Abstract
Volatile organic compounds (VOCs) play an important role in the defense against pest infestations on plants. The analysis of these VOCs using gas chromatography mass spectrometry (GC-MS) enables the detection of pests by analyzing the VOC composition (VOC profiles) for specific patterns and markers. The analysis of such complex datasets with high biovariability poses a particular challenge. For this reason, a multivariate evaluation method based on a self-written Python script, using principal component analysis (PCA) and linear discriminant analysis (LDA), was developed and tested for functionality using a dataset, which has been evaluated manually and has identified five specific markers (2,4-dimethyl-1-heptene, 3-carene, α-longipinene, cyclosativene, and copaene) for Anoplophora glabripennis (ALB) infestation on Acer trees. The results obtained in the present study did not only match the manually evaluated results, but lead to further insight into the dataset. Another sesquiterpene which is assumed to be α-zingiberene was identified as an ALB specific marker in addition to 2,4-dimethyl-1-heptene and 3-carene. Furthermore, the European native beetle species goat moth Cossus cossus (CC) and poplar long-horned beetle Saperda carcharias (SC) were also analyzed for their VOCs to differentiate ALB specific VOC from other pest infestations. This comparison lead to the conclusion that the compounds α-longipinene, cyclosativene, and copaene are not specific for ALB but for pest infestation in general. It was possible to identify not only specifically produced VOCs, but also differences in concentrations that arise specifically during ALB infestation. Therefore, the evaluation method for the detection of plant pests presented in this study represents a time-saving alternative to conventional non computing methods, which in addition provides more detailed results.