Butter Volatilome: Vacuum-Assisted Headspace Solid Phase Microextraction Improves Fingerprinting Information Capacity

The volatilome, also referred to as volatome, “contains all of the volatile metabolites as well as other volatile organic and inorganic compounds that originate from an organism” [1] super-organism, or ecosystem. Therefore, all volatile metabolites present in the volatilome could be considered part of sample’s metabolome. In the case of cow butter, the volatilome encrypts information on many functional variables directly and indirectly related to its overall quality. Pasture versus indoor feeding systems, for example, produce diagnostic signatures of terpenoids (i.e., monoterpenes and sesquiterpenes) and differently impact on aroma precursors distribution (e.g., short-chain fatty acids, hydroxyl-substituted fatty acids). This contribution explores the information capacity of headspace solid phase micro extraction combined to comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (HS-SPME-GC×GC-ToF MS) toward the complex volatilome of high-quality dairy butter. In particular, the potentials of GC×GC combined with regular or vacuum-assisted HS-SPME are evaluated in terms of concentration factor (CF), fingerprint discrimination power, and number of detectable features for several homologue series (e.g., short chain fatty acids, primary alcohols, linear saturated and unsaturated aldehydes, δ- and γ-lactones etc.) or informative chemical classes (i.e., mono/sesqui terpenoids, key-aroma compounds etc.) altogether concurring to delineate the sample’s chemical dimensionality [2]. Results confirm the fundamental role of GC×GC with cryogenic modulation in generating “high-resolution” fingerprints of volatiles (see Figure 1A and 1B-C), the high method sensitivity achieved with a band-compression in space that enables regular and vacuum-assisted HS-SPME sampling in linearity conditions without losses in terms of analytes coverage. Moreover, by vacuum-assisted HS-SPME the fingerprinting information capacity is improved with several advantages in terms of samples discrimination power as described by the Principal Component Analysis based on a selection of target analytes for regular and vacuum-assisted HS-SPME sampling of five different butter samples – Figure 1D. Moreover, for most of the key-aroma compounds (γ-Dodecalactone, (E)-2-Nonenal, 1-Octen-3-one, Nonanal, 2-Methyl Butanoic acid, δ-Octalactone, (E)-2-Octenal, δ-Decalactone, Hexanal, Butanoic acid, 2-Butanone, Hexanoic acid, Dimethyl sulfide, and 3-Hydroxy-2-butanone) CFs of vacuum-assisted HS sampling resulted higher than one showing a great potential for the development of an Artificial Intelligence smelling machine capable to detect butter aroma blueprint without the use of a human panel.