# 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
In this way, methodologies that use hyperspectral imaging have been developed in order to foretell the content of certain substances related to meat spoilage, such as the total volatile basic nitrogen (TVB-N) and the thiobarbituric acid-reactive substances (TBARS), which are related to the degradation of proteins and the oxidation of lipids, respectively. Specifically, these prediction tools have been used in determining the freshness of pork [96; 97] and chicken [92]. Specifically, in cured pork meat, [PERSON] et al. [97] developed simplified models for monitoring TVB-N based on a system of hyperspectral images in the spectral range of 400-1000 nm. For this, they initially elaborated calibration models using PLSR and least-squares support vector machines (LS-SVM), which were later simplified with the selection of 9 wavelengths (using the regression coefficient for it). Subsequently, [PERSON] et al. [97] evidenced the suitability of the MLR model, which allowed the mapping of TVB-N content in cured pork slices. For their part, [PERSON] et al. [96] predicted the TVB-N using a hyperspectral imaging and colorimetric sensors array system for data acquisition and processing with the utilization of an efficient back-propagation adaptive boosting (BP-AdaBoost) algorithm for data fusion and modelling. [PERSON] et al. [92] used a hyperspectral imaging system and selected 10 optimal wavelengths through SPA (located in the 400-1000 nm spectral range), to establish a simplified SPA-PLSR model for chicken freshness prediction. Finally, these authors developed an image algorithm for displaying TBARS values on distribution maps. On the other hand, as can be deduced from hyperspectral imaging technology, the acquisition and possession of previous data plays a fundamental role in the elaboration of predictive models that are robust. That is why BD could provide advances in meat quality, since the creation of meaningful data resources is essential for the derivation of solid predictive models and rigorous validation tests that help predict different parameters of meat quality [101]. Moreover, other IR 4.0 technologies are being used in the search for new solutions and innovations for meat quality inspection. Thus, [PERSON] et al. [102] investigated the remote post mortem veterinary inspection of pigs using AR. Concretely, the authors used a remote two-way video communication where they only needed a non-veterinary technician who carried a smartphone and a wireless headset that allowed communication between the veterinarian and the technician. The remote control tests were carried out at a large-scale pig slaughter plant where standard on-site inspections were also carried out for later comparison. Thus, it was observed that the remote inspection using AR showed a high level of agreement with respect to the standard inspection (located between 75% and 92% for vague findings and subjective decisions and clear, easily distinguished findings, respectively). Additionally, these findings may represent a way to reduce the inspection costs in small slaughterhouses established in remote places without reducing the quality of the carcasses produced, since it avoids the displacement of veterinarians.