Variation law and prediction model to determine the moisture content in tea during hot air drying

Author:

Duan Dongyao1ORCID,Ma Fangyan1,Zhao Liqing1,Yin Yuanyuan1,Zheng Yinghui1,Xu Xin1,Sun Ying1,Xue Yiwei1

Affiliation:

1. College of Mechanical and Electrical Engineering Qingdao Agricultural University Qingdao China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering,Food Science

Reference31 articles.

1. Commonly used methods and comparative analysis of tea moisture content;Chen S.;China Tea Processing,2013

2. Pellet feed quality prediction model based on particle swarm parameter optimization and BP neural network;Chen X.;Transactions of the Chinese Society of Agricultural Engineering,2016

3. Quality evaluation for appearance of needle green tea based on machine vision and process parameters;Dong C.;Transactions of the Chinese Society of Agricultural Machinery,2017

4. Research on intelligent sorting system of fresh tea based on convolutional neural network;Gao Z.;Transactions of the Chinese Society of Agricultural Machinery,2017

5. Irrigation demand forecasting using artificial neuro‐genetic networks;González Perea R.;Water Resources Management,2015

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