Multi-way calibration based on second-order data constitutes a revolutionary milestone for analytical applications. However, most classical chemometric models assume that these data fulfil the property of low rank bilinearity, ...

Analyte quantitation can be achieved from second-order data in the presence of uncalibrated components using multivariate calibration methods such as partial leastsquares with residual bilinearization. However, the latter ...

A general expression is derived for estimating the sensitivity of second-order bilinear calibration models, particularly parallel factor analysis (PARAFAC) and bilinear least-squares (BLLS), whether the second-order advantage ...

A maximum likelihood model is described for performing second-order multivariate calibration with unfolded principal component regression with residual bilinearization (MLU-PCR/RBL). It differs from the conventional RBL ...

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the ...

Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available ...

Second-order instrumental signals showing a non-linear behaviour with respect to analyte concentration can still be adequately processed in order to achieve the important second-order advantage. The combination of unfolded ...

Achieving second order advantage with multi-way partial least squares and residual bi-linearization with total synchronous fluorescence data of monohydroxy–polycyclic aromatic hydrocarbons in urine samples