Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.