## 4 Conclusions  
The objective of this work, namely, the varietal discrimination of _Trebbiano d'Abruzzo_ white wine and two recently rediscovered ancient varieties, _Pecorino_ and _Passerina_, was ambitious, since the three investigated varieties belong to the same family of grapes and the winemaking processes are quite similar. In addition, cultivation of the three grape varieties, rather than being localized in specific and different areas of the Abruzzo region, are spread over the whole regional territory, with the only exception being _Passerina_ grape which is mainly cultivated in the Northern part of the region. In this regard, this factor did not allow an optimal representativeness of the last variety, 11 being the only available samples. Sensory analysis revealed that _Trebbiano_ and _Pecorino_ exhibit very similar attributes, while _Passerina_ wines present less-ample values of most of the sensory indicators, and the samples of this variety--although coming from a restricted area--exhibit a relatively high variability in some sensorial attributes. In summary, the differences in the sensorial attributes seem too small to consider them efficient markers of the grape varietal origin. Despite the relatively high homogeneity in the sensorial properties of the three wine varieties, the approaches proposed here to attempt a varietal discrimination were severely tested by generation of an external data set formed by 18 wine samples out of the 46 collected ones. While conventional PLS-DA based on individual classes of molecular/atomic markers (polyphenols and organic acids, trace elements, or volatile compounds, respectively) did not provide a satisfactory predictive performance, data fusion by SO-PLS-LDAmisclassified only three samples of the external set. It is worth noting that two classification errors regarded _Passerina_ samples that were erroneously attributed to the _Pecorino_ variety, while the _Passerina_ class wrongly accepted one _Trebbiano_ sample. These classification errors may be ascribed to the inadequate description of the compositional variability of the _Passerina_ category in calibration, because of the low number of training samples compared to the relatively high variability in the sensorial properties of this kind of wine. On the other hand, _Pecorino_ and _Trebbiano_ wines, which are both represented by a greater, although still not ideal, number of samples (10 and 11), were never confused for each other. Bearing in mind that these two varieties exhibit almost coincident sensorial attributes, this outcome is particularly encouraging and supports the adequacy of the proposed multi-block approach for the varietal discrimination of wines produced in close territories with grapes of a same family. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12199794/s1](https://www.mdpi.com/article/10.3390/app12199794/s1)]([https://www.mdpi.com/article/10.3390/app12199794/s1](https://www.mdpi.com/article/10.3390/app12199794/s1)). Figure S1: Typical GC-MS chromatogram of volatile components; Figure S2: Typical UHPLC chromatogram of polyphenols and organic acids; Table S1: Gradient profile of the binary mobile phase consisting of water (eluent A) and acetonitrile (eluent B) used in the UHPLC separation of organic acids and polyphenols; Table S2: List of the volatiles detected by HS-SPME/GC-MS: observed and literature values of the retention index (RI), mean peak areas (%) in the aroma profiles of the three wine varieties and related standard error (SE) values; Table S3: Organic acids and polyphenols detected in the UHPLC chromatograms of the analyzed wines; Table S4: Mean concentrations (mg/L) of the elements detected by ICP-OES in the three wine varieties and related standard error (SE) values. Conceptualization, A.A.D., A.B. and M.C. ; methodology, A.B., F.P. and L.R. ; software, A.B. ; validation, S.R., F.R. and A.B. ; formal analysis, A.B. ; investigation, M.F., G.C., S.R. and F.P. ; resources, M.C. ; data curation, M.C. and F.P.