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Before multivariate analysis, the correcte … Before multivariate analysis, the corrected FT-NIR spectral datasets were mean-centered and the GC-TOF/MS dataset were scaled to unit variance following log10-transformation. The multivariate models were calculated using PCA, OPLS-DA, and O2PLS implemented in SIMCA-P + version 12 (Umetrics AB, Umeå, Sweden). The ellipse in the PC score plot represents the confidence region of the model based on Hotteling’s T2 statistic (Hotelling 1931; Mason et al. 2001). The significance level of the confidence region was defined at 0.05, and the data that fell outside the ellipse were determined to belong to candidate lines. These models were validated using 7-fold cross-validation or analysis of variance of cross-validated predictive residuals (CV-ANOVA) (Eriksson et al. 2008). Cross-validation is an internal predictive validation method for determining the number of significant components by calculating the total amount of explained X-variance (R2X), Y-variance (R2Y), and cross-validated predictive ability (Q2Y). A component is significant when Q2Y is positive value. Additionally, the variance related to class separation (RP2X) was calculated by OPLS-DA. CV-ANOVA is based on an ANOVA assessment of the cross-validatory predictive residuals of the models. The statistical Welch’s t test was performed and false discovery rate (FDR), which have been proven to be reliable for determining the significance of multiple testing (Storey 2002), were calculated using Microsoft Office Excel 2003 software. Q-value for FDR less than 0.05 was regarded as significant. ess than 0.05 was regarded as significant.
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