0000000000181498
AUTHOR
Mercedes Cascant
showing 4 related works from this author
An infrared spectroscopic tool for process monitoring: sugar contents during the production of a depilatory formulation.
2012
Abstract 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. Employing a multivariate calibration model established from data of 15 well characterized samples, prediction errors equal or below 3.04 mg mL−1 for the quantitative determination of fructose, glucose, sucrose, maltose and maltotriose were obtained. Results found in this preliminary study indicate a g…
Determination of sugars in depilatory formulations: a green analytical method employing infrared detection and partial least squares regression.
2011
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 7.04 to 12.55 mg sugar g(-1) sample. As a reference procedure, gradient liquid chromatography with on-line infrared detection, employing background correction based on cubic smoothing splines, was used. The analysis revealed changes in the suga…
Determination of total phenolic compounds in compost by infrared spectroscopy
2016
Abstract 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 ( R pred 2 ) and residual predictive deviation (RPD) being obtained for this la…
Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by Infrared spectroscopy and partial least square regre…
2017
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. Attenuated total reflectance (PLS-ATR-MIR) and diffuse reflectance (PLS-DR-NIR) models were built and evaluated from root mean square error of cross validation and prediction (RMSECV and RMSEP), coefficients of determ…