Optimal design of experiments applied to headspace solid phase microextraction for the quantification of vicinal diketones in beer through gas chromatography-mass spectrometric detection

Abstract

Vicinal diketones, namely diacetyl (DC) and pentanedione (PN), are compounds naturally found in beer that play a key role in the definition of its aroma. In lager beer, they are responsible for off-flavors (buttery flavor) and therefore their presence and quantification is of paramount importance to beer producers. Aiming at developing an accurate quantitative monitoring scheme to follow these off-flavor compounds during beer production and in the final product, the head space solid-phase microextraction (HS-SPME) analytical procedure was tuned through experiments planned in an optimal way and the final settings were fully validated. Optimal design of experiments (O-DOE) is a computational, statistically-oriented approach for designing experiences that are most informative according to a well-defined criterion. This methodology was applied for HS-SPME optimization, leading to the following optimal extraction conditions for the quantification of VDK: use a CAR/PDMS fiber, 5 ml of samples in 20 ml vial, 5 min of pre-incubation time followed by 25 min of extraction at 30 °C, with agitation. The validation of the final analytical methodology was performed using a matrix-matched calibration, in order to minimize matrix effects. The following key features were obtained: linearity (R(2) > 0.999, both for diacetyl and 2,3-pentanedione), high sensitivity (LOD of 0.92 μg L(-1) and 2.80 μg L(-1), and LOQ of 3.30 μg L(-1) and 10.01 μg L(-1), for diacetyl and 2,3-pentanedione, respectively), recoveries of approximately 100% and suitable precision (repeatability and reproducibility lower than 3% and 7.5%, respectively). The applicability of the methodology was fully confirmed through an independent analysis of several beer samples, with analyte concentrations ranging from 4 to 200 g L(-1).

Brief Insight into the Underestimated Role of Hop Amylases on Beer Aroma Profiles

“…Odor perception threshold values vary considerably and ranges are commonly used. The respective thresholds and aroma impressions are summarized as follows: diacetyl (100-200 [21,23] µg L −1 ), pentanedione (900-100 [24] µg L −1 ), isoamyl Table 3. no reference standard being available for polymerization degree from 3 to 7, the peaks were tentatively identified (*) based on relative retention times available from floridi et al [18] acetate (1.2-2, [25] 0.6-1.2 [26] mg L −1 , banana), ethyl acetate (25-30, [25] 20-30, [26] 33 [27] mg L −1 , solvent), propanol (600, [25] , 700, [28] , 800 [27] mg L −1 , alcohol/sweet), isobutanol (100 [25] , 200 [29] mg L −1 , solvent) , isoamyl alcohols (50-65, [25] 70 [29] mg L −1 , banana/alcoholic), ethyl caproate (0.2-0.23, [25] , 0.21 [29,30] mg L −1 , apple/fruity), ethyl caprylate (0.9-1.0, [25] 0.9 [29,30] mg L −1 , apple/aniseed), acetaldehyde (25, [25] 0.11 [31] mg L −1 , green leaves/fruity).…”

Nuclear Magnetic Resonance and Headspace Solid-Phase Microextraction Gas Chromatography as Complementary Methods for the Analysis of Beer Samples

“…They found the two methods to have similar performance with high repeatability and good linearity. Several others have demonstrated the application of SPME GCMS for the analysis of volatile compounds in beer [9][10][11].…”

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

“…In these cases, the number of parameters involved in the optimization phase is in general in the range of 3-5 , being the time and temperature of extraction the parameters most frequently considered, followed by the salting out effect, the ratio of sample/headspace volume and the stirring influence. Although fewer in number, there are also other works that include in the optimization phase other HS-SPME parameters with known influence in the extraction process, namely the fiber coating [47], the pre-incubation time and temperature [47][48][49], the desorption time and temperature [49][50][51] and the derivatization conditions in situations when this process is also involved [49,52].…”

“…In the scope of HS-SPME tuning, the class of full factorial [29,36] and fractional factorial designs (with resolution IV and V) [25,[33][34][35]39,42,44] are the most often used approaches. As to optimization, the response surface methodologies (RSM), namely the Central Composite design [26,[30][31][32]37,38,52] and the Doehlert designs [27], together with the algorithmic Optimal designs [28,45,47,48,53], are the dominant techniques for establishing the optimal settings for each factor. Other studies also adopt both categories of designs in a sequential manner, first identifying the important factors and then looking for their optimum settings [24,40,41,43,46,49,50].…”