Detection of cold chain breaks using partial least squares-class modelling based on biogenic amine profiles in tuna.

The maintenance of the cold chain is essential to ensure foodstuff conformity and safety. However, gaps in the cold chain may be expected so designing analytical methods capable to detect cold chain breaks is a worthwhile issue. In this paper, the possibility of using the amount of nine biogenic amines (BAs) determined in Thunnus albacares by HPLC-FLD for detecting cold chain breaks is approached. Tuna is stored at 3 different temperature conditions for 8 storage periods. The evolution of the content of BAs is analyzed through parallel factor analysis (PARAFAC), in such a way that storage temperature, BAs and storage time profiles are estimated. PARAFAC has made it possible to observe two spoilage routes with different relative evolution of BAs. In addition, it has enabled to estimate the storage time, by considering the three storage temperatures, with errors of 0.5 and 1.0 days in fitting and in prediction, respectively. Furthermore, a class-modelling technique based on partial least squares is sequentially applied to decide, from the amount of BAs, if there has been a cold chain break. Firstly, samples stored at 25 °C are statistically discriminated from those kept at 4 °C and -18 °C; next, frozen samples are distinguished from those refrigerated. In the first case, the probabilities of false non-compliance and false compliance are almost zero, whereas in the second one, both probabilities are 10%. Globally, the results of this work have pointed out the feasibility of using the amount of BAs together with PLS-CM to decide if the cold chain has been maintained or not.

Ad-hoc blocked design for the robustness study in the determination of dichlobenil and 2,6-dichlorobenzamide in onions by programmed temperature vaporization-gas chromatography-mass spectrometry.

An 'ad-hoc' experimental design to handle the robustness study for the simultaneous determination of dichlobenil and its main metabolite (2,6-dichlorobenzamide) in onions by programmed temperature vaporization-gas chromatography-mass spectrometry (PTV-GC-MS) is performed. Eighteen experimental factors were considered; 7 related with the extraction and clean up step, 8 with the PTV injection step and 3 factors related with the derivatization step. Therefore, a high number of experiments must be carried out that cannot be conducted in one experimental session and, as a consequence, the experiments of the robustness study must be performed in several sessions or blocks. The procedure to obtain an experimental design suitable for this task works by simultaneously minimizing the joint confidence region for the coefficient estimates and the correlation among them and with the block. In this way, the effect of the factors is not aliased with the block avoiding possible misinterpretations of the effects of the experimental factors on the analytical responses. The developed experimental design is coupled to the PARAFAC2 method, which allows solving some specific problems in chromatography when working with complex matrix such as co-elution of interferents (including silylation artifacts from the derivatization step) and small shifts in the retention time and, besides, the unequivocal identification of the target compounds according to document SANCO/12571/2013.

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).

Robust and non-parametric statistics in the evaluation of figures of merit of analytical methods. Practices for students.

A set of laboratory practices is proposed in which evaluation of the quality of the analytical measurements is incorporated explicitly by applying systematically suitable methodology for extracting the useful information contained in chemical data. Non-parametric and robust techniques useful for detecting outliers have been used to evaluate different figures of merit in the validation and optimization of analytical methods. In particular, they are used for determination of the capability of detection according to ISO 11843 and IUPAC and for determination of linear range, for assessment of the response surface fitted using an experimental design to optimize an instrumental technique, and for analysis of a proficiency test carried out by different groups of students. The tools used are robust regression, least median of squares (LMS) regression, and some robust estimators as median absolute deviation (m.a.d.) or Huber estimator, which are very useful as an alternatives to the usual centralization and dispersion estimators.

Methodology of multicriteria optimization in chemical analysis Some applications in stripping voltammetry.

Multicriteria optimization, widely used in engineering, does not much used in the optimization of analytical signals. The aim of this paper is to show the usefulness of the desirability function to optimize instrumental responses obtained in instrumental analysis. The simultaneous optimization of a signal and of its variability is a generic question of interest to any chemical analyst. It is clear that the improvement of the two responses forms the basis of the validation of any analytical method, and affects all the figures of merit: accuracy (trueness and precision), capability of detection, robustness, sensitivity, etc. Furthermore, in the specific case of electroanalysis, an improvement in the signal may implicitly mean an increase of the signal in the blank, such that the "net signal" may not improve. This experimental approach (surface response methodology plus desirability) to multicriteria optimization has been applied to three cases of growing complexity. Thus, in the determination of Cu(II) by differential pulse anodic stripping voltammetry the simultaneous maximization of the peak current and minimization of its standard deviation is looked for. Whereas, in the determinations of Ni(II) and indomethacin by differential pulse adsorptive stripping voltammetry, the simultaneous maximization of the peak current and minimization of the blank signal is desired. In all the cases, the experimental conditions where the optima are found for each individual response are just opposite, so it is required to look for a certain compromise, that is achieved using the desirability function.