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Tropical Journal of Pharmaceutical Research
Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, Nigeria
ISSN: 1596-5996
EISSN: 1596-9827
Vol. 14, No. 11, 2015, pp. 2071-2075
Bioline Code: pr15271
Full paper language: English
Document type: Research Article
Document available free of charge

Tropical Journal of Pharmaceutical Research, Vol. 14, No. 11, 2015, pp. 2071-2075

 en Artificial Neural Network Analysis of Xinhui Pericarpium Citri Reticulatae Using Gas Chromatography - Mass Spectrometer - Automated Mass Spectral Deconvolution and Identification System
Ou, Xiaoqun; Li, Hao; Yang, Xiumei; Tan, Maolan; Ao, Hui & Wang, Jin

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-MSAMDIS) 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 System; Peels

 
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