Classification and Modeling Methods
This chapter provides awareness on the increasing applications of two- and three-way methodologies and their advantages in the classification context. It also provides an explanation of data structure properties recorded from different analytical platforms with multivariate detection. The chapter explores models and algorithms for fingerprinting classification along with the relevant works applying these techniques, focusing on classification results and their potential uses. The multiway data structures also provide potential methodologies for food fingerprinting development. In case of a multiway data structure, the analysis can be done through multiway chemometric methods. According to the acquired data, there are three main methods that are used to obtain a preliminary data analysis, namely principal component analysis (PCA), parallel factor analysis (PARAFAC), and Tucker3. PCA is the most widely used method and it is applied to 2D data; whereas PARAFAC is performed on 3D data. The Tucker3 model may be a better choice for maintaining the data structure.