Department of Analytical Chemistry, University of Valencia, Edificio Jerónimo Muñoz, 50th Dr. Moliner, E-46100 Burjassot, Spain.
Anal Chem. 2011 Jun 15;83(12):4855-62. doi: 10.1021/ac2004407. Epub 2011 May 17.
The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.
多元曲线分辨-交替最小二乘法（MCR-ALS）在液相色谱-红外检测（LC-IR）中的应用由于梯度洗脱过程中强烈的背景吸收变化而变得麻烦。由于反相梯度应用中使用的常见流动相系统，先前已经去除了溶剂背景红外贡献的很大一部分，从而使其变得更加方便。本文提出了两种基于简单易用的交互自建模混合物分析（SIMPLISMA）和主成分分析（PCA）的简单背景校正方法，并在分析碳水化合物和硝基苯酚混合物时获得的反相梯度 LC-IR 数据集上进行了评估。扣除计算出的背景信号后，MCR-ALS 提供了更好的信噪比，去除了剩余的流动相和背景信号贡献，并解析了重叠的色谱峰。本方法倾向于实现易于使用的背景校正，以促进 MCR-ALS 在在线 LC-IR 中的应用，即使在使用梯度条件且仅实现较差的色谱分辨率的具有挑战性的情况下也是如此。因此，它显示出很大的潜力，可以促进充分利用在线红外检测同时定量和鉴定大量分析物的优势，使新的令人兴奋的应用更容易实现。
