Definitive Screening Designs and latent variable modelling for the optimization of solid phase microextraction (SPME): Case study - Quantification of volatile fatty acids in wines

Abstract

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 parameters 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 quantitative 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 underlying 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 (R 2 > 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.

Quantification of Volatile Compounds in Wines by HS-SPME-GC/MS: Critical Issues and Use of Multivariate Statistics in Method Optimization

“…The Box Behnken design (BBD) was applied in research for characterizing aroma-active monoterpenes in berries to determine optimum levels of three variables influencing the terpene recovery on SPME fiber: extraction temperature, extraction time and equilibrium time and consequently to display their linear and quadratic effects [29]. Recently, the DoE approach consisting of a definitive screening design (DSD) and latent variable modelling has been reported for the optimization of a method based on the HS-SPME-GC/MS analysis of volatile fatty acids in wine [14]. A DSD is a particular class of three levels of screening designs and is capable of providing estimates of the main effects that are unbiased or unconfounded regarding all second-order interactions and among themselves.…”

“…The use of multivariate statistics approaches, in particular those pertaining to the design of experiments (DoE) instead of the classical univariate (one-at-a-time, OVAT) approach, has become increasingly relevant in recent years [13][14][15]. The increasing relevance of HS-SPME in wine analysis applications is illustrated by the linear increase in the number of publications on the topic occurred since the introduction of SPME (Figure 1).…”

Key information related to quality by design (QbD) applications in analytical methods development

“…In a review paper, Hibbert [11] described the use of several DoE processes applied to chromatographic separation and highlighted the most used in optimization and validation studies such as: (1) Factorial design [12][13][14]; (2) Plackett-Burman (PB) design [15][16][17]; (3) Central Composite design (CCD) [18][19][20]; (4) Box-Behnken design (BBD) [21][22][23]; (5) Doehlert design (DD) [24][25][26] and (6) Mixture design [27][28][29]. Other types of DoE have been used and often cited in the literature, as examples Mixed-level fractional factorial design [30][31][32]; Definitive Screening design (DSD) [33][34][35] and D-optimal design [36][37][38].…”