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• The proposed peak-aware conditional generative framework improves the reliability of GC-MS measurements under interference conditions.method 95%
• The peak-aware mechanism highlights characteristic peaks in GC-MS data, enabling more faithful generation of important spectral features.method 90%
• Chemical and solvent information is encoded in a latent vector for conditional generation of synthetic GC-MS signals.method 90%
• The generative model creates synthetic GC-MS datasets for training AI discrimination models, especially when experimental acquisition is limited.method 95%
• The proposed method consistently achieves cosine similarity and Pearson correlation coefficient values above 0.9.result 95%
• The proposed method preserves peak number diversity in the discrimination model.result 90%
• The proposed method reduces false alarms in the discrimination model.result 90%

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