - & XGBoost & [162] \\ \hline Soy Sauce & Analyzing the composition of alcohols and esters & Precise non-invasive detection of alcohol and eastern composition inside the solution & EPO & - & RF & [163] \\ \hline Lonicerae Japonicae & Obtaining information on the morphology and moisture content changes in droplets & Rapid method suitable for real-time monitoring & & Faster R-CNN-ANN & [164] \\ \hline Oils & Adulteration detection & High accuracy offering a robust pipeline for non-invasive food safety and quality control & Various techniques & & [165] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Hyperspectral imaging indeed shows high potential in mapping the quality attributes or chemical composition of food products in both 2D and 3D spatial domains. However, this is confronted with the development of proper models that account for physical and physiological factors. These shall be accompanied by the development of more effective methods of validation and acquisition of accurate ground-truth data at pixel level, enabling higher resolutions of heterogeneities in this spatial domain.