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

Authors

  • Daiyu Jiang College of Automation, Chongqing University of Posts and Telecommunications, China
  • Gang Hu Computer Information Systems Department, State University of New York at Buffalo State, USA
  • Guanqiu Qi Computer Information Systems Department, State University of New York at Buffalo State, USA https://orcid.org/0000-0001-9562-3865
  • Neal Mazur Computer Information Systems Department, State University of New York at Buffalo State, USA

DOI:

https://doi.org/10.37965/jait.2020.0037

Keywords:

nicotine, tobacco leaves, near-infrared spectroscopy, fully convolutional network, cloud computing

Abstract

As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and non-destructive 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 NIRspectroscopy data in a cloud environment. This 1D-FCN model uses one-dimension 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 spectra data in the tobacco industry.

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Published

2021-01-05

How to Cite

Jiang, D., Hu, G., Qi, G., & Neal Mazur. (2021). A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud. Journal of Artificial Intelligence and Technology, 1(1), 74–82. https://doi.org/10.37965/jait.2020.0037

Issue

Section

Research Article