Browsing by Author "Rodrigues, Pedro M."
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- Definitive screening designs and latent variable modelling for the optimization of solid phase microextraction (SPME): case study-quantification of volatile fatty acids in winesPublication . Pereira, Ana C.; Reis, Marco S.; Leça, João M.; Rodrigues, Pedro M.; Marques, José C.In the present study, we apply the recently proposed Definitive Screening Designs (DSD) to optimize HS-SPME extraction in order to analyze volatile fatty acids (VFA) present in wine samples. This is the first attempt to apply this new class of designs to one of the most well-known and widely applied extraction techniques. The latent structure of the responses is also explored for defining the optimal extraction conditions. DSD is a new screening design with the potential to significantly reduce the number of experiments required to estimate the model pa rameters and to establish the optimum operation conditions. Therefore, there is an obvious interest in assessing the benefits of DSD in practice. In this work, this design framework is applied to the simultaneous optimization of seven extraction parameters (responses). Both qualitative and quantitative extraction parameters are considered, in order to test the flexibility of DSD designs: a two-level qualitative variable, the fiber coating, and six quanti tative variables, namely the pre-incubation time, the extraction time and temperature, the headspace/sample volume, the effect of agitation during extraction and the influence of the ethanol content (sample dilution). Optimization of analytes' chromatographic responses was carried out both individually (response by response) and altogether, by modelling the responses in the latent variable space (i.e., explicitly considering their under lying correlation structure). In the end, a consensus analysis of all perspectives was considered in the definition of the overall optimal extraction conditions for the quantification of VFA in fortified wines. The solution found was to use a DVB/Car/PDMS fiber, 10 mL of samples in 20 mL vial, 40 min of extraction at 40 C. The analysis also revealed that the factors incubation time, agitation and sample dilution do not play a significant role in explaining the variability of extraction parameters. Therefore, they were set to the most convenient levels. The methodology followed was thoroughly validated and the following figures of merit were obtained: good linearity (R2 > 0.999, for all compounds), high sensitivity (LOD and LOQ are close or below the values found in literature), recoveries of approximately 100% and suitable precision (repeatability and reproducibility lower than 7.21% and 8.61%, respectively). Finally, the optimized methodology was tested in practice. Several wine samples were analyzed and the odor activity value calculated to facilitate the identification of their importance as odor active compounds in different aged fortified wines. This work demonstrates the benefits of using DSD and latent variable modelling for the optimization of analytical techniques, contributing to the implementation of rigorous, systematic and more efficient optimization protocols.
- Multiresponse and multiobjective latent variable optimization of modern analytical instrumentation for the quantification of chemically related families of compounds: case study—Solid‐phase microextraction (SPME) applied to the quantification of analytes with impact on wine aromaPublication . Reis, Marco S.; Pereira, Ana C.; Leça, João M.; Rodrigues, Pedro M.; Marques, José C.The optimized operation of modern analytical instrumentation is a critical but complex task. It involves the simultaneous consideration of a large number of factors, both qualitative and quantitative, where multiple responses should be quantified and several goals need to be adequately pondered, such as global quantification performance, selectivity, and cost. Furthermore, the problem is highly case specific, depending on the type of instrument, target analytes, and media where they are dispersed. Therefore, an optimization procedure should be conducted frequently, which implies that it should be efficient (requiring a low number of experiments), as simple as possible (from experi mental design to data analysis) and informative (interpretable and conclusive). The success of this task is fundamental for achieving the scientific goals and to justify, in the long run, the high economic investments made and significant costs of operation. In this article, we present a systematic optimization proce dure for the prevalent class of situations where multiple responses are available regarding a family of chemical compounds (instead of a single analyte). This class of problems conducts to responses exhibiting mutual correlations, for which, furthermore, several goals need to be simultaneously considered. Our approach explores the latent variable structure of the responses created by the chemical affinities of the compounds under analysis and the orthogonality of the interpretable extracted components to conduct their simultaneous opti mization with respect to different analysis goals. The proposed methodology was applied to a real case study involving the quantification of a family of analytes with impact on wine aroma.