## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Spectroscopic Techniques
#### 4.1.1 Near-Infrared (NIR) Spectroscopy
 & 77 & Vis-NIR & 400-2498 &Concerning the texture traits of red meat (tenderness, hardness, crumbliness, cohesiveness, springiness, chewiness), which are summarised in Table 2, [PERSON] et al. [127] and [PERSON] et al. [129], using the same wavelength range, statistical analysis and reference method, reported similar prediction values for beef tenderness. The study by [PERSON] et al. [136], using the Vis-NIR range and full internal cross-validation for the assessment of PLSR models, focused on the analysis of beef sensory traits and the best predictive performances were achieved for crumbliness and instrumental tenderness. On the other hand, [PERSON] et al. [137], using a similar wavelength range and reference method but a different validation method (external validation), achieved higher performance values for the instrumental tenderness of beef meat. Finally, the best performance for beef tenderness was achieved by [PERSON] et al. [125], using a different reference method compared to previous authors. In addition to pork colour, [PERSON] et al. [133] also reported moderate R\({}^{2}\)\({}_{\text{P}}\) values for pork sensory traits, using WBSF and TPA as reference methods. Also, focusing on pork tenderness, [PERSON] et al. [138], used support vector machine regression (SVMR) and PLSR, reporting high R\({}^{2}\)\({}_{\text{P}}\) values, but they are considered possible to be improved, increasing the number of samples. Regarding lamb meat, [PERSON] et al. [134] and [PERSON] et al. [135] investigated several lamb muscles using the same wavelength range and reference method for instrumental tenderness, resulting in higher performance values compared to [PERSON] et al. [139], who studied two lamb muscles and used WBSF as a reference method. The results from various studies on the use of NIR spectroscopy for analysing meat sensory traits show a high variability in terms of the performance values. The reasons for this variability in the performance values could be attributed to several factors, such as the type and number of samples, sample variability, reference method, spectra pre-processing techniques, calibration models, validation method, and data analysis. The composition and quality of meat samples can differ significantly based on factors such as the breed, age, diet, and post-mortem handling. Also, considering the same animal, composition and quality show variability among muscles, which highlights the importance of the considered muscles in the NIR-based studies and for their comparison. All these variations can influence the spectral characteristics measured by NIR spectroscopy, leading to differences in the prediction accuracies for sensory traits. The accuracy of the prediction values also relies on the development of robust calibration models. The calibration process involves establishing a relationship between the spectral data and the reference values obtained from sensory evaluation or other measurement techniques. The complexity and effectiveness of these calibration models can influence the predictive performance of NIR spectroscopy for sensory traits. The methods used to collect NIR spectral data and sensory evaluation scores can affect the results obtained. Factors such as the sample preparation, instrument settings, data pre-processing, and sensory panel training can all impact the accuracy and consistency of the predicted texture values. The choice of data analysis method, such as SVMR and PLSR, can also play a role in determining the predictive performance of NIR spectroscopy. Different algorithms and approaches may yield varying results in terms of the accuracy and reliability of sensory trait predictions. The size and variability of the sample set used for calibration and validation can influence the generalisability of the calibration models. The validation process (i.e., type, number of samples, sources of variability considered) is, in general, a crucial factor that makesa difference, even when all the other factors are optimised or when similar factors are used in the compared research studies.