Performance limitations of resonant refractive index sensors with low-cost components.

Resonant biosensors are attractive for diagnostics because they can detect clinically relevant biomarkers with high sensitivity and in a label-free fashion. Most of the current solutions determine their detection limits in a highly stabilised laboratory environment, which does, however, not apply to real point-of-care applications. Here, we consider the more realistic scenario of low-cost components and an unstabilised environment and consider the related design implications. We find that sensors with lower quality-factor resonances are more fault tolerant, that a filtered LED lightsource is advantageous compared to a diode laser, and that a CMOS camera is preferable to a CCD camera for detection. We exemplify these findings with a guided mode resonance sensor and experimentally determine a limit of detection of 5.8 ± 1.7×10-5 refractive index units (RIU), which is backed up by a model identifying the various noise sources. Our findings will inform the design of high performance, low cost biosensors capable of operating in a real-world environment.

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

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.