Utilizing excitation-emission matrix fluorescence spectroscopy and convolutional neural networks for dark tea brand and aging period identification-Reference-Cited by-同舟云学术

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Utilizing excitation-emission matrix fluorescence spectroscopy and convolutional neural networks for dark tea brand and aging period identification

Published:2024-09 Issue: Volume:133 Page:106458
ISSN:0889-1575
Container-title:Journal of Food Composition and Analysis
language:en
Short-container-title:Journal of Food Composition and Analysis

Author:

Tan Xin,He Juan,Li Haipu,Jiang Jin,He Zihui,Guo Jinglin,Qiu Bo

Funder

Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Central South University

Publisher

Elsevier BV

Reference49 articles.

1. Multivariate comparison of classification performance measures;Ballabio;Chemom. Intell. Lab. Syst.,2018

2. Convolutional neural networks for estimating the ripening state of Fuji Apples using visible and near-infrared spectroscopy;Benmouna;Food Bioprocess Technol.,2022

3. Intelligent deep models based on scalograms of electrocardiogram signals for biometrics;Byeon;Sensors,2019

4. A comparative analysis for the volatile compounds of various Chinese dark teas using combinatory metabolomics and fungal solid-state fermentation;Cao;J. Food Drug Anal.,2018

5. Combining excitation-emission matrix fluorescence spectroscopy, parallel factor analysis, cyclodextrin-modified micellar electrokinetic chromatography and partial least squares class-modelling for green tea characterization;Casale;J. Pharm. Biomed. Anal.,2018

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