Combining NIR spectroscopy with DD-SIMCA for authentication and iSPA-PLS-DA for discrimination of ethyl route and oil feedstocks of biodiesels in biodiesel/diesel blends

The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near-infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non-target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares-Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA-PLS-DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. As advantages, the proposed analytical methodology contributes to the United Nations' Sustainable Development Goal (SDG) #7 (affordable and clean energy) as well as aligns with the principles of Green Analytical Chemistry.