Ester Rojo

Estimation of toxicity of ionic liquids in Leukemia Rat Cell Line and Acetylcholinesterase enzyme by principal component analysis, neural networks and multiple lineal regressions

Multiple linear regression (MLR), radial basis network (RB), and multilayer perceptron (MLP) neural network (NN) models have been explored for the estimation of toxicity of ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium and quinolinium ionic liquid salts in the Leukemia Rat Cell Line (IPC-81) and Acetylcholinesterase (AChE) using only their empirical formulas (elemental composition) and molecular weights. The toxicity values were estimated by means of decadic logarithms of the half maximal effective concentration (EC(50)) in microM (log(10)EC(50)). The models performances were analyzed by statistical parameters, analysis of residuals and central tendency and statistical dispersion tests. The MLP model estimates the log(10)EC(50) in IPC-81 and AchE with a mean prediction error less than 2.2 and 3.8%, respectively.

A Novel Method To Quantify the Adulteration of Extra Virgin Olive Oil with Low-Grade Olive Oils by UV−Vis

A simple and novel method to quantify adulterations of extra virgin olive oil (EVOO) with refined olive oil (ROO) and refined olive-pomace oil (ROPO) is described here. This method consists of calculating chaotic parameters (Lyapunov exponent, autocorrelation coefficients, and two fractal dimensions, CPs) from UV-vis scans of adulterated EVOO samples. These parameters have been successfully linearly correlated with the ROO or ROPO concentrations in 396 EVOO adulterated samples. By an external validation process, when the adulterating agent concentration is less than 10%, the integrated CPs/UV-vis model estimates the adulterant agent concentration with a mean correlation coefficient (estimated versus real concentration of low grade olive oil) greater than 0.97 and a mean square error of less than 1%. In light of these results, this detector is suitable not only to detect adulterations but also to measure impurities when, for instance, a higher grade olive oil is transferred to another storage tank in which lower grade olive oil was stored that had not been adequately cleaned.

Linear and non linear chemometric models to quantify the adulteration of extra virgin olive oil

Two mathematical methods to quantify adulterations of extra virgin olive oil (EVOO) with refined olive oil (ROO), refined olive-pomace oil (ROPO), sunflower (SO) or corn (CO) oils have been described here. These methods are linear and non linear models based on chaotic parameters (CPs, Lyapunov exponent, autocorrelation coefficients and two fractal dimensions) which were calculated from UV-vis scans (190-900 nm wavelength) of 817 adulterated EVOO samples. By an external validation process, linear and non linear integrated CPs/UV-vis models estimate concentrations of adulterant agents with a mean correlation coefficient (estimated versus real concentration of cheaper oil) greater than 0.80 and 0.97 and a mean square error less than 1% and 0.007%, respectively. In the light of the results shown in this paper, the adulteration of EVOO with ROO, ROPO, SO and CO can be suitably detected by only one chaotic parameter integrated on a radial basis network model.

Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils.

Self-organizing map (SOM) and learning vector quantification network (LVQ) models have been explored for the identification of edible and vegetable oils and to detect adulteration of extra virgin olive oil (EVOO) using the most common chemicals in these oils, viz. saturated fatty (mainly palmitic and stearic acids), oleic and linoleic acids. The optimization and validation processes of the models have been carried out using bibliographical sources, that is, a database for developing learning process and internal validation, and six other different databases to perform their external validation. The models performances were analyzed by the number of misclassifications. In the worst of the cases, the SOM and LVQ models are able to classify more than the 94% of samples and detect adulterations of EVOO with corn, soya, sunflower, and hazelnut oils when their oil concentrations are higher than 10, 5, 5, and 10%, respectively.

Chaotic parameters and their role in quantifying noise in the output signals from UV, TGA and DSC apparatus

Two fractal dimensions and the Liapunov exponent (LE) have been applied to detect noisy output signals from UV spectrophotometer (UV), thermogravimetric analyzer (TGA) and differential scanning calorimeter (DSC) apparatus of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid ([emim][EtSO(4)]). The data collected from these three pieces of equipment were classified before calculating LE, regularization (RD) and box dimensions (BD). The RD and LE are able individually to detect and quantify noisy output signals with a mean error value less than 5% in all cases tested. Given that the LE can be calculated using a really simple method, this chaotic parameter has been selected as the most suitable to detect noise of signals from these apparatus.

Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils and detect adulterations of extra virgin olive oil

Abstract Unsupervised models have been explored for the identification of edible and vegetable oils and to detect adulteration of extra virgin olive oil (EVOO) using the most common chemicals in these oils such as saturated fatty, oleic and linoleic acids. The optimization and validation processes of the models have been carried out using bibliographical sources. A database for developing learning process and internal validation, and six other different databases to perform their external validation has been used. In the worst of the cases, the unsupervised models are able to classify more than the 94 % of samples and detect adulterations of EVOO with promising results. The adulteration of EVOO with corn, soya, sunflower and hazelnut oils can be detected when their oil concentrations are higher than 10, 5, 5 and 10 %, respectively.

Ionic liquids: determination of their aqueous content using differential scanning calorimeter equipment, chaotic parameters and a radial basis network model.

A new computerized approach to the determination of water in 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide, 1-butyl-3-methylimidazolium hexafluorophosfate and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide ionic liquids (ILs) using the differential scanning calorimeter (DSC) scans of their mixtures with water is presented here. This approach consists of a combination of chaotic algorithms and a radial basis network (RBN). The data collected (heat flow signal) from DSC scans of ILs and water mixtures are used to calculate six chaotic parameters (two Liapunov exponents, two correlation parameters and two fractal dimensions), and then, these values are transferred into an RBN trained computer for modeling and estimating output. The predicted results using the RBN were compared with the measurements of water content carried out by the Karl Fischer technique and the difference between the real and predicted values was less than 0.05 and 4.9% in the internal and external validation, respectively. Such an integrated chaotic parameters (CPs)/RBN system is capable of detecting and quantifying water content in the aforementioned ILs, based on the created models and patterns, without any previous knowledge of this thermal process.