Abstract
One-class partial least squares (OCPLS) classifier is investigated as a tool for multivariate statistical quality control (MSQC). According to the OCPLS score distance (SD) and absolute centered residual (ACR) of predicted response, an object can be classified into one of the four groups: regular points (with a small SD and a small ACR), class outliers (with a small SD and a large ACR), good leverage points (with a large SD and a small ACR) and bad leverage points (with a large SD and a large ACR). The correlation between OCPLS distance measures and some existing methods, including D-statistic, Q-statistic and correlation coefficient (Pearson's r), is briefly discussed. OCPLS is applied to non-targeted detection of adulterations in whole milk powder using near-infrared (NIR) spectroscopy. The results demonstrate OCPLS can provide an effective tool for MSQC by including both SD and ACR of predicted response.
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