Extended Kalman filtering projection method to reduce the 3σ noise value of optical biosensors

Kezheng Li, Roopam Gupta, Alexander Drayton, Isabel Barth, Donato Conteduca, Christopher Reardon, Kishan Dholakia, Thomas F. Krauss

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
3 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)3474–3482
Number of pages9
JournalACS Sensors
Issue number11
Early online date27 Oct 2020
Publication statusPublished - 25 Nov 2020


  • Optical biosensors
  • Signal processing
  • Signal-to-noise ration
  • Extended Kalman filter
  • Guided mode resonance
  • Microring resonator


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