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Multivariate statistical investigations we … Multivariate statistical investigations were performed using SIMCA-P + 12 software (Umetrics, Umeå, Sweden). All variables were log10-transformed, centered, and scaled to unit variance for the analysis. To connect the information of two-block variables (X and Y) to each other, we used an orthogonal projection to latent structures (OPLS). OPLS is one of the supervised methods which is commonly applied in metabolomics. An OPLS regression model (Trygg and Wold, 2002) was calculated to investigate potential relationships between the metabolic compositions (X) of the aspen leaves and their positions (Y) on the stem. Peak areas under the resolved GC–MS peaks were used as descriptors (X) and the leaf positions as the response (Y) in the OPLS model. R2X is the cumulative modeled variation in X, R2Y is the cumulative modeled variation in Y, and Q2Y is the cumulative predicted variation in Y, according to cross-validation. The range of these parameters is 0–1, where 1 indicates a perfect fit. s is 0–1, where 1 indicates a perfect fit.
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