On-Line Monitoring of the Tobacco Leaf Composition during Flue-Curing by Near-Infrared Spectroscopy and Deep Transfer Learning. (2nd September 2022)

• Record Type: Journal Article
• Authors: Wei, Kesu; Bin, Jun; Wang, Feng; Kang, Chao
• Is Part Of: Analytical letters. Volume 55:Number 13(2022)
• Page Start: 2089
• Page End: 2107
• Publication Date: 2022-09-02
• Subjects: Convolutional neural networks -- deep transfer learning -- near-infrared spectroscopy -- on-line monitoring -- tobacco leaf flue-curing; Chemistry, Analytic -- Periodicals; Chemistry, Analytic -- Abstracts; 543
• DOI: 10.1080/00032719.2022.2046021
• Languages: English
• ISSNs: 0003-2719

Abstract

Abstract: The yield, quality, and output value of tobacco leaves are strongly affected by the flue-curing conditions. To effectively control the flue-curing and guarantee quality, it is necessary to quantify the chemical composition of tobacco leaves and provide timely feedback during this process. Therefore, the practicability of on-line monitoring of moisture, starch, protein, and soluble sugars for tobacco leaves by near-infrared (NIR) spectroscopy and deep transfer learning was explored. The results showed that the use of an NIR spectrometer equipped with a fiber-optic probe with a deep learning algorithm accurately predicted the content of these components during the curing process. The convolutional neural networks model showed greater potential for on-line monitoring than partial least squares and support vector machines. Furthermore, a network-based deep transfer learning strategy was crafted to include seasonal and temperature variability to accurately predict samples from a new harvest season in a curing barn. The overall studies indicated the efficacy of NIR diffuse reflectance spectroscopy as a rapid and nondestructive method for on-line and simultaneous determination of moisture, starch, protein, and soluble sugars in the flue-curing process to assist in making decisions.