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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：27  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

    1. J. C. Brookes, F. Hartoutsiou, A. P. Horsfield, and A. M. Stoneham. Could humans recognize odor by phonon assisted tunneling? Phys. Rev. Lett., 98:038101, Jan 2007. doi: 10.1103/PhysRevLett.98.038101. URL https://link.aps.org/doi/10.1103/PhysRevLett.98.038101.
    2. A. David and S. Vassilvitskii. k-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, page 1027–1035, 2007.
    3. M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. V. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. J. Bearpark, J. J. Heyd, E. N. Brothers, K. N. Kudin, V. N. Staroverov, T. A. Keith, R. Kobayashi,
    J. Normand, K. Raghavachari, A. P. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, and D. J. Fox. Gaussian˜16 Revision C.01, 2016. Gaussian Inc. Wallingford CT.
    4. S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, and E. E. Bolton. PubChem 2023 update. Nucleic Acids Research, 51(D1):D1373–D1380, 10 2022. ISSN 0305-1048. doi: 10.1093/nar/gkac956. URL https://doi.org/10.1093/nar/gkac956.
    5. S. LAMROUS and M. TAILEB. Divisive hierarchical k-means. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA’06), pages 18–18, 2006. doi: 10.1109/CIMCA.2006.89.
    6. J. MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297, 1967.
    7. S. L. Pashkovski, G. Iurilli, D. Brann, D. Chicharro, K. Drummey, K. M. Franks, S. Panzeri, and S. R. Datta. Structure and flexibility in cortical representations of odour space. Nature, 583:253–258, 2020.
    8. P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65, 1987. ISSN 0377-0427. doi: https://doi.org/10.1016/0377-0427(87) 90125-7. URL https://www.sciencedirect.com/science/article/pii/0377042787901257.
    9. The Good Scents Company. The good scents company information system. http://www.thegoodscentscompany.com/, 1980-2021.
    10. L. Turin. A spectroscopic mechanism for primary olfactory reception. Chem. Senses., 21(6):773–91, 1996.

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