Mass spectral search method using the Neural Network approach

Chong Sze Tong; K. C. Cheng

Mass spectral search method using the Neural Network approach

Chong Sze Tong^*
, K. C. Cheng

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to conference › Conference paper › peer-review

Abstract

This paper investigates the use of Neural Networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for mining and testing. A total of 355 spectra were used for training the neural networks, and a further set of 163 spectra was used for evaluation. All the neural networks considered performed better than the conventional benchmark, with recognition rates above 97.5%.

Original language	English
Pages	3962-3967
Number of pages	6
Publication status	Published - 1999
Event	1999 International Joint Conference on Neural Networks, IJCNN 1999 - Washington, DC, USA Duration: 10 Jul 1999 → 16 Jul 1999

Conference

Conference	1999 International Joint Conference on Neural Networks, IJCNN 1999
City	Washington, DC, USA
Period	10/07/99 → 16/07/99

Cite this

@conference{591a42236cc44ec98ebd2cd5782635fb,

title = "Mass spectral search method using the Neural Network approach",

abstract = "This paper investigates the use of Neural Networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for mining and testing. A total of 355 spectra were used for training the neural networks, and a further set of 163 spectra was used for evaluation. All the neural networks considered performed better than the conventional benchmark, with recognition rates above 97.5\%.",

author = "Tong, \{Chong Sze\} and Cheng, \{K. C.\}",

note = "Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.; 1999 International Joint Conference on Neural Networks, IJCNN 1999 ; Conference date: 10-07-1999 Through 16-07-1999",

year = "1999",

language = "English",

pages = "3962--3967",

}

TY - CONF

T1 - Mass spectral search method using the Neural Network approach

AU - Tong, Chong Sze

AU - Cheng, K. C.

PY - 1999

Y1 - 1999

N2 - This paper investigates the use of Neural Networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for mining and testing. A total of 355 spectra were used for training the neural networks, and a further set of 163 spectra was used for evaluation. All the neural networks considered performed better than the conventional benchmark, with recognition rates above 97.5%.

AB - This paper investigates the use of Neural Networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for mining and testing. A total of 355 spectra were used for training the neural networks, and a further set of 163 spectra was used for evaluation. All the neural networks considered performed better than the conventional benchmark, with recognition rates above 97.5%.

UR - https://www.scopus.com/pages/publications/0033324639

M3 - Conference paper

AN - SCOPUS:0033324639

SP - 3962

EP - 3967

T2 - 1999 International Joint Conference on Neural Networks, IJCNN 1999

Y2 - 10 July 1999 through 16 July 1999

ER -

Mass spectral search method using the Neural Network approach

Abstract

Conference

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