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涂料和涂层材料的衰减全反射傅里叶变换红外光谱(ATR-FTIR)及定量多变量分析

ATR-FTIR spectroscopy and quantitative multivariate analysis of paints and coating materials.

作者信息

Hayes Philippa Alice, Vahur Signe, Leito Ivo

机构信息

University of Tartu, Institute of Chemistry, 14a Ravila str, 50411 Tartu, Estonia; National Oceanography Centre, University of Southampton Waterfront Campus, European Way, Southampton SO14 3ZH, United Kingdom.

University of Tartu, Institute of Chemistry, 14a Ravila str, 50411 Tartu, Estonia.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2014 Dec 10;133:207-13. doi: 10.1016/j.saa.2014.05.058. Epub 2014 May 29.

DOI:10.1016/j.saa.2014.05.058
PMID:24945861
Abstract

The applicability of ATR-FTIR spectroscopy with partial least squares (PLS) data analysis was evaluated for quantifying the components of mixtures of paint binding media and pigments, and alkyd resins. PLS methods were created using a number of standard mixtures. Validation and measurement uncertainty estimation was carried out. Binary, ternary and quaternary mixtures of several common binding media and pigments were quantified, with standard measurement uncertainties in most cases below 3g/100g. Classes of components - aromatic anhydrides and alcohols - used in alkyd resin synthesis were also successfully quantified, with standard uncertainties in the range of 2-3g/100g. This is a more demanding application because in alkyd resins aromatic anhydrides and alcohols have reacted to form a polyester, and are not present in their original forms. Once a PLS method has been calibrated, analysis time and cost are significantly reduced from typical quantitative methods such as GC/MS. This is beneficial in the case of routine analysis where the components are known.

摘要

采用偏最小二乘法(PLS)数据分析的衰减全反射傅里叶变换红外光谱法(ATR-FTIR)用于定量油漆粘结介质与颜料混合物以及醇酸树脂的成分,其适用性得到了评估。使用多种标准混合物建立了PLS方法,并进行了验证和测量不确定度估计。对几种常见粘结介质和颜料的二元、三元和四元混合物进行了定量分析,在大多数情况下标准测量不确定度低于3g/100g。醇酸树脂合成中使用的成分类别——芳香酸酐和醇——也成功实现了定量,标准不确定度在2 - 3g/100g范围内。这是一个要求更高的应用,因为在醇酸树脂中芳香酸酐和醇已经发生反应形成了聚酯,不再以原始形式存在。一旦校准了PLS方法,与典型的定量方法如GC/MS相比,分析时间和成本会显著降低。在已知成分的常规分析情况下,这是有益的。

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