Building robust calibration models for the analysis of estrogens by gas chromatography with mass spectrometry detection

Five hormonal growth promotants (diethylstilbestrol, hexestrol, dienestrol, 17-(beta)-estradiol and 17-(alpha)-ethynylestradiol) have been analysed by gas chromatography with mass spectrometry detection (GC/MS, SIM mode) for four non-consecutive days. The aim is to build models with stable predictions. The strategies applied are internal standardization and global models carried out by gathering signals recorded on several days. Two models were examined: univariate models (with standardized peak area) and PARAFAC2 (the analyte scores were standardized by the scores of the internal standard). Internal standardization has been proved to be efficient for both models of dienestrol and ethynylestradiol. The mean relative error in absolute value when samples recorded on a different day to the calibration set are quantified by PARAFAC2 is 7.00% and 7.11% for dienestrol and ethynylestradiol, respectively. For diethylstilbestrol and estradiol, internal standardization was combined with global calibration models built with signals recorded under several sources of variability (different days). Thus predictions become steadier over time and in the estradiol example, errors decrease from 33.10% to 9.76%. The mean relative error in absolute value with PARAFAC2 updated models oscillates between 6.34% for ethynylestradiol and 10.74% for diethylstilbestrol. For univariate updated models errors range from 6.42% to 14.19% for ethynylestradiol and estradiol respectively. The combination of both strategies has been proved to be efficient independently of the analyte and of the signal order.