A large number of algal biofuels projects rely on a lipid screening technique for selecting a particular algal strain with high lipid content from a large pool of candidates. We have developed a multivariate calibration model for predicting the levels of spiked neutral and polar lipids as well as the total measured fatty acid content and level of unsaturation in microalgae. This model is based on infrared (IR), both near IR (NIR) and Fourier-Transform IR (FTIR) spectroscopy. The advantage of a spectroscopic technique over traditional, labor and time consuming, chemical methods is the fast (< 1 min), and non-destructive nature of the screening method. A first feasibility study is based on nine levels of exogenous lipid spikes (between 1 and 3 % (w/w)) of trilaurin as a triglyceride and phosphatidylcholine as a phospholipid model compound in lyophilized algal biomass from Nannochloropsis sp., Chlorococcum sp., Spirulina sp. and an unknown diatom. We used a chemometric approach (Partial-Least-Squares (PLS) regression) to identify the main spectral variation upon increasing phospholipid and triglyceride content in algal biomass collected from either single species or multiple species combined. Recently we built a calibration model based on FTIR spectra correlated to the fatty acid content measured in Chlorella vulgaris biomass and applied this model to samples from cultures grown under nitrogen replete and deplete conditions. Our results show that NIR and FTIR spectra of algal biomass from five different species can be used to accurately predict the levels of exogenously added lipids and measured fatty acid content.