Tropical Journal of Pharmaceutical Research

Original Research Article | OPEN ACCESS

Artificial neural network analysis of Xinhui pericarpium Citri Reticulatae using gas chromatography - mass spectrometer - automated mass spectral deconvolution and identification system

Xiaoqun Ou¹, Hao Li², Xiumei Yang¹, Maolan Tan¹, Hui Ao¹, Jin Wang¹

¹College of Pharmacy, Chengdu University of Traditional Chinese Medicine; ²College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.

For correspondence:- Jin Wang Email: wangjin0816@126.com Tel:+ 8613880900787

Received: 6 May 2015 Accepted: 5 October 2015 Published: 29 November 2015

Citation: Ou X, Li H, Yang X, Tan M, Ao H, Wang J. Artificial neural network analysis of Xinhui pericarpium Citri Reticulatae using gas chromatography - mass spectrometer - automated mass spectral deconvolution and identification system. Trop J Pharm Res 2015; 14(11):2071-2075 doi: 10.4314/tjpr.v14i10.17

© 2015 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..

Abstract

Purpose: To develop an effective analytical method to distinguish old peels of Xinhui Pericarpium citri reticulatae (XPCR) stored for > 3 years from new peels stored for < 3 years.

Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer feedforward neural network (MLFN), were used to analyze the Gas Chromatography - Mass Spectrometer - Automated Mass Spectral Deconvolution and Identification System (GC-MS-AMDIS) data of the essential oils of the XPCR. The Root Mean Square (RMS) errors of each ANN model was obtained through judging the characteristic of old peels and new peels.

Results: The Root Mean Square (RMS) error of GRNN was 0.22, less than the error MLFN at different levels, indicating that GRNN model is more reliable and accurate for judging the characteristics of old peels and new ones.

Conclusion: The general regression neural network model is established to reliably distinguish between old peels and new peels.

Keywords: Artificial neural networks, Xinhui, Pericarpium, Citri reticulatae, Gas Chromatography, Automated Mass Spectral Deconvolution and Identification Syste