# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
## 1 Introduction
This study illustrated key considerations involved in the selection of a SPME fiber, including: (1) the polarity and functionality of the polymer absorbent; and (2) the volatility and functional groups of the target analyte. The separation of MVOCs is efficiently accomplished with the use of gas chromatography (GC) [33; 36]. Detection methods include flame ionization detection (FID) [34; 37; 38] and mass spectrometry (MS) [8; 39; 40; 41; 42] for the quantification and qualification of metabolic profiles. Among the MS techniques, single quadrupole mass detection is most widely used; however, more advanced techniques, such as triple quadrupole (MS/MS) [43; 44], time of flight (TOF) [45] and ion mobility mass spectrometry (IMS-MS) [46; 47], are utilized depending on the purpose of the analysis. In metabolomics research, different data pretreatment methods are applied in order to generate \"clean\" data in the form of normalized peak areas that reflect metabolite concentrations. These clean data can then be used as the input for data analysis. Data pretreatment aids to enhance relevant (biological) information and to reduce the influence of confounding factors from random error and spurious chemicals from column and absorbent bleeding [48]. Three classes of data pretreatment methods are normally utilized, including centering, scaling and transformations. Centering converts all of the concentrations to fluctuations on zero [49]. Scaling enables the adjustment of fold differences between the metabolites, increasing the importance of low abundant metabolites. Transformations including log and power transformations are generally applied to correct for heteroscedasticity [50]. Statistical data analysis methods including multivariate data analysis (MVDA) can be used for extracting important features from large or small datasets containing a number of variables and observations. MVDA includes multivariate ANOVA (MANOVA), linear discriminant analysis (LDA), cluster analysis (CA), principal component analysis (PCA) and partial least square analysis (PLS). These methods are widely used in fungal detection and classification [11; 51; 52; 53]. In this study, the effect of sample collection strategy using SPME fibers and sample preparation (fungal growth parameters) on the MVOCs' production from _A. flavus_ was investigated. For SPME fiber evaluation, the extraction efficiencies of three commercially available SPME fibers coated with carboxen/polydimethylsiloxane (CAR/PDMS), divinylbenzene/polydimethylsiloxane (DVB/PDMS) and carboxen/divinylbenzene /PDMS were compared. A single _A. flavus_ isolate was selected for this study in an attempt to reduce variations due to phenotype differences associated with different isolates [32]. For the growth parameters' effect study, the _A. flavus_ isolate was grown under varied conditions using different temperatures and numbers of spores inoculated and on different substrates to evaluate the influence of these factors on MVOCs' production. The aim of this study was to optimize fungal growth conditions for large MVOCs' production and to determine MVOCs' variability within a single isolate. Different data pretreatment methods, including scaling, centering and transformations, were evaluated using MVOCs' datasets from _A. flavus_ grown on five different media to select the proper method for PCA analysis. To our knowledge, this is the first study focused on the evaluation of the growth parameters and data analysis methods for fungal MVOCs analysis.