## 3 Results
### Non-Volatile Profile  
The optimization of the dilution procedure for the wine samples was achieved using pooled QC samples. Direct injection and dilution ratios of 1:1, 1:2 and 1:5 were tested. Samples were analysed and processed to extract the features, and two parameters were considered to select the best dilution conditions: total sum area (response) and number of features with RSD <30 (stability). The dilution of the samples yielded a reduction in the response but increased the stability of the features. In positive ionization mode, the compounds were most stable at a dilution ratio of 1:2, which captured close to 2-fold more compounds with RSD <30 than direct injection. Meanwhile, in negative ionization mode, the dilution ratio of 1:5 displayed slightly better results than 1:2 in terms of response. However, this dilution ratio was discarded due to loss of sensitivity (around 50% compared to 1:2). Therefore, the best consensus in terms of sensitivity and stability was obtained for a dilution ratio of 1:2, which was used for the analysis of the real samples. Respective totals of 1801 and 4275 features for positive and negative ionization modes were obtained after XCMS pre-processing, and 1167 and 1701 features remained respectively after data pre-treatment step. PCAs including QC samples were again used during the pre-treatment process to verify the quality of the data (Figures 4 and 5 for negative and positive ESI modes, respectively). Data acquired in negative ESI mode displayed an important batch effect which was highlighted by the QC samples, and after data pre-treatment this effect was successfully eliminated (Figure 4). Although a batch effect was not observed in positive ionization mode, an improvement in the final quality of the data was also achieved after the pre-treatment process, since QCs and samples were grouped closer and better separated (Figure 5). As for the volatile data, ANOVA and PLS-DA models were used to disclose the relevant biological information contained in the data. The ANOVA showed clear differences in the non-volatile profile of the rose wines due to the fermentation strategy regardless of the grape variety, with amino acids and organic acids being the most impacted compound families (Supplementary Table S4). The variable reduction procedure allowed us to obtain a PLS-DA with improved classification performance compared to the model fitted with all the metabolites, displaying a BER of 0.02 \(\pm\) 0.04 and \(p\)-values from the permutation test below 0.05 using the three diagnostic statistics (Table 1). A total of 38 metabolites were selected as markers and the model was optimized for two components accounting for 88% of the total variance found in the samples, which were clearly grouped according to the fermentation strategy (Figure 6). As found for the volatile profile, _Sc-Td_ wines displayed the most distinct non-volatile profile, since these wines were separated from the rest on component 1 (X-variate 1), that explained the 71% of the total variance of samples. Among the annotated compounds, glutamic acid and D-mannitol/dulcitol were selected as potential markers of the fermentation strategy, displaying higher overall concentrations in _Sc-Td_ wines (Figure 6). Meanwhile the variance explained in component 2 made it possible to differentiate _Sc-Mp_ from _Sc_ wines, being the non-volatile metabolites quintic acid and imidazole acetic acid selected as potential markers of _Sc-Mp_ wines (Figure 6). Figure 4: Principal component analysis (PCA) performed for the liquid chromatography (LC) negative ESI mode data during pre-treatment. (**a**) Raw data, (**b**) after smoothed cubic spline and (**c**) pre-treated data. Quality control samples (QCs) are included to verify the quality of the data. _TD_: wines fermented with sequential inoculation of _Torulaspora delbrueckii_ and _Saccharomyces cerevisiae_. _MP_: wines fermented with sequential inoculation of _Metchnikova pulcherrima_ and _Saccharomyces cerevisiae_. _SC_: wines fermented with monoculture of _Saccharomyces cerevisiae_.