研究生: |
吳承駿 Wu, Cheng-Chun |
---|---|
論文名稱: |
K他命與K菸快速偵測電子鼻系統 An Electronic Nose System for Rapid Detection of Ketamine and Ketamine Cigarette Gas |
指導教授: |
鄭桂忠
Tang, Kea-Tiong |
口試委員: |
楊家銘
Yang, Chia-Min 饒達仁 Yao, Da-Jeng 劉奕汶 Liu, Yi-Wen |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | 電子鼻 、K他命 、K菸 、毒品氣體收集 、毒品檢驗 |
外文關鍵詞: | Electronic Nose, Ketamine, Ketamine Cigarette, Gas Collection of Drug, Drug Detection |
相關次數: | 點閱:2 下載:0 |
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在臺灣,K他命為常見毒品,而其中一種常見的K他命吸食方式為將K他命粉末捲入香菸內後點燃吸食,此種吸食方式造成K他命的氣味在空氣中飄散,嚴重危害人們的居住品質與身體健康。目前並沒有有效且便利的科學方法或工具去立即辨識K菸或是K他命的氣味,由於K他命加熱後會產生一股類似燒塑膠的濃烈氣味,因此本研究利用仿生電子鼻系統,以氣味來辨識K他命。
本研究提出了K他命與K菸氣體定量收集方法,並使用穩定流速之感測系統,讓環境和流速壓力的影響降至最低。利用金屬半導體氧化物感測器陣列感測先前收集之氣體樣本後,藉由訊號前處理,對感測器電阻變化進行特徵擷取,並使用降低維度演算法,使資料複雜度及資料分布之觀察難度降低。透過線性迴歸及分類器,對K他命、不同混合比例之K菸、香菸、K他命混合干擾氣體等氣體進行辨識結果分析及比較。由辨識結果可看出,本系統對K他命、K菸、香菸,三種氣體進行樣本收集及分析後,對於偵測樣本氣體是否含有K他命最高可達到95.92%辨識率;上述三種氣體另外增加含干擾氣體(甲醛)的數種樣本後,對於偵測樣本氣體是否含有K他命最高可達到96.83%辨識率。
Drug abuse has become a serious problem in Taiwan, while ketamine is the most widespread among drugs. There are many methods to take ketamine; among those methods, it is very common in Taiwan that people take ketamine in the way of ketamine cigarettes. By heating up ketamine cigarettes, it produces an odor that smells similar to burning plastic. There are no effective and convenient methods for immediately detecting and identifying the smell of ketamine. Therefore, this research proposed to develop an electronic nose system to identify the gas of ketamine.
In the thesis, standard operating procedures were established for collecting ketamine gas and ketamine cigarette gas at constant flow to quantitatively collect those gas samples least affected by environmental factors. After injecting the gas samples, the variation signal of the sensor array was noted. By feature extraction and dimensionality reduction algorithms, the complexity of data decreased. The classification results were also analyzed through linear regression and classification algorithms. The classification accuracy was up to 95.92% in identifying ketamine, ketamine cigarette, and cigarette; the classification accuracy was up to 96.83% in identifying all gas samples.
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