A Review on Artificial Intelligence in Gas Chromatography Ms Generation

One particularly powerful technique, gas chromatography–mass spectrometry (GC-MS) was done by AI is widely used for its high sensitivity and precise quantification. Recent studies have leveraged AI to enhance GC-MS data analysis, particularly in pattern recognition and predictive modeling. However, the performance of such AI approaches heavily depends on access to comprehensive and diverse training datasets— something that can be limited when experimental data is sparse or constrained to specific chemicals(2,3). In this work, we explore the use of AI-driven generative approaches, including Variational Autoencoders (VAE) and Generative Adversarial Networks (GANs), to synthesize highquality data for GC-MS analysis. We also conduct exploratory data analysis (EDA) to investigate the complexity and features of real-world GC-MS datasets, establishing a foundation for evaluating the effectiveness of generated data in downstream tasks.(4)