Feature Recognition of Tobacco by Independent Component Analysis - Back Propagation Neural Network

As the most important base for studying the quality stability of tobacco products, the characteristics of flue-cured tobacco are of great significance for both cigarette enterprises and producing areas. In this study, the characteristics of tobacco were qualitatively recognized based on the directly acquired gas chromatography-mass spectrometry (GC-MS) accumulative data via the pattern recognition, in an effort to rapidly and conveniently identify the grades information of tobacco. Specifically, an independent component analysis -back propagation neural network (ICA-BPNN) method was proposed to process the GC-MS ion accumulation. First, independent components were extracted after cumulating all the spectrum peaks of the acquired mass data. Next, a BPNN recognition model was established for the obtained independent components and then used to qualitatively discriminate four tobacco grades from Yunnan Province in China. Finally, a comparison was made between ICA-BPNN and principal component analysis (PCA)-BPNN models in the qualitative effect. Given nodes of 40, the ICA-BPNN model achieved the best accuracy, with the accuracy of calibration and prediction set of 72.86% and 80.82%, respectively. Results revealed that the proposed pattern recognition method, where mass data of tobacco were directly accumulated as overall information, is of certain potentials in the fast discrimination of agricultural product quality.