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Method validation is a mandatory step in bioanalysis, to evaluate the ability of developed methods in providing reliable results for their routine application. Even if some organisations have developed guidelines to define the different parameters to be included in method validation (FDA, EMA); there are still some ambiguous concepts in validation criteria and methodology that need to be clarified. The methodology to calculate fundamental parameters such as the limit of quantification has been defined in several ways without reaching a harmonised definition, which can lead to very different values depending on the applied criterion. Other parameters such as robustness or ruggedness are usually omitted and when defined there is not an established approach to evaluate them. Especially significant is the case of the matrix effect evaluation which is one of the most critical points to be studied in LC-MS methods but has been traditionally overlooked. Due to the increasing importance of bioanalysis this scenario is no longer acceptable and harmonised criteria involving all the concerned parties should be arisen. The objective of this review is thus to discuss and highlight several essential aspects of method validation, focused in bioanalysis. The overall validation process including common validation parameters (selectivity, linearity range, precision, accuracy, stability…) will be reviewed. Furthermore, the most controversial parameters (limit of quantification, robustness and matrix effect) will be carefully studied and the definitions and methodology proposed by the different regulatory bodies will be compared. This review aims to clarify the methodology to be followed in bioanalytical method validation, facilitating this time consuming step., (Copyright © 2014 Elsevier B.V. All rights reserved.)

This work was conducted to study a new separation and evaluation approach for the chemical fingerprinting of petroleum biomarkers in biota samples. The final aim of this work was to study the correlation between the observed effects in the shore habitats (mussels and limpets) and one pollution source: the oil spill of the Prestige tanker. The method combined a clean-up step of the biota extracts (mussels and limpets), the retention-time locking of the gas chromatographic set up, and the multivariate data analysis of the chromatograms. For clean-up, solid-phase extraction and gel permeation chromatography were compared, and 5g Florisil cartridges assured the lack of interfering compounds in the last extracts. In order to assure reproducible retention times and to avoid the realignment of the chromatograms, the retention-time locking feature of our gas chromatography-mass spectrometry (GC-MS) set up was used. Finally, in the case of multivariate analysis, the GC-MS chromatograms were treated, essentially by derivatization and by normalization, and all the chromatograms at m/z 191 (terpenes), m/z 217-218 (steranes and diasteranes) and m/z 231 (triaromatic steranes) were treated by means of principal component analysis. Furthermore, slightly different four oil samples from the Prestige oil spill were analyzed following the Nordtest method, and the GC-MS chromatograms were considered as the reference chemical fingerprints of the sources. In this sense, the correlation between the studied samples, including sediments and biota samples, and the source candidate was completed by means of a supervised pattern recognition method. As a result, the method proposed in this work was useful to identify the Prestige oil spill as the source of many of the analyzed samples.