We report a statistical approach to model the resonant peak wavelength (RPW) equation(s) of a photonic crystal fibre (PCF)-based surface plasmon resonance (SPR) sensors in terms of the PCF structural parameters (air-hole diameter, pitch, core diameter and gold layer thickness) at various tolerance levels. Design of experiments (statistical tool) is used to investigate the role played by the PCF structural parameters for sensing performance evaluation—RPW, across three tolerance levels ($\pm 2$%, $\pm 5$% and $\pm 10$%). Pitch of the hollow-core PCF was discovered to be the major influencing parameter for the sensing performance (RPW) of the PCF-based SPR sensor while the inner metal (gold) layer thickness and core diameter are the least contributing parameters. This novel statistical method to derive the sensing performance parameter(s) of the PCF-based SPR sensors can be applied effectively and efficiently in the designing, characterisation, tolerance analysis not only at the research level, but also in optical fibre sensor fabrication industry to improve efficiency and lower cost.

We report an innovative design of a multi-core photonic crystal fibre-based surface plasmon resonance temperature sensor using ethanol and benzene as temperature-sensitive materials with a segmented outer-surface metal coating scheme. A stable sensing performance for a detection range of 10–80 ${}^{\circ }$ C was found while using ethanol as the temperature-sensitive material; while using benzene both blue and red frequency shifts were observed. The maximum temperature sensitivities obtained from this proposed temperature sensor were 360 pm/ ${}^{\circ }$ C and 23.3 nm/ ${}^{\circ }$ C with resolutions of $2.78\times {10}^{-1}$ ${}^{\circ }$ C and $4.29\times {10}^{-3}$ ${}^{\circ }$ C, respectively, when using ethanol or benzene as the sensing medium.