Adaptive Asymmetric Least Squares baseline estimation for analytical instruments

S. Oller-Moreno, A. Pardo, Juan Manuel Jiménez-Soto, J. Samitier, S. Marco
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引用次数: 8

Abstract

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.
分析仪器的自适应非对称最小二乘基线估计
自动化信号处理的分析仪器是今天需要高度复杂的生物医学样品的分析。基线估计技术通常用于纠正长期仪器污染或退化。它们对于精确的峰面积积分是必不可少的。有些方法迭代地接近基线估计,试图忽略不属于基线的峰值。本文提出的方法是对Eilers和Boelens开发的非对称最小二乘(ALS)基线去除技术的改进。ALS技术在存在强烈峰值时(相对于噪声水平)存在偏差。这是典型的各种仪器技术,如气相色谱-质谱(GC-MS)或气相色谱-离子迁移谱(GC-IMS)。在这项工作中,我们对原始ALS方法的不对称权重提出了一种修改(命名为psalsa),以便更好地拒绝基线以上的大峰。我们的方法将使用合成的和真实的GC信号与几个版本的ALS算法进行比较。结果表明,该方法改进了以前的方法,对参数变化具有更强的鲁棒性,并提供了更准确的峰面积。
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