## 6 Conclusions  
The development of optical sensing techniques for the prediction of red meat sensory quality is promising, since it could make possible the creation of measuring, grading, and quality control methods that are highly desired in the industrial context. These non-destructive and objective methods, including NIRS, Raman spectroscopy, and HSI, have been successfully applied to provide moderate- to high-accuracy predictions of important sensory attributes of red meat, such as the colour, tenderness, and flavour, with the colour being, in general, and unsurprisingly, the best predicted trait. Conventional laboratory methods involve laborious and extensive sample preparation. In contrast, optical sensing techniques coupled with multivariate analysis and image processing have shown great promise in the non-invasive analysis of red meat sensory quality, promoting sustainability and improving the environmental impact of meat processing by reducing food waste and minimising the use of natural resources. Optical sensing techniques tend to predict sensory quality according to predefined standards and cannot be set independently of the gold standard; hence, there is a requirement for an ongoing and complementary relationship between optical sensing techniques and conventional sensory analysis (human and instrumental) methods. Furthermore, it is always possible that with alternative chemometric and machine learning approaches, improved predictions could beachieved from existing datasets. Therefore, future work should take cognisance of contemporary developments in AI, including deep learning, and ensure that innovative approaches are tested for their potential alongside classical chemometrics, with a focus on accuracy and robustness for industrial real-time applications. Testing these techniques in large-scale meat production environments to assess their applicability and robustness is a critical next step, alongside a thorough evaluation of their scalability and cost-effectiveness under industrial conditions. Additionally, fostering collaborations between academia, industry and technology will be crucial to overcome the lack of standardisation in spectral data acquisition and analysis by adopting and implementing comprehensive guidelines that cover all the stages of data collection, from sample handling to final analysis. The Open Science principles in data sharing could be more widely adopted where possible to maximally exploit existing data and to allow continuing exploration of the potential of new modelling approaches to extract optimal optical sensor information and features predictive of traits of interest. Among the methods reviewed, HSI offers high accuracy, versatility and a wide range of potential applications in the prediction of meat sensory quality. However, its application in an industrial setting is still very limited and most results emanate from laboratory research. If technical hurdles are overcome, implementation of optical sensors, especially HSI, would permit processors to have access to information on a non-destructive basis and in real-time on many important parameters, satisfying the needs of the modern meat industry and consumers and supporting the adoption of Industry 4.0 principles.  
This work was supported by Enterprise Ireland, through Meat Technology Ireland (contract TC-2021-0031) and Teagasc, through the Walsh Scholarship Scheme (WS 2022028).  
Not applicable.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.