ABSTRACT
Detection of the drug Levodopa (3,4-dihydroxyphenylalanine, L-Dopa) is essential for the medical treatment of several neural disorders, including Parkinson's disease. In this paper, we employed surface-enhanced Raman scattering (SERS) with three shapes of silver nanoparticles (nanostars, AgNS; nanospheres, AgNP; and nanoplates, AgNPL) to detect L-Dopa in the nanoparticle dispersions. The sensitivity of the L-Dopa SERS signal depended on both nanoparticle shape and L-Dopa concentration. The adsorption mechanisms of L-Dopa on the nanoparticles inferred from a detailed analysis of the Raman spectra allowed us to determine the chemical groups involved. For instance, at concentrations below/equivalent to the limit found in human plasma (between 10-7-10-8 mol/L), L-Dopa adsorbs on AgNP through its ring, while at 10-5-10-6 mol/L adsorption is driven by the amino group. At even higher concentrations, above 10-4 mol/L, L-Dopa polymerization predominates. Therefore, our results show that adsorption depends on both the type of Ag nanoparticles (shape and chemical groups surrounding the Ag surface) and the L-Dopa concentration. The overall strategy based on SERS is a step forward to the design of nanostructures to detect analytes of clinical interest with high specificity and at varied concentration ranges.
ABSTRACT
Polyphenols have attracted attention due to their antioxidant capacity and beneficial effects to health. Therefore, fast, inexpensive, and efficient methods to discriminate and to quantify polyphenols are of interest for food industry. In this paper, Layer-by-Layer films of poly(allylamine hydrochloride) and iron tetrasulfonated phthalocyanine were employed as sensor for determination of polyphenols in green tea (camellia sinensis), and green and roasted mate teas (ilex paraguariensis). The polyphenol sensor was tested in catechol standard solution by differential pulse voltammetry (DPV), reaching a limit of detection of 1.76 × 10-7 mol/L. The determination of polyphenols in the tea samples was obtained by analytical curve and catechol standard addition using electrochemical techniques. Projection techniques (information visualization) were applied to the DPV results of the tea samples and a pattern of separation following the phenolic content was obtained. The results support the application of the sensor in fast classification of beverages according to their polyphenol content.
