Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective.

Area of Science:

• Analytical Chemistry
• Cheminformatics
• Artificial Intelligence

Background:

• Interpreting spectral data (mass, NMR, IR, UV-Vis) is crucial for molecular structure elucidation.
• Advanced sensing technologies generate massive spectral datasets, overwhelming traditional analysis methods.
• Manual spectral interpretation requires extensive expertise and is time-consuming.

Purpose of the Study:

• To highlight recent innovations in AI-based spectral interpretation techniques.
• To discuss limitations and obstacles in current AI spectral analysis.
• To propose future research directions and outlooks for AI in spectral interpretation.

Main Methods:

• Review of emerging artificial intelligence (AI) methods applied to spectral data analysis.
• Discussion of traditional algorithms and neural network approaches for information extraction.
• Analysis of computer-aided expert systems and their reliance on predefined strategies.

Main Results:

• AI methods, including neural networks, show significant potential in extracting information from spectral data.
• AI significantly reduces the difficulty and time required for analyzing large spectral datasets.
• AI offers promising solutions for overcoming limitations of traditional expert systems in spectral interpretation.

Conclusions:

• AI-based spectral interpretation is a rapidly advancing field with transformative potential.
• Further research is needed to address current limitations and optimize AI applications.
• AI is poised to play a pivotal role in the future of molecular structure determination and chemical analysis.