The Development of Optical Sensing Techniques as Digital Tools to Predict the Sensory Quality of Red Meat: A Review

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.