已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning

计算机科学 鉴定(生物学) 中医药 人工智能 传统医学 质量(理念) 构造(python库) 组分(热力学) 理论(学习稳定性) 机器学习 医学 替代医学 植物 物理 生物 哲学 认识论 病理 程序设计语言 热力学
作者
Xiaotong Xin,Xuecong Tian,Cheng Chen,Chen Chen,Keao Li,Xuan Ma,Lu Zhao,Xiaoyi Lv
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:315: 124251-124251 被引量:6
标识
DOI:10.1016/j.saa.2024.124251
摘要

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.
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
Hello应助zhl采纳,获得10
2秒前
可乐龙猫完成签到,获得积分10
3秒前
2231131发布了新的文献求助10
3秒前
4秒前
乐乐应助尊敬爆米花采纳,获得10
7秒前
Serene完成签到 ,获得积分10
8秒前
10秒前
完美世界应助牛市棋手采纳,获得10
10秒前
聪明大神发布了新的文献求助10
10秒前
所所应助勿忸采纳,获得10
10秒前
Melody发布了新的文献求助10
13秒前
快乐的窝瓜完成签到 ,获得积分10
13秒前
13秒前
14秒前
15秒前
科研通AI6.1应助ccc采纳,获得10
15秒前
凝云完成签到 ,获得积分10
15秒前
16秒前
16秒前
16秒前
17秒前
flxz5286发布了新的文献求助10
18秒前
normankasimodo完成签到 ,获得积分10
18秒前
General发布了新的文献求助30
18秒前
19秒前
zhl发布了新的文献求助10
20秒前
21秒前
牛市棋手发布了新的文献求助10
21秒前
Tulip发布了新的文献求助10
21秒前
dan发布了新的文献求助10
22秒前
wwwww完成签到 ,获得积分10
23秒前
yudada完成签到 ,获得积分10
24秒前
25秒前
palegg发布了新的文献求助10
25秒前
甄晓溪完成签到,获得积分10
27秒前
大模型应助茹茹采纳,获得10
28秒前
斯文败类应助cds采纳,获得10
29秒前
chang发布了新的文献求助10
30秒前
xiaolizi发布了新的文献求助10
31秒前
32秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Psychopathic Traits and Quality of Prison Life 1000
Development Across Adulthood 1000
Chemistry and Physics of Carbon Volume 18 800
The formation of Australian attitudes towards China, 1918-1941 660
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6450759
求助须知:如何正确求助?哪些是违规求助? 8262873
关于积分的说明 17604647
捐赠科研通 5515299
什么是DOI,文献DOI怎么找? 2903417
邀请新用户注册赠送积分活动 1880438
关于科研通互助平台的介绍 1722363