## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)
#### 4.3.5 Visualisation  
The visualisation of chemical imaging is a capability of HSI that sets it apart from traditional imaging and spectroscopy techniques [216]. Unlike imaging, which provides information about the appearance of a sample but not its chemical composition, and spectroscopy, which only yields average chemical information for the entire sample without revealing its distribution, chemical imaging combines both aspects [222]. Table 4 provides a summary of the studies conducted on red meat to measure various sensory attributes using HSI. [PERSON] et al. [233], using the slice shear force (SSF) as a reference method for beef samples (three different muscles) and the PLSR model, achieved high R\({}^{2}_{\text{CV}}\) values for tenderness and colour. In comparison to the approach of [PERSON] et al., [PERSON] et al. [234] used two statistical methods (PLSR and SVM) and external validation, resulting in similar values for colour. Meanwhile, [PERSON] et al. [235], who focused on instrumental tenderness and used a different reference method, attained lower performance scores. Additionally, [PERSON] et al. [236] used the optical scattering by HSI to predict beef colour parameters and WBSF scores, obtaining excellent R\({}^{2}_{\text{CV}}\) values. Finally, [PERSON] et al. [237] and [PERSON] et al. [238] applied different methods (PLSR and decision tree, respectively) but the same reference method (semi-trained judges), achieving outstanding performance values for marbling. In pork, [PERSON] et al. [239], using fresh samples, and [PERSON] et al. [244], using frozen samples, followed the same statistical approach and reference method but used different wavelength ranges, obtaining similar R\({}^{2}_{\text{p}}\) values for colour attributes. Similarly, [PERSON] et al. [242] demonstrated the feasibility of using an HSI system to predict the quality of the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source** & **Species** & **Muscle** & **Number of** & **Wavelength** & **Statistical** & **Measured** & **Performance** \\  & & **Samples** & **Range (nm)** & **Method** & **Attribute** & & \\ \hline
[233] & Beef & \begin{tabular}{c} _M. longissimus dorsi,_ \\ _M. sentitendinosus,_ \\  & & 321 & 900–1700 & PLSR & \begin{tabular}{c} Tenderness (SSF) \\ Colour (L*) \\ R\({}^{2}_{\text{CV}}=0.88\) \\ Tenderness (WBSF) \\ \end{tabular}  & \begin{tabular}{c} R\({}^{2}_{\text{CV}}=0.83\) \\ R\({}^{2}_{\text{CV}}=0.88\) \\ R\({}^{2}_{\text{CV}}=0.81\) \\ R\({}^{2}_{\text{CV}}=0.83\) \\ R\({}^{2}_{\text{CV}}=0.82\) \\ R\({}^{2}_{\text{CV}}=0.83\) \\ R\({}^{2}_{\text{CV}}=0.92\) \\ R\({}^{2}_{\text{CV}}=0.94\) \\ R\({}^{2}_{\text{p}}=0.90\) \\ R\({}^{2}_{\text{p}}=0.99\) \\ R\({}^{2}_{\text{p}}=0.99\) \\ \end{tabular}  & 321 & 900–1700 & PLSR & \ \begin{tabular}{c} Tenderness (SSF) \\ Colour (L*) \\ R\({}^{2}_{\text{CV}}=0.88\) \\ R\({}^{2}_{\text{CV}}=0.88\) \\ R\({}^{2}_{\text{CV}}=0.83\) \\ R\({}^{2}_{\text{CV}}=0.92\) \\ R\({}^{2}_{\text{CV}}=0.94\) \\ R\({}^{2}_{\text{p}}=0.90\) \\ R\({}^{2}_{\text{p}}=0.72\) \\ R\({}^{2}_{\text{p}}=0.85\) \\ R\({}^{2}_{\text{CV}}=0.90\) \\ R\({}^{2}_{\text{CV}}=0.63\) \\ R\({}^{2}_{\text{CV}}=0.90\) \\ R\({}^{2}_{\text{CV}}=0.92\) \\ \end{tabular}  & 322 & 900–1700 & PLSR &
\begin{tabular}{c} Tenderness (SSF) \\ Colour (L*) \\ R\({}^{2}_{\text{CV}}=0.88\) \\ R\({}^{2}_{\text{CV}}=0.83\) \\ R\({}^{2}_{\text{p}}=0.816\) \\ R\({}^{2}_{\text{p}}=0.691\) \\ R\({}^{2}_{\text{p}}=0.691\) \\ R\({}^{2}_{\text{p}}=0.691\) \\ R\({}^{2}_{\text{p}}=0.71\) \\ R\({}^{2}  
attributes, such as colour, in salted porcine meat samples, resulting in high R\({}^{2}_{cv}\) values. Moreover, [PERSON] et al. [249] used a Vis-NIR HSI system on frozen pork samples for the prediction of colour and freshness indicators, yielding satisfactory performance values. [PERSON] and [PERSON] [241] showed that combining HSI with MLR provides a fast, non-destructive approach for predicting pork meat tenderness, with a high coefficient of determination (R\({}^{2}_{cv}\)).