Application of Chemometric Analysis to Complexity in Isothermal Calorimetric Data

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• 作者：Michael A.A. O'Neill ; Anthony E Beezer ; John Tetteh ; Simon Gaisford ; Meena Dhuna
• 刊名：Journal of Physical Chemistry B
• 出版年：2007
• 出版时间：July 19, 2007
• 年：2007
• 卷：111
• 期：28
• 页码：8145 - 8149
• 全文大小：65K
• 年卷期：v.111,no.28(July 19, 2007)
• ISSN：1520-5207

文摘

The interpretation of complexity in isothermal calorimetric data is demanding. The observed power signal isa composite of the powers arising from each of the individual events occurring (which can involve physical,as well as chemical, change). The challenge, therefore, lies in deconvoluting the observed data into theircomponent parts. Here, we discuss the potential use of chemometric analysis, because it offers the significantadvantage of being model-free, using principal component analysis to deconvolute data. Using model data,we discovered that the software required a minimum trivariate data matrix to be constructed. Two variables,power and time, were available from the raw data. Selection of a third variable was more problematic, but itwas found that by running multiple experiments the small variation in the number of moles of compound ineach experiment was sufficient to allow a successful analysis. In general we noted that it required a minimum2n + 2 repeat experiments to allow analysis (where n is the number of reaction processes). The data outputtedfrom the chemometric software were of the form intensity (arbitrary units) versus time, reflecting the factthat the software was written for analysis of spectroscopic data. We provide a mathematical treatment of thedata that allows recovery of both reaction enthalpy and rate constants. The study demonstrates that chemometricanalysis is a promising approach for the interpretation of complex calorimetric data.