Physical-chemical and ecotoxic evaluation of different deep eutectic solvents for green analytical applications.

The search for new analytical methods is a latent reality in the so-called green analytical chemistry area, which aims at correlating analytical demands to environmental issues. Among the approaches used, it is possible to highlight green solvents as substitutes to the dangerous and conventional organic solvents as the most prominent alternative for this purpose. In the last few years, the amount of research focused on the usage of deep eutectic solvents (DESs) has been growing as an alternative to these issues. Thus, this work aimed to investigate the main physical-chemical and ecotoxical properties of seven different DESs. The results showed that DESs' evaluated properties are influenced by the chemical structure of their precursors, which may regulate DESs' viscosity, superficial tension, and antagonistic action against vegetable tissues and microbial cells. The constatations pointed here introduce a new perspective about the conscious usage of DESs on a green analytical point of view.

Detection of adulteration in hydrated ethyl alcohol fuel using infrared spectroscopy and supervised pattern recognition methods.

This paper proposes an analytical method to detect adulteration of hydrated ethyl alcohol fuel based on near infrared (NIR) and middle infrared (MIR) spectroscopies associated with supervised pattern recognition methods. For this purpose, linear discriminant analysis (LDA) was employed to build a classification model on the basis of a reduced subset of wavenumbers. For variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA) and a stepwise formulation (SW). For comparison, models based on partial least squares discriminant analysis (PLS-DA) were also employed using full-spectrum. The method was validated in a case study involving the classification of 181 hydrated ethyl alcohol fuel samples, which were divided into three different classes: (1) authentic samples; (2) samples adulterated with water and (3) samples contaminated with methanol. LDA/GA and PLS-DA models were found to be the best methods for classifying the spectral data obtained in NIR region, which achieved a correct prediction rate of 100% in the test set, while the LDA/SPA and LDA/SW were correctly classified at 84.4% and 97.8%, respectively. For MIR data, all models (PLS-DA and LDA coupled with the SW, SPA and GA) employed in this study correctly classified all samples in the test set.