Introduction: Novel manipulations of the well-known classical least squares multivariate
calibration model were explained in detail as a comparative analytical study in this research work. In
addition to the application of plain classical least squares model, two preprocessing steps were tried,
where prior to modeling with classical least squares, first derivatization and orthogonal projection to
latent structures were applied to produce two novel manipulations of the classical least square-based
model. Moreover, spectral residual augmented classical least squares model is included in the present
Quantitative determination of pyridostigmine bromide in the presence of its two related substances;
impurity A and impurity B was considered as a case study to construct the comparison.
Method: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different
concentrations of the studied components. To investigate the predictive ability of the studied models;
a test set consisting of 9 mixtures was constructed.
Results: The key performance indicator of this comparative study was the root mean square error of
prediction for the independent test set mixtures, where it was found 1.367 when classical least squares
applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when
orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral
residual augmented classical least squares was performed.
Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures
preprocessing method produced significant improvement of the predictive ability of it.