A 3layered skin model is built to simulate hyperspectral reflectance using Monte Carlo simulations based on biological components, which include melanin volume fraction, water level, blood volume fraction, oxygen saturation, etc. A forward neural network is trained for mapping biological components and reflectance. Then a database, which contains 50,000 samples spectra from 450 to 750 nm with randomly given biological components information, are generated by this forward neural network. Support vector regression, inverse neural networks and random forest are applied for the regression analysis of reflectance data and melanin volume fraction. Dimensionality reduction is used to accelerate the training time. The performances of three regression methods are measured and show promising prediction results.