Abstract
Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free. Many optical biosensors rely on tracking the change in a resonance signal or an interference pattern caused by the change in refractive index that occurs upon binding to a target biomarker. The most commonly used method for tracking such a signal is based on fitting the data with an appropriate mathematical function, such as a harmonic function or a Fano, Gaussian, or Lorentz function. However, these functions have limited fitting efficiency because of the deformation of data from noise. Here, we introduce an extended Kalman filter projection (EKFP) method to address the problem of resonance tracking and demonstrate that it improves the tolerance to noise, reduces the 3σ noise value, and lowers the limit of detection (LOD). We utilize the method to process the data of experiments for detecting the binding of C-reactive protein in a urine matrix with a chirped guided mode resonance sensor and are able to improve the LOD from 10 to 1 pg/mL. Our method reduces the 3σ noise value of this measurement compared to a simple Fano fit from 1.303 to 0.015 pixels. These results demonstrate the significant advantage of the EKFP method to resolving noisy data of optical biosensors.
Highlights
Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free
Many different types of optical biosensors have been reported, based on the principles of surface plasmon resonances (SPR),[1] microring resonances,[2] dual mode[3] or Mach−Zehnder interferences,[4] porous silicon nanostructures,[5] photonic crystals,[6] or guided mode resonances (GMRs).[7−9] The unifying feature of all of these modalities is that the resonance signal is recorded, fitted to a mathematical function, and tracked as a function of time, for example, to record a protein−
Together with the standard deviation σ of the noise taken for a constant refractive index (Figure 2e), we determined the limit of detection (LOD) as 3σ/S and compared the values for four methods, that is, Fano, principal component analysis (PCA), projection, and Kalman filter (KF) projection (KFP)
Summary
Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free. These include the centroid and full width at half-maximum method,[10] locally weighted parametric regression,[11] polynomial curve fitting,[12] and principal component analysis (PCA).[13,14] It is difficult to compare these methods directly from the literature because of differences in experimental arrangements, but the stated experimental error is typically around 10−4 RIU.[15] The double projection method[16] was introduced more recently and has been shown to be more accurate in the determination of the resonance position with an estimated error around 2.2 × 10−5 RIU This method uses eigenvector analysis to compare the measurement with simulated results, solving the unknown refractive index by projecting the vectors to the basis twice. Both the Lorentzian and Fano functions are tracked as the measurement progresses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
