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Lin Y, Ma J, Sun DW, Cheng JH, Zhou C. Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment. Food Chem 2024; 448:139078. [PMID: 38527403 DOI: 10.1016/j.foodchem.2024.139078] [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: 01/12/2024] [Revised: 03/03/2024] [Accepted: 03/18/2024] [Indexed: 03/27/2024]
Abstract
A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Chenyue Zhou
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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2
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Xu X, Wang X, Ding Y, Zhou X, Ding Y. Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring. Int J Biol Macromol 2024; 265:131011. [PMID: 38518947 DOI: 10.1016/j.ijbiomac.2024.131011] [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: 12/25/2023] [Revised: 03/03/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.
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Affiliation(s)
- Xia Xu
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China.
| | - Xinyu Wang
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Yicheng Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Xuxia Zhou
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China
| | - Yuting Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China
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Zhao J, Ni Y, Tan L, Zhang W, Zhou H, Xu B. Recent advances in meat freshness "magnifier": fluorescence sensing. Crit Rev Food Sci Nutr 2023:1-17. [PMID: 37555377 DOI: 10.1080/10408398.2023.2241553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
To address the serious waste of meat resources and food safety problems caused by the decrease in meat freshness due to the action of microorganisms and enzymes, a low-cost, time-saving and high-efficiency freshness monitoring method is urgently needed. Fluorescence sensing could act as a "magnifier" for meat freshness monitoring due to its ability to sense characteristic signal produced by meat spoilage. Here, the magnification mechanism of meat freshness via sensing the water activity, adenosine triphosphate, hydrogen ion, total volatile basic nitrogen, hydrogen sulfide, bioamines was comprehensively analyzed. The existing "magnifier" forms including paper chips, films, labels, arrays, probes, and hydrogels as well as the application in livestock, poultry and aquatic meat freshness monitoring were reviewed. Future research directions involving innovation of principles, visualization and quantification capabilities for various meats freshness were provided. By critically evaluating the potential and limitations, efficient and reliable meat freshness monitoring strategies wish to be developed for the post-epidemic era.
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Affiliation(s)
- Jinsong Zhao
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Yongsheng Ni
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Lijun Tan
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Wendi Zhang
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Hui Zhou
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Baocai Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
- Engineering Research Center of Bio-Process of Ministry of Education, School of Food & Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
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Li J, Li J, Tang Y, Liu Z, Zhang Z, Wu H, Shen B, Su M, Liu M, Li F. Touchable Gustation via a Hoffmeister Gel Iontronic Sensor. ACS NANO 2023; 17:5129-5139. [PMID: 36876910 DOI: 10.1021/acsnano.3c00516] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A particular sense, touchable gustation, was achieved. We proposed a chemical-mechanical interface strategy with an iontronic sensor device. A conductive hydrogel, amino trimethylene phosphonic acid (ATMP) assisted poly(vinyl alcohol) (PVA), was employed as the dielectric layer of the gel iontronic sensor. The Hofmeister effect of the ATMP-PVA hydrogel was well investigated to establish the quantitative description of the gel elasticity modulus to chemical cosolvents. The mechanical properties of hydrogels can be transduced extensively and reversibly by regulating the aggregation state of polymer chains with hydrated ions or cosolvents. Scanning electron microscopy (SEM) images of ATMP-PVA hydrogel microstructures stained with different soaked cosolvents present different networks. The information on different chemical components will be stored in the ATMP-PVA gels. The flexible gel iontronic sensor with a hierarchical pyramid structure performed high linear sensitivity of 3224.2 kPa-1 and wide pressure response in the range of 0-100 kPa. The finite element analysis proved the pressure distribution at the gel interface of the gel iontronic sensor and the capacitation-stress response relation. Various cations, anions, amino acids, and saccharides can be discriminated, classified, and quantified with the gel iontronic sensor. The Hofmeister effect regulated chemical-mechanical interface performs the response and conversion of biological/chemical signals into electrical output in real time. The particular function to tactile with gustation percept will contribute promising applications in the human-machine interaction, humanoid robot, clinic treatment, or athletic training optimization.
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Affiliation(s)
- Jiang Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- School of Chemistry, Beihang University, Beijing 100191, China
| | - Jianliang Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- School of Chemistry, Beihang University, Beijing 100191, China
| | - Yongtao Tang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China
| | - Zhihao Liu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Zilu Zhang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China
| | - Hao Wu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Bin Shen
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China
| | - Meng Su
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
| | - Mingjie Liu
- School of Chemistry, Beihang University, Beijing 100191, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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Tan X, Tang Y, Yang T, Dai G, Ye C, Meng J, Li F. Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification. Anal Chem 2023; 95:3309-3316. [PMID: 36716054 DOI: 10.1021/acs.analchem.2c04346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for β-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six β-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.
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Affiliation(s)
- Xiaoqing Tan
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yongtao Tang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Tingting Yang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Guoliang Dai
- Research Center for Green Printing Nanophotonic Materials, Jiangsu Key Laboratory for Environmental Functional Materials, School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Changqing Ye
- Research Center for Green Printing Nanophotonic Materials, Jiangsu Key Laboratory for Environmental Functional Materials, School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Jianxin Meng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China.,College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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6
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Machine learning-assisted optical nano-sensor arrays in microorganism analysis. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Liu Z, Li J, Li J, Yang T, Zhang Z, Wu H, Xu H, Meng J, Li F. Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip. Anal Chem 2022; 94:15864-15872. [DOI: 10.1021/acs.analchem.2c03927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Zhihao Liu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
| | - Jiang Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
| | - Jianliang Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
| | - Tingting Yang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Zilu Zhang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
| | - Hao Wu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
| | - Huihua Xu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Jianxin Meng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100045, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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