Characterization of Cultivar Differences of Blueberry Wines Using GC-QTOF-MS and Metabolic Profiling Methods

Abstract

A non-targeted volatile metabolomic approach based on the gas chromatography-quadrupole time of fight-mass spectrometry (GC-QTOF-MS) coupled with two different sample extraction techniques (solid phase extraction and solid phase microextraction) was developed. Combined mass spectra of blueberry wine samples, which originated from two different cultivars, were subjected to orthogonal partial least squares-discriminant analysis (OPLS-DA). Principal component analysis (PCA) reveals an excellent separation and OPLS-DA highlight metabolic features responsible for the separation. Metabolic features responsible for the observed separation were tentatively assigned to phenylethyl alcohol, cinnamyl alcohol, benzenepropanol, 3-hydroxy-benzenethanol, methyl eugenol, methyl isoeugenol, (E)-asarone, (Z)-asarone, and terpenes. Several of the selected markers enabled a distinction in secondary metabolism to be drawn between two blueberry cultivars. It highlights the metabolomic approaches to find out the influence of blueberry cultivar on a volatile composition in a complex blueberry wine matrix. The distinction in secondary metabolism indicated a possible O-methyltransferases activity difference among the two cultivars.

Keywords: GC-QTOF-MS analysis; blueberry wine; multivariate analysis; volatile composition.

Figure 1

Example of β-Phellandrene detection in a blueberry wine sample (a) The average spectrum of β-Phellandrene extracted by conventional manual background subtraction; (b) the deconvoluted spectrum of β-Phellandrene. (c) β-Phellandrene spectrum from NIST library; (d) Predicted GC-MS Spectrum-GC-MS (Non-derivatized)-70 eV, Positive (HMDB0036081), obtained from The Human Metabolome Database (HMDB).

Figure 2

PCA of the metabolite features detected in the blueberry wines by different sample extraction method (SPE and SPME) followed by GC-QTOF-MS. The explained variances are shown in brackets. (a) Score plot of SPE; (b) Loading plot of SPE; (c) Score plot of SPME; (d) Loading plot of SPME. The PCA showing that the volatile metabolites are clearly different between Misty blueberry wine (M1, M2, M3, M4) and O’Neal blueberry wine OBW (O1, O2, O3, O4) despite of the extraction methods.

Figure 3

OPLS-DA of metabolite features detected in the blueberry wines by SPE-GC-QTOF-MS. (a) Score plot of all metabolite features; (b) Model overview of the OPLS-DA model; (c–e) Loadings S-plot showing the variable importance in a model, combining the covariance and the correlation (p(corr)) loading profile. The box-plots at bottom showed the significantly different volatile metabolites between Misty blueberry wine (M1, M2, M3, M4) and O’Neal blueberry wine (O1, O2, O3, O4) in the OPLS-DA model (Line, mean; box, standard error; whisker, standard deviation).

Figure 4

OPLS-DA of metabolite features detected in the blueberry wines by SPME-GC-QTOF-MS. (a) Score plot of all metabolite features; (b) Model overview of the OPLS-DA model; (c–e) Loadings S-plot showing the variable importance in a model, combining the covariance and the correlation (p(corr)) loading profile. The box-plots at bottom showed the significantly different volatile metabolites between Misty blueberry wine (M1, M2, M3, M4) and O’Neal blueberry wine (O1, O2, O3, O4) in the OPLS-DA model (Line, mean; box, standard error; whisker, standard deviation).