Multivariate calibrations in Fourier transform infrared spectrometry for prediction of kerosene properties

Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) is being proposed more and more as a high-throughput technology to assess the elemental composition of materials. When a specific element is of interest, semiquantification is possible by building a calibration model using the emission line intensity of this element for known samples. However, a unique element has usually more than one emission line, and there are many examples where several emission lines used in combination give dramatically better results than any of the individual variables used alone. With a multivariate approach, models can be constructed that take into account all the emission lines related to a specific element; therefore more robust models can be developed. In this work, chemometric methods such as principal component analysis and partial least squares are proposed to resolve and extract useful information from the LIBS spectral data collected on biological materials.

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.