Deep learning-assisted smartphone-based portable and visual ratiometric fluorescence device integrated intelligent gel label for agro-food freshness detection

Lü, Zhiwei; Li, Mengjiao; Chen, Maoting; Wang, Qirui; Wu, Chun; Sun, Mengmeng; Su, Gehong; Wang, Xianxiang; Wang, Yanying; Zhou, Xinguang; Ye, Jianshan; Liu, Tao; Rao, Hanbing

doi:10.1016/j.foodchem.2023.135640

Cited by 35 publications

(9 citation statements)

References 32 publications

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“…Lu et al [190] proposed a machine learning-assisted multivariate detection smartphoneintegrated optical platform to detect the signals generated via green fluorescent carbon dots and a blue-emitting Fe/Zn metal organic framework in the presence of biogenic amines (BAs), including spermine (SP) and histamine (HIS). The detection and classification of the images obtained was performed using a YOLO V3 architecture and image processing techniques.…”

Section: Luminescent Sensorsmentioning

confidence: 99%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

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Section: Luminescent Sensorsmentioning

confidence: 99%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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“…Lu et al, prepared CDs@Fe/Zr-MOF based on a smartphone-assisted dual-color ratiometric fluorescence smart gel label-based visual sensing platform. The results show that the fluorescence intensity and basic morphology of hydrogel tags do not change with time (over 6 consecutive days) at different temperatures (−20 to 35 °C), indicating that the smart tags used in this system have good stability [ 162 ]. Jia et al, developed a simple and effective strategy for the visual determination of BAs by dual-emissive metal–organic framework (MOF) probes and color-transition hydrogels, to real-time monitor biogenic amines (BAs).…”

Section: Sensing Applicationsmentioning

confidence: 99%

Luminescent Guests Encapsulated in Metal–Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review

Sun

et al. 2023

Biosensors

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Metal–organic frameworks (MOFs) have excellent applicability in several fields and have significant structural advantages, due to their open pore structure, high porosity, large specific surface area, and easily modifiable and functionalized porous surface. In addition, a variety of luminescent guest (LG) species can be encapsulated in the pores of MOFs, giving MOFs a broader luminescent capability. The applications of a variety of LG@MOF sensors, constructed by doping MOFs with LGs such as lanthanide ions, carbon quantum dots, luminescent complexes, organic dyes, and metal nanoclusters, for fluorescence detection of various target analyses such as ions, biomarkers, pesticides, and preservatives are systematically introduced in this review. The development of these sensors for portable visual fluorescence sensing applications is then covered. Finally, the challenges that these sectors currently face, as well as the potential for future growth, are briefly discussed.

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“…Biogenic amines (BAs) are nitrogenous organic compounds with low molecular weight and biological activity, which can be used as representative markers of food spoilage. , Spermine is an important kind of endogenous biogenic amine, which is not only a marker of food deterioration but also a biomarker of cancers. − The excessive intake of spermine poses serious health threats to human beings, including breathing problems, irregular blood pressure, headache symptoms, and even cancers. − Traditional detection techniques of biogenic amines include electrochemical sensing, colorimetric sensing, capillary electrophoresis, and mass spectrometry . Nevertheless, the majority of these techniques are costly, time-consuming, and well-equipped. , The optical detection of spermine mainly focuses on cuvette-based dual-emission mode development. , Therefore, the development of a high-throughput and portable sensing strategy for on-site spermine detection is still limited.…”

Section: Introductionmentioning

confidence: 99%

“…37,38 The optical detection of spermine mainly focuses on cuvette-based dual-emission mode development. 39,40 Therefore, the development of a highthroughput and portable sensing strategy for on-site spermine detection is still limited.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted High-Throughput Strategy for Real-Time Detection of Spermine Using a Triple-Emission Ratiometric Probe

Wu,

Tan,

Chen

et al. 2023

ACS Appl. Mater. Interfaces

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In this study, we designed and fabricated a spermine-responsive triple-emission ratiometric fluorescent probe using dual-emissive carbon nanoparticles and quantum dots, which improve the sensor's accuracy and reduce interfering environmental effects. The probe is advantageous for the proportionate detection of spermine because it has good emission resolution, and the maximum points of the two emission peaks differ by 95 nm. As a proof of concept, cuvettes and a 96-well plate were combined with a smartphone and YOLO series algorithms to accomplish real-time, visual, and high-throughput detection of seafood and meat freshness. In addition, the reaction mechanism was verified by density functional theory and fundamental characterizations. Upon exposure to different amounts of spermine, the intensity of the fluorescent probe changed linearly, and the fluorescent color shifted from yellow-green to red, with a limit of detection of 0.33 μM. To enable visual identification of food-originated spermine, a hydrogel-based visual sensing platform was successfully developed utilizing the triple-emission fluorescent probe. Consequently, spermine could be identified and quantified without complicated equipment.

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Deep learning-assisted smartphone-based portable and visual ratiometric fluorescence device integrated intelligent gel label for agro-food freshness detection

Cited by 35 publications

References 32 publications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Luminescent Guests Encapsulated in Metal–Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review

Machine Learning-Assisted High-Throughput Strategy for Real-Time Detection of Spermine Using a Triple-Emission Ratiometric Probe

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