# Analysis of Volatile Components in Different _Ophiocordyceps sinensis_ and Insect Host Products
## 2 Results and Discussion
### Multivariate PLS-DA Analysis  
PLS-DA was performed to evaluate the variations among the volatile compound profiles obtained from GC\(\times\)GC-QTOFMS data for different products. The PLS-DA scores plot shows clear classification of the three groups: fruiting bodies and fermented cultures of \(O\). _sinensis_ fungus, _T. xiaojinensis_ insects, and insect-fungus complexes (Figure 3A). PLS (Partial least square) component 1 (PLS 1) and PLS component 2 (PLS 2) explained 21.5% and 19.0% of the variance, respectively, and hence together, they explained 40.05% of the total variance. The parameters of the cross-validation modeling for the fifth PLS component were R\({}^{2}\)X = 0.73, R\({}^{2}\)Y = 0.992, and Q\({}^{2}\)Y = 0.896, showing high levels of an explained variance and predictability. A permutation test involving 200 iterations was also conducted to validate the model, which yielded R\({}^{2}\) = 0.770 and Q\({}^{2}\) = \(-\)0.480.  
Figure 3: Partial least squares-discriminant analysis (PLS-DA) analysis of 16 products. **(A)**: Scores plot, **(B)**: variable importance projection (VIP) score plot, **(C)**: loadings plot. F: Fungus of _Ophiocordyceps sinensis_; I: Larvae or pupae of _Thitarodes xiaojinensis_; FI: Fungus–insect complexes.  
To explain the relationships between variables and products, loading scatter plots were performed (Figure 3B). As shown in the loadings plot PLS-DA model (Figure 3B), X-variables situated in the vicinity of the dummy Y-variables had the highest discriminatory power among the groups and had higher VIP (variable importance projection) values, thus contributing more to the differences of different groups. The VIP values of each compound were calculated (Figure 3C). The compounds with larger VIP values represent higher contributions to the discrimination of different groups. In the study, volatile components with VIP values \(>\) 1 and \(p\) \(<\) 0.05 were considered as representative differential compounds. A total of 28 differential volatile compounds were identified, although there were 48 volatile compounds with VIP values \(>\) 1. It showed that the majority of variables gave a not significant contribution to the model. The 28 differential volatile compounds included thieno [2,3-c] pyridine (C71), 2,6,11-trimethyldodecane (C78), 2,3,4-trimethyldodecane (C79), 4,7-dimethyllanden (C75), 2,6-dimethylundecane (C65), farnesane (C94), 2,3-dimethyldodecane (C92), [but-2-en-2-yl] benzene (C51), 2-methylcyclopentanone (C8), 2-undecanone (C83), 2,8-dimethylundecane (C69), \(\alpha\)-methyl-1\(H\)-indene-1-methanol acetate (C88), 2,6-dimethylheptadecane (C108), 1,2-dimethoxyethylbenzene (C38), 2,6-dimethyl-6-trifluoroacetoxyoctane (C34), modephene (C97), 4'-methylproiphenone (C53), 2,6-di-tert-butyl-4-methyl-p-quinol (C110), o-xylene (C5), 3-methyl-undecane (C56), tridecane (C84), 2,5,6-trimethyldecane (C19), sabinene (C13), dodecane (C61), \(\beta\)-sesquiphellandrene (C113), 2,3-dimethyldecane (C52), 3,4-dimethylcumene (C63), and 4,7-dimethylundecane (C64). Among them, the first eight volatiles including thieno [2,3-c] pyridine (C71), 2,6,11-trimethyldodecane (C78), 2,3,4-trimethyldodecane (C79), 4,7-dimethyllandan (C75), 2,6-dimethylundecane (C65), farnesane (C94), 2,3-dimethyldodecane (C92), and [but-2-en-2-yl] benzene (C51) showed higher discriminatory potential with VIP values greater than 1.5.