Tea quality estimation based on multi-source information from leaf and soil using machine learning algorithm-Reference-Cited by-同舟云学术

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Tea quality estimation based on multi-source information from leaf and soil using machine learning algorithm

Published:2023-12 Issue: Volume:20 Page:100975
ISSN:2590-1575
Container-title:Food Chemistry: X
language:en
Short-container-title:Food Chemistry: X

Author:

Yang Bin,Jiang Jie,Zhang Huan,Han Zhaolan,Lei Xiaogang,Chen Xuejin,Xiao Yao,Njeri Ndombi Salome,Zhu Xujun,Fang Wanping

Publisher

Elsevier BV

Subject

Food Science,Analytical Chemistry

Reference41 articles.

1. Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels;Bian;ISPRS Journal of Photogrammetry and Remote Sensing,2013

2. Monitoring of essential and heavy metals in green tea from different geographical origins;Brzezicha-Cirocka;Environmental monitoring and assessment,2016

3. Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration;Chen;Applied Optics,2015

4. Mellow and Thick Taste of Pu−Erh Ripe Tea Based on Chemical Properties by Sensory−Directed Flavor Analysis;Deng;Foods,2022

5. Machine learning and remote sensing techniques applied to estimate soil indicators – Review;Diaz-Gonzalez;Ecological Indicators,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ongoing Multivariate Chemometric Approaches in Bioactive Compounds and Functional Properties of Foods—A Systematic Review;Processes;2024-03-14

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