A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

Abstract

As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and nondestructive quantitative analysis of nicotine is an important task in the tobacco industry. Near-infrared (NIR) spectroscopy as an effective chemical composition analysis technique has been widely used. In this paper, we propose a one-dimensional fully convolutional network (1D-FCN) model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud environment. This 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy data. It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss. Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing. Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches. This research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.