###### Abstract  
The sensory quality of meat, encompassing the traits of appearance, texture, and flavour, is essential to consumer acceptance. Conventional quality assessment techniques, such as instrumental methods and trained sensory panels, often face limitations due to their destructive and time-consuming nature. In recent years, optical sensing techniques have emerged as a fast, non-invasive, and non-destructive technique for the prediction of quality attributes in meat and meat products, achieving prediction accuracies of over 90%. This review critically examines the potential of optical sensing techniques, such as near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), to inform about the sensory attributes of red meat, aligning with industrial demands for early information on the predicted sensory performance of inventory to support meeting consumer requirements. Recent trends and the remaining challenges associated with these techniques will be described. While technical issues related to spectral data acquisition and data processing are important challenges when considering industrial implementation, overall, optical sensing techniques, in tandem with recent developments in digitalisation and data analytics, provide potential for the online prediction of meat sensory quality in the meat processing industries. Establishing technologies for enhanced information on the product and improved possibilities for quality control will help the industry to meet consumer demands for a consistent quality of product. beef; flavour; near-infrared spectroscopy; Raman spectroscopy; hyperspectral imaging; chemometrics +
Footnote †: journal: Applied sciences  
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across cultures, societies and individuals, red meat quality is expected to become more influential in shaping consumer preferences and purchase decisions [5]. Meat quality refers to a set of characteristics that determine the level of satisfaction consumers experience when purchasing and consuming meat [6]. According to [PERSON] [7], these characteristics can be grouped into four main classes: sensory, technological, nutritional, and safety aspects, while extrinsic features such as the provenance and production system also contribute. The sensory attributes of meat include its appearance/colour, texture, flavour and odour, while technological quality involves parameters such as the rate and extent of pH decline, water-holding capacity (WHC), oxidative stability, emulsifying and gelling capacities, and ratios of protein, fat, and connective tissue [8]. Red meat is a high-value product and its quality is highly affected by pre-slaughter (breed, category, production system, and animal welfare), peri-mortem (stress) and post-mortem influences (electrical stimulation, carcass suspension methods, chilling regime, ageing, etc.) (Figure 1). As meat quality is perceived subjectively by consumers, the assessment of meat quality may differ across individuals, societies, and cultures [9]. Meat quality assessment is defined as the determination of a variety of characteristics in meat to evaluate its suitability for preservation or consumption for a specific period [10]. Widely utilized conventional methodological approaches for assessing meat sensory quality include sensory evaluation of its appearance, tenderness, juiciness and flavour by either untrained consumers or trained sensory panellists, as well as instrumental proxies, including texture measures such as the Warner-Batzler shear force (WBSF), among others, and colorimetric assessment [11]. The limitations of these conventional methods include their destructive and time-consuming nature, and the requirement for skilled personnel, as well as the fact that sensory meat quality cannot be estimated by current in-plant cameras or other vision technologies, such as video image analysis (VIA).