Advances in Liquid Chromatographic and Voltammetric Analysis of Underivatized Amino Acids

Underivatized amino acids detection by anion-exchange chromatography coupled to a nanostructured detector

This work reports the development of a fast and reliable amperometric sensor for the detection of amino acids. The detector was constructed using copper nanoparticles (CuNPs) supported on reduced graphene oxide (RGO) modified glassy carbon electrode (CuNPs-RGO/GCE) and based on the application of high performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Under optimized isocratic HPAEC-PAD conditions (using 40 mmol L^-1 NaOH as mobile phase, flow rate of 0.30 mL min^-1 and detection potential of 0.45 V vs. Pd/PdO), the linear dynamic ranges of the concentration of amino acids obtained were 0.50-50 µmol L^-1 for lysine, 1.0-100 µmol L^-1 for alanine, glycine and serine, and 5.0-100 µmol L^-1 for leucine. The limits of detection (S/N = 3) obtained ranged from 0.10 (for lysine and leucine) to 0.50 µmol L^-1 (for alanine, glycine and serine) and sensitivity varied from 6.1 (for leucine) to 21.5 nA µmol^-1 L (for serine). The average recovery percentages ranged from 97% (for glycine) to 102% (for leucine and serine). The results obtained showed that the CuNPs-RGO/GCE has good long-term stability, repeatability and reproducibility; this makes the device suitable for application as an electrochemical detector. The successful application of the proposed method for the analysis of sugarcane vinasse demonstrates its suitability for separation and determination of amino acids in complex matrices.

Separation optimization in reversed-phase liquid chromatography by using alkanol additives in the mobile phase: application to amino acids

In an effort to enhance complex mixture separations by using small amounts of a homologous series of alkanols as additives in the mobile phases, it was proposed an optimization algorithm based on a sixth-parameter retention model. This model considers simultaneously the contents of the main organic modifier and of the alkanol additive in the mobile phase as well as of the number of alkyl chain of the additive. This model is in fact a modification of a previously one derived in a recently published paper for the retention description of a mixture of purely hydrophobic alkylbenzenes under isocratic conditions with mobile phases containing alkanol additives. The effectiveness of the new retention model as well as the optimization algorithm was successfully applied to the separation of ten o-phthalaldehyde (OPA) derivatives of amino acids. Indeed, the new retention model exhibited an excellent prediction performance since the obtained overall predictive error between calculated and experimental times was only 2.8% for all isocratic runs by using a variety of mobile phase compositions containing any alkanol homologue even different than those used in the starting/fitting experiments. Moreover, a perfect resolution of the above amino acid mixture was achieved within only 7.4 min in the chromatogram recorded using the optimal mobile phase determined by means of the simple optimization algorithm proposed in this study.