## 3 Results and Discussion
Figure 3 shows a distinction between the three classes of microorganisms under study. A higher dispersion along LV1 was observed for _A. niger_, which may be explained as this data set comprises samples with 1, 3, and 5 days of growth (Table S1). Additionally, it is possible to observe that most of the related loadings are grouped closed to the _A. niger_ samples, which confirms this set of metabolites may be considered as a biomarker pattern of this species. Thus, to verify the classification model established for the data set, it was evaluated using cross-validation. Figure 2: GC \(\times\) GC-ToFMS total ion chromatogram contour plot of the VOCs released from: (**a**) Yeast Glucose Chloramphenicol Agar medium (YGC\({}_{A}\)), used as control and (**b**) _A. niger_ culture headspace volatile components inoculated in YGC\({}_{A}\), for 1 day of growth, at 37 \({}^{\circ}\)C. (**c**) The wide 2-phenylethanol GC \(\times\) GC peak was identified at a mass spectral acquisition of 100 spectra/s for 1 day of growth and absent for the YGC\({}_{A}\). Peak number assignment in Table S1 and bold in Table 1.  
Cross-validation was performed by using a specific number of samples that are reserved for the construction of the model (training set) and another portion for testing the built model (validation set). The proper choice of cross-validation method from those available basically depends on the number of samples available for use in the training set and validation. In this case, LOOCV was used for the data set in this study once the number of samples is small and it is not possible to assign a sample set to the validation set. The LOOCV consists of choosing one of the samples to compose the validation set and the remaining samples are used for the training set. A new sample is then taken to make up the validation set and the sample that was previously used for the validation set this time will make up the training set; these operations are repeated iteratively several times until all the samples have been part of the validation set at least once [55]. Table 2 presents the results for the statistical parameters obtained in the model validation according to the number of main components used to describe the data. By looking at the three components, the values of R\({}^{2}\), Q\({}^{2}\), and accuracy are 0.9152, 0.8252, and 0.9815, respectively. However, statistical parameters R\({}^{2}\) and Q\({}^{2}\) are the most important diagnostic tools for evaluating the performance of the built model. The R\({}^{2}\) parameter refers to the fitting quality of the model and it is responsible for measuring the performance considering adjusting the raw data. Thus, the values of R\({}^{2}\) range from 0 to 1, where 1 indicates a perfect model and 0 indicates no adjustment or modeling [56]. The main disadvantage of parameter R\({}^{2}\) is that it can be adjusted to close to 1 including more terms to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Latent Variable 1** & **Latent Variable 2** & **Latent Variable 3** & **Latent Variable 4** & **Latent Variable 5** \\ \hline Accuracy & 0.7778 & 0.7778 & 0.9815 & 1.0 & 1.0 \\ R\({}^{2}\) & 0.4548 & 0.8821 & 0.9152 & 0.9312 & 0.9557 \\ Q\({}^{2}\) & 0.2957 & 0.7888 & 0.8252 & 0.81084 & 0.8128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical parameters for the evaluation of the PLS-DA model according to the number of components for the classification model. Figure 3: PLS-DA biplot of the samples (LV1\(\times\)LV2) using the set of 44 metabolites listed in Table 1, which shows a distinction between the three classes of microorganisms under study. The areas were previously standardized by CFU mL\({}^{-1}\) (Table S1, gray-highlighted). Results obtained by performing the pure culture growth in solid YGC\({}_{\text{A}}\) culture medium and using different growth times. the model (in this case, latent variables), and therefore is not a sufficient indicator to assess the validity of the model.