## 4 Conclusions  
The results show that the combination of HS-SPME-MS fingerprints and chemometrics is a promising technique for use as a TAS system working as a high throughput solution for the prediction of the in-cup coffee sensory quality of incoming coffee beans. Sensory quality control and evaluation is crucial if the coffee industry is to satisfy the ever-increasing demand for coffee with specific sensory attributes. The described methods would allow trained panellists to be exempted (at least partially) from routine tasting and focus their activity on new products and sensory attributes. The ambitious challenge of this study was based on the exploration of the ability of this analytical approach to predict in-cup coffee quality, including representative coffee samples of different origins, species and postharvest treatments, as occurs in industry quality control upon the acceptance of incoming beans. The study has demonstrated that this approach and the use of a multi-note model to predict global coffee sensory profiles requires a number of compromises, in terms of model robustness and acceptance of the errors in prediction. The high errors in prediction, in particular for samples with high sensory scores, probably occurred because the number of high scored samples was lower than that of the low scored samples, making this part of the score range less well represented in the sample sets. A second explanation may involve the sensory scores measured by the panel; high scores are more difficult to define and require a precise alignment.  
As a general consideration, the main limit of this study is the number of coffee samples, which is only a snapshot of the number of coffees that may be processed in a plant, and is therefore not sufficient to obtain fully reliable and robust models. Automatic screening to predict the cup-quality of the raw material requires a huge repository of sensory and instrumental data. Furthermore, this approach operates, in terms of chemometric data processing, within an order of magnitude of hundreds of samples with similar qualities, as shown by [PERSON] on fifty three Arabica coffees and [PERSON] on 18 espresso coffees (Ristretto and Lungo types). For higher numbers of samples, other data mining approaches should be considered in the development of the prediction tool, e.g., artificial neural networks and deep learning algorithms [23; 41; 43].  
The following is present in supplementary material available online, Figure S1: PCA scores plots of coffee samples.  
Conceptualization, E.L. and C.B.; methodology, E.L., B.S. and C.B.; software, D.B. and E.L.; validation, G.P., M.R.R. and C.C.; formal analysis, D.B. and G.S.; investigation, D.B. and G.S.; resources, G.P. and M.R.R.; data curation, D.B. and G.S.; writing--original draft preparation, E.L. and D.B.; writing--review and editing, E.L., C.B., G.P., M.R.R.; visualization, B.S. and C.C.; supervision, E.L. and C.B.; project administration, E.L.  
The research is part of a PhD project financially supported by Luigi Lavazza S.p.A, Torino.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.