ABSTRACT
A simple pH-independent optical method for the sensing of heparin, as a biomedically important polyionic drug, based on aggregation of gold nanoparticles (AuNPs) is described. The polyanionic heparin induces the aggregation of positively charged ionic liquid stabilized AuNPs, which results in a shift in the surface plasmon band and a consequent color change of the AuNPs from red to blue. The color change was monitored using UV-vis spectrophotometry and image analysis methods. The aggregation was confirmed by transmission electron microscopic measurements. The degree of aggregation was found to be proportional to the concentration of the added heparin, allowing its quantitative detection. The change in the absorbance and color-value has been used to monitor the concentration of heparin. This optical method can quantify heparin as low as 0.010 µg mL(-1) and the calibration is linear for a wide range of concentration.
Subject(s)
Gold/chemistry , Heparin/analysis , Ionic Liquids/chemistry , Metal Nanoparticles/chemistry , Optical Phenomena , Color , Heparin/chemistry , Hydrogen-Ion Concentration , Optical Imaging , SpectrophotometryABSTRACT
This paper addresses the solution of peak overlapping, as a fundamental problem in TLC, by multivariate analysis of the images recorded by a digital camera. We report the results of our study on the application of multivariate image analysis (MIA) for simultaneous determination of several species on thin layer chromatography (TLC) sheet for the first time. An imaging system, composed of a dark cabinet, a digital camera and a multivariate image analysis program, was prepared for recording the images of TLC plates after development of a multi-component solution. The written program was able to produce 2- and 3-dimensional chromatograms of the solutions, which were subsequently used as inputs of partial least squares, as an efficient multivariate calibration method. The ability of the proposed MIA-TLC method for simultaneous determination of the co-eluting components was validated by analysis of ternary synthetic mixtures of indicators of highly overlapped chromatograms (i.e., methyl yellow, bromocresol green and creseol red) and a real mixture of nifedipine and its photo-degradation product. By application of different strategies like principal component analysis and variable selection, models were obtained that could estimate the concentration of indicators in the external prediction set with relative errors of lower than 10% and in most cases lower than 5%.
