Projection method for improving signal to noise ratio of localized surface plasmon resonance biosensors

Abstract

This paper presents a simple and accurate method (the projection method) to improve the signal to noise ratio of localized surface plasmon resonance (LSPR). The nanostructures presented in the paper can be readily fabricated by nanoimprint lithography. The finite difference time domain method is used to simulate the structures and generate a reference matrix for the method. The results are validated against experimental data and the proposed method is compared against several other recently published signal processing techniques. We also apply the projection method to biotin-streptavidin binding experimental data and determine the limit of detection (LoD). The method improves the signal to noise ratio (SNR) by one order of magnitude, and hence decreases the limit of detection when compared to the direct measurement of the transmission-dip. The projection method outperforms the established methods in terms of accuracy and achieves the best combination of signal to noise ratio and limit of detection.

Extended Kalman Filtering Projection Method to Reduce the 3σ Noise Value of Optical Biosensors

“…The only computation part of the EKFP method is the projection part. As has already been pointed out, the computational complexity of the projection method is O ( pN ), where p is the number of reference spectra and N is the number of wavelength samples. In this case, where p = 40, the computational complexity is O (40 N ), while for the Fano fit, the algorithm is based on the least squares regression with N examples and C parametric variables, the total computational complexity is O ( C 2 N ) .…”

“…The single and double projection methods were initially described in refs. , In brief, the double projection method is an eigenvector analysis, which uses singular value decomposition to create a basis for each simulated spectrum at different RIs. Then each simulation is projected against the basis to form a weight matrix.…”

“…Because the eigenvector analysis is based on a singular value decomposition, the algorithm relies on a large matrix operation which is computationally expensive and experimentally rather complex. However, a simpler one-step projection method was subsequently introduced by Abumazwed et al 17 with a slightly worse stated LOD of 7.5 × 10 −4 RIU. The one-step projection method is attractive because it eliminates the use of the eigenvector analysis, directly convoluting the measured spectrum with the simulated spectrum database, which reduces the computational complexity by more than 2 orders.…”

Improved method for estimating adlayer thickness and bulk RI change for gold nanocrescent sensors

“…We previously introduced the projection method that directly measures the effective refractive index with improved signal to noise ratio (SNR) for LSPR sensors. The effective refractive index is influenced by the adlayer thickness and the change in bulk refractive index 4 . Although the projection method provides a direct measurement for the effective bulk RI change, it cannot be used to distinguish between the effects due to biomolecular adlayer and those due to the bulk RI change, and it was affected by systematic errors and noise.…”

Ultrasensitive nanosensors based on localized surface plasmon resonances: From theory to applications

“…[75] Although most of them are created to readout the signal from a SPR sensor, they can also be straightforwardly applied to determine the spectral shift of the LSPR peaks in a similar manner. Beyond the simplest recognition method that directly reads the strongest point of a peak fitted by a Lorentz or Gaussian function, the most commonly used algorithms include the centroid method, [71,[76][77][78][79] polynomial fitting, [71,76,80,81] optimal linear data analysis, [82] projection method, [83] integrated response technique, [84] etc. In the centroid method, the determination of the LSPR peak position is based on the calculation of the center-of-mass from the part of the peak above a certain baseline.…”