Simultaneous determination of quality parameters in biodiesel/diesel blends using synchronous fluorescence and multivariate analysis

Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration ([Formula: see text]), root mean square errors of calibration (RMSEC), determinant coefficients of prediction ([Formula: see text]), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ([Formula: see text] = 0.907, RMSEC = 0.425%, [Formula: see text] = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ([Formula: see text] = 0.888, RMSEC = 0.446%, [Formula: see text] = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples_×Δλ_×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.

Single excitation-emission fluorescence spectrum (EEF) for determination of cetane improver in diesel fuel

A highly sensitive spectrofluorimetric method has been developed for the determination of 2-ethylhexyl nitrate in diesel fuel. Usually, this compound is used as an additive in order to improve cetane number. The analytical method consists in building the chemometric model as a first step. Then, it is possible to quantify the analyte with only recording a single excitation-emission fluorescence spectrum (EEF), whose data are introduced in the chemometric model above mentioned. Another important characteristic of this method is that the fuel sample was used without any pre-treatment for EEF. This work provides an interest improvement to fluorescence techniques using the rapid and easily applicable EEF approach to analyze such complex matrices. Exploding EEF was the key to a successful determination, obtaining a detection limit of 0.00434% (v/v) and a limit of quantification of 0.01446% (v/v).