Multivariate evaluation method for the detection of pest infestations on plants via VOC analysis using gas chromatography mass spectrometry

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.

Keywords: Linear discriminant analysis; Multivariate evaluation method; Pest infestations; Principal component analysis; Volatile organic compounds.

Fig. 1

Flowchart of the Python script. The relevant steps of data selction, data processing, and chemometrical evaluation are shown. GC/MS: gas chromatography mass spectrometry, RT: retention time, TIC: total ion chromatogramm, PCA: principal component analysis, LDA: linear discriminant analysis.

Fig. 2

Stacking of GC-MS example chromatograms of the four investigated classes. Red: healthy Acer tree (HT), blue: Anoplophora glabripennis (ALB) infested Acer tree, green: Cossus cossus (CC) larvae, purple: Saperda carcharias (SC) infested Acer tree; RT: retention time. Compounds specific to one pest are marked with diamond symbol. Compounds which occur during pest infestation are labeled with cycle symbol. Increasing concentrations of compounds during infestation are characterized with an upward triangle symbol, decreasing concentrations with a downward triangle symbol, respectively. The peak No. can be assigned to the compounds in Table 3.

Fig. 3

LD-Plots of the six different class comparisons. (a) Anoplophora glabripennis (ALB) infested Acer tree and healthy tree (HT), (b) Saperda carcharias (SC) larvae infested Acer tree and HT, (c) Cossus cossus (CC) larvae and HT, (d) ALB infested Acer tree and SC larvae infested Acer tree, (e) ALB infested Acer tree and CC larvae, (f) SC larvae infested Acer tree and CC larvae.

Fig. 4

(a) Loading plots of the class comparison of Anoplophora glabripennis (ALB) infested Acer tree and healthy tree (HT). PC: principal component, RT: retention time, the Peak No. can be assigned to the compounds in Table 3; (b) overlay of example chromatograms of HT and ALB between retention time 15.2 min and 16.3 min with identified compounds cyclosativene, -longipinene, copaene, and caryophyllene. Red: ALB, black: HT.