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

• 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.

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Publication Details

Journal
Journal of Future Foods
Published
2026-01-01
DOI
https://doi.org/10.1016/j.jfutfo.2025.12.012
Primary Topic
Carbon and Quantum Dots Applications
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article
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article

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

Yingzhe Zhao, Lirong Han, Mingyuan Yin, X.G. Qiao et al.
Journal of Future Foods
Carbon and Quantum Dots Applications
article

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

Yingzhe Zhao, Lirong Han, Mingyuan Yin, X.G. Qiao, Yiran Dong, Yali Chen, Kaiwen Jiao
article en

Abstract

• 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.

Journal of Future Foods
North China University of Science and Technology (CN), Hebei University (CN)
Reduced inequalities
Openalex Percentile: Top 96%
Carbon and Quantum Dots Applications
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