In-situ ultrasensitive label-free DNA hybridization detection using optical fiber specklegram

Feng, Fu; Chen, Wang; Chen, Dihan; Lin, Wei; Chen, Shih‐Chi

doi:10.1016/j.snb.2018.05.099

Cited by 43 publications

(27 citation statements)

References 37 publications

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“…Among the several techniques used for ssDNA detection using optical fiber sensors based on intensity, fluorescence and the interferometers based sensors are the most frequently used. Within the latter group, the specklegram interferometer presented the lowest detected concentration of ssDNA with 0.1 fM ssDNA concentration detection [42], which equals the minimum LOD achieved with wavelength based optical fiber sensors [33]. On the other hand, with fluorescence based sensors the minimum LOD was achieved with a novel enzyme-enhanced fluorescence detection on etched optical fiber sensor, 1pM ssDNA [43].…”

Section: Discussion and Outlookmentioning

confidence: 84%

“…Optical fiber interferometers have also relevance in the detection of DNA hybridization process based on intensity. A quite new interferometer design, known as specklegram interferometer, has been proposed for the detection of ssDNA [42]. The base of this new design is a large-core optical fiber that can support hundreds of eigenmodes.…”

Section: B Intensity Based Sensorsmentioning

confidence: 99%

“…The base of this new design is a large-core optical fiber that can support hundreds of eigenmodes. The interference pattern of the different eigenmodes propagating along the fiber forms a fiber specklegram [42]. Thus, the large-core fiber can be considered as an interferometer itself.…”

Section: B Intensity Based Sensorsmentioning

confidence: 99%

“…The specklegram interferometer presents the lowest detected concentration of ssDNA hybridization in the domain of intensity based optical fiber sensors. This compact and ultrasensitive sensor based on fiber specklegram was functionalized with a monolayer of poly-Llysine and achieved a detection of 0.1 fM ssDNA concentration [42]. Moreover, the well-known temperature cross-sensitivity issue in many optical fiber devices has been entirely removed in this biosensor by the introduction of a reference optical fiber channel along with the rest of microfluidic channels of the device.…”

Section: B Intensity Based Sensorsmentioning

confidence: 99%

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Advances in Fiber Optic DNA-Based Sensors: A Review

et al. 2021

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DNA is becoming increasingly important in the domain of optical fiber sensors, either as a tool for biosensing, or as a target to detect. In this review the main contributions of the last years are presented both in the domain of wavelength and intensity based configurations. This review comprises the use of natural single strand DNA (ssDNA) sequences as receptors for the detection of ssDNA sequences through hybridization, synthetic nucleic acids receptors for detection of complementary ssDNA sequences, and sensors based on natural and synthetic ssDNA receptors used for the detection of non-DNA targets. Parameters such as sensitivity, detection range and limit of detection are analyzed and discussed in detail to the purpose of comparing the different technologies and knowing the future lines to follow in the domain of fiber optic DNA-based sensors.

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Section: Discussion and Outlookmentioning

confidence: 84%

Section: B Intensity Based Sensorsmentioning

confidence: 99%

Section: B Intensity Based Sensorsmentioning

confidence: 99%

Section: B Intensity Based Sensorsmentioning

confidence: 99%

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Advances in Fiber Optic DNA-Based Sensors: A Review

et al. 2021

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“…Dessa forma, diversas pesquisas têm sido desenvolvidas para o processamento das imagens capturadas. Autores como Yu et al (1993);Fujiwara et al (2017); Li et al (2019);Feng et al (2018) quantificaram a diferença entre os padrões speckle por meio do coeficiente normalizado do produto interno (NIPC -Normalized inner-product coefficient). Ainda que apresentassem bons resultados, melhorias na precisão foram obtidas por Efendioglu et al (2011) ao lançar mão de atributos estatísticos, chamados de momentos de primeira e segunda ordem, aplicados a uma Rede Neural Artificial (RNA).…”

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Extração de características de padrões speckle para sensores a fibra óptica

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2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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This work presents a classification system, based on texture features extracted from the speckle pattern, to determine 3 different regions in which an optical fiber is mechanically perturbed. Two different descriptors are used: the Local Binary Pattern (LBP) and the Gray Level Co-occurrence Matrix (GLCM) for extracting characteristics, which are applied to an Artificial Neural Network. Accuracy was analyzed for seven different characteristic amounts. The results show that, for an optical fiber of 50 µm automatically disturbed, both descriptors were able to classify the location of the deformation, reaching 94.2% accuracy with 236 features extracted with LBP and 79.3% accuracy with 16 features extracted with GLCM. Resumo: Neste trabalho é apresentado um sistema de classificação, baseado na extração de características de texturas de padrões speckle, para determinação de 3 regiões em que uma fibra óptica é perturbada mecanicamente. São utilizados dois diferentes descritores de textura, o Local Binary Pattern (LBP) e a Gray Level Co-occurrence Matrix (GLCM) para extração de características, as quais são empregadas em uma Rede Neural Artificial feedforward. A acurácia foi analisada para sete diferentes quantidades de características. Os resultados mostram que, para uma fibra óptica de 50 µm perturbada de forma automática, ambos descritores foram capazes de classificar a localização da deformação, alcançando 94,2% de acurácia com 236 características extraídas com LBP e 79,3% de acurácia com 16 características extraídas com GLCM.

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