## 5 Industrial Considerations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **NIRS** & **Raman Spectroscopy** & **HSI** \\ \hline Fundamentals & Wave-absorption frequencies exhibited by chemical bonds present in functional groups such as C–H, O–H, N–H, and C=O & Frequency shift of inelastic scattered light, which occurs when incident light photons interact with particles and produce scattered photons & Combination of spectral and spatial information \\ Accuracy & High accuracy, which is determined by the detector’s features but also the physical attributes of the sample & High accuracy though the well-resolved and non-overlapping Raman bands & High accuracy, fusion of spectral and spatial data \\ Cost & Low cost & High cost & High cost Scanning is quick but processing is affected by factors such as the number of wavelengths and the hardware and software systems \\ Applicability for industrial setting & Inteasing trend of testing portable NIR spectrometers in abattoirs and meat processing facilities & Availability of portable Raman spectrometers but restricted performance in abattoirs and meat processing facilities & Great potential, but the industrial application is limited to date \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of spectroscopic and imaging techniques. ever, industrial application for meat quality prediction is limited to date [252]. Challenges include the large volume of data that is generated with every hyperspectral image recorded, sensitivities to external lighting conditions rendering the need for careful calibration and higher cost of hyperspectral relative to point-based systems. In particular, while examination of the application of NIR HSI in meat plants has been initiated, Raman imaging has been very limited in terms of industrial assessment and is also less studied relative to NIR HSI at the research level. The high dimensionality of all the forms of hyperspectral data poses limitations on the processing speed, highlighting the importance of efficiency in data processing algorithms. However, approaches are available to refine down the size of those datasets, including the potential to develop multiband camera systems with the ability to narrow down the spectral range required for prediction models, scanning only selected feature wavelengths, leading to less data being generated. The concept is that the HSI technique should be used to develop models and identify feature wavelengths with industrial rollout focused on more robust, targeted systems. It is important to ensure that regular updates of models with gold standard sensory and technological data are built into any industrial system which is deployed, to ensure the highest accuracies are maintained. Ultimately, using rapid methods in tandem with traditional methods applied judiciously for subsets of samples, on an ongoing basis, allows the process of model refinement for the changing target population to be expedited and the utility of models to be maintained, while simultaneously keeping costs minimised. While cost and time constraints can be addressed through customised solutions, the key challenge hindering the industrial implementation of optical sensor solutions is the requirement for extensive calibration procedures before utilisation. Establishing a balance between high prediction accuracy and industrial feasibility is crucial, as achieving robust predictions often involves increased complexity and costs, which can limit the scalability in real-time applications. These steps must be acknowledged by meat industry stakeholders as essential for establishing a dependable and effective real-time system for monitoring overall meat quality in the industrial setting. Since all the optical sensing techniques require complex multivariate analysis to extract useful predictive information from spectra, it is essential to identify the most appropriate multivariate approach (pre-processing techniques, modelling approaches) applicable for each specific application.