Study on Near Infrared Spectroscopy Model of Tobacco Leaves Based on Regression CNN
ZONG Qianqian
DING Xiangqian
HAN Feng
GONG Huili
ZHANG Lei
Abstract：In order to extract the deep critical features of near-infrared spectroscopy to a greater extent,a novel prediction method,namely,regression-based convolutional neural network(CNNR),which removes the pooling layer and substitute the re?gression layer for the linear softmax classification layer at the top of the general CNN's structure,has been proposed to develop the quantitative model for the tobacco constituents. In order to verify the effectiveness of the CNNR algorithm,the models for total sug?ar,total nicotine and chlorine in tobacco are built for which correlation coefficient R are 0.9318,0.941,0.933,respectively and RMSECV of cross validation were 0.7052,0.0710,0.0971,respectively. The results indicate that the extracted features have a strong ability to interpret the spectral data and have better prediction performance and comprehensive expression ability of tobacco chemical components.
Keywords：tobacco chemical componentsconvolutional neural networknear infrared spectrumquantitative modeltopol?ogy structure
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 275-280 )