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研究生： 游雅棠
Yu, Ya-Tang
論文名稱： 解碼氣味: 基於IR頻譜的芳香分子分類
Decoding Smell: An IR Spectral Approach to Odorant Classification
指導教授： 林秀豪
Lin, Hsiu-Hau
口試委員: 黃文敏
Huang, Wen-Min
陳柏中
Chen, Po-Chung
學位類別： 碩士
Master
系所名稱： 理學院 - 物理學系
Department of Physics
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 37
中文關鍵詞： 芳香分子 、k-平均演算法 、紅外光譜
外文關鍵詞： odorant, k-means clustering, IR spectrum
相關次數： 點閱：29  下載：0 
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    • 基於聲子輔助穿隧(phonon-assisted tunneling)模型，氣味偵測與分子的低能
    量激發態相關。本研究基於芳香分子的紅外光譜(IR spectrum)對其做編碼。
    由Gaussian計算軟體計算分子的紅外光譜，並考慮體溫造成之熱效應，建構了
    芳香分子的頻譜碼。將芳香分子頻譜碼做k-平均演分群法(k-means clustering)，
    我們成功將一組芳香分子資料集以氣味分類。這個分類結果輔助了芳香分子低
    能量激發態可以編碼氣味的觀點。

    The phonon-assisted tunneling (PAT) model suggests that odor detection is
    related to the low-energy excitations of odorants. This research codes odorants
    based on their IR spectra of odorants, which represent these low-energy excitations.
    Starting with the computation of IR spectra using Gaussian software and
    considering thermal effects at body temperature, we constructed the spectral codes
    of odorants. Using k-means clustering to the spectral codes, we successfully classified
    a set of odorants based on their odors. The results support the view that
    the low-energy excitations of odorants encodes the information of odors.

    Abstract (Chinese) Abstract Acknowledgements (Chinese) Contents 1 Introduction------------------------------------------------1 2 An Overview of Smell Process 2.1 The Smell Process Inside Our Nose-------------------------3 2.2 The PAT Model---------------------------------------------5 3 How We Code Odorants 3.1 What Ways Were Tried to Code Odorants?--------------------9 3.2 Code Odorants as Spectral Codes---------------------------11 4 Techniques for Quantum Chemistry 4.1 Simulate the IR Spectra of Odorants with Gaussian---------13 4.2 The Spectral Coding with Temperature Consideration--------15 5 Odorant Clsutering 5.1 Design of Dataset-----------------------------------------17 5.2 K-Means Clustering----------------------------------------20 5.2.1 The Main Body of K-Means Clustering.....................20 5.2.2 The K-Means++ Improvement...............................21 5.2.3 Index for Evaluating Clustering Quality.................21 5.3 Result of Clustering--------------------------------------23 6 Outlook-----------------------------------------------------25 A Analysis to Other Datasets----------------------------------26 A.1 Results of the K-Means Clustering-------------------------28 A.2 Divisive Hierarchical K-Means Clustering------------------33 A.3 Cluster by the Divisive Hierarchical K-Means Clustering---33 Bibliography--------------------------------------------------35

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