Alejandro C. Olivieri

Franco Allegrini2
Claudio J. Salomon1
Marina Soazo1
2Franco Allegrini
1Claudio J. Salomon
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Genetic algorithms and other procedures mimicking natural processes are being increasingly used for variable selection, to improve the predictive ability of partial least-squares multivariate calibration. Two issues are critical for the success of genetic algorithms: initialization (setting the first candidates for solving the problem at hand) and(More)
Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the(More)
Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or(More)
  • Daniel A. Real, María V. Martinez, Agustín Frattini, Marina Soazo, Alicia G. Luque, Marisa S. Biasoli +3 others
  • 2012
The objective of the present paper was the development and the full characterization of antifungal films. Econazole nitrate (ECN) was loaded in a polymeric matrix formed by chitosan (CH) and carbopol 971NF (CB). Polyethylene glycol 400 and sorbitol were used as plasticizing agents. The mechanical properties of films were poorer when the drug was loaded,(More)
Appropriate closed-form expressions are known for estimating analyte sensitivities when calibrating with one-, two-, and three-way data (vectors, matrices, and three-dimensional arrays, respectively, built with data for a group of samples). In this report, sensitivities are estimated for calibration with four-way data using the quadrilinear parallel factor(More)
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