Abstract
Generative artificial intelligence (AI) techniques are advancing rapidly and are becoming increasingly challenging to implement. Researchers, practitioners, and enthusiasts alike now require an understanding of complex concepts far beyond the scope of simple feed-forward neural networks to implement the current state-of-the-art methods for their research interests. In contrast, while data augmentation methods may not perform at the same level, they are easier to understand and implement, and are well demonstrated. For these reasons, this review aims to bridge the knowledge gap between the sciences of chemometrics and generative AI and provide a starting point for new researchers. In the context of spectroscopy, this work collects, categorizes, and describes the most popular preprocessing techniques and the state-of-the-art in generative AI and data augmentation, spanning over 104 peer-reviewed journals and proceedings across 32 publishers and organisations. We provide intuitive explanations of the methods, highlighting their strengths and weaknesses, and we include graphical and practical examples of their applications.