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These limitations hinder its clinical utility in applications such as tumor detection and treatment monitoring. This study introduces a spectral decomposition method to improve absolute CA concentration estimation in PA imaging. By using a reference spectral library, the approach corrects for signal distortions and nonlinear behavior, overcoming key limitations of traditional intensity-based methods. Validation was performed through in vitro experiments using a prostate-specific membrane antigen (PSMA)-targeted CA in both saline and blood, as well as dynamic tracking of indocyanine green (ICG) in ex vivo tissue. The method achieved significantly lower concentration estimation errors, with average absolute errors of 1.80 \u00b5M in saline and 3.34 \u00b5M in blood. Compared to conventional techniques, the proposed method demonstrated enhanced reliability and robustness. 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