The concentrations of blood and melanin in skin can be estimated based on the reflectance of light. Many models for this estimation have been built, such as Monte Carlo simulation, diffusion models, and the differential modified Beer–Lambert law. The optimization-based methods are too slow for chromophore mapping of high-resolution spectral images, and the differential modified Beer–Lambert is not often accurate enough. Optimal coefficients for the differential Beer–Lambert model are calculated by differentiating the diffusion model, optimized to the normal skin spectrum. The derivatives are then used in predicting the difference in chromophore concentrations from the difference in absorption spectra. The accuracy of the method is tested both computationally and experimentally using a Monte Carlo multilayer simulation model, and the data are measured from the palm of a hand during an Allen's test, which modulates the blood content of skin. The correlations of the given and predicted blood, melanin, and oxygen saturation levels are correspondingly r = 0.94, r = 0.99, and r = 0.73. The prediction of the concentrations for all pixels in a 1-megapixel image would take ∼20 min, which is orders of magnitude faster than the methods based on optimization during the prediction.