摘要： In our previous work, we have demonstrated the great diagnostic potential of a low‐resolution Raman sensing system for bladder cancer through ex vivo experiments. Before forwarding this technique into clinical applications, the system's performance under different experimental conditions must be thoroughly understood. In this paper, a comparison study of this system under different experimental conditions and post‐experiment analysis methods is presented. The different experiment conditions includes two major parts: (a) varying the incident laser power at sample from 30 to 150 mW (30‐mW interval) with fixed integration time of 1 s; (b) varying integration time of 1 s, 2 s, 3 s, and 5 s, with a fixed incident laser power of 150 mW. A total number of 2,916 spectra were collected on 42 bladder tissue specimens under different experimental conditions. Three principal component analysis (PCA)‐based classification methods, including linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN), are used in this study for comparison. Results show that increasing the incident laser power has little influence on the overall prediction accuracy; increasing the integration time from 1 to 5 s has a clear improvement on the prediction accuracy; PCA–ANN outperforms PCA–LDA and PCA–SVM consistently under the parameter settings in this study.