Background: Diclofenac (DCF) is an important widely used non-steroidal antiinflammatory
drug. Disposal of expired formulation, excretion from administered dose, the poor performance
of sewage treatment process, contributes to its frequent detection in environment. Analysis
of DCF in environmental sample requires time consuming pretreatment, extraction steps. Though,
UV absorption analysis of DCF is simple but spectral interference of soil organic matter is a problem.
The aim of this paper is to establish appropriate partial least square chemometric model for DCF
quantitation through variable selection, and validation of analytical method through multivariate figure
of merit analysis.
Methods: Spectral data of DCF spiked soil solution is recorded and variants of partial least squares
(PLS) regression viz., backward-interval PLS (biPLS), synergy-interval PLS (siPLS) and genetic algorithm
(GA) based PLS models (GA-PLS) are developed from autoscaled and 2nd order differential
spectrum. Prediction fidelity of the selected models was evaluated from a blind-folded semi-synthetic
spectral data. The method was validated through figures of merit estimates, such as selectivity, analytical
sensitivity, limits of detection and quantitation.
Results: The siPLS model developed offered the minimum root mean square error of crossvalidation
(RMSECV) of 0.1896 mg/l and 0.1910 mg/l for autoscaled data (9 variables) and derivative
spectra (12 variables), respectively. Refinement of the derivative spectrum with GA offered a
simplified model (RMSECV:0.1712, 10 variable).
Conclusion: The GA based variable selection for PLS regression analysis offers robust analytical
tool for DCF in environmental samples. Further research is warranted to model variable interference
in spectral data unknown to analyst in priori.