Table 1.
Whole cluster analysis. Volatiles emitted from Frontenac and Marquette clusters, grown in Iowa (IA) and South Dakota (SD). These VOCs were indicated to be the most representative variable from hierarchical clustering analysis (HCA) after the PCA (JMP Pro 12.0.1, SAS Institute Inc., Cary, NC, USA).
| Sample | Cluster | No. of Members 2 | Most Representative Variable 3 | Cluster Proportion of Variation Explained 4 | Total Proportion of Variation Explained 5 |
|---|---|---|---|---|---|
| IA Frontenac (0.709) 1 | 1 | 5 | Heptanal | 0.937 | 0.173 |
| 3 | 5 | 4-Methyl-3-penten-2-one | 0.745 | 0.138 | |
| 5 | 6 | Nonanal | 0.512 | 0.114 | |
| 2 | 3 | 3-Methyl-1-butanol | 0.845 | 0.094 | |
| 4 | 4 | 1,4-Butanolide | 0.576 | 0.085 | |
| 6 | 2 | 5-(Hydroxymethyl)-2-furancarboxaldehyde | 0.858 | 0.064 | |
| 7 | 2 | Benzophenone | 0.548 | 0.041 | |
| SD Frontenac (0.686) 1 | 1 | 7 | Toluene | 0.712 | 0.208 |
| 2 | 5 | Nonanal | 0.820 | 0.171 | |
| 5 | 3 | 3-Phenyl-2-propenal | 0.810 | 0.101 | |
| 3 | 4 | 4-Methyl-3-penten-2-one | 0.592 | 0.099 | |
| 4 | 3 | Acetic acid | 0.502 | 0.063 | |
| 6 | 2 | Benzyl alcohol | 0.536 | 0.045 | |
| IA Marquette (0.739) 1 | 1 | 10 | 1-Octanol | 0.805 | 0.310 |
| 2 | 5 | Acetaldehyde | 0.814 | 0.156 | |
| 3 | 3 | Methyl ethyl ketone | 0.616 | 0.071 | |
| 4 | 3 | 1-Hexadecanol | 0.505 | 0.063 | |
| 5 | 3 | Acetophenone | 0.536 | 0.062 | |
| 6 | 1 | Acetic acid | 1.000 | 0.038 | |
| 7 | 1 | 2-Ethyl-1-hexanol | 1.000 | 0.038 | |
| SD Marquette (0.783) 1 | 1 | 7 | Acetone | 0.817 | 0.249 |
| 2 | 4 | 4-Methyl-3-penten-2-one | 0.799 | 0.139 | |
| 6 | 4 | Decane | 0.797 | 0.139 | |
| 3 | 2 | 1-Pentanol | 1.000 | 0.087 | |
| 4 | 3 | 2-Ethyl-1-hexanol | 0.532 | 0.069 | |
| 7 | 2 | 1-Hexadecanol | 0.654 | 0.057 | |
| 5 | 1 | Indene | 1.000 | 0.043 |
1 The total proportion of variation explained by all the cluster components. 2 The number of variables in the cluster. 3 The cluster variable that has the largest squared correlation with its cluster component. 4 The cluster’s proportion of variance explained by the first principal component amount the variables in the cluster, based only on variables within the cluster. 5 The overall proportion of variance explained by the cluster component, using only the variables within each cluster to calculate the first principal component.