Sustainable biofuels and bioproducts technologies are being developed by fermentation of sugars present and released from pretreated cellulosic biomass to lipids using oleaginous yeasts. Detailed analytical characterization of lipid content through cultivation under different scenarios not only is a bottleneck that slows down development of improved strains and processes, this process also creates significant chemical waste. Since lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum, the use of multivariate linear regression of respective wavelengths can be used for the prediction of intracellular lipid content present in the yeast biomass. In this work, we present data on the multivariate quantitative correlation of NIR spectra with measured lipid content in different oleaginous yeast strains. This work is the first demonstration of the rapid, non-destructive, lipid quantification on as little as 10 mg of yeast biomass in a 96-well format, preventing significant chemical pollution by applying a real-time monitoring process. We demonstrate a distinct correlation of lipid content with the accumulation of select fatty acids of the lipids for 5 different yeast species, among which, for <
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S. cerevisiae<
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and <
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L. starkeyi<
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, in-depth calibration curves were developed from 65 and 154 unique samples, respectively. Laslty, we demonstrate that NIR spectra can be used to accurately predict intracellular lipid content using multivariate linear regression analysis in a manner of minutes, avoiding the need for lengthy chemical analyses that are resource intensive.