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Abstract
The generation of poly-hydroxilated transient species during the photochemical treatment of phenol usually impedes the spectrophotmetric monitoring of its degradation process. Frequently, the appearance of compounds such as pyrocatechol, hydroquinone and benzoquinone produces serious spectral interference, which hinder the use of the classical univariate calibration process. In this work, the use of multivariate calibration is proposed to permit the spectrophotometric determination of phenol in the presence of these intermediates. Using 20 synthetic mixtures containing phenol and the interferents, a calibration model was developed by using a partial least square regression process (PLSR) and processing the absorbance signal between 180 and 300 nm. The model was validated by using 3 synthetic mixtures. In this operation, typical errors lower than 3% were observed. Close correlation between the results obtained by liquid chromatography and the proposed method was also observed.
Highlights
Multivariate calibration processes are very useful in analytical chemistry, mainly in the analysis of samples composed by several components that need individual determination from instrumental data with low selectivity (Miller 1995)
This paper describes an analytical methodology for determination of phenol in the presence of typical products of its photochemical degradation by using a spectrophotometric method and a multivariate calibration technique (Partial Least-Squares Regression, partial least-squares regression (PLSR))
The calibration step consists of the development of a mathematical model which can reproduce a concentration matrix Y from a matrix X
Summary
Multivariate calibration processes are very useful in analytical chemistry, mainly in the analysis of samples composed by several components that need individual determination from instrumental data with low selectivity (Miller 1995). This paper describes an analytical methodology for determination of phenol in the presence of typical products of its photochemical degradation by using a spectrophotometric method and a multivariate calibration technique (Partial Least-Squares Regression, PLSR). The process is composed of two steps: calibration and prediction In this case, the calibration step consists of the development of a mathematical model which can reproduce a concentration matrix Y (contains ‘‘n’’ lines which correspond to samples and ‘‘q’’ columns of different phenol concentrations) from a matrix X (with ‘‘n’’ lines of samples and ‘‘p’’ columns of selected wavelength values)
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