Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing
Phase-sensitive surface plasmon resonance biosensors are known for their high sensitivity. One of the technology bottle-necks of such sensors is that the phase sensorgram, when measured at fixed angle set-up, can lead to low reproducibility as the signal conveys multiple data. Leveraging the sensiti...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Biosensors |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-6374/11/3/95 |
id |
doaj-54ac184865464038ac9a5a0227ddb4d3 |
---|---|
record_format |
Article |
spelling |
doaj-54ac184865464038ac9a5a0227ddb4d32021-03-24T00:03:10ZengMDPI AGBiosensors2079-63742021-03-0111959510.3390/bios11030095Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance BiosensingTzu-Heng Wu0Ching-Hsu Yang1Chia-Chen Chang2Hui-Wen Liu3Chia-Yu Yang4Tang-Long Shen5Chii-Wann Lin6Aurélien Bruyant7Department of Biomedical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanGraduate Institute of Bio-Electronics and Bio-Informatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanDepartment of Medical Biotechnology and Laboratory Sciences, College of Medicine, Chang Gung University, Taoyuan 333, TaiwanDepartment of Biomedical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanDepartment of Plant Pathology and Microbiology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanDepartment of Plant Pathology and Microbiology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanDepartment of Biomedical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, TaiwanLaboratory Light, Nanomaterials & Nanotechnologies (L2n), CNRS ERL 7004, University of Technology of Troyes, 12 rue Marie Curie, 10004 Troyes, FrancePhase-sensitive surface plasmon resonance biosensors are known for their high sensitivity. One of the technology bottle-necks of such sensors is that the phase sensorgram, when measured at fixed angle set-up, can lead to low reproducibility as the signal conveys multiple data. Leveraging the sensitivity, while securing satisfying reproducibility, is therefore is an underdiscussed key issue. One potential solution is to map the phase sensorgram into refractive index unit by the use of sensor calibration data, via a simple non-linear fit. However, basic fitting functions poorly portray the asymmetric phase curve. On the other hand, multi-layer reflectivity calculation based on the Fresnel coefficient can be employed for a precise mapping function. This numerical approach however lacks the explicit mathematical formulation to be used in an optimization process. To this end, we aim to provide a first methodology for the issue, where mapping functions are constructed from Bayesian optimized multi-layer model of the experimental data. The challenge of using multi-layer model as optimization trial function is addressed by meta-modeling via segmented polynomial approximation. A visualization approach is proposed for assessment of the goodness-of-the-fit on the optimized model. Using metastatic cancer exosome sensing, we demonstrate how the present work paves the way toward better plasmonic sensors.https://www.mdpi.com/2079-6374/11/3/95surface plasmon resonance biosensorphase sensitive detectionexosomealgorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tzu-Heng Wu Ching-Hsu Yang Chia-Chen Chang Hui-Wen Liu Chia-Yu Yang Tang-Long Shen Chii-Wann Lin Aurélien Bruyant |
spellingShingle |
Tzu-Heng Wu Ching-Hsu Yang Chia-Chen Chang Hui-Wen Liu Chia-Yu Yang Tang-Long Shen Chii-Wann Lin Aurélien Bruyant Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing Biosensors surface plasmon resonance biosensor phase sensitive detection exosome algorithm |
author_facet |
Tzu-Heng Wu Ching-Hsu Yang Chia-Chen Chang Hui-Wen Liu Chia-Yu Yang Tang-Long Shen Chii-Wann Lin Aurélien Bruyant |
author_sort |
Tzu-Heng Wu |
title |
Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing |
title_short |
Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing |
title_full |
Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing |
title_fullStr |
Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing |
title_full_unstemmed |
Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing |
title_sort |
multi-layer reflectivity calculation based meta-modeling of the phase mapping function for highly reproducible surface plasmon resonance biosensing |
publisher |
MDPI AG |
series |
Biosensors |
issn |
2079-6374 |
publishDate |
2021-03-01 |
description |
Phase-sensitive surface plasmon resonance biosensors are known for their high sensitivity. One of the technology bottle-necks of such sensors is that the phase sensorgram, when measured at fixed angle set-up, can lead to low reproducibility as the signal conveys multiple data. Leveraging the sensitivity, while securing satisfying reproducibility, is therefore is an underdiscussed key issue. One potential solution is to map the phase sensorgram into refractive index unit by the use of sensor calibration data, via a simple non-linear fit. However, basic fitting functions poorly portray the asymmetric phase curve. On the other hand, multi-layer reflectivity calculation based on the Fresnel coefficient can be employed for a precise mapping function. This numerical approach however lacks the explicit mathematical formulation to be used in an optimization process. To this end, we aim to provide a first methodology for the issue, where mapping functions are constructed from Bayesian optimized multi-layer model of the experimental data. The challenge of using multi-layer model as optimization trial function is addressed by meta-modeling via segmented polynomial approximation. A visualization approach is proposed for assessment of the goodness-of-the-fit on the optimized model. Using metastatic cancer exosome sensing, we demonstrate how the present work paves the way toward better plasmonic sensors. |
topic |
surface plasmon resonance biosensor phase sensitive detection exosome algorithm |
url |
https://www.mdpi.com/2079-6374/11/3/95 |
work_keys_str_mv |
AT tzuhengwu multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT chinghsuyang multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT chiachenchang multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT huiwenliu multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT chiayuyang multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT tanglongshen multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT chiiwannlin multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing AT aurelienbruyant multilayerreflectivitycalculationbasedmetamodelingofthephasemappingfunctionforhighlyreproduciblesurfaceplasmonresonancebiosensing |
_version_ |
1724205478474416128 |