Artificial Intelligence for Spectral Analysis: Challenges and Opportunities

As a well-developed technology, spectral analysis is intensively utilized in enormous application domains. Despite the variety of spectrometry and spectrometers, classic approaches to spectral analysis typically exploit complex physical modeling to solve the tasks. Although such procedures are powerful and have been widely acknowledged in the academic as well as industry world, they still face many limitations. To address these issues, artificial intelligence (AI)-based approaches have been proposed in the last decades and proven to be successful. Previous work in this emerging interdisciplinary topic enables a fast, accurate and efficient spectral analysis compared to the classic approaches. However, in the meantime, new challenges arise with the introduction of AI methods due to its model-free, data-driven and blackbox behavior. Firstly, as spectral analysis is a highly physics-related topic, there are scenarios where the task is by definition unresolvable but the AI models still try to make predictions, which leads to undesired behavior. Besides, since AI approaches lack interpretability and reliability by nature, in real-world (especially commercial and medical) applications, abnormal results are inevitable and will potentially result in huge losses. In addition, data privacy issue also becomes crucial for cloud-based AI solutions. Thus, in this paper, we thoroughly explain these challenges that current state-of-the-art (SOTA) faces and discuss opportunities for possible outlets in future work.