Develop a method that can identify, separate and quantify the unique spectral signatures of malignant melanoma in a photoacoustic image. A key feature of photoacoustic imaging is its ability to illuminate tissue at multiple wavelengths, and thus record images with a spectral dimension. Spectral imaging allows sensing of intrinsic chromophores that can reveal physiological, cellular and subcellular functions. However, the identification of spectral signatures within images obtained at multiple wavelengths requires spectral unmixing techniques. Several techniques are available, such as adaptive matched filtering, independent component analysis, and principal component analysis. Preliminary results have shown that a modified variant of adaptive matched filtering shows promise. We will evaluate this further using a phantom, ex vivo, and in vivo measurements.