Noninvasive monitoring of evolving urinary metabolic patterns in neonatal encephalopathy.

Background: Infants with moderate and severe neonatal encephalopathy (NE) frequently suffer from long-term adverse outcomes. We hypothesize that the urinary metabolome of newborns with NE reflects the evolution of injury patterns observed with magnetic resonance imaging (MRI).

Methods: Eligible patients were newborn infants with perinatal asphyxia evolving to NE and qualifying for therapeutic hypothermia (TH) included in the HYPOTOP trial.

Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy.

Near-infrared (NIR) spectroscopy was evaluated as a rapid method for the determination of oleic, palmitic, linoleic and linolenic acids as well as omega-3, omega-6, and to predict polyunsaturated, monounsaturated and saturated fatty acids, together with triacylglycerides, diglycerides, free fatty acids and ergosterol in salmon oil. To do it, Partial Least Squares (PLS) regression models were applied to correlate NIR spectra with aforementioned fatty acids and lipid classes. Results obtained were validated in front of reference procedures based on high performance thin layer and gas chromatography.

A green analytical chemistry approach for lipid extraction: computation methods in the selection of green solvents as alternative to hexane.

There is a great interest in finding alternatives and green solvents in extraction processes to replace petroleum based solvents. In order to investigate these possibilities, computational methods, as Hansen solubility parameters (HSP) and conductor-like screening model for real solvent (COSMO-RS), were used in this work to predict the solvation power of a series of solvents in salmon fish lipids. Additionally, experimental studies were used to evaluate the performance in lipids extraction using 2-methyltetrahydrofurane, cyclopentyl methyl ether, dimethyl carbonate, isopropanol, ethanol, ethyl acetate, p-cymene and d-limonene compared with hexane.

Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by Infrared spectroscopy and partial least square regression.

Middle and near infrared (MIR and NIR) were employed to determine organic carbon (OC) and total nitrogen (TN) in different soil organic amendments including wastes, composts and mixtures of composts and organic wastes. Prediction models based on partial least squares (PLS) regression from the spectra of untreated samples were built. Different spectra preprocessing strategies were adopted and the best number of latent variable was evaluated using leave-one-out cross-validation.

Prediction of alkaline earth elements in bone remains by near infrared spectroscopy.

An innovative methodological approach has been developed for the prediction of the mineral element composition of bone remains. It is based on the use of Fourier Transform Near Infrared (FT-NIR) diffuse reflectance measurements. The method permits a fast, cheap and green analytical way, to understand post-mortem degradation of bones caused by the environment conditions on different skeletal parts and to select the best preserved bone samples.

Determination of total phenolic compounds in compost by infrared spectroscopy.

Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred(2)) and residual predictive deviation (RPD) being obtained for this latter values of 5.

Direct determination of major components in human diets and baby foods.

A fast method has been developed for the determination of fat, proteins, carbohydrates, and energy value in baby food, infant fast food, and canteen menus, using near-infrared (NIR) and mid-infrared (MIR) spectroscopy measurements and multivariate calibration methods based on partial least square regression. Reference standard methods were employed to build and validate the infrared methods for direct determination of nutrients. Coefficients of determination obtained between predicted values and reference ones for total fat, proteins, carbohydrates, and energy value were 96.

An infrared spectroscopic tool for process monitoring: sugar contents during the production of a depilatory formulation.

A fast, reliable and economical methodology has been developed to control the production process of sugar-based depilatories. The method is based on the use of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with multivariate data analysis. A very simple sample preparation process involving the dissolution of samples in water was applied.

Determination of sugars in depilatory formulations: a green analytical method employing infrared detection and partial least squares regression.

A green analytical method was developed for the analysis of sugar-based depilatories. Three independent partial least squares (PLS) regression models were built for the direct determination of glucose, fructose and maltose without any sample pretreatment based on their attenuated total reflectance - Fourier transform infrared (ATR-FTIR) spectra. The models showed adequate prediction capabilities with root-mean-square-errors of prediction ranging from 7.