Table 1.

The pros and cons of MVDA and machine learning

Method	Pros	Cons
MVDA	Simple to set up; excellent computational tools with graphical user interface readily available Fast to optimize Models are often understandable linear equations Suitable when number of CPPs are small Useful for data visualization (2D and 3D)	Linear equations-based algorithms like PCA/ PLSR can lose information Cannot model complex relationships between CPP and CQA when the data is noisy and involves non-linear relationships
ML	Can capture complex relationships/functions including non-linear relationships that may model the underlying process more effectively Can handle very large datasets obtained from different sources e.g., multi-omics, in-situ spectra, conventional analytical methods such as HPLC, LC-MS and MALDI-TOF ML feature selection algorithms can find novel levers/CPPs in high-dimensional data	Large amounts of data are usually required for efficient model training Often slow to optimize – may need high computational power. Complicated to set up and therefore can often be incorrectly designed