Non-destructive analytical strategies based on NIR spectroscopy for monitoring the quality of biodiesel/diesel blends in terms of the authentication of second-generation biodiesel feedstock using DD-SIMCA and quantification of the biodiesel content using iSPA-PLS

Abstract

Considering relevant environmental, economic, and social factors, the discussion on the use of second-generation biofuels has gained prominence in recent years, especially supported by the Sustainable Development Goals (SDGs) and the Paris COP21 climate change agreement. One of the policies implemented by different countries has been the mandatory blend of biodiesel in commercial petrodiesel. Thus, this work proposes the use of NIR spectroscopy to monitor the quality of biodiesel/diesel blends. The analytical strategies involve the authentication of second-generation biodiesel feedstock using Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and the quantification of biodiesel content regardless of the biodiesel feedstock using the Successive Projections Algorithm for interval selection in Partial Least Squares (iSPA-PLS). To avoid dilution of the samples with organic solvents, two spectral regions (881–1651 and 1911–2203 nm) were investigated. As a result, the spectral range of 1911–2203 nm achieved the best results for both the qualitative and quantitative analysis. Accordingly, the authentication efficiencies for DD-SIMCA were 97.6% and 99.2% for jatropha and beef tallow biodiesel/diesel blends using multiplicative scattering correction (MSC) and Savitzky-Golay smoothing (with a second-order polynomial and a 15-point window) coupled with the first derivative (SGD) as pre-processing algorithms, respectively. Furthermore, a relative error of prediction of only 2.85% was obtained for the biodiesel content quantification using SGD/iSPA-PLS. Hence, the proposed analytical methodology provides valuable support to governments and regulatory bodies in overseeing the quality of this product, ensuring that consumers are safeguarded against economic repercussions resulting from improper or fraudulent blending. Moreover, it aligns with the principles of Green Analytical Chemistry and contributes to the SDG 7 (affordable and clean energy) and SDG 13 (climate action).