摘要
In this work a new method is presented for simultaneous colorimetric determination of morphine （MOR） and ibuprofen （IBU） based on the aggregation of citrate-capped gold nanoparticles （AuNPs）. Citrate-capped AuNPs were aggregated in the presence of MOR and IBU. The difference in kinetics of AuNPs aggregation in the presence of MOR/IBU was used for simultaneous analysis of MOR and IBU. The formation and size of synthesized AuNPs and the aggregated forms were monitored by infra-red （IR） spectroscopy and transmission electron microscopy （TEM）, respectively. By adding MOR or IBU the absorbance was decreased at 520 nm and increased at 620 nm. The difference in kinetic profiles of aggregation was applied for simultaneous analysis of MOR and IBU using partial least square （PLS） regression as an efficient multivariate calibration method. The number of PLS latent variables was optimized by leave-one-out cross-validation method using predicted residual error sum of square. The proposed model exhibited a high capability in simultaneous prediction of MOR and IBU concentrations in real samples. The results showed linear ranges of 1.33-33.29 μg/mL （R2=0.9904） and 0.28-6.9 μg/mL （R2=0.9902） for MOR and IBU respectively with low detection limits of 0.15 and 0.03 μg/mL（S/N=5）.
In this work a new method is presented for simultaneous colorimetric determination of morphine （MOR） and ibuprofen （IBU） based on the aggregation of citrate-capped gold nanoparticles （AuNPs）. Citrate-capped AuNPs were aggregated in the presence of MOR and IBU. The difference in kinetics of AuNPs aggregation in the presence of MOR/IBU was used for simultaneous analysis of MOR and IBU. The formation and size of synthesized AuNPs and the aggregated forms were monitored by infra-red （IR） spectroscopy and transmission electron microscopy （TEM）, respectively. By adding MOR or IBU the absorbance was decreased at 520 nm and increased at 620 nm. The difference in kinetic profiles of aggregation was applied for simultaneous analysis of MOR and IBU using partial least square （PLS） regression as an efficient multivariate calibration method. The number of PLS latent variables was optimized by leave-one-out cross-validation method using predicted residual error sum of square. The proposed model exhibited a high capability in simultaneous prediction of MOR and IBU concentrations in real samples. The results showed linear ranges of 1.33-33.29 μg/mL （R2=0.9904） and 0.28-6.9 μg/mL （R2=0.9902） for MOR and IBU respectively with low detection limits of 0.15 and 0.03 μg/mL（S/N=5）.
