Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Sensors, Год журнала: 2024, Номер 24(7), С. 2200 - 2200

Опубликована: Март 29, 2024

The constant monitoring and control of various health, infrastructure, natural factors have led to the design development technological devices in a wide range fields. This has resulted creation different types sensors that can be used monitor environments, such as fire, water, temperature, movement, among others. These detect anomalies input data system, allowing alerts generated for early risk detection. advancement artificial intelligence improved sensor systems networks, resulting with better performance more precise results by incorporating features. aim this work is conduct bibliometric analysis using PRISMA 2020 set identify research trends machine learning applications fiber optic sensors. methodology facilitates dataset comprised documents obtained from Scopus Web Science databases. It enables evaluation both quantity quality publications study area based on specific criteria, trends, key concepts, advances concepts over time. found deep techniques Bragg gratings been extensively researched focus structural health future research. One main limitations lack use novel materials, graphite, designing presents an opportunity studies.

Язык: Английский

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Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Agronomy, Год журнала: 2024, Номер 14(2), С. 341 - 341

Опубликована: Фев. 7, 2024

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention recent years since it 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 sustainability, have promoted pushed forward the use of agroindustry environmental applications. However, issues with noise confounding signals make these tools non-trivial technical challenge. Great advances artificial intelligence, more particularly machine learning, provided new that allowed researchers improve quality functionality nanosensor systems. This short review presents latest work analysis data from using learning agroenvironmental It consists an introduction topics application field nanosensors. The rest paper examples techniques utilisation electrochemical, luminescent, SERS colourimetric classes. final section discussion conclusion concerning relevance material discussed future sector.

Язык: Английский

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A Wash-Free Spheres-on-Sphere Strategy for On-Site and Multiplexed Biosensing

ACS Nano, Год журнала: 2024, Номер 18(11), С. 8270 - 8282

Опубликована: Март 7, 2024

Respiratory infections and food contaminants pose severe challenges to global health the economy. A rapid on-site platform for simultaneous detection of multiple pathogens is crucial accurate diagnosis, appropriate treatment, a reduced healthcare burden. Herein, we present spheres-on-sphere (SOS) multiplexed using portable Coulter counter, which employs millimeter- micron-sized spheres coupled with antibodies as multitarget probes. The assay allows quantitative analytes within 20 min by simple mixing, enabling detection. shows high accuracy in identifying three respiratory viruses (SARS-CoV-2, influenza virus, parainfluenza virus) from throat swab samples, LOD 50.7, 32.4, 49.1 pg/mL. It also demonstrates excellent performance quantifying mycotoxins (aflatoxin B1, deoxynivalenol, ochratoxin A) samples. SOS offers approach sensitivity specificity applications resource-limited settings.

Язык: Английский

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Progress and Challenge of Sensors for Dairy Food Safety Monitoring

Sensors, Год журнала: 2024, Номер 24(5), С. 1383 - 1383

Опубликована: Фев. 21, 2024

One of the most consumed foods is milk and products, guaranteeing suitability these products one major concerns in our society. This has led to development numerous sensors enhance quality controls food chain. However, this not a simple task, because it necessary establish parameters be analyzed often, only compound responsible for contamination or degradation. To attempt address problem, multiplex analysis together with non-directed (e.g., general such as pH) are relevant alternatives identifying safety dairy food. In recent years, use new technologies devices/platforms optical electrochemical signals accelerated intensified pursuit systems that provide simple, rapid, cost-effective, and/or multiparametric response presence contaminants, markers various diseases, indicators levels. achieving simultaneous determination two more analytes situ, single measurement, real time, using working ‘real sensor’, remains daunting challenges, primarily due complexity sample matrix. requirements, different approaches have been explored. The state art on will summarized review including optical, electrochemical, other sensor-based detection methods magnetoelastic mass-based sensors.

Язык: Английский

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Functionalized metal-organic frameworks with biomolecules for sensing and detection applications of food contaminants

Critical Reviews in Food Science and Nutrition, Год журнала: 2024, Номер unknown, С. 1 - 33

Опубликована: Сен. 26, 2024

The increasing demand for toxin-free food, driven by the rise in fast food consumption and changing dietary habits, necessitates advanced efficient detection methods to address potential risks associated with contaminated food. Nanomaterial-based have shown significant promise, particularly using metal-organic frameworks (MOFs) combined biomolecules. This review article provides an overview of recent advancements functionalized (FMOFs) biomolecules detect various contaminants, including heavy metals, antibiotics, pesticides, bacteria, mycotoxins other chemical contaminants. We discuss fundamental principles detecting evaluate existing analytical techniques, explore development biomacromolecule-functionalized MOF-based sensors encompassing colorimetric, optical, electrochemical, portable variants. also examines sensing mechanisms, uses FMOFs as signal probes carriers capture probes, assesses sensitivity. Additionally, we opportunities challenges producing biomacromolecules contaminant assessment. Future directions include improving sensor sensitivity specificity, developing more cost-effective production methods, integrating these technologies into real-world safety monitoring systems. work aims pave way innovative reliable solutions ensure our supply.

Язык: Английский

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Ultrabright fluorescence probe mediated biosensor for rapid and ultrasensitive quantification of chloramphenicol in milk

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 112883 - 112883

Опубликована: Янв. 1, 2025

Язык: Английский

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A washing-less biosensor based on the dual functions of magnetic separation and signal output of magnetic nanoparticles for the rapid and visual detection of enrofloxacin

Analytica Chimica Acta, Год журнала: 2025, Номер 1352, С. 343923 - 343923

Опубликована: Март 9, 2025

Язык: Английский

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Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods

Foods, Год журнала: 2025, Номер 14(6), С. 922 - 922

Опубликована: Март 8, 2025

Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing data analysis capabilities and have proven to be powerful tools assessing the of Animal-Source Foods (ASFs). Studies that link ML with HACCP in ASFs are limited. The present review provides an overview ML, feature extraction, selection employed safety. Several non-destructive presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, hyperspectral imaging combined algorithms. Prospects include enhancing predictive models development hybrid Artificial Intelligence (AI) automation quality control processes using AI-driven computer which could revolutionize inspections. However, handling conceivable inclinations AI is vital guaranteeing reasonable exact hazard assessments assortment nourishment generation settings. Moreover, moving forward, interpretability will make them more straightforward dependable. Conclusively, applying allows real-time analytics can significantly reduce risks associated ASF consumption.

Язык: Английский

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Deep-Learning-Assisted Microfluidic Immunoassay via Smartphone-Based Imaging Transcoding System for On-Site and Multiplexed Biosensing

Nano Letters, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics food safety. This study presents a deep-learning-assisted microfluidic immunoassay platform that uses smartphone-based imaging transcoding system, polystyrene microsphere-based encoding, artificial-intelligence-assisted decoding. Microspheres of varying sizes act as multiprobes, their quantities correlating to target concentrations after an immunoreaction separation-filtration within the chip. A smartphone intelligent decoding software captures images multiprobes from chip performs classification, counting, concentration calculations. The "encoding-decoding" strategy integrated design allow these processes be completed in simple steps, eliminating need additional immunomagnetic separation. As proof concept, this successfully detected multiple respiratory viruses antibiotics various real samples high sensitivity 30 min, demonstrating great potential smart, universal toolkit next-generation POCT applications.

Язык: Английский

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A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk

Food Chemistry, Год журнала: 2024, Номер 449, С. 139050 - 139050

Опубликована: Март 20, 2024

Язык: Английский

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