Moisture contents and product quality prediction of Pu‐erh tea in sun‐drying process with image information and environmental parameters

芳香 均方误差 环境科学 水分 风味 数学 气象学 化学 统计 食品科学 地理
作者
Cheng Chen,Wuyi Zhang,Zhiguo Shan,Chunhua Zhang,Tianwu Dong,Zhouqiang Feng,Chengkang Wang
出处
期刊:Food Science and Nutrition [Wiley]
卷期号:10 (4): 1021-1038 被引量:7
标识
DOI:10.1002/fsn3.2699
摘要

In this study, moisture contents and product quality of Pu-erh tea were predicted with deep learning-based methods. Images were captured continuously in the sun-drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep-learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R2 of .9997, .9882, .9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R2 of .9688, .9772, .9752, .9741, .8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun-drying system.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lyn发布了新的文献求助30
刚刚
充电宝应助科研通管家采纳,获得10
刚刚
Twikky完成签到,获得积分10
刚刚
柚子皮应助科研通管家采纳,获得10
刚刚
1秒前
1秒前
1秒前
852应助科研通管家采纳,获得10
1秒前
李健应助科研通管家采纳,获得10
1秒前
1秒前
搜集达人应助科研通管家采纳,获得10
1秒前
Akim应助夏末采纳,获得10
1秒前
CipherSage应助科研通管家采纳,获得10
1秒前
迟大猫应助想学习采纳,获得10
1秒前
隐形曼青应助科研通管家采纳,获得10
1秒前
香蕉觅云应助科研通管家采纳,获得10
1秒前
领导范儿应助科研通管家采纳,获得10
1秒前
2秒前
期刊应助科研通管家采纳,获得10
2秒前
思源应助科研通管家采纳,获得10
2秒前
香蕉觅云应助科研通管家采纳,获得10
2秒前
bkagyin应助科研通管家采纳,获得10
2秒前
最卷的卷心菜完成签到,获得积分10
2秒前
科研通AI5应助科研通管家采纳,获得50
2秒前
田様应助科研通管家采纳,获得100
2秒前
2秒前
共享精神应助科研通管家采纳,获得10
3秒前
yun尘世应助科研通管家采纳,获得10
3秒前
3秒前
JamesPei应助科研通管家采纳,获得10
3秒前
英姑应助科研通管家采纳,获得10
3秒前
知性的映之完成签到,获得积分10
3秒前
3秒前
小蘑菇应助圈圈采纳,获得10
3秒前
万能图书馆应助七块采纳,获得10
4秒前
yatou5651发布了新的文献求助10
4秒前
小二郎应助futing采纳,获得10
4秒前
天天快乐应助阿金采纳,获得10
4秒前
flyabc完成签到,获得积分10
5秒前
qp发布了新的文献求助10
5秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527469
求助须知:如何正确求助?哪些是违规求助? 3107497
关于积分的说明 9285892
捐赠科研通 2805298
什么是DOI,文献DOI怎么找? 1539865
邀请新用户注册赠送积分活动 716714
科研通“疑难数据库(出版商)”最低求助积分说明 709678