Rapid authentication of <i>Chaenomeles</i> species by visual volatile components fingerprints based on headspace gas chromatography‐ion mobility spectrometry combined with chemometric analysis

化学 线性判别分析 化学计量学 色谱法 主成分分析 人工智能 计算机科学
作者
Shanming Tian,Huanying Guo,Minmin Zhang,Huijiao Yan,Xiao Wang,Hengqiang Zhao
出处
期刊:Phytochemical Analysis [Wiley]
标识
DOI:10.1002/pca.3170
摘要

Chaenomeles, including Chaenomeles speciosa (ZP), Chaenomeles sinensis (GP), Chaenomeles tibetica (XZ), and Chaenomeles japonica (RB), has been widely used as food in China for thousands of years. However, only ZP, was recorded to be the authentic medicinal Chaenomeles. Therefore, the rapid and accurate method for the authenticity identification of Chaenomeles species is urgently needed.To develop a method for rapid differentiation of Chaenomeles species.The visual volatile components fingerprints based on headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometric analysis, including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA), were utilised for the authentication of Chaenomeles species.The visual volatile components fingerprints by the GC-IMS intuitively showed the distribution features of the volatile components for different Chaenomeles samples. The LDA and PLS-DA models successfully discriminated Chaenomeles species with original discrimination accuracy of 100%. Fifteen volatile compounds (VOCs) (peaks 9, 12, 13, 19, 23, 24, 35, 48, 57, 65, 67, 76, 79, 80, 83) were selected as the potential species-specific markers of Chaenomeles via variable importance of projection (VIP > 1.2) and one-way analysis of variance (P < 0.05).This study showed that the visual volatile components fingerprints by HS-GC-IMS combined with chemometric analysis is a meaningful method in the Chaenomeles species authentication.

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
lzd发布了新的文献求助10
1秒前
负责的鸵鸟完成签到,获得积分10
1秒前
1秒前
1秒前
高大的大米完成签到,获得积分10
2秒前
3秒前
3秒前
晓澈发布了新的文献求助10
3秒前
WngZho发布了新的文献求助10
3秒前
乐乐应助spridrop采纳,获得10
3秒前
陈洋发布了新的文献求助10
3秒前
Rico发布了新的文献求助10
4秒前
4秒前
zky完成签到,获得积分20
4秒前
蓦然发布了新的文献求助10
4秒前
西坡万岁发布了新的文献求助10
4秒前
4秒前
lzd发布了新的文献求助10
4秒前
5秒前
valimar完成签到,获得积分10
5秒前
KK发布了新的文献求助10
5秒前
深情安青应助123456lyf采纳,获得10
5秒前
5秒前
专注乐荷发布了新的文献求助30
5秒前
大模型应助阿南采纳,获得30
6秒前
6秒前
鲤鱼晓瑶应助VV采纳,获得10
6秒前
CipherSage应助nabe采纳,获得10
6秒前
737发布了新的文献求助10
7秒前
7秒前
zcx发布了新的文献求助10
7秒前
Luka发布了新的文献求助10
7秒前
7秒前
bc老师完成签到,获得积分10
7秒前
7秒前
LF完成签到,获得积分20
8秒前
直率的一笑应助www采纳,获得10
8秒前
鸟兽兽应助www采纳,获得10
8秒前
lzd发布了新的文献求助10
8秒前
8秒前
高分求助中
The Wiley Blackwell Companion to Diachronic and Historical Linguistics 3000
Standards for Molecular Testing for Red Cell, Platelet, and Neutrophil Antigens, 7th edition 1000
HANDBOOK OF CHEMISTRY AND PHYSICS 106th edition 1000
ASPEN Adult Nutrition Support Core Curriculum, Fourth Edition 1000
Signals, Systems, and Signal Processing 610
脑电大模型与情感脑机接口研究--郑伟龙 500
GMP in Practice: Regulatory Expectations for the Pharmaceutical Industry 500
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 6295724
求助须知:如何正确求助?哪些是违规求助? 8113316
关于积分的说明 16980974
捐赠科研通 5357999
什么是DOI,文献DOI怎么找? 2846655
邀请新用户注册赠送积分活动 1823851
关于科研通互助平台的介绍 1678994