## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Spectroscopic Techniques
#### 4.1.2 Raman Spectroscopy
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Source** & **Animal** & **Muscle** & **Number of Samples** & **Spectroscopy Method** & \
\begin{tabular}{c} **Wavelength Range** \\ **(nm)** \\ \end{tabular} & **Statistical Method** &
\begin{tabular}{c} **Measured Attribute** \\ \end{tabular} & **Performance** \\ \hline
[127] & Beef & \
\begin{tabular}{c} _M. longissimus_ \\ _thoraxis_ \\ _M. longissimus_ \\ _lumborum_ \\ \end{tabular} & 644 & NIR & 1000–2500 & PLSR &
\begin{tabular}{c} Tenderness \\ (WBSF) \\ (Crumblines) \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.4\) \\
[136] & Beef & \
\begin{tabular}{c} _M. longissimus_ \\ _thoraxis_ \\ \end{tabular} & 595 & Vis-NIR & 350–2500 & PLSR &
\begin{tabular}{c} Tenderness \\ (PSory) \\ \end{tabular} & \(\text{R}^{2}_{\text{CV}}=0.4\) \\
[137] & Beef & \
\begin{tabular}{c} _M. longissimus_ \\ _thoraxis_ \\ \end{tabular} & 396 & Vis-NIR & 400–1395 & PLSR &
\begin{tabular}{c} Tenderness \\ (WBSF) \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.46\) \\
[125] & Beef & \
\begin{tabular}{c} _M. longissimus_ \\ _thoraxis_ \\ \end{tabular} & 120 & NIR & 740–1070 & PLSR &
\begin{tabular}{c} Tenderness \\ (AKSF) \\ \end{tabular} & \(\text{R}^{2}_{\text{CV}}=0.79\) \\
[133] & Pork & \
\begin{tabular}{c} _M. longissimus_ \\ _thoraxis_ \\ \end{tabular} & 287 & FT-NIR & 1000–2500 & PLSR &
\begin{tabular}{c} Tenderness \\ (WBSF) \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.6\) \\
[133] & Pank & \
\begin{tabular}{c} _M. longissimus_ \\ _lumborum_ \\ \end{tabular} & 287 & FT-NIR & 1000–2500 & PLSR &
\begin{tabular}{c} Congress \\ (TPA) \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.6\) \\
[139] & Lamb & \
\begin{tabular}{c} _M. longissimus_ \\ _lumborum_ \\ \end{tabular} & 205 & Vis-NIR & 400–2500 & PLSR &
\begin{tabular}{c} Tenderness \\ (WBSF) \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.13\) \\
[134] & Lamb & \
\begin{tabular}{c} _M. semienthema-_ \\ _mosus_ \\ \end{tabular} & 150 & Vis-NIR & 200–1100 & PLSR &
\begin{tabular}{c} Tenderness \\ (Texture \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.83\) \\
[135] & Lamb & \
\begin{tabular}{c} _M. multiple_ \\ _muscles_ \\ \end{tabular} & 210 & Vis-NIR & 400–1000 & PLSR &
\begin{tabular}{c} Tenderness \\ (Texture \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.88\) \\
[135] & Lamb & \
\begin{tabular}{c} _M. multiple_ \\ _muscles_ \\ \end{tabular} & 210 & Vis-NIR & 400–1000 & PLSR &
\begin{tabular}{c} Tenderness \\ (Texture \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.88\) \\
[135] & Lamb & \
\begin{tabular}{c} _M. multiple_ \\ _muscles_ \\ \end{tabular} & 210 & Vis-NIR & 400–1000 & PLSR &
\begin{tabular}{c} Tenderness \\ (Texture \\ \end{tabular} & \(\text{R}^{2}_{\text{P}}=0.88\) \\
[136] & \multicolumn{6}{c}{NIR: near-infrared, Vis-NIR: visible and near-infrared, PLSR: partial least squares regression, SVMR: support vector machine regression WBSF: Warner–Batzler shear force, AKSF: Allo–Kramer shear force, TPA: texture profile analysis, \(\text{R}^{2}_{\text{CV}}\): coefficient of determination of cross-validation, \(\text{R}^{2}_{\text{P}}\): coefficient of determination of cross-validation, \(\text{R}^{2}_{\text{P}}\): coefficient of determination of prediction.} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected studies on the prediction of the texture attributes of red meat using NIR and Vis-NIR spectroscopy. variations in frequency due to changes in the photon energy, resulting in inelastic scattering, known as Raman scattering [147]. When the scattered light has a frequency lower than that of the incident photon, it is referred to as Stokes-Raman scattering [148]. Conversely, if the scattered light has a higher frequency than the incident photon, it is known as anti-Stokes-Raman scattering [146]. The resulting Raman spectrum is comprised of bands caused by the inelastic scattering from chemically bonded structures [142]. In the Raman spectrum, organic compounds and functional groups display unique vibration frequencies, represented by peaks [149].