Rapid Determination of Amphotericin B in Serum and Urine by Third-Order Derivative Spectrophotometry

Botsoglou, Nikolaos; Fletouris, Dimitrios; Παπαγεωργίου, Γεώργιος; Florou-Paneri, Panagiota; Mantis, Antonios

doi:10.1021/js950398a

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Both features are strong incentives for deploying them in monitoring networks designed for environmental, biomedical and industrial purposes. Numerous potential monitoring-type applications of mini-and microspectrophotometers may be found in the relevant literature; the most typical are those in food monitoring [23][24][25][26][27], in biomedical monitoring [28][29][30][31][32], in industrial monitoring [33,34], in environmental monitoring [35,36] and in biochemical security-related monitoring [37]. The monitoring-type problems have two characteristic features:…”

Section: Demand For Dsp In Spectrophotometrymentioning

confidence: 99%

Spectrophotometric applications of digital signal processing

Morawski¹

2006

Meas. Sci. Technol.

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Spectrophotometry is more and more often the method of choice not only in analysis of (bio)chemical substances, but also in the identification of physical properties of various objects and their classification. The applications of spectrophotometry include such diversified tasks as monitoring of optical telecommunications links, assessment of eating quality of food, forensic classification of papers, biometric identification of individuals, detection of insect infestation of seeds and classification of textiles. In all those applications, large numbers of data, generated by spectrophotometers, are processed by various digital means in order to extract measurement information. The main objective of this paper is to review the state-of-the-art methodology for digital signal processing (DSP) when applied to data provided by spectrophotometric transducers and spectrophotometers. First, a general methodology of DSP applications in spectrophotometry, based on DSP-oriented models of spectrophotometric data, is outlined. Then, the most important classes of DSP methods for processing spectrophotometric data—the methods for DSP-aided calibration of spectrophotometric instrumentation, the methods for the estimation of spectra on the basis of spectrophotometric data, the methods for the estimation of spectrum-related measurands on the basis of spectrophotometric data—are presented. Finally, the methods for preprocessing and postprocessing of spectrophotometric data are overviewed. Throughout the review, the applications of DSP are illustrated with numerous examples related to broadly understood spectrophotometry.

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Section: Demand For Dsp In Spectrophotometrymentioning

confidence: 99%

Spectrophotometric applications of digital signal processing

Morawski¹

2006

Meas. Sci. Technol.

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“…Numerous potential monitoring-type applications of MSPMs may be found in the relevant literature; the most typical are those in food monitoring [11][12][13][14][15], in biomedical monitoring [16][17][18][19][20], in industrial monitoring [21,22], in environmental monitoring [23,24] and in biochemical securityrelated monitoring [25]. The monitoring-type problems have two characteristic features.…”

Section: Introductionmentioning

confidence: 99%

Estimation of low concentrations in the presence of high concentrations using Bayesian algorithms for interpretation of spectrophotometric data

Niedzinski

Morawski

2004

Journal of Chemometrics

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A Bayesian method for interpretation of spectral data, dedicated to the monitoring-type applications of mini-and micro-spectrometers, is addressed. The method generates estimates of the concentrations of the components of an analyzed substance on the basis of the data representative of its absorption spectrum, provided that both the normalized spectra of the components and statistical information on historical measurements of the monitored concentrations are available. The Bayesian method is systematically compared with the constrained least-squares method, under an assumption that the estimated concentrations differ considerably and the processed data are subject not only to random instrumental noise but also to some random disturbances introduced by the residual content of components of the analyzed substance being not identified. A study, performed using both synthetic and real-world spectrophotometric data, is aimed at the assessment of the robustness of the Bayesian method to the maximum-to-minimum ratio of concentrations and imprecision of a priori information.

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“…The amount of drug in these samples has been determined by various techniques. Chromatography has been the method most employed in recent years, although other analytical techniques such as second and third-order derivative UV spectrophotometry [13,14], spectrofluorometry [15], Raman spectroscopy [16], and colorimetric methods [17] have also been used. Recently, capillary electrophoresis [18] has also been employed for amphotericin B analysis in pharmaceuticals.…”

Section: Introductionmentioning

confidence: 99%

Rapid determination of intramacrophagic amphotericin B by direct injection HPLC

et al. 2000

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Key WardsColumn liquid chromatography Cell cultures Amphotericin B SummaryThe uptake and kinetic release of the antifungal amphotericin B by phagocytic cells appears to be important in the toxicily-activily mechanism of the drug at the cellular level. An analytical HPLC method has been developed, that is selective, sensitive and reproducible for the determination of amphotericin B uptake by J774.2 macrophage-like cells. Chromatography was on a reversed-phase, end-capped column (Lichrospher Select B Cs) and samples were eluted with a mobile phase of acetonitrile, water, ammonium acetate, acetic acid and 0.02 M triethylamine. The limit of quantitation of amphotericin B was 1 ng ml 1. Precision and accuracywas <5%.

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Rapid Determination of Amphotericin B in Serum and Urine by Third-Order Derivative Spectrophotometry

Cited by 7 publications

References 42 publications

Spectrophotometric applications of digital signal processing

Spectrophotometric applications of digital signal processing

Estimation of low concentrations in the presence of high concentrations using Bayesian algorithms for interpretation of spectrophotometric data

Rapid determination of intramacrophagic amphotericin B by direct injection HPLC

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