近年來，在電子鼻的相關領域中，許多研究已投入於利用電子鼻進行氣體分析之開發。這些分析方法主要針對於利用現有的已知氣體資料，稱為訓練資料，來分辨待測氣體的種類。然而，一個更為實際的應用是對氣體做完整的成分分析。本篇論文所探討的即是如何應用電子鼻來進行氣體成分之分析。首先，本論文提出了一個以K最鄰近演算法為基礎的分類法。在此方法的訓練階段，氣體的訓練資料會先依照所包含的氣體成分做分類。在測試階段時，待測氣體會被歸類到最接近的氣體類別。此外，針對每個氣體類別，每個感測器所量測到的反應會被給予權重以反映該感測器對於分辨該氣體類別的精準度。除此之外，為了更進一步預估混合氣體各成分的濃度，本論文採用了迴歸分析的方法來建立氣體的模型，並運用加權最小平方法來預估混合氣體各成分的濃度。經由實驗的檢驗，上述的兩種氣體分析的方法可提供電子鼻系統用來分辨混合氣體的各成分以及預估各成分的濃度。

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