Resampling as a Robust Measure of Model Complexity in PARAFAC Models

IF 2.3 4区 化学 Q1 SOCIAL WORK
Helene Fog Froriep Halberg, Marta Bevilacqua, Åsmund Rinnan
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引用次数: 0

Abstract

Fluorescence spectroscopy has been applied for analysis of complex samples, such as food and beverages. Parallel factor analysis (PARAFAC) is a well‐known decomposition method for fluorescence excitation–emission matrices (EEMs). When the complexity of the system increases, it becomes considerably more difficult to determine the optimal number of PARAFAC components, especially when the fluorophores of the system are unknown. The two commonly applied diagnostics, core consistency and split‐half analysis, appear to underestimate the model complexity due to covarying components and local minima, respectively. As a more robust alternative, we propose a resampling approach with multiple initializations and submodel comparisons for estimating the optimal number of PARAFAC components in complex data.
将重采样作为 PARAFAC 模型复杂性的稳健衡量标准
荧光光谱法已被用于分析食品和饮料等复杂样品。平行因子分析(PARAFAC)是一种著名的荧光激发-发射矩阵(EEM)分解方法。当系统的复杂性增加时,确定 PARAFAC 成分的最佳数量就变得相当困难,尤其是当系统中的荧光团未知时。两种常用的诊断方法--核心一致性和分割半分析--似乎分别由于共变成分和局部最小值而低估了模型的复杂性。作为一种更稳健的替代方法,我们提出了一种具有多重初始化和子模型比较的重采样方法,用于估计复杂数据中 PARAFAC 成分的最佳数量。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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