Optimisation of headspace solid-phase microextraction for analysis of aromatic compounds in vinegar

Modeling and optimization of acetic acid fermentation: A polynomial-based approach

“…However, they require greater numbers of experimental data to fit coefficients; also, the number of experiments needed depends on the polynomial order, the number of factors and the number of levels (values) used to discretize each factor range. Experimental design is used to identify the factors most strongly influencing a process under specific experimental conditions, minimize the effects of uncontrolled factors (perturbations), isolate and assess the effect of each individual factor by statistical analysis [30] and rationalize (reduce) the number of experiments required [31]. Experimental design allows obtaining the simplest algebraic equations used to construct polynomial models.…”

Analyzing volatiles in brown rice vinegar by headspace solid-phase microextraction (SPME)–Arrow: Optimizing the extraction conditions and comparisons with conventional SPME

“…These headspace techniques are optimized in terms of several extraction parameters including fiber type, extraction temperature, extraction time, desorption time, salt content, pH, and the possible interactions between independent variables, which can significantly affect the extraction efficacy. [9][10][11][12][13][14][15][16] All experiments were performed in triplicate and the experimental conditions were optimized on the basis of normalized peak areas.…”

“…The main VOCs in vinegar can be classified into several groups, including aldehydes, acids, alcohols, acetates, and furan derivatives. [6,12] Accordingly, we selected seven major VOCs and determined their extraction yields using the above-mentioned SPME-Arrow fibers; these seven VOCs were tentatively identified and the normalized peak areas obtained using each tested fiber are shown in Figure 1 efficacies of the 120-μm CAR/PDMS, 100-μm PDMS, and 120-μm DVB/PDMS SPME-Arrow fibers toward ethanol and acetic acid extraction were not significantly (p > .05) different. The selection of the appropriate coating material is a crucial step in the optimization and development of the SPME method.…”

“…[11] A considerable number of headspace-SPME-based (HS-SPME-based) studies that analyze VOCs in vinegar derived from a variety of fruits and grains have been reported in recent years. [7,12,13] However, the key VOCs in brown rice vinegars have been reported to a lesser extent. Moreover, the extraction efficacy of HS-SPME-Arrow for VOCs in brown rice vinegar has not been studied as this novel technique has only appeared recently.…”

Discrimination of Two Kinds of Geographical Origin Protected Chinese Vinegars Using the Characteristics of Aroma Compounds and Multivariate Statistical Analysis

“…Based on the advantages of implementation of synchronized extraction and concentration steps, headspace-solidphase microextraction (HS-SPME) is the most commonly used method for volatile compounds extraction (Aceña et al 2011;Castro et al 2002;Damascelli and Palmisano 2013;Natera et al 2002;Pizarro et al 2008;Ye et al 2012). After determined by the dynamic head-space gas chromatography with FID detector (HS-GC-FID) (Del Signore 2001;Natera Marín et al 2002;Pizarro et al 2008), gas chromatography-mass spectrometer (GC-MS) (Callejón et al 2008a, b), or direct gas chromatographyolfactometry (D-GCO) techniques (Aceña et al 2011;Callejón et al 2008a, b), the volatile compounds data were analyzed by multivariate statistical technique, such as principal component analysis (PCA) (Luo et al 2013;Zheng et al 2014), linear principal component analysis (LPCA) (Del Signore 2001), cluster analysis (CA) (Xiao et al 2011), linear discriminant analysis (LDA) (Callejón et al 2008a;Casale et al 2006a), stepwise linear discriminant analysis (SLDA) (Pizarro et al 2008), and parallel factor analysis (PARAFAC) (Cocchi et al 2007).…”