# Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry
## 4 Industry 4.0 Technologies Applied for Meat Quality, Safety, and Authenticity
### Application of Industry 4.0 Technologies for Meat Quality  
High quality is a key factor in today's hypercompetitive market [55]. Hence, predicting quality attributes including chemical composition, physicochemical parameters (pH, colour, water holding capacity, and texture) and sensory attributes of a meat product in the processing line quickly and non-destructively is a major challenge [79]. From this perspective, IR 4.0 technologies can be particularly beneficial in ensuring food quality, since methods using image techniques have been studied for the determination of different quality parameters (e.g., colour, texture and texture-related features, flavour, and freshness) with good results in different meat matrices [80, 81, 82, 83, 84]. Specifically in meat, the use of hyperspectral images has been extensively investigated for the prediction of many parameters related to its quality (Table 2). This imaging technology encompasses a computer with appropriate software, a spectrograph, a camera, and an illumination unit [85]. Thus, spatial and spectral information of the meat is obtained simultaneously, which forms three-dimensional (3D) data cubes that allow objects to be detected, identified, and quantified in more detailed images (two-dimensional (2D) data matrix). After a previous pre-treatment of these acquired data to reduce noise and redundant information and the selection of the image region of interest (ROI), the information is processed with multivariate analysis (principal component analysis (PCA), partial least squares regression (PLSR), stepwise regression, correlation co-efficient, artificial neural network (ANN), successive projection algorithm, etc.) with the purpose of original data modelling for classification or regression [80, 86]. One example of this advancement was the work carried out by [PERSON] et al. [81], in which they successfully developed a technology based on hyperspectral imaging to determine the quality of pork fat (in terms of fatty acid content). Concretely, these authors related textural information with intramuscular fat content obtained through the traditional gas chromatography (GC) technique. Thus, using a PLSR algorithm, they concluded that texture features from hyperspectral images could be used to rapidly predict intramuscular fat without the need for tedious traditional techniques. Similarly, [PERSON] et al. [82] successfully employed hyperspectral imaging to predict intramuscular fat content and fatty acid composition of lamb meat. Additionally, methods for detecting marbling (which is related to sensory quality) through hyperspectral images in beef have also been developed [87, 88]. Hyperspectral imaging has also been used to predict the colour of various meat products during their processing because porphyrins of some pigments can absorb energy at particular wavebands during electromagnetic radiation [83, 89]. An example is pork sausages, where this IR 4.0 technology has been used to predict the colouration associated with the employ of different casings [98, 99]. Similarly, hyperspectral images have been utilised to determine the influence of microwave treatment on beef colour parameters, allowing monitoring of the changes linked with heating [83]. For this, a typical pushbroom hyperspectral imaging system (308-1105 nm) was employed and spectral information was