Computational Tool for Curve Smoothing Methods Analysis and Surface Plasmon Resonance Biosensor Characterization

Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the acc...

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Veröffentlicht in:Inventions (Basel) Jg. 10; H. 2; S. 31
Hauptverfasser: Villarim, Mariana Rodrigues, Villarim, Andréa Willa Rodrigues, Gazziro, Mario, Cavallari, Marco Roberto, Belfort, Diomadson Rodrigues, Ando Junior, Oswaldo Hideo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.04.2025
Schlagworte:
Accuracy
Algorithms
Biosensors
Data analysis
Data smoothing
Data transfer (computers)
Detectors
Dimensional analysis
hybrid analysis
Light
mathematical modeling
Medical equipment
noise reduction
Optimization
Physiological apparatus
Real time
Scientific visualization
Sensors
Signal processing
Smoothing
Software
Spectrum analysis
Spline functions
Surface plasmon resonance
surface plasmon resonance (SPR)
White noise
ISSN:2411-5134, 2411-5134
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Zusammenfassung:Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the accuracy of results. To address these limitations, this study presents the development of an open-source computational tool to optimize SPR biosensor characterization, implemented using MATLAB App Designer (Version R2024b). The tool enables the importation of experimental data, application of different smoothing methods, and integration of traditional and hybrid approaches to enhance accuracy in determining the resonance angle. The proposed tool offers several innovations, such as integration of both traditional and hybrid (angle vs wavelength) analysis modes, implementation of four advanced curve smoothing techniques, including Gaussian filter, Savitzky–Golay, smoothing splines, and EWMA, as well as a user-friendly graphical interface supporting real-time data visualization, experimental data import, and result export. Unlike conventional approaches, the hybrid framework enables multidimensional optimization of SPR parameters, resulting in greater accuracy and robustness in detecting resonance conditions. Experimental validation demonstrated a marked reduction in spectral noise and improved consistency in resonance angle detection across conditions. The results confirm the effectiveness and practical relevance of the tool, contributing to the advancement of SPR biosensor analysis.
Bibliographie:ObjectType-Article-1
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ISSN:2411-5134
2411-5134
DOI:10.3390/inventions10020031