Main components determination and rapid geographical origins identification in Gentiana rigescens Franch. based on HPLC, 2DCOS images combined to ResNet

模式识别(心理学) 环境科学 数学 人工智能 计算机科学
作者
Chunlu Liu,Tao Shen,Furong Xu,Yuanzhong Wang
出处
期刊:Industrial Crops and Products [Elsevier]
卷期号:187: 115430-115430 被引量:14
标识
DOI:10.1016/j.indcrop.2022.115430
摘要

As an important resource in many prescriptions, the geographical origins of Gentiana rigescens Franch. influences its chemical characteristics, quality and price greatly. Hence, a simple and rapid method for the correct classification and identification of the geographical origins of G. rigescens is of significance. In this work, marker components of iridoids were measured by high performance liquid chromatography (HPLC) and were applied as a reference to characterize chemical profiles of samples from different geographical origins. The effects of climate factors on the content differences of G. rigescens were examined by correlation analysis. Afterward, a novel two-dimensional correlation spectroscopy (2DCOS) images acquired based on Fourier transform infrared (FT-IR) spectroscopy was proposed combined to deep learning to identify geographical origins of G. rigescens. Through analyzing the iridoid components of G. rigescens, which discovered that there were significant differences in its five marker components. In addition, the marker components of gentiopicroside based on Northwestern Yunnan (DXB) were higher, and the climate environment of low temperature, temperate, and high precipitation was more suitable for the cultivation and growth of G. rigescens. In the residual convolutional neural network (ResNet), the train set and test set accuracy of synchronous 2DCOS images for the feature bands (1800–400 cm-1) was 100%, and the external validation set of all samples was correctly identified. The results indicated the synchronous 2DCOS images of feature bands were suitable for the correct identification of the geographic origin of G. rigescens, and it reduced the amount of computation and time, and saved computing resources. This study provided a powerful and useful tool for the cultivation and geographical origins identification of G. rigescens.
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
刚刚
1秒前
不要再打游戏了完成签到,获得积分10
2秒前
3秒前
3秒前
1257应助傻瓜子采纳,获得10
3秒前
hehe完成签到,获得积分20
3秒前
上官若男应助Lit-Tse采纳,获得10
4秒前
5秒前
陈陈陈晨发布了新的文献求助10
5秒前
认真平蝶完成签到 ,获得积分10
6秒前
6秒前
阿黎发布了新的文献求助10
8秒前
hehe发布了新的文献求助10
9秒前
10秒前
10秒前
染东发布了新的文献求助10
11秒前
shimenwanzhao完成签到 ,获得积分0
11秒前
13秒前
13秒前
小罗黑的完成签到,获得积分10
13秒前
深情安青应助动听的雪碧采纳,获得10
13秒前
华仔应助只想梳油头采纳,获得10
13秒前
14秒前
奶油橙子完成签到,获得积分10
14秒前
阿黎完成签到,获得积分10
14秒前
麞欎完成签到,获得积分10
16秒前
Yummy完成签到 ,获得积分10
17秒前
123发布了新的文献求助10
18秒前
Lilybiu完成签到,获得积分10
18秒前
西风发布了新的文献求助10
19秒前
鲤鱼月饼完成签到 ,获得积分10
20秒前
20秒前
大咖完成签到 ,获得积分10
21秒前
二丫完成签到,获得积分10
23秒前
nanm完成签到,获得积分20
23秒前
哈哈哈哈哈完成签到,获得积分10
23秒前
LLL完成签到,获得积分10
24秒前
zzqx完成签到,获得积分20
24秒前
24秒前
高分求助中
Sustainability in Tides Chemistry 1500
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Threaded Harmony: A Sustainable Approach to Fashion 799
Livre et militantisme : La Cité éditeur 1958-1967 500
Retention of title in secured transactions law from a creditor's perspective: A comparative analysis of selected (non-)functional approaches 500
"Sixth plenary session of the Eighth Central Committee of the Communist Party of China" 400
Introduction to Modern Controls, with illustrations in MATLAB and Python 310
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3057090
求助须知:如何正确求助?哪些是违规求助? 2713644
关于积分的说明 7436720
捐赠科研通 2358721
什么是DOI,文献DOI怎么找? 1249510
科研通“疑难数据库(出版商)”最低求助积分说明 607166
版权声明 596314