Celia Reguera

Optimization of a FIA system with amperometric detection by means of a desirability function: determination of sulfadiazine, sulfamethazine and sulfamerazine in milk.

A sensitive and cheap FIA, with amperometric detection, analytical procedure is developed in this paper to determine sulfadiazine, sulfamethazine and sulfamerazine in milk. A multicriteria optimization based on the use of a desirability function is used for optimizing two analytical responses (peak height and its variability) since single-objective optimizations lead to conflicting experimental conditions. In the optimum conditions, the determination of the three sulfonamides in milk samples is carried out, the analytical procedure being validated according to Commission Decision 2002/657/EC. The decision limit at 0 and 100 microg L(-1) (which is the maximum residue limit in milk) are 13.9 and 110.2, 9.5 and 107.1 and 9.1 and 107.1 microg L(-1) for sulfadiazine, sulfamethazine and sulfamerazine, respectively. Whereas the values of capability of detection, CCbeta, obtained at 0 and 100 microg L(-1) were 26.9 and 119.8, 18.2 and 113.6, and 17.5 and 113.7 microg L(-1) for sulfadiazine, sulfamethazine and sulfamerazine, respectively. Recovery values between 67.4% and 119.1% are found for milk test samples of two brands of milk. The accuracy of the method is confirmed. The ruggedness of the procedure is evaluated by means of a Plackett-Burman design. The relative errors were lower than 2.5% (n=7).

Pareto-optimal front as a tool to study the behaviour of experimental factors in multi-response analytical procedures.

This work presents a methodology to analyse the behaviour of an analytical procedure, above all when optimization of the procedure is needed. The methodology starts by the design of an experiment suitable to fit response surfaces to some analytical responses of interest in the problem being studied. Then, a pareto-optimal front is estimated that accounts for the optimal possibly trading-off solutions among the responses. The analysis of the behaviour of the optimal values of the response surfaces and the experimental conditions that provide these values allows going deeply into the analytical procedure.

Chapter 2 – Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data

Abstract Numerous research activities generate data that are organized as a three-way tensor, which can be described with PARAFAC. This chapter starts by briefly describing the PARAFAC model and the properties that support its practical utility. Besides, two alternative models are presented, PARAFAC2 and Tucker3, which are useful when the data are inconsistent with the PARAFAC model. The properties are illustrated throughout four cases that consider different analytical scenarios: (1) quantification of a target analyte in presence of interferents (excitation-emission fluorescence spectra); (2) quantification and identification of an analyte for regulated analyses ensuring the probabilities of false positive and false negative, even in the presence of interferents (gas chromatography-mass spectrometry data for pesticides); (3) prediction of a quantitative response with no specific signals (electronic nose data to predict storage time); and (4) description of a complex temporal process (for deciding on harvesting grapes).

Impact of time and temperature of storage on the spoilage of swordfish and the evolution of biogenic amines through a multiway model

A new multiway/multivariate approach is proposed to study and model the spoilage of swordfish with time and temperature of storage through the profiles of putrescine, spermidine, histamine, tyramine, tryptamine, cadaverine, spermine, and 2‐phenylethylamine. The evolution of these biogenic amines in food is a complex process that cannot be characterized by a single parameter but by a modification of the amine profiles. An experimental strategy is designed to determine these profiles in such a way that data are structurally 3‐way. Modeling the joint evolution of the biogenic amines with a PARAFAC model which explains 97.8% of variability (CORCONDIA index equals 100%) leads to estimate the storage time, storage temperature, and biogenic amines profiles. A multiple regression (determination coefficient of 0.98) based on the loadings of the 2 factors of the time profile of the PARAFAC model enables the estimation of the storage time with an error of 0.5 days.