Abstract

A simulation study was conducted to evaluate the feasibility of using chemometrics methods to analyze process nuclear magnetic resonance (NMR) data. Using the computer-generated NMR data, training sets and validation sets were constructed to represent several real-world application scenarios. The experimental factors (the spectral noise, the reference measurement error, and the nonlinearity) that affect the performance of a partial least-square (PLS) model were systematically investigated.

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