Processing gas chromatographic data and confidence interval calculation for partition coefficients determined by the phase ratio variation method

• Published in: Journal of Chromatography A Vol. 1110; no. 1; pp. 146 - 155
• Main Authors: Atlan, Samuel, Trelea, Ioan Cristian, Saint-Eve, Anne, Souchon, Isabelle, Latrille, Eric
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 31.03.2006; Elsevier
• Subjects: Analytical chemistry; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography, Gas - methods; Confidence interval; Confidence Intervals; Exact sciences and technology; Gas chromatographic methods; Henry's law constant; Life Sciences; Linear regression; Mathematics; Nonlinear regression; Organic Chemicals - chemistry; Parametric method; Partition coefficient; Pharmaceutical Preparations - analysis; Phase ratio variation (PRV); Regression Analysis; Solubility; Temperature; Volatile organic compound; Volatilization; Parametric method; Linear regression; Partition coefficient; Volatile organic compound; Henry's law constant; Nonlinear regression; Phase ratio variation (PRV); Confidence interval; Statistical analysis; Henry constant; Flame ionization detector; Regression analysis; Gas chromatography; Lipophilicity; Physicochemical properties; nonlinear regression; partiparametric method; PRV; linear regression; confidence interval; phase ratio variation; phase ratio variation (PRV); partition coefficient; volatile organic compound
• ISSN: 0021-9673; 1873-3778
• DOI: 10.1016/j.chroma.2006.01.055

The phase ratio variation (PRV) method is widely used for the determination of partition coefficient values (dimensionless Henry's law constants) by headspace gas chromatography. Traditional data processing by linear regression has several drawbacks: potential bias introduced by linearization, absence of quality indicator of the resulting value and, in case of replicate determinations, poor utilisation of the existing measurements leading to unnecessarily large confidence intervals. The paper compares existing PRV data processing methods (linear and nonlinear regression, parametric) and derives confidence intervals for the resulting partition coefficient values. The possibility of using several series of measurements to derive a single partition coefficient value with tighter and more reliable confidence intervals is presented for all three processing methods. The methods are tested on published literature data and new experimental data for 12 volatile organic compounds in water at 25 °C. The nonlinear regression based on several series of measurements appears to be the method of choice.