This study focuses on hazelnuts (Corylus avellana L.) primary metabolome (i.e., amino acids, mono and disaccharides, low molecular weight acids and amines) and its characteristic fingerprint as a function of geographical origin, harvest year, post-harvest drying and storage time. Its information potential is of great interest to predict hazelnuts sensory quality after industrial roasting. Most of the (key)-aroma compounds and potent odorants [1] derive from non-volatile precursors that, once mapped, may objectively represent nuts potential quality. Comprehensive two-dimensional gas chromatography coupled to Time of Flight Mass Spectrometry (GC×GC-TOF MS) featuring Tandem Ionization by varying electron energies across the analytical run is exploited and 2D-patterns of derivatized primary metabolites (oximation-sylilation) are explored by combined untargeted/targeted fingerprinting (UT fingerprinting) based on the template matching strategy [2]. The primary metabolome accounts of about 500 2D-peak-regions as illustrated in the color plot of Figure 1; within these detectable analytes, a sub-group of about 150 can be reliably identified by matching linear retention indices (LRI) and MS spectra at 70 eV. Characteristic fingerprints at 70 and 12 eV ionization energy enable both sample clustering on the basis of key-variables (origin and cultivar) and results cross-validation. In addition, soft ionization energy at 12 eV produces spectra with a complementary information power and higher specificity for most of the informative analytes. Spectra at 12 eV have higher relative ratio for heavier fragments and lower intensity for derivatization agents. By combining data from primary metabolite distribution and volatiles, produced after lab-scale model roasting, several positive correlations (statistically relevant) between precursors and Maillard reaction products confirm the consistency of the proposed approach and the high flexibility of the analytical platform.
COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY COUPLED TO TIME OF FLIGHT MASS SPECTROMETRY FEATURING TANDEM IONIZATION: ADDING AN EXTRA-DIMENSIONS TO HAZELNUTS (Corylus avellana L.) PRIMARY METABOLOME FINGERPRINTING
M. Cialiè Rosso;C. Cagliero;E. Liberto;P. Rubiolo;B. Sgorbini;C. Bicchi;C. Cordero
2019-01-01
Abstract
This study focuses on hazelnuts (Corylus avellana L.) primary metabolome (i.e., amino acids, mono and disaccharides, low molecular weight acids and amines) and its characteristic fingerprint as a function of geographical origin, harvest year, post-harvest drying and storage time. Its information potential is of great interest to predict hazelnuts sensory quality after industrial roasting. Most of the (key)-aroma compounds and potent odorants [1] derive from non-volatile precursors that, once mapped, may objectively represent nuts potential quality. Comprehensive two-dimensional gas chromatography coupled to Time of Flight Mass Spectrometry (GC×GC-TOF MS) featuring Tandem Ionization by varying electron energies across the analytical run is exploited and 2D-patterns of derivatized primary metabolites (oximation-sylilation) are explored by combined untargeted/targeted fingerprinting (UT fingerprinting) based on the template matching strategy [2]. The primary metabolome accounts of about 500 2D-peak-regions as illustrated in the color plot of Figure 1; within these detectable analytes, a sub-group of about 150 can be reliably identified by matching linear retention indices (LRI) and MS spectra at 70 eV. Characteristic fingerprints at 70 and 12 eV ionization energy enable both sample clustering on the basis of key-variables (origin and cultivar) and results cross-validation. In addition, soft ionization energy at 12 eV produces spectra with a complementary information power and higher specificity for most of the informative analytes. Spectra at 12 eV have higher relative ratio for heavier fragments and lower intensity for derivatization agents. By combining data from primary metabolite distribution and volatiles, produced after lab-scale model roasting, several positive correlations (statistically relevant) between precursors and Maillard reaction products confirm the consistency of the proposed approach and the high flexibility of the analytical platform.File | Dimensione | Formato | |
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