## 3 Results and Discussion  
Evaluating the Potential of A. niger Metabolite Biomarkers Pattern for Strain Distinction at 1-Day of Culture Growth  
The initial experiments were performed based on a previous study which included evaluation of the volatile profile of the fungus _A. niger_ in comparison with other species and under different growth conditions [14]. Thus, using previously optimized method and other growth conditions, the growth time was reduced to one day in order to minimize the detection time of the fungus in relation to the previous method that spends 3 and 5 days. Table 1 presents the list of the 44 metabolites named as the _A. niger_ molecular biomarkerpattern [14] and determined by HS-SPME/GC\(\times\)GC-ToFMS in the samples under study. The 44 metabolites set was chosen since they were identified in all the conditions studied for _A. niger_ and, therefore, was defined as a standard of molecular biomarkers for this microorganism. This data set includes hydrocarbons, including aliphatic and aromatic (31.8%), alcohols (22.7%), aldehydes (20.5%), ketones (11.4%), esters (6.8%), terpene compounds (4.6%), and norisoprenoids (2.3%). According to Figure 2, it is possible to verify differences between the culture medium and one day of growth, highlighting the compounds (1) 1-Butanol, (9) 2-Phenylethanol, (26) Toluene, and (43) Endobornyl acetate, assigned in Table S1. For 2-Phenylethanol (Figure 2c), the peak was identified at a mass spectral acquisition of 100 spectra/s for 1 day of growth  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Peak** & **1 \({}_{\text{R}}\)\({}^{\text{a}}\) (s)** & **2 \({}_{\text{R}}\)\({}^{\text{a}}\) (s)** & **Metabolite** & **CAS** & **Formula** & **MSI** & **RI\({}_{\text{Calc}}\)\({}^{\text{c}}\)** & **RI\({}_{\text{Lit}}\)\({}^{\text{d}}\)** \\
**Number** & & & & & & & & & \\ \hline  
ethyl 2-methylpropenoate & 80-62-6 & C\({}_{\text{S}}\)H\({}_{\text{S}}\)O & 2 & 685 & 710 [41] \\
21 & 695 & 0.920 & 3-Hydroxy-2,4,4-trimethylpentyl & 74367-34-3 & C\({}_{\text{L}}\)H\({}_{\text{H}}\)2\({}_{\text{A}}\)O\({}_{\text{S}}\) & 2 & 1376 & 1376 [13] \\
22 & 545 & 1.050 & 2-Phenylethylacetate & 103-45-7 & C\({}_{\text{L}}\)H\({}_{\text{H}}\)2\({}_{\text{O}}\) & 2 & 1196 & 1196 [13] \\
23 & 880 & 0.490 & Hexadecane & 544-76-3 & C\({}_{\text{G}}\)H\({}_{\text{M}}\) & 1 & 1601 & 1600 [49] \\
24 & 965 & 0.430 & Heptadecane & 629-78-7 & C\({}_{\text{G}}\)H\({}_{\text{S}}\) & 1 & 1701 & 1700 [49] \\
25 & 115 & 0.460 & Benzene & 71-43-2 & C\({}_{\text{G}}\)H\({}_{\text{S}}\) & 1 & 643 & 643 [13] \\
26 & 170 & 0.540 & Toluene & 108-88-3 & C\({}_{\text{G}}\)H\({}_{\text{S}}\) & 1 & 759 & 771 [41] \\
27 & 250 & 0.590 & 1,3-Dimethylbenzene & 108-38-3 & C\({}_{\text{G}}\)H\({}_{\text{S}}\) & 2 & 871 & 871 [13] \\
28 & 270 & 0.640 & 1,2-Dimethylbenzene & 95-47-6 & C\({}_{\text{S}}\)H\({}_{\text{S}}\) & 2 & 901 & 900 [42] \\
29 & 325 & 0.580 & Proylbenzene & 103-65-1 & C\({}_{\text{G}}\)H\({}_{\text{I}}\) & 2 & 953 & 959 [41] \\
30 & 335 & 0.590 & 1-Ethyl-4-methylbenzene & 622-96-8 & C\({}_{\text{G}}\)H\({}_{\text{I}}\) & 2 & 964 & 970 [41] \\
31 & 365 & 0.640 & 1,3,5-Timethylbenzene & 108-67-8 & C\({}_{\text{G}}\)H\({}_{\text{I}}\) & 2 & 995 & 974 [41] \\
32 & 390 & 0.580 & 1-Methyl-2-(1-methylylethyl)benzene & 527-84-4 & C\({}_{\text{I}}\)H\({}_{\text{I}}\) & 2 & 1023 & 1023 [13] \\
33 & 390 & 0.690 & 1,2-3 Trimethylbenzene & 526-73-8 & C\({}_{\text{G}}\)H\({}_{\text{I}}\) & 1 &and absent for the YGC\({}_{A}\), corroborating the fact that the indicated compounds are directly related to the presence of the microorganism. Therefore, the data set was submitted for evaluation using PLS-DA. This type of tool has been widely used in metabolomics approaches, essentially when each of the selected classes is known. The variation of the samples can be explained by the latent variable which in statistics can be considered the variables that are not directly observed but are rather inferred (through a mathematical model) [53; 54].