Machine learning assisted ratiometric fluorescence sensor array integrated multi-emission signal single sensing element for recognition of diverse tea based on boronic acid functional bimetallic lanthanide metal-organic frameworks

双金属片 硼酸 荧光 镧系元素 信号(编程语言) 材料科学 纳米技术 传感器阵列 人工智能 计算机科学 化学 硼酸 共价键 人为噪声 荧光寿命成像显微镜 分子识别 紧身衣
作者
Kaiwen Jiao,Yingzhe Zhao,Yiran Dong,Lirong Han,Yali Chen,X.G. Qiao,Mingyuan Yin
出处
期刊:Journal of future foods [Elsevier BV]
标识
DOI:10.1016/j.jfutfo.2025.12.012
摘要

• A LMOFs ratiometric fluorescence sensor array based on multi-emission signal single sensing element composites was prepared. • A highly sensitive LMOFs ratiometric fluorescence sensor array for TPs was designed and developed. • The machine learning techniques were combined with the LMOFs ratiometric fluorescence sensor array for the recognition of diverse tea products. The quality control and authentication of tea is important in the food safety. The developed fluorescence sensor array that enables to simultaneously determinate and discriminate diverse tea is still a nontrivial task. Herein, we proposed a ratiometric fluorescence sensor array based on a multi-emission signal single sensing element of bimetallic lanthanide metal-organic frameworks composites (LMOFs), wherein LMOFs were constructed by the coordination polymerization of lanthanide metals (europium and terbium ions) and the functional ligands (3,5-dicarboxybenzeneboronic acid) through one-pot method. The resulting LMOFs (excitation/emission with 260 nm/430 nm, 497 nm, 552 nm, 597 nm, and 622 nm) exhibited obvious differential fluorescence response change against tea polyphenols (TPs) due to that the antenna effect interfered by the borate ester covalent structure formed between the phenolic hydroxyl group of TPs with the boric acid of LMOFs. The generated ratiometric logical operation in the LMOFs fluorescence sensor array could reflect the “fluorescence fingerprints” of TPs and tea products, which were combined with machine learning techniques (linear discriminant analysis, hierarchical cluster analysis, and artificial neural networks) to realize the excellent recognition for five TPs (even at 1.0 µM) and the effective identification of 15 teas and 7 tea beverages as well as 100 % accuracy in blind samples test. Such LMOFs ratiometric fluorescence sensor array might provide a simple and efficient detection method for the authentication of tea products.
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
丁帅完成签到,获得积分10
刚刚
huanhuanhuan发布了新的文献求助10
刚刚
huanhuanhuan发布了新的文献求助10
刚刚
huanhuanhuan发布了新的文献求助10
刚刚
1秒前
2秒前
啊啊缘发布了新的文献求助10
3秒前
3秒前
4秒前
吴家豪完成签到,获得积分10
4秒前
中岛悠斗完成签到,获得积分10
5秒前
ylq完成签到,获得积分10
6秒前
深情安青应助Nature已接受采纳,获得20
6秒前
lxl完成签到,获得积分10
6秒前
6秒前
tamo发布了新的文献求助10
6秒前
8秒前
初景应助周雪采纳,获得20
8秒前
翟延恶发布了新的文献求助10
8秒前
Owen应助等待书桃采纳,获得10
10秒前
林志坚完成签到 ,获得积分10
10秒前
ztt完成签到,获得积分10
10秒前
molihuakai应助懵懂的柚子采纳,获得10
11秒前
JamesPei应助清逸飞扬采纳,获得30
11秒前
12秒前
13秒前
15秒前
乐乐应助科研通管家采纳,获得10
15秒前
15秒前
琉璃苣应助科研通管家采纳,获得10
15秒前
李爱国应助科研通管家采纳,获得30
16秒前
天天快乐应助科研通管家采纳,获得30
16秒前
慕青应助科研通管家采纳,获得10
16秒前
阳阳完成签到,获得积分10
16秒前
16秒前
慕青应助科研通管家采纳,获得10
16秒前
大知闲闲应助科研通管家采纳,获得10
16秒前
17秒前
ztt发布了新的文献求助10
17秒前
发一篇sci发布了新的文献求助10
18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
Römisch-Germanische Forschungen 1000
APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior 1000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7610296
求助须知:如何正确求助?哪些是违规求助? 9186099
关于积分的说明 19678680
捐赠科研通 7184053
什么是DOI,文献DOI怎么找? 3270360
关于科研通互助平台的介绍 2434021
邀请新用户注册赠送积分活动 2265050