Metal Ion‐Assisted Infrared Optical Sensor for Selective Determination of Tryptophan in Urine Samples

Abstract

In this study, a simple infrared chemical sensor was developed for the selective detection of tryptophan in biological fluids. This sensor was capable of trapping tryptophan molecules through the formation of relatively stable metal ion complexes on the surface of the sensing element. A proline-modified sensing phase was immobilized on the surface of the internal reflection element. With the assistance of appropriate metal ions, tryptophan molecules were selectively attracted nearby the evanescent field such that analytical signals were generated. Factors that affected the chemical equilibria in this detection system were examined including the species and concentration of metal ion, the pH of the sample solution, and the concentration of the chelating agent. Among the examined metal ions, nickel provided the best selectivity toward the detection of tryptophan as a result of its extremely high formation constant with tryptophan. Under the optimal conditions, the detected signals were related linearly (R 2 > 0.99) to concentrations of tryptophan up to 600 mM. Based on three times the baseline variation of blank samples, the detection limit was ca. 5 mM. From a study of possible interfering agents-metal ions and organic species-in the sample solution, the recoveries of tryptophan were greater than 95%.

Development of molecularly ımprınted polymer based quartz crystal mıcrobalance nanosensor for the determınatıon of tryptophan

“…Today, many methods such as high performance liquid chromatography (HPLC) [9,10], fluorescent detector HPLC [11], liquid chromatography-tandem mass spectrometry [12], spectrophotometry [13], spectrofluorimetry [14], amperometry [15], capillary electrophoresis technique [16], colorimetric method [17] and infrared optical sensor [18] are used to determine the amount of tryptophan. Although these methods are very important for quantitative analysis, most of them have disadvantages such as high costs, complex analysis process, long analysis time, long optimisation conditions and variables that depend on the analyst's ability.…”