Classification of Teas Using Machine and Deep Learning Methods on a Custom e-nose Platform

This publication has 17 references indexed in Scilit:

• Relevance of Indian traditional tisanes in the management of type 2 diabetes mellitus: A review
  Saudi Pharmaceutical Journal, 2023
• A CNN-Based E-Nose Using Time Series Features for Food Freshness Classification
  IEEE Sensors Journal, 2023
• Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals
  Computers in Biology and Medicine, 2022
• Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
  Journal of Big Data, 2021
• Tea quality evaluation by applying E-nose combined with chemometrics methods
  Journal of Food Science and Technology, 2020
• Black tea classification employing feature fusion of E-Nose and E-Tongue responses
  Journal of Food Engineering, 2019
• Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis
  PLOS ONE, 2018
• Composition of volatile compounds and in vitro antimicrobial activity of nine Mentha spp.
  SpringerPlus, 2016
• Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach
  IEEE Sensors Journal, 2011
• LIBSVM
  ACM Transactions on Intelligent Systems and Technology, 2011