Spectral unmixing techniques for photoacoustic images are often used to isolate signal origins (e.g., blood, contrast agents, lipids). However, these techniques tend to exploit the optical properties of different biological chromophores and do not typically consider acoustic properties. Analysis of the acoustic frequency response of photoacoustic signals has the potential to provide additional discrimination of photoacoustic responses from different materials, with the added benefit of potentially requiring few optical wavelength emissions. This study presents our initial results testing this hypothesis in a phantom experiment, given the task of differentiating between photoacoustic signals from deoxygenated hemoglobin (Hb) and methylene blue (MB). Coherence-based beamforming, principal component analysis, and nearest neighbor classification were employed to determine ground-truth labels, perform feature extraction, and classify image contents, respectively. The mean ± one standard deviation of classification accuracy was increased from.67pm 0.05 to.81 pm 0.11 when increasing the number of wavelength emissions from one to two, respectively. When using an optimal laser wavelength pair of 690 and 870 nm, the sensitivity and specificity of detecting MB over Hb were 1.00 and 1.00, respectively. Results are highly promising for the differentiation of photoacoustic-sensitive materials with comparable performance to that achieved with a more conventional multispectral laser wavelength approach.