Over the years the demand for fast analytical methods to assess the authenticity of food products has constantly increased. Authenticity is a key aspect used to avoid frauds and to define the quality of food products. In this respect origin determination is at the basis of the PGI and PDO labels, used by the European Community to define high quality food products.

Several approaches, involving different disciplines can be used to achieve the purpose:
Metabolomics represents a powerful tool when dealing with a great number of samples.
In the present study, metabolomics was used to create a classification method able to discriminate 2 rum samples differing in the raw material (sugarcane vs molasses).

The volatile fraction of rums differing in the raw material was isolated by solvent assisted flavor evaporation and analyses were carried out using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS). Non targeted methods were used to collect and catalogue the whole informative content present in the chromatograms composing the analytical set.
After the pre-treatment of raw data, sparse-partial-least-squares discriminant analysis (sPLS-DA) was performed for variable reduction. The sample set was then divided into three sub set (training, validation and test set) to be processed using PLS-DA. The first set was used to create the model while the second set was used to validate the model by means of leave-one-out cross validation.
Finally, an independent data set (test set) was submitted to the model to evaluate its predictive power.
Classification of unknown samples resulted in 100% of correct classification for sugar cane and 71% for molasses revealing only 2 wrong classifications.
Finally, the most significant markers were identified (targeted analyses) revealing 1-decanol, γ-dodecalactone, ethyl 3-methylbutanoate, ethyl nonanoate, 3-furancarboxaldehyde, 1-hexanol, β-ionone, 2- and 3-methylbutanol, methyl decanoate, 3-octanol, and 2-undecanone as key compounds for classification.

Luca Nicolotti

Luca Nicolotti was born on September, 24th 1986.
He studied Pharmaceutical Chemistry and Technology at the University of Turin and in 2010 he received his 5 years degree.
From January 2011 to December 2013 he attended the Doctoral School in Pharmaceutical and Bio-molecular Sciences working in the Laboratory of Phytochemistry and Food Analysis under the supervision of Prof. Dr. Bicchi. In February 2014 he received the PhD from the Uiversity of Turin.
Luca Nicolotti is currently working at the DFA as scientific co-worker (Postdoctoral position).
His main research topics are listed as follows:
1.Detailed characterization and classification (targeted and un-targeted fingerprinting) of complex food samples (sensory quality and technological fingerprint of hazelnuts) by GC×GC-MS followed by advanced statistical data processing.
2.Development of techniques and approaches (GC×GC-MS, HS-SPME-GC-MS, DHS-GC-MS) for the monitoring and screening of technological and aroma markers in food matrices.