Development of an optical biosensor for the detection of antibiotics in the environment

Weber, P.; Vogler, Julian; Gauglitz, Günter

doi:10.1117/12.2267467

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The detection limit determined was 10 μM [ 54 ]. A different approach to the detection of antibiotics was developed by Weber et al [ 103 ] using an interferometer for the quantification of penicillin, with 0.25 μg/mL being the minimum concentration tested.…”

Section: Detection Of Selected Water Pollutantsmentioning

confidence: 99%

Optical Biosensors and Their Applications for the Detection of Water Pollutants

et al. 2023

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The correct detection and quantification of pollutants in water is key to regulating their presence in the environment. Biosensors offer several advantages, such as minimal sample preparation, short measurement times, high specificity and sensibility and low detection limits. The purpose of this review is to explore the different types of optical biosensors, focusing on their biological elements and their principle of operation, as well as recent applications in the detection of pollutants in water. According to our literature review, 33% of the publications used fluorescence-based biosensors, followed by surface plasmon resonance (SPR) with 28%. So far, SPR biosensors have achieved the best results in terms of detection limits. Although less common (22%), interferometers and resonators (4%) are also highly promising due to the low detection limits that can be reached using these techniques. In terms of biological recognition elements, 43% of the published works focused on antibodies due to their high affinity and stability, although they could be replaced with molecularly imprinted polymers. This review offers a unique compilation of the most recent work in the specific area of optical biosensing for water monitoring, focusing on both the biological element and the transducer used, as well as the type of target contaminant. Recent technological advances are discussed.

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Section: Detection Of Selected Water Pollutantsmentioning

confidence: 99%

Optical Biosensors and Their Applications for the Detection of Water Pollutants

et al. 2023

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“…The continued advancement of materials, biology, computer science and other disciplines has led to the gradual development and widespread application of biosensors across various fields. These include laboratory-based biological research [3][4][5][6], environmental pollution monitoring [7][8][9], food health and safety [10,11], and disease prevention and diagnosis [12][13][14][15][16]. In recent years, technological breakthroughs have been made in materials science, nanoscience and other disciplines, and the research of biosensors in their own structure, components and strategy optimisation has also made great progress.…”

Section: Introductionmentioning

confidence: 99%

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Wu,

Yang,

Zhou

et al. 2023

Electroanalysis

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“…Biosensors are a type of detection or diagnostic device. − Compared with conventional or larger analytical instruments, biosensors have the advantages of speed, low cost, nondestructive property, and on-site detection. They have been extensively used in fundamental bioresearch, − food safety, , environmental monitoring, − disease diagnosis, − and drug screening. − In the past decades, with the extensive progression of nanotechnology, signal amplification strategies, and transducers, biosensors have been substantially advanced. However, all biosensors inevitably have some irregular signal noise.…”

mentioning

confidence: 99%

Advancing Biosensors with Machine Learning

et al. 2020

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Chemometrics play a critical role in biosensorsbased detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

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Development of an optical biosensor for the detection of antibiotics in the environment

Cited by 5 publications

References 4 publications

Optical Biosensors and Their Applications for the Detection of Water Pollutants

Optical Biosensors and Their Applications for the Detection of Water Pollutants

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Advancing Biosensors with Machine Learning

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