Shinzawa Hideyuki, Jiang Jian-Hui, Iwahashi Makio, Ozaki Yukihiro
Department of Chemistry, School of Science and Technology, Research Center for Near-Infrared Spectroscopy, Kwansei-Gakuin University, Hyogo, Japan.
Anal Sci. 2007 Jul;23(7):781-5. doi: 10.2116/analsci.23.781.
A curve fitting technique for optical spectra based on a robust estimator, least median squares (LMedS), is introduced in this study. For the effective calculation of LMedS, particle swarm optimization (PSO) is also introduced. Unlike a standard curve fitting method using least squares (LS) estimator, the method based on LMedS estimator is less influenced by outliers in experimental data. Two kinds of data sets, simulated data with outliers and temperature-dependent near-infrared (NIR) spectra of oleic acid (OA) are applied for the demonstration of the proposed method. The results clearly reveal that, compared with the LS estimator, the proposed method can effectively reduce undesirable effects of low SN ratio and can yield more accurate fitting results.
本研究介绍了一种基于稳健估计器——最小中位数平方（LMedS）的光谱曲线拟合技术。为有效计算LMedS，还引入了粒子群优化（PSO）算法。与使用最小二乘法（LS）估计器的标准曲线拟合方法不同，基于LMedS估计器的方法受实验数据中异常值的影响较小。使用两种数据集对所提方法进行验证，即含有异常值的模拟数据以及油酸（OA）的温度相关近红外（NIR）光谱。结果清楚地表明，与LS估计器相比，所提方法能够有效降低低信噪比的不良影响，并能产生更准确的拟合结果。
