## 4 Conclusions  
In conclusion, the developed tools can be used to ensure the quality and food safety of grapes by employing early and sophisticated metabolomics strategies. It was possible to perform the construction of an experimental model from previously selected metabolites by reducing the growth time to one day. The model was validated employing statistical tests, and the results were considered satisfactory in modeling the microorganism's behavior. In addition, the model was tested to verify the presence of the microorganism _A. niger_ in contaminated grapes for two varieties, and proved the presence of the microorganism even with one day of detection time. Fungus detection from the set of metabolites was also used for kinetic follow-up of fungus growth over 7 days. Using a PLS-DA approach, it was possible to observe that the matrix is an important factor for fungi development, as different concentrations of the metabolites were observed for the red and white grape varieties. The results obtained throughout this study suggest that even when the analysis was performed for the samples with one day of growth time, the pattern of metabolites associated with the _A. niger_ species was detected, which may be extremely useful in making this type of method possible for the rapid detection of food contamination. Moreover, it is hoped that the methodology developed can be extended to other problems involving the detection of this microorganism, such as its detection in other foods, or even in other environments where it may be present in a way that is harmful to human health. Although this article represents a step forward in the evaluation of the applicability of the metabolome of _A. niger_ in contaminated real samples, for its further application in other scenarios, it is mandatory to look for other parameters, such as limits of detection, evaluation of confounders, matrix effects, and the presence of a mixture of species (co-cultures), among others. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/foods10112870/s1](https://www.mdpi.com/article/10.3390/foods10112870/s1)]([https://www.mdpi.com/article/10.3390/foods10112870/s1](https://www.mdpi.com/article/10.3390/foods10112870/s1)), Figure S1: Dendrogram and heatmap representation of the 42 metabolites putatively identified from red (RG) and white (Do) table grapes contaminated with _A. niger_, determined by in situ HS-SPME/GC\(\times\)GC-ToFMS over 7 days, which illustrates the formation of two main clusters related to the two grape varieties, within each one there is an organization of the samples as a function of the growth time. Table S1. Full data set used for statistical processing including the chromatographic data of the 44 metabolites putatively identified and normalized by CFU mL\({}^{-1}\) from _A. niger_, _P. chrysogenum_ and _C. albicans_ cultures in different growth times, using HS-SPME/GC\(\times\)GC-ToFMS. Results obtained by performing the pure culture growth in solid YGC\({}_{\text{A}}\) culture medium and by contaminated grapes (Red Globe and Dominga) with _A. niger_ after 7 days of growth. Table S2. Full-data set used for statistical processing including the chromatographic data of the 42 metabolites putatively identified from red and white tables grapes contaminated with _A. niger_ and determined using in situ-HS-SPME/GC\(\times\)GC-ToFMS. **Author Contributions:** Conceptualization, S.M.R. and F.A. ; methodology, S.M.R., A.A., J.R.B. and C.P.C. ; formal analysis, J.R.B. and C.P.C. ; resources, F.A and S.M.R. ; data curation, S.M.R., F.A., A.A.,J.R.B. and C.P.C. ; writing--original draft preparation, J.R.B. and C.P.C. ; writing--review and editing, S.M.R., F.A. and A.A. All authors have read and agreed to the published version of the manuscript. The authors thank the Foundation for Research Support of the State of Sio Paulo (Fapesp)--Process Number 2016/20547-2) and The National Institute of Science and Technology in Bioanalysis (INCTBio).