## 3 Results  
### Volatile Profile  
Prior to GC-MS injection of the real samples, pooled QCs were used to optimize the dilution factor and the extraction fibre employed. Dilution ratios of 1:1, 1:2, 1:4 and non-dilution were tested in three different fibres, which covered a vast range of polarities, such as PDMS, CAR/PDMS and DVB/CAR/PDMS. The combination of the DVB/CAR/PDMS fibre and non-dilution yielded the best results in terms of total chromatographic peak signal (sensitivity). However, the DVB/CAR/PDMS fibre and 1:1 dilution ratio were selected, since 10% more features could be detected with low deviation coefficients (20% RSD), improving stability. Subsequently, the real rose wine samples were analysed with the optimised methodology. PARAllel FACtor analysis2 (PARAFAC2) deconvolution algorithm [13] was used to pre-process the converted data, with a total of 505 molecular features assigned to detected peaks. After the pre-treatment step, 429 features remained in the data set, among which 126 were definitive--or putatively identified (Supplementary Table S3). PCAs including QC samples were used during data pre-treatment to check the quality of the data (Figure 2). The quality of the raw data (before pre-treatment) was quite satisfactory, since QCs were allocated in a narrow region and samples were grouped according to the fermentation strategy. However, the pre-treatment workflow proposed in this study led to a slight improvement of the final data, with QCs and samples belonging to the same fermentation strategy being allocated in closer areas. Afterwards, a two-way ANOVA (Supplementary Table S3) was performed to study the effect of the fermentation strategy, that impacted to different chemical groups, especially esters, alcohols and terpenes, independently of the grape variety of the wine (Supplementary Table S3). Rose wines that followed a sequential inoculation with the Flavia _Metschnikowia pulcherrina_ yeasts (_Sc-Mp_) showed statistically significant differences compared with those fermented with the monoculture Red Fruit _Saccharomyces cerevisiae_ yeasts (_Sc_). However, the sequential inoculation with Biodiva _Torulaspora delbruveckii_ yeasts (_Sc-Td_) produced rose wines with the most distinct volatile profile, with overall lower contents of esters and aldehydes, but an increase in several alcohols and terpenes (Supplementary Table S3). Subsequently, the main wine volatile metabolites linked to the fermentation strategy were  
Figure 2: Principal component analysis (PCA) performed for the gas chromatography (GC) data during pre-treatment. (**a**) Raw data and (**b**) pre-treated data. Quality control samples (QCs) are included to verify the quality of the data. _TD_: wines fermented with sequential inoculation of _Torulaspora delbruveckii_ and _Saccharomyces cerevisiae_. _MP_: wines fermented with sequential inoculation of _Metchnikowia pulcherrina_ and _Saccharomyces cerevisiae_. _SC_: wines fermented with monoculture of _Saccharomyces cerevisiae_. selected by means of a variable reduction procedure based on variable importance in projection (VIP) from the PLS-DA model. These metabolites were considered as potential markers of the fermentation strategy and a new model was fitted using the reduced data. The performance of the PLS-DA including the potential volatile markers displayed a mean overall BER of 0.08 \(\pm\) 0.06 and \(p\)-values from the permutation test below 0.05 in the three diagnostic statistics (Table 1). This resulted in a slight improvement compared to the model fitted with all the variables (BER of 0.11 \(\pm\) 0.08), and the complexity of the model was reduced from 3 to 2 components. The X-variate 1 explained a high 65% of the total variance found in the rose wine samples, that separated the _Sc-Td_ wines from the rest (Figure 3). This result supported the more distinct volatile profile of _Sc-Td_ wines previously observed in the ANOVA.