## 1 Introduction  
Ochratoxin A is a mycotoxin reported to be a potential human carcinogen (group 2B) defined by the International Agency for Research on Cancer (IARC) and it is common in grape and grape-related products [1; 2]. _A. carbonarius_ in section _Nigri_ is known to infect grape and is a main source of ochratoxinA in grape products [3; 4]. Identifying different toxigenic strains is a crucial task to control the safety of foodstuffs. A typical approach is to assess volatile organic compounds generated by the fungi [5; 6; 7]. Microbial volatile organic compounds (MVOCs) are generated by the metabolism of microorganisms such as bacteria and filamentous fungi [8; 9; 10; 11]. The MVOCs have been studied for various reasons including predicting spoilage processes caused by microorganisms during the period of storing foodstuffs, taxonomy research to identify different fungal species [8; 9] and investigating the relation between volatile compounds in indoor air environments with contamination by fungi [10; 11]. Also, MVOCs have been used to discover the relationships with the mycotoxins [5; 6; 12; 13]. For example, [PERSON] et al. [13], investigated the volatile sesquiterpenes generated by both toxicigenic and nontoxician _Fusarium sambucinum_ strains, and the toxigenic strains produced more sesquiterpenes with greater chemical diversity compared with nontoxician strains. However, a later study investigated volatile compounds produced by _Aspergillus_ strains with different OTA-forming ability and showed that the profile of volatiles generated by toxic strains could not be distinguished from non-toxic strains [5]. Therefore, further research needs to be applied to characterize different toxigenic strains. Selected previous studies were performed by gas chromatography-mass spectrometry (GC-MS) technology to analyze MVOCs based on relative quantitative data [5; 6; 12; 13]. Limitations of this time-consuming quantitative analysis approach included incomplete peak resolution [14] and limited breadth of analysis [15]. Data analysis strategies have developed over many years and suggested chemometrics as a useful and efficient way to analyze large data sets generated by modern information-rich analytical techniques [15; 16; 17; 18; 19; 20]. The data generated from GC-MS experiments exhibit high dimensionality with numerous variables, and in order to better understand the information between the different samples, untargeted metabolic fingerprinting of GC-MS data coupled with chemometrics has proven to be a robust tool [21; 22]. However, untargeted profiling of MVOCs to distinguish different toxigenic strains is not always available or precisely identified in reference library data. A critical step for metabolomics study is to analyze high-dimensional data generated from the GC-MS data. A variety of chemometrics methods have been developed to project the multi-dimensional data to lower dimensions and explore the differences between group samples [23] including partial least squares discriminant analysis (PLS-DA) [24], orthogonal projection to latent structures discriminant analysis (OPLS-DA) [25], principle component analysis (PCA) and support vector machine (SVM) [26; 27]. Of these, PLS-DA is one of the most attractive classification methods in chemometrics and has been successfully implemented in metabolomics research [22; 28; 29]. OPLS-DA is an extension of PLS-DA, which improves the interpretation of constructed models by removing variance orthogonal to the variation of interest [30]. The advantage of OPLS-DA is that one single component is used to predict the group or class whereas the rest of the components are used to define the variation orthogonal to the first predicting component [31; 32]. In addition, PLS-DA and OPLS-DA can provide statistical information, such as loading weight, sensitivity ratios (SR), regression coefficients and variable importance on projection (VIP), which can be performed to find out important variables [28; 33; 34].