Evaluating the signal processing chain employed in surface plasmon resonance biosensing

Sousa, Tiago Abreu Tavares de; Oliveira, Leiva Casemiro; Loureiro, F.C.C.L.; Lima, Antonio Marcus Nogueira; Neff, H.

doi:10.1109/i2mtc.2014.6861017

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…As show in [6], [13] the preprocessing before extracting the interest parameters and even the techniques used to extract these parameters can minimize the degradation effect of the noise in SPR signal. The raw image vector capture by the image sensor is given by…”

Section: Operational Aspectsmentioning

confidence: 99%

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SPR sensor for tampering detection in biofuels

Oliveira¹,

Pereira²,

Oliveira³

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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This work presents a sensor based on surface plasmon resonance phenomenon and combined with dc-sheet resistance monitoring for detecting tampering of ethanol fuel. To demonstrated the feasibility of the proposed sensor, hydrated and anhydride ethanol fuel were tested. Tampering ethanol with methanol and with increasing water content have been used to evaluate the capabilities of the proposed sensing arrangement.

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Section: Operational Aspectsmentioning

confidence: 99%

“…witch the i-th pixel represented by x i (t) , i = 1, • • • , P with P the number of pixels of the image sensor. After spatial, temporal and frequency noise removal [13] the SPR image was given by smooth pixelsx i [6]:…”

Section: Operational Aspectsmentioning

confidence: 99%

SPR sensor for tampering detection in biofuels

Oliveira¹,

Pereira²,

Oliveira³

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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“…The SPRc and SPRi sensors track this position/intensity for sensing proceedings. Algorithms to improve SPRc data extraction [6], curve characteristics enhancement [7], and for better selection of the region-of-interest (ROI) in SPRi [8,9] are early reports. Attempts to model and remove image sensor noise [10], with machine learning approach [11], or based on transformation domain operation in SPR images for intensity processing [12] and noise removal [13] can be found elsewhere.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the arising noises during the SPR sensor usage, related to the processes of transformation, generation, conversion, amplification, and digitization of incident light are also expressed in the SPR image. Brightness, contrast, and sharpness issues are commonly noticed in SPR images.Algorithms to improve SPRc data extraction[6], curve characteristics enhancement[7], and for better selection of the region-of-interest (ROI) in SPRi[8,9] are early reports. Attempts to model and remove image sensor noise[10], with machine learning approach[11], or based on transformation domain operation in SPR images for intensity processing[12] and noise removal[13] can be found elsewhere.…”

mentioning

confidence: 99%

Combination of Digital Image Processing and Statistical Data Segmentation to Enhance SPR and SPRi Sensor Responses

Aguiar

Souza

et al. 2022

Plasmonics

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The work reports the combination of basics digital image processing (DIP) techniques and statistical segmentation strategy (SDS) to improve surface plasmon resonance curve (SPRc) and SPR imaging (SPRi) sensors' performance. The SPR image is used for sensing and monitoring biological events in the so-called SPR imaging process. In the traditional SPR process based on the attenuated total reflection (ATR) method, the image is used to create the SPR curve, and the curve features tracking is employed on sensing applications. The SPR curve features are enhanced after the pixels of the SPR image have been processed with lowcomplexity filters in the spatial domain (brightness, contrast, threshold, and morphological). The bootstrap was used as a statistical processing approach, selecting lines and columns from the image that was less affected by imperfections and noises in the image detector, and consequently reducing the SPR sensor instrumentation disturbances. Experimental tests with reversible binding water-mixture were performed, and both image and statistical processing were reported. The combination of DIP and SDS approaches improves the extraction of the curve features, increasing the performance in terms of resonance position sensitivity until 81%.

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