Artificial Neural Network Analysis of Xinhui Pericarpium Citri Reticulatae Using Gas Chromatography - Mass Spectrometer - Automated Mass Spectral Deconvolution and Identification System

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