## 2 Materials and Methods
### Wine Samples
The Thermo Xcalibur v. 2.2 software was used to control both the Combipal autosampler and GC-MS. Raw untargeted GC-MS data from rose wines were acquired in Xcalibur file format (.raw) and converted to the international ANDI file format (.cdf) by means of the file converter tool implemented in Xcalibur. They were then pre-processed (Figure 1) to convert the chromatograms into extracted data (tables of metabolite chromatographic areas). The raw.cdf files with a three-way array structure (elution time \(\times\) mass spectra \(\times\) samples) were deconvoluted by means of the PARAFAC2 algorithm. The chromatograms were first divided into 335 intervals in the elution time dimension in order to reduce the complexity and a single model was built for each interval. For each model, one to eight components were fitted and the selection of the optimal number of components was based on model fit (%), core consistency, distribution of the residuals, comparison of the resolved mass spectral profiles against the raw profiles, and retention times of the weighted elution profiles. The freely-available platform PARADISe v. 3.88 was used to implement this framework [12]. To verify the quality of the data, pooled quality control (QC) samples were repeatedly injected throughout the whole sequence. Different levels of metabolite identification confidence were categorized in accordance with the proposed minimum reporting criteria defined by the metabolomic standard initiative [20]. Two orthogonal properties: linear retention index (LRI) calculated with the equation of Van [PERSON] and Kratz, and fragmentation mass spectrum, were used for metabolite identification. Three levels of identification were considered. The resolved PARAFAC2 mass spectrums and linear retention index were compared against an authentic chemical standard analysed under the same experimental conditions for definitive identification (level 1). Level 2 was considered for metabolites showing a match factor (MF) > 850 and LRI \(\pm\) 30 compared to the NIST database. Level 3 was considered for metabolites with 650 \(\leq\) MF \(\leq\) 85 a and LRI \(\pm\) 30 compared to the NIST database. Since close values of MF and reverse match factor (RMF) are related to low levels of background, RMF was used to control the quality of each metabolite background subtraction. Analysis, Data Acquisition and Processing of Using Ultra-High-Performance Liquid Chromatography High-Resolution Mass Spectrometry (UHPLC-HRMS) Data  
The relative non-volatile metabolome of rose wines was determined using ultra-high-performance liquid chromatography coupled to an Exactive Orbitrap mass spectrometer (UHPLC-HRMS) controlled by the Thermo Xcalibur v. 2.2 software. A total of 1 mL of each rose wine sample was centrifuged at 15,000 rpm for 10 min at 20 \({}^{\circ}\)C (Eppendorf(tm) 5424 Microcentifuges, Hamburg, Germany) and 200 \(\mu\)L of the supernatant were transferred into a 1.5 mL. amber glass vial, mixed with 380 \(\mu\)L of acetonitrile and placed in the autosampler refrigerated at 10 \({}^{\circ}\)C of a Dionex Ultimate 3000 RS UHPLC system coupled to an Exactive Orbitrap mass spectrometer detector (Thermo Fisher Scientific, San Jose, CA, USA). Hydrophilic interaction separation was carried out on a 2.1 mm \(\times\) 150 mm ACQUITY UPLC 1.7 \(\mu\)m BEH amide column, equipped with an ACQUITY UPLC BEH amide 1.7 \(\mu\)m van-guard pre-column (Waters, Barcelona, Spain). The temperature of the column was maintained at 40 \({}^{\circ}\)C and two mobile phases were used: A: acetonitrile and B: acetonitrile-water (2:98) + 1 mM ammonium formate. The injection volume was 5 \(\mu\)L and the separation was obtained at a flow rate of 0.4 mL/min with a 40 min gradient. The gradient started at 5% B for 4 min, raised to 28% B in 25 min, raised to 60% B in 30 min, maintained for 0.5 min before rising to 80% B in 33 min. After that, the column was equilibrated to the previous conditions within 10 min.