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Li M, Xu J, Peng C, Wang Z. Deep learning-assisted flavonoid-based fluorescent sensor array for the nondestructive detection of meat freshness. Food Chem 2024; 447:138931. [PMID: 38484548 DOI: 10.1016/j.foodchem.2024.138931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 04/10/2024]
Abstract
Gas sensors containing indicators have been widely used in meat freshness testing. However, concerns about the toxicity of indicators have prevented their commercialization. Here, we prepared three fluorescent sensors by complexing each flavonoid (fisetin, puerarin, daidzein) with a flexible film, forming a fluorescent sensor array. The fluorescent sensor array was used as a freshness indication label for packaged meat. Then, the images of the indication labels on the packaged meat under different freshness levels were collected by smartphones. A deep convolutional neural network (DCNN) model was built using the collected indicator label images and freshness labels as the dataset. Finally, the model was used to detect the freshness of meat samples, and the overall accuracy of the prediction model was as high as 97.1%. Unlike the TVB-N measurement, this method provides a nondestructive, real-time measurement of meat freshness.
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Affiliation(s)
- Min Li
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China
| | - Jianguo Xu
- Key Laboratory of Molecular Recognition and Sensing, College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, PR China
| | - Chifang Peng
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China; School of Life Science and Health Engineering, Jiangnan University, Wuxi 214122, PR China; International Joint Laboratory On Food Safety, Jiangnan University, Wuxi 214122, PR China.
| | - Zhouping Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China; School of Life Science and Health Engineering, Jiangnan University, Wuxi 214122, PR China; International Joint Laboratory On Food Safety, Jiangnan University, Wuxi 214122, PR China
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Ni W, Yu Y, Gao X, Han Y, Zhang W, Zhang Z, Xiao W, Hu Q, Zhang Y, Huang H, Li F, Chen M, Han J. Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay. Anal Chem 2024; 96:301-308. [PMID: 38102984 DOI: 10.1021/acs.analchem.3c04107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.
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Affiliation(s)
- Weiwei Ni
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Yu
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Xu Gao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Han
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Wenhui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Zerui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Wenqi Xiao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Qin Hu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Yanliang Zhang
- Nanjing Research Center for Infectious Diseases of Integrated Traditional Chinese and Western Medicine, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing 211109, China
| | - Hui Huang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Fei Li
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Mingqi Chen
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Jinsong Han
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
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