# 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
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Meat/Meat Product** & **Predetermined Quality Parameters** & **Reference** \\ \hline Beef & Marbling level & [87, 88] \\  & Colour (L\({}^{*}\), a\({}^{*}\), b\({}^{*}\)), texture (shear force) & [89] \\  & Colour, pH, texture ( tenderness) & [90] \\  & Colour (L\({}^{*}\), a\({}^{*}\)), moisture & [83] \\  & Colour (L\({}^{*}\), a\({}^{*}\), b\({}^{*}\)), pH & [84] \\  & Texture (springiness) & [91] \\  & TBRS & [92] \\ Lamb & Fatty acid content, chemical composition, pH & [82] \\  & Colour, texture (CLCM), drip loss & [93] \\  & Intramuscular fat (fatty acid composition) & [81] \\  & Colour (L\({}^{*}\)), pH, drip loss & [94] \\  & Texture ( tenderness) & [95] \\  & TVB-N & [96, 97] \\  & Colour (L\({}^{*}\), a\({}^{*}\), b\({}^{*}\)) & [98, 99] \\ \hline \hline \end{tabular}  
* \({}^{*}\): Lightness; a\({}^{*}\): Redness; b\({}^{*}\): Yellowness; TBARS: Thiobarbituric acid-reactive substances; GLCM: Gray level co-occurrence matrix; TVB-N: Total volatile basic nitrogen.  
\end{table} 
Table 2: Application of hyperspectral imaging for meat quality prediction. extracted from each image obtained in the range of 400-1000 nm (after an adequate calibration of the camera). Thus, quantitative prediction models (Savitzky-Golay-regression coefficients-multiple linear regressions (SG-RC-MLR) model) were established which allowed eight optimal wavelengths to be related to the values of a*, permitting visualization of dynamic colour changes during heating through distribution maps [83]. Similarly, other studies have focused on the prediction of colour in fresh pork [94], beef [89], lamb [93], and chicken [84] meat with the aim of characterizing this important quality attribute. Texture has also been another quality attribute widely monitored by imaging technologies [100]. In this way, hyperspectral imaging techniques are currently being utilised to predict meat structure and its texture attributes (juiciness, tenderness, hardness, gumminess, springiness, chewiness, etc.) in different types of meat such as chicken [91], beef [89; 90], lamb [93], and pork [95]. For example, in chicken, [PERSON] et al. [91] employed a hyperspectral imaging system (400-1000 nm) and a PLSR and ANN for textural model calibration. Subsequently, they selected 10 optimal wavelengths through the successful projections algorithm (SPA) and established optimized SPA-PLSR and SPA-ANN models. Since SPA-PLSR showed better results, [PERSON] et al. [91] developed an image processing algorithm that allowed obtaining maps for chicken meat that permitted visualizing springiness parameter. Similarly in beef, hyperspectral imaging technology was also employed. Thus, [PERSON] et al. [90] developed an image processing algorithm to visualize the texture after the selection of 15 different wavelengths through PLSR (starting from the initial region 900-1700 nm). For their part, [PERSON] et al. [89] utilised a hyperspectral imaging system (400-1100 nm) to capture hyperspectral scattering images of beef steak. Thus, they established a multilinear regression (MLR) model to predict meat tenderness after selecting the appropriate wavelengths through stepwise regression. Moreover, hyperspectral imaging has been used to evaluate other parameters related to the texture, such as the water holding capacity, drip loss, and the level of marbling already mentioned previously [87; 88; 93; 94]. Even other parameters related to meat quality such as pH and the chemical composition (moisture, protein, and fat) have been predicted in various meat matrices [82; 83; 84; 93; 94] due to the characteristic absorption bands possessed by the functional groups of the meat constituents (C-H, N-H, O-H, and S-H). Thus, spectral imaging systems (400-1700) permit meat properties to be evaluated, mainly using linear regression algorithms (PLSR and MLR) [80]. In addition to the above attributes, imaging techniques can be applied to predict freshness indices of meat.