高光谱成像
弗洛斯
生物系统
人工智能
过程(计算)
材料科学
模式识别(心理学)
工艺工程
计算机科学
化学
工程类
生物化学
生物
操作系统
芦丁
抗氧化剂
作者
Pengdi Cui,Yang Yu,Jing Zhao,Peiqi Miao,Qilong Xue,Changqing Liu,Zheng Li
出处
期刊:Measurement
[Elsevier]
日期:2023-08-01
卷期号:218: 113246-113246
被引量:1
标识
DOI:10.1016/j.measurement.2023.113246
摘要
The process of drying solute-containing droplets can lead to dynamic redistribution of solutes. Tracking morphological changes and obtaining drying kinetics will help optimize the spray drying process, but there are few techniques available for measuring the spatio-temporal concentration of solutes in drying droplets. In this study, hyperspectral imaging was used as a non-invasive method to simultaneously obtain information on the morphology and moisture content changes of droplets, and was applied to investigate the drying process of Lonicerae Japonicae Flos extract. The Faster R-CNN algorithm was employed to locate the target droplet and identify its size through the series of droplet images recorded by a hyperspectrometer. Droplet moisture content prediction model was established using PLS and ANN algorithms, with the ANN showing better prediction accuracy. The hyperspectral imaging combined with artificial intelligence algorithms as a promising method can be used for investigating the drying kinetics of solutions with different drying methods.
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