# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
## 3 Results and Discussion
### Effect of Data Pretreatment Methods  
Data pretreatment methods can be utilized to convert raw data to a different scale (for instance, logarithmic scale or relative scale), which reduces unwanted biases to more clearly depict important biological signals. The effect of data pretreatment has been illustrated through the application of six data pretreatment methods on the MVOCs' data of _A. flavus_ grown on the five different media substrates. The results of these methods are shown in Figure 5, where (A) is the raw data. The other graphs represent: (B) centering; (C) autoscaling; (D) Pareto scaling; (E) log transformation; (F) power transformation; and (G) area normalization. The pretreatment methods were performed according to the equations listed in Table 3.  
The MVOCs' raw data obtained from the five growth media study was used for the data pretreatment methods' evaluation. The raw peak area data for the 132 identified MVOCs are shown in Figure 5A (MVOC profile from CDL media Replication 1). Mean centering was applied to obtain a mean value of zero in order to improve the interpretability of the model (Figure 5B). Autoscaling is a combination of mean centering and scaling to unit variance where the scaling weight employed is \(1/s\), and \(s\) represents the standard deviation of the variable (peak area of a specific MVOC). After autoscaling, \"long\" variables are \"shrunk\", and \"short\" variables are \"stretched\" (Figure 5C). In Pareto scaling, the scaling weight is \(1/\surd s\), and it is intermediate between the extremes of no scaling and  
Figure 5: Effect of data pretreatment on the original data. autoscaling (Figure 5D). The data do not become dimensionless after autoscaling, so this method stays closer to the original measurement than autoscaling. Another objective of data pre-treatment is converting a non-normal distribution of the specific variable into a normal one. One way to accomplish this is through log transformation (Figure 5E). The benefits of this sort of transformation include: (1) simplifying the response function by linearizing a non-linear response-factor relationship; (2) stabilizing the variance of the residuals; and (3) making the distribution of the residuals more normal, which can serve to eliminate outliers. Power transformation plots the square root of the data (Figure 5F) and is similar to the Pareto scaling method. Finally, the area normalization method (Figure 5G) showed similar results when compared to the original data (Figure 5A). The area normalization method is a semi-quantitative approach using the relative percentage of each compound of the total MVOCs extracted. Each of the six data pretreatment methods was applied to the entire dataset. PCA was used to analyze the effect of each method (Figure 6). PCA can also identify important MVOCs contributing to classification by analysis of the loadings. Suitable data pretreatment methods help provide good cluster separation where the distance within the cluster of a specific category (media type) and the distance between the clusters of the categories are favorable. The application of log transformation (Figure 6E) provided the best clustering results in the score plots. PCA analysis of the centering (Figure 6B), Pareto scaling (Figure 6D) and the original data (Figure 6A) provided poor clustering results compared to the other pretreatment methods. Power transformed data showed intermediate cluster separation. Tight clusters were produced with the area normalization method (Figure 6G); however, the CDA, CSA and CDL clusters were not well separated. Figure 6: \(Cont.\)  
In the original (Figure 6A) and centering data (Figure 6B), MEA, CMA and CSA clusters \"squeeze\" together because CDA and CDL have much larger variances in the data caused by a higher concentration of MVOCs. Large variances play an important role for the classification of different categories (media) in PCA analysis. Poor cluster separation resulted from data pretreatment without a \"hard\" scaling method, such as autoscaling.