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This paper systematically evaluates time-series foundation models (TSFMs) such as Chronos-2 and MOMENT on electronic nose (E-Nose) data for gas identification and concentration prediction. It finds that fine-tuning is necessary and that fusing TSFM embeddings with specialized models can improve performance.