2-(2-Phenyl-1H-phenanthro-[9,10-d]imidazole-1-yl)-acetic acid (PPIA) and its application for determination of amines by high performance liquid chromatography with fluorescence detection and identification with mass spectroscopy/atmospheric pressure chemical ionization

A simple, sensitive, and mild method for the determination of amino compounds based on a condensation reaction with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride (EDC-HCI) as the dehydrant with fluorescence detection has been developed. Amines were derivatized to their acidamides with labeling reagent 2-(2-phenyl-1H-phenanthro-[9,10-d]imidazole-1-yl)-acetic acid (PPIA). Studies on derivatization conditions indicated that the coupling reaction proceeded rapidly and smoothly in the presence of a base catalyst in acetonitrile to give the corresponding sensitively fluorescent derivatives with an excitation maximum at lambda(ex) 260nm and an emission maximum at lambda(em) 380nm. The labeled derivatives exhibited high stability and were enough to be efficiently analyzed by high-performance liquid chromatography. Identification of derivatives was carried out by online post-column mass spectrometry (LC/APCI-MS/MS) and showed an intense protonated molecular ion corresponding m/z [MH](+) under APCI in positive-ion mode. At the same time, the fluorescence properties of derivatives in various solvents or at different temperature were investigated. The method, in conjunction with a gradient elution, offered a baseline resolution of the common amine derivatives on a reversed-phase Eclipse XDB-C-8 column. LC separation for the derivatized amines showed good reproducibility with acetonitrile-water as mobile phase. Detection limits calculated from 0.78 pmol injection, at a signal-to-noise ratio of 3, were 3.1-18.2 fmol. The mean intra- and inter-assay precision for all amine levels were < 3.85% and 2.11%, respectively. Excellent linear responses were observed with coefficients of > 0.9996. The established method for the determination of aliphatic amines from real wastewater and biological samples was satisfactory. (c) 2006 Elsevier B.V. All rights reserved.

Recognizing 3-D Objects Using 2-D Images

We discuss a strategy for visual recognition by forming groups of salient image features, and then using these groups to index into a data base to find all of the matching groups of model features. We discuss the most space efficient possible method of representing 3-D models for indexing from 2-D data, and show how to account for sensing error when indexing. We also present a convex grouping method that is robust and efficient, both theoretically and in practice. Finally, we combine these modules into a complete recognition system, and test its performance on many real images.

Robust 2-D Model-Based Object Recognition

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

Informe técnico : análisis de pruebas físicas y cualidades de las arcillas 2 A- 2 B- 2 C- 2 D Veredea Palomitas - Floridablanca - Santander.

35 hojas : ilustraciones.