No abstract

The partial least-squares (PLS) calibration method as a chemometric tool was used to develop a calibration model using Fourier transform infrared spectroscopy (FTIR) spectra data of biodiesel samples from different sources, such as cotton, castor, and palm, which were mixed with raw soybean oil to simulate an adulteration system. The PLS calibration method was applied with and without variable selection to quantify the amount of raw soybean oil present in these samples. Classic methods of variable selection, such as forward and stepwise, were applied to all origins together and each one separately. Variable selection improves not only the stability of the model to the colinearity in multivariate spectra but also the interpretability of the relationship between the model and the sample composition, which means that it becomes easier to determine and quantify the amount of raw soybean oil mixed in each biodiesel source.

Focusing on the O 2 class, a set of crude oils from Llanos Orientales Basin, Colombia, were classified in terms of biodegradation levels using negative ion mode electrospray Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and chemometric analysis. The O 2 class, which is mainly composed of naphthenic carboxylic acids, was monitored because these polar crude oil constituents are known to be substantially affected by microbial activity. Principal component analysis (PCA) applied on the O 2 profile was able to classify the crude oils into three groups: biodegraded, mixture, and nonbiodegraded. From the relative abundances of the O 2 class, a clear trend on acid distribution could be directly correlated with biodegradation: a rising in abundance of saturated acids with low double-bond equivalent (DBE) values (despite the lowering observed for fatty acids with DBE = 1), expressed by the A/C index. The combined use of two indexes, the A/C index and a new index also based on saturated acid abundances, the SA index, is proposed as an effective strategy to monitor biodegradation. This approach showed to be particularly useful to fill blanks on discrete biodegradation classification and when samples are actually composed of a mixture of oils with contrasting biodegradation levels. Results are in good agreement with predictions based on classical hydrocarbon biomarker analysis.