Development of an Intelligent Surface Plasmon Resonance Based Sensor: practical examples
Resumo
Intelligent sensors based on Surface Plasmon Resonance (SPR) require careful integration of the artificial intelligence (AI) capabilities into the operational aspects of measurement, with the development of new tasks, activities, software, processes, or services. This paper presents practical examples of AI interventions for the construction of smart SPR sensors built with the PPBIO prism.
Palavras-chave:
surface plasmon resonance, machine learning, intelligent sensor
Referências
V.G. Barra and et al. 2025. Autoencoder-Based Method for Enhancing and Manipulating Surface Plasmon Resonance Sensor Responses. In Proceedings of the IEEE LC-IoT 2025, Vol. 1. IEEE, Fortaleza, 1–5.
J.C.S. Batista and et al. 2020. Smart noise reduction in SPR sensors response using multiple-ANN design. IEEE Sensors Journal 21 (2020), 4517–4524.
P.J. Boltryk and et al. 2005. Intelligent sensors - a generic software approach. Phys.: Conf. Ser. 15 (2005), 155–160.
J.C. Gomes and et al. 2021. SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors. Biosen. & Bioelec. 172 (2021).
M. Gwiyeong and et al. 2022. Machine learning and its applications for plasmonics in biology. Cell Reports Physical Science 3 (2022), 1–16.
A. Meireles and et al. 2021. Towards a Grounded Theory for a Development Process Model for Machine Learning Based Systems. In Proceedings of the ISE 2021, Vol. 1. Sociedade Brasileira de Computação, Porto Alegre Brazil, 19–24.
L.C. Oliveira and et al. 2013. A Surface Plasmon Resonance Biochip That Operates Both in the Angular and Wavelength Interrogation Modes. IEEE TIM 62 (2013).
L.C. Oliveira and et al. 2016. A Prism-based Polymeric Surface Plasmon Resonance Biochip for Angular and Spectral Modes. Proc. Engin. 168 (2016).
L.C. Oliveira and et al. 2019. Surface Plasmon Resonance Sensors: A Materials Guide to Design, Characterization, Optimization, and Usage (2 ed.). Springer.
L.C. Oliveira and et al. 2025. Intelligent Surface Plasmon Resonance Sensor For Refractive Index Substance Identification With Convolutional Neural Networks Image-Based Model. IEEE TIM 74 (2025), 1–10.
A. Radford and et al. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434
F.J. Silva and el al. 2021. n automated platform for surface plasmon resonance based sensors. IEEE TIM 70 (2021), 1–7.
J.C.S. Batista and et al. 2020. Smart noise reduction in SPR sensors response using multiple-ANN design. IEEE Sensors Journal 21 (2020), 4517–4524.
P.J. Boltryk and et al. 2005. Intelligent sensors - a generic software approach. Phys.: Conf. Ser. 15 (2005), 155–160.
J.C. Gomes and et al. 2021. SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors. Biosen. & Bioelec. 172 (2021).
M. Gwiyeong and et al. 2022. Machine learning and its applications for plasmonics in biology. Cell Reports Physical Science 3 (2022), 1–16.
A. Meireles and et al. 2021. Towards a Grounded Theory for a Development Process Model for Machine Learning Based Systems. In Proceedings of the ISE 2021, Vol. 1. Sociedade Brasileira de Computação, Porto Alegre Brazil, 19–24.
L.C. Oliveira and et al. 2013. A Surface Plasmon Resonance Biochip That Operates Both in the Angular and Wavelength Interrogation Modes. IEEE TIM 62 (2013).
L.C. Oliveira and et al. 2016. A Prism-based Polymeric Surface Plasmon Resonance Biochip for Angular and Spectral Modes. Proc. Engin. 168 (2016).
L.C. Oliveira and et al. 2019. Surface Plasmon Resonance Sensors: A Materials Guide to Design, Characterization, Optimization, and Usage (2 ed.). Springer.
L.C. Oliveira and et al. 2025. Intelligent Surface Plasmon Resonance Sensor For Refractive Index Substance Identification With Convolutional Neural Networks Image-Based Model. IEEE TIM 74 (2025), 1–10.
A. Radford and et al. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434
F.J. Silva and el al. 2021. n automated platform for surface plasmon resonance based sensors. IEEE TIM 70 (2021), 1–7.
Publicado
23/09/2025
Como Citar
OLIVEIRA, Leiva C.; SARAIVA, José G. O.; ARAÚJO, Silvio R. F. de; MELO, Arthur A. de; LIMA, Antonio M. N..
Development of an Intelligent Surface Plasmon Resonance Based Sensor: practical examples. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 4. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 49-52.
DOI: https://doi.org/10.5753/ise.2025.14326.
