Separation and identification of various vulcanization agents and antioxidants in two types of rubber by chromatographic and spectrometric methods.
The aim of this study was to separate and identify by chromatographic and spectrometric methods, the various allergenic vulcanization agents and antioxidants used in the manufacture of industrial rubber. Specimens of elastomers were manufactured specially for this study. The specificity of the gas chromatographic method developed allows separation of all the manufacturing additives in the selected rubber types after one injection only, even though they belong to extremely varied chemical categories. The GLC method was coupled with mass spectrometry, which permitted identification of the peaks obtained and the study of the fragmentation of the 4 reference products under various conditions. Separation by TLC was performed in parallel on the same extracts, allowing rapid identification of the products tested for, and showed new spots after vulcanization.
Spectrofluorimetric determination of phenyl-β-naphthylamine used as rubber antioxidant
Phenyl-β-naphthylamine (PBN) used as rubber antioxidant was found to have native fluorescence. A spectrofluorimetric method for determination of PBN in multicomponent mixtures of polymer additives is described. The apparent excitation and fluorescence wavelengths used are 348 and 413.5 nm, respectively. Maximum fluorescence intensity is obtained by irradiating PBN dissolved in ethanol, at room temperature. The fluorescence varies linearly with the concentration of PBN in the range of 0.04–4 μg mL−1. The accuracy and precision of the method are reported.
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.