Self-modeling curve resolution (SMCR) by particle swarm optimization (PSO).
Self-modeling curve resolution (SMCR) by particle swarm optimization (PSO).
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DOI：
10.1016/j.aca.2006.12.004
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发表时间：
2007-07
影响因子：
6.2
通讯作者：
H. Shinzawa;Jian-hui Jiang;M. Iwahashi;I. Noda;Y. Ozaki
中科院分区：
文献类型：
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作者：
H. Shinzawa;Jian-hui Jiang;M. Iwahashi;I. Noda;Y. Ozaki
将粒子群优化算法与交替最小二乘算法相结合应用于自建模曲线求解，以获得有效的初始估计。该方法的目的是搜索浓度剖面或纯光谱，给出最佳的分辨率结果的粒子群算法。SMCR有时会由于初始估计不佳而陷入局部最小值，从而产生分辨率不足的结果。该方法能够减少由于PSO的优点SMCR局部极小值的不良影响。此外，还提出了一种新的基于全局相位角的SMCR性能更有效的标准。它充分利用了数据结构的优点，即在SMCR中可以考虑扰动的连续变化。为了证明它的潜力，SMCR的PSO应用于浓度依赖的近红外光谱的油酸（OA）和乙醇的混合溶液。用演化因子分析法（EFA）比较了该方法与SMCR的曲线分辨性能.结果表明，粒子群算法的SMCR比进化模糊算法的曲线分辨性能有显著提高。结果表明，粒子群算法的SMCR对局部极小值不敏感，可以作为曲线分辨率分析的一种新的有效工具。
Particle swarm optimization (PSO) combined with alternating least squares (ALS) is introduced to self-modeling curve resolution (SMCR) in this study for effective initial estimate. The proposed method aims to search concentration profiles or pure spectra which give the best resolution result by PSO. SMCR sometimes yields insufficient resolution results by getting trapped in a local minimum with poor initial estimates. The proposed method enables to reduce an undesirable effect of the local minimum in SMCR due to the advantages of PSO. Moreover, a new criterion based on global phase angle is also proposed for more effective performance of SMCR. It takes full advantage of data structure, that is to say, a sequential change with respect to a perturbation can be considered in SMCR with the criterion. To demonstrate its potential, SMCR by PSO is applied to concentration-dependent near-infrared (NIR) spectra of mixture solutions of oleic acid (OA) and ethanol. Its curve resolution performances are compared with SMCR with evolving factor analysis (EFA). The results show that SMCR by PSO yields significantly better curve resolution performances than those by EFA. It is revealed that SMCR by PSO is less sensitive to a local minimum in SMCR and it can be a new effective tool for curve resolution analysis.