Cholesteric liquid crystals (CLCs) are sensitive to environmental temperature changes, and have been employed as a specific intermediary for biosensors. Considering the temperature-dependent structural changes of CLCs, this study aimed to determine the sensing properties of side-polished fibers (SPFs) after coating with CLCs. The experimental results demonstrated that, with regard to the transmitted spectrum, the loss peak of CLC-coated SPFs exhibited a positive linear relationship with temperature changes over a range of 20 to 50 °C. The linear correlation coefficient achieved 97.8% when the temperature increased by 10 °C, and the loss peak drifted by 12.72 nm. The reflectance spectrum of CLCs coated on the polished surface were obtained using optical fiber sensors. The feasibility of measuring the helical structure of CLCs was further verified using SPF transmission spectroscopy. The findings indicated that the transmitted spectrum of SPFs could be adopted to characterize the helical structure of CLCs, which lays a solid foundation for further study on SPF-based biosensors.

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.