To explore the use of side-polished fibre ( SPF) for microprobe-type"lab-on-fibre", this paper analyzes the surface roughness in side-polished fiber ( SPF ) by employing the gray level co-occurrence matrix ( GLCM) texture feature analysis method.Our experimental results show 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.By employing the random forest ( RF ) feature importance ranking method based on the Gini coefficient and out-of-bag ( OOB ) error estimation, our 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.Through utilizing this subset, an RF classification validation experiment is conducted on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95 .65％.Our 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.
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