Edta como extractante universal : I- cationes mayores (Ca, Mg y K)

p.133-137Se escogieron 15 muestras de suelos de diferentes lugares del país procediéndose a la extracción de cationes mayores con solución 0,1 M de EDTA pH 7,0. Paralelamente las mismas muestras se trataron con solución de acetato de amonio pH 7,0, determinándose en ambas soluciones calcio, magnesio y potasio por espectro fotometría de absorción atómica. Los resultados analíticos indican una estrecha correlación para calcio, magnesio y potasio obtenidos con los dos extractantes

Edta como extractante universal : II parte, fósforo y elementos menores

p.139-144En la primera parte de este trabajo se estimó la factibilidad del uso de una solución de EDTA 0,1M en reemplazo de la tradicional de acetato de amonio 1M con el objeto de utilizarlo como extractante universal. En esta segunda parte, que debe tomarse como un intento preliminar, se prueba la extracción de fósforo y elementos menores. Se considera que los resultados son promisorios como primera aproximación para el fósforo, cobre, zinc y manganeso. Por ello se estima recomendable insistir en esta línea de trabajo que de lograr éxito permitirá agilizar los análisis químicos de suelo con el consiguiente incremento en la eficiencia de los laboratorios

Calibration transfer of intact olive NIR spectra between a pre-dispersive instrument and a portable spectrometer

The recent development of new portable devices enables the establishment of NIRS technology at industrial setting (on-line). However, the numerous existing NIRS databases based on a particular parameter, have been constructed with laboratory instruments (off-line), which have required a considerably effort in terms of time, labor and costs. For this reason, the transfer of calibrations between devices of different characteristics is a clearly crucial step. In this study, three different standardization algorithms: Slope/Bias Correction (SBC), Piecewise Direct Standardization (PDS) and Transfer by Orthogonal Projection (TOP) were tested and evaluated for transferring olives quality databases between an off-line NIRS monochromator (FOSS NIRSystem 6500) and a portable NIRS diode-array spectrometer (CORONA 45 visNIR). The results obtained showed that the use of TOP yielded the best Standard Error of Prediction (SEP) values for the fat content (1.97%) and free acidity (2.52%) parameters, while PDS for moisture content (2.24%). These results suggest that good calibration models for quality evaluation in intact olives can be obtained, based on spectral databases transferred between diverses NIRS spectrometers

Perempuan di persidangan : Pemantauan peradilan berperspektif perempuan

Jakartaxx, 234 p.; 21 c

Noise Robustness Comparison for near Infrared Prediction Models

The effect of six artificial noises added to near infrared spectra on the predictive robustness of ten calibration algorithms was investigated for the prediction of whole corn moisture. White noise, multiplicative noise, baseline shift, wavelength shift, spectral stretch/ shrink and stray light were independently added to a set of whole corn near infrared spectra used to validate various regression models based on partial least squares regression, locally weighted regression, variable selection, artificial neural networks and least-squares support vector machines. The quantitative increase in the root mean square error of prediction relative to a unit of noise was used as the robustness criterion. The effectiveness of standard normal variate (SNV) was also tested to remove artificial noises for all models. All models were highly sensitive to white noise and stray light (0.92% pt increase in error for 0.1% of white noise; 3.01% pt increase in error for 0.1% of stray light added to the data, with partial least squares regression). Model robustness was improved by the use of SNV. Variable selection techniques were particularly sensitive to wavelength shift, white noise and stray light. However, SNV pre-treatment before modelling improved the results. </jats:p

Blending process modeling and control by multivariate curve resolution

The application of the Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) method to model and control blending processes of pharmaceutical formulations is assessed. Within the MCR-ALS framework, different data analysis approaches have been tested depending on the objective of the study, i.e., knowing the effect of different factors in the evolution of the blending process (modelling) or detecting the blending end-point and monitoring the concentration of the different species during and at the end of the process (control). Data analysis has been carried out studying multiple blending runs simultaneously taking advantage of the multiset mode of the MCR-ALS method. During the ALS optimization, natural constraints, such as non-negativity (spectral and concentration directions) have been applied for blending modelling. When blending control is the main purpose, a correlation constraint in the concentration direction has been additionally used. This constraint incorporates an internal calibration procedure, which relates resolved concentration values (in arbitrary units) with the real reference concentration values in the calibration samples (known references) providing values in real concentration scale in the final MCR-ALS results. Two systems consisting of pharmaceutical mixtures of an active principle (acetaminophen) with two or four excipients have been investigated. In the first case, MCR results allowed the description of the evolution of the individual compounds and the assessment of some physical effects in the blending process. In the second case, MCR analysis allowed the detection of the end-point of the process and the assessment of the effects linked to variations in the concentration level of the compounds.Peer reviewe

Blending process modeling and control by multivariate curve resolution

The application of the Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) method to model and control blend processes of pharmaceutical formulations is assessed. Within the MCR-ALS framework, different data analysis approaches have been tested depending on the objective of the study, i.e., knowing the effect of different factors in the evolution of the blending process (modeling) or detecting the blend end-point and monitoring the concentration of the different species during and at the end of the process (control). Data analysis has been carried out studying multiple blending runs simultaneously taking advantage of the multiset mode of the MCR-ALS method. During the ALS optimization, natural constraints, such as non-negativity (spectral and concentration directions) have been applied for blend modeling. When blending control is the main purpose, a variant of the MCR-ALS algorithm with correlation constraint in the concentration direction has been additionally used. This constraint incorporates an internal calibration procedure, which relates resolved concentration values (in arbitrary units) with the real reference concentration values in the calibration samples (known references) providing values in real concentration scale in the final MCR-ALS results. Two systems consisting of pharmaceutical mixtures of an active principle (acetaminophen) with two or four excipients have been investigated. In the first case, MCR results allowed the description of the evolution of the individual compounds and the assessment of some physical effects in the blending process. In the second case, MCR analysis allowed the detection of the end-point of the process and the assessment of the effects linked to variations in the concentration level of the compounds. © 2013 Elsevier B.V