## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Qualitative Methods  
In meat analysis, a range of chemometric methods, such as tree-based and regression-based approaches, discriminant analyses, and neural networks, are utilised for classification analysis [185]. PLS-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), soft independent modelling of class analogies (SIMCA), decision tree (DT) and random forest (RF) are commonly employed for the spectral analysis of meat [120]. PLS-DA employs the quantitative regression approach of PLS to conduct a qualitative analysis, decomposing the spectra into linear combinations of PCs to capture essential information in the dataset [193]. LDA utilises a space defined by linear discriminants (LDs), similar to the PCs in PCA, to achieve probabilistic classification by seeking canonical variables that maximise the separation between categories [194]. In contrast to PLS-DA and LDA, which focus on finding directions in a shared space for class separation, SIMCA establishes unique spaces for each class, creates class-specific models using these spaces, and then evaluates the class membership of unknown samples by applying them to the respective models [195]. A decision tree (DT) is a non-parametric structure comprising nodes organised in a tree layout. These nodes contain decision rules derived from input data to determine the traversal direction towards either the right- or left-side nodes, leading to the output data at the bottom nodes. DTs are employed for both classification and regression purposes, with their interpretability being a key advantage of the algorithm [196]. An RF is a collection of DTs, where each tree is constructed using a different subset of the input data. This results in each tree having its own unique structure, and the final decisions are determined by a majority vote among the trees. To ensure the effectiveness of a random forest, the subsets of input data used to build the individual trees must be independent of one another [196].  
Conventional chemometric techniques such as PCA and PLS are widely used for handling large datasets due to their ability to reduce dimensionality [197]. However, most of these methods are limited by their assumptions of linearity and their dependence on pre-processing steps [198]. The increasing size and complexity of datasets amplify these challenges, limiting the scalability of conventional chemometric techniques. In addition to the chemometric techniques discussed above, pre-trained deep learning algorithms have been demonstrated to hold important potential via transfer learning in relation to the prediction of important meat and carcass attributes, in particular in computer vision datasets derived from RGB imaging. Deep learning is being increasingly used in red meat processing to enhance models' capability to identify complex features. The main benefit of deep learning is its feature learning ability, unlike traditional methods that rely on raw data [199]. Studies on beef [200,201], pork [202,203] and lamb meat [204] highlight its promising application. While they are less studied in relation to spectral application, deep learning approaches also have high potential in spectral/spectral image data for target outcomes, with further development.