# 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 Authenticity  
Meat and meat derivatives have certain intrinsic (specie, breed, sex, etc.) and extrinsic (geographical origin, production system, food supplied, processing techniques,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Meat/Meat Product**} & **IR 4.0** & \multicolumn{2}{c}{**Predetermined Safety**} \\  & **Technology** & **Parameters/Security** & **Reference** \\  & & **Improvement** & \\ \hline Possible application & Robotization and & Reduce human handling and & [29] \\  & automization & processing time & [29] \\  & hesi & TVC & [112] \\  & hesi & _Pseudomonas fluorescens_ & [113] \\  & hesi & Fecal matter & [105] \\  & hesi & TVC & [108] \\  & hesi & TVC, _Pseudomonas_ spp. & [107] \\  & hesi & _Enterobacteria_ & [111] \\  & hesi & _Enterobacteriaceae_ & [111] \\  & hesi & _Pseudomonas_ spp. & [110] \\  & hesi & _Pseudomonas_ spp. & [109] \\  & hesi & TVC, _Pseudomonas_ spp. & [107] \\  & hesi & Allergers & [117] \\  & & & [118] \\  & & & [119] \\  & & & [118] \\  & & Monitoring of CCP, security & \\  & & & assurance, and traceability & [75] \\  & & & during processing \\  & & & Improved traceability and & [76] \\  & & consumer confidence & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Application and possible applications of IR 4.0 technologies for meat safety.  
etc.) characteristics that on many occasions are exploited for commercial purposes. For instance, certain breeds tend to present some qualities that are more appreciated by the consumer (see autochthonous breeds vs. commercial breeds). Identically, some farming systems (extensive vs. intensive), feeding (natural vs. commercial feed) or processing (traditional/artisanal vs. industrial) affect the perception and preference of consumers. Considering these circumstances, the ability to determine the authenticity of meat is a basic pillar to prevent food fraud, which continues to represent a health, ethical, religious, and economic danger to modern society [119]. Authentication can be defined as the facts to establish or confirm the authenticity of food, that is, the acts that lead to determining that the claims made about the meat are reliable [120]. In this context, IR 4.0 technologies in the meat industry are shown as a valuable tool to detect and reduce different types of food fraud (Table 4).  
Regarding fraud related to breed, origin, breeding systems, feeding, and processing techniques, these can be avoided using technologies such as IoT and blockchain, since both allow exhaustive monitoring of traceability during the entire food chain [24,121]. Furthermore, IoT allows the implementation of various useful sensors (such as temperature, humidity, oil, salt, metal, colour, pH, and viscosity sensors) in determining food fraud through the Raspberry pi that controls the system sensors and the ZigBee module used to transfer the results [122]. Due to this, the increase of consumer confidence in the meat industry can be favoured since these technologies increment transparency [123].  
Finally, one of the most common frauds in meat industry is the total or partial substitution of high-value meat by poor meat or offal or by including proteins from various origins to lower costs. On this matter, imaging technologies have been identified as successful tools to determine adulteration in minced beef [124, 125, 126, 127], chicken [106, 128], lamb [129], and pork [130]. In general, these technologies (i.e., hyperspectral imaging and laser induced breakdown spectroscopy) made it possible to develop prediction models to determine different percentages of adulteration in meat. To do this, different samples of pure and adulterated meat (in different concentrations) are used. The images obtained from the samples were acquired and calibrated (using different mathematical tools such as PLSR, regression coefficients, random forest, support vector machines, etc.) with the aim of obtaining simplified models capable of predicting meat adulteration [124,127,131].