Publication date: 1 April 2018
Source:Talanta, Volume 180
Author(s): Sarmento Júnior Mazivila
Discrimination of biodiesel feedstock present in diesel-biodiesel blend is challenging due to the great similarity in the spectral profile as well as digital image profile of each type of feedstock employed in biodiesel production. Once the marketed diesel-biodiesel blend is subsidized, in which motivates adulteration in biofuel blend by cheaper supplies with high solubility to obtain profits associated with the subsidies involved in biodiesel production. Non-destructive analytical methods based on qualitative and quantitative analysis for detecting marketed diesel-biodiesel blend adulteration are reviewed. Therefore, at the end is discussed the advantage of the qualitative analysis over quantitative analysis, when the systems require immediate decisions such as to know if the marketed diesel-biodiesel blend is unadulterated or adulterated in order to aid the analyst in selecting the most appropriate green analytical procedure for detecting diesel-biodiesel blend adulteration proceeding in fast way. This critical review provides a brief review on the non-destructive analytical methods reported in scientific literature based on different first-order multivariate calibration models coupled with spectroscopy data and digital image data to identify the type of biodiesel feedstock present in diesel-biodiesel blend in order to meets the strategies adopted by European Commission Directive 2012/0288/EC as well as to monitoring diesel-biodiesel adulteration. According to that Directive, from 2020 biodiesel produced from first-generation feedstock, that is, oils employed in human food such as sunflower, soybean, rapeseed, palm oil, among other oils should not be subsidized. Therefore, those non-destructive analytical methods here reviewed are helpful for discrimination of biodiesel feedstock present in diesel-biodiesel blend according to European Commission Directive 2012/0288/EC as well as for detecting diesel-biodiesel blend adulteration.
Graphical abstract
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