## 5 Industrial Considerations  
Spectroscopic and imaging techniques are well known for their rapid analysis speed, which compared to classical laboratory-based methods offers promising potential for the prediction of technological and sensory quality, and they have been demonstrated to be able to classify red meat into sensory quality classes with high sensitivity and predict sensory quality scores with moderate accuracy. This demonstrates their ability to provide information content to meat processors regarding the sensory performance of meat, at the (future) point of consumption, as it is being processed early post-mortem in meat plants, and therefore, their potential to be integrated within meat management systems. The overview of these techniques is summarised in Table 5. However, utilising spectral analysis necessitates conducting large physicochemical training experiments, as the development of reliable calibration models is challenging due to the structural and chemical diversity across meat types, including differences in fat and moisture content. In an industrial setting, external factors such as the ambient temperature, humidity, lighting, spectrophotometer temperature, sample handling, etc., can affect the results and various correction methods,including optical techniques, orthogonal methods, and bias correction, can be implemented to help develop reliable calibration models. Where these dynamic conditions are changing over time, the performance value of the models can deteriorate and it is important to ensure regular model updates based on evolving sample classes and dynamic conditions in the factory. Among the most commonly used spectral techniques, NIR analysis has wide applications in industrial sectors due to its speed and non-destructive nature [99; 121; 124; 125; 128]. It could be said, therefore, that NIRS is the most compatible method with direct online application in a meat plant, subject to availability of predictive models. This is because NIR analysis makes use of relatively straightforward and robust instruments. As with NIRS, Raman spectroscopy is non-destructive, non-invasive and non-contact, preserving the sample integrity and providing safe procedures for both samples and operators. However, some technical limitations currently impede the widespread commercial application of the Raman spectroscopy technique for meat in an industrial setting [253]. The complexity of the meat substrate and the limited size of the laser spot leads to slow performance in recording the signal and in capturing representative information compared to NIRS. Effective approaches to improving the reproducibility and selectivity in meat quality prediction modelling using Raman spectroscopy include addressing the signal overlap and minimising interference from irrelevant factors. While Raman is less compatible currently with online application, and studies are more limited compared with NIRS, the accuracy of the models developed for meat quality prediction with Raman suggests higher performance compared to NIRS, where comparison is feasible. This may relate to the well-resolved and non-overlapping Raman bands, while Raman spectra are more conducive to material identification through spectral library searching, as they provide a clearer and more distinctive fingerprint of the sample's composition. Point-based methods have advantages but also limitations. Imaging combined with spectroscopy in HSI has shown promising early indications for more accurate non-destructive analysis and higher-quality prediction models for meat sensory quality; how