Machine Learning-Assisted Confinement Loss Prediction for a Highly Sensitive PCF-SPR Sensor

A photonic crystal fiber (PCF) based surface plas-
mon resonance (SPR) sensor is proposed for the simultaneous
detection of two analytes over a wide refractive index (RI)
range. Surface plasmons are excited by a single gold (Au) film
deposited on two open channels formed on the outer surface
of the fiber. Across the RI range
of 1.25– 1.43 RIU, a maximum wavelength sensitivity of 23,200
nm/RIU, a peak amplitude sensitivity of 2235.71 RIU−1 and
a best sensor resolution of 4.31 × 10−6 RIU are achieved,
and the two channels are shown to respond selectively to their
own analytes without noticeable crosstalk. The suggested sensor
with a wide sensing range, excellent detection capability, and
compact structure has great potential for biochemical sensing,
chemical analysis, environmental monitoring, and point-of-care
diagnostics. Furthermore, six machine learning regression models
are implemented to predict the confinement loss, among which
Random Forest exhibits the best predictive accuracy with an R2
of 0.9759.