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. Author manuscript; available in PMC: 2018 Oct 25.

Published in final edited form as: J Am Chem Soc. 2017 Sep 8;139(42):14954–14960. doi: 10.1021/jacs.7b06371

The results of simultaneous quantitative linear regression analysis of the morphine in ternary mixtures of morphine and its metabolites using support vector machine (SVM) algorithm, with 2 components of partial least squares regression (PLS) for multivariate X-block data compression. The root-mean-square errors (RMSEs) of calibration (C), cross-validation (CV), and prediction (P) attest to the quality of the model and prediction. Two unknown/validation samples (red circle ) were simultaneously correctly analyzed for MOR (50 and 400 ng/mL).

Inline graphic — The results of simultaneous quantitative linear regression analysis of the morphine in ternary mixtures of morphine and its metabolites using support vector machine (SVM) algorithm, with 2 components of partial least squares regression (PLS) for multivariate X-block data compression. The root-mean-square errors (RMSEs) of calibration (C), cross-validation (CV), and prediction (P) attest to the quality of the model and prediction. Two unknown/validation samples (red circle ) were simultaneously correctly analyzed for MOR (50 and 400 ng/mL).