校正峰电位漂移偏最小二乘极谱催化波同时测定轻稀土

应用Ｍ２７３．Ａ电化学系统中的线性扫描技术，确定了８．０×１０－５ｍｏｌ／Ｌ二甲酚橙及ｐＨ４．０ＨＡｃ－ＮａＡｃ缓冲体系为偏最小二乘极谱法同时测定Ｌａ、Ｃｅ、Ｐｒ、Ｎｄ、Ｓｍ、Ｅｕ的最佳极谱体系。６种稀土元素的线性范围均在１．０× １０－７～１．２ × １０－５ｍｏｌ／Ｌ。并对极谱峰电位随浓度漂移进行了校正，提高了方法的准确度。     

单扫描极谱-偏最小二乘法同时测定丁基锡混合物

在pH6．0的乙酸-乙酸钠缓冲液中，三丁基锡、二丁基锡和一丁基锡有各自的极谱峰电流，但彼此之间存在相互干扰，难以同时进行极谱测定。文中运用偏最小二乘的多元校正能力，对三种丁基锡混合物极谱图进行多元校正，获得了较好的定量分析结果。回收率为96．2％～101．2％，相对标准偏差为3．2％～11．9％。     

优化偏最小二乘催化极谱法同时测定发样中Co、Ni、Cr、Zn的研究

在M273A电化学系统及2.0mmol/LNH4Cl-丁二酮肟体系中采用优化偏最小二乘催化极谱法实现了发样中钴、镍、铬、锌等元素的同时测定,找到了最佳工作条件.方法的准确度和精密度较常规的催化极谱法均有较大提高.钴、镍、铬、锌的线性范围分别为5×10-4～1.0μg/mL、5×10-3～10.0μg/mL、5×10-2～20.0μg/mL、0.1～200.0μg/mL.回收率分别为98%～102%、96%～101%、96%～103%、94%～103%.     

模糊聚类-偏最小二乘回归光度法同时测定地质样品中金铂钯铑的研究

将模糊聚类分析与偏最小二乘法相结合,对地质样品中吸收光谱严重重叠的贵金属多组份体系进行解析,较好地解决了计算光度分析中校准模型的优化问题,使计算结果的精度得到了显著提高,分析结果的相对误差小于 10%,标准偏差小于 0.67,明显优于一般偏最小二乘 (PLS)法。采用小锍试金法消除样品中贱金属元素的干扰,其回收率为 92%～107%,标准偏差为 0.10～0.67;相对标准偏差为 4.7%～11.0%。并对影响聚类效果的参数选择作了讨论。     

偏最小二乘法在多组分阳极溶出伏安分析中的应用—铜,锑,铋的同时测定

本文利用微分脉冲阳极溶出伏安法对微量铜、锑、铋混合溶液进行同进分析，实验在盐酸－氯化钠介质中进行，悬汞电极作工作电极，在－４００ｍＶ处富集电解５ｍｉｎ，然后由－４００ｍＶ向０Ｖ作微分脉冲溶出伏安扫描，扫描速度为２ｍＶ／ｓ，脉冲振幅为４０ｍＶ，由于计算机采集溶出伏安数据后采用偏最小二乘法计算并建立数学模型，然后据此模型对未知溶液进行定量分析，获得了较好的结果。     

偏最小二乘光度法同时测定多种酚的研究有应用

利用Ｃｕ（Ⅱ）、吡啶能与酚形成稳定的三元配合物的特点，研究了Ｃｕ（Ⅱ）－吡啶－酚三元显色新体系，并以偏最小二乘法建立模型预测，同时测定了模拟水样和环境水样中的对苯二酚、间苯二酚、邻苯三酚和对硝基苯酚，取得满意效果。     

偏最小二乘-分光光度法同时测定芦丁与槲皮素的方法研究

芦丁和槲皮素在紫外区的最大吸收波长接近,吸收光谱严重重叠,普通光度法分析难以实现两者的直接测定.该文通过测定以1∶4甲醇-水为溶剂的芦丁、槲皮素标准混合溶液在200～500 nm波长范围的吸收光谱,采用偏最小二乘法(PLS法)建立校正模型,对样品中的芦丁和槲皮素的含量进行预测,建立了偏最小二乘-分光光度法同时测定芦丁和...     

阳离子交换树脂分离-偏最小二乘光度法测定地质样品中的金、铂、钯、铑

本文采用阳离子交换树脂分离地质样品中的贱金属元素，利用偏最小二乘回归光度法在Mn＋－SnCl2－丁基罗丹明B－阿拉伯胶－乳化剂OP高灵敏的离子缔合物体系中对金、铂、钯、铑同时进行测定。结果表明，该方法灵敏度高，操作简便，可同时测定地质样品中痕量贵金属元素金、铂、钯、铑，其标准回入回收率为92．4％～107％。     

Genetic Algorithm Applied to Selection of Wavelength in Partial Least Squares for Simultaneous Spectrophotometric Determination of Nitrophenol Isomers

Abstract

Ternary mixtures of nitrophenol isomers have been simultaneously determined in synthetic and real matrix by application of genetic algorithm and partial least squares model. All factors affecting the sensitivity were optimized and the linear dynamic range for determination of nitrophenol isomers found. The simultaneous determination of nitrophenol mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares modeling was used for the multivariate calibration of the spectrophotometric data. A genetic algorithm is a suitable method for selecting wavelength for PLS calibration of mixtures with almost identical spectra without loss prediction capacity. The experimental calibration matrix was designed by measuring the absorbance over the range 300–520 nm for 21 samples of 1–20 µg mL^?1, 1–20 µg mL^?1, and 1–10 µg mL^?1 of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol, respectively. The root mean square error of prediction for m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol with genetic algorithms and without genetic algorithms were 0.3732, 0.5997, 0.3181 and 0.7309, 0.9961, 1.0055, respectively. The proposed method was successfully applied for the determination of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol in synthetic and water samples.     

催化极谱法测定痕量铬的研究

本文研究了含氨介质中铬(Ⅲ、Ⅵ)-α,α’-联吡啶-亚硝酸钠体系的电化学行为。建立了测定痕最铬的方法。测定铬的范围为0.004～0.028μg/mL和0.04～0.23μg/mL,检测下限为2.0×10~(-3)μg/mL,用以测定生物样品中良量铬,变异系数为0.8%。     

偏最小二乘法用于荧光光度法同时测定铈,镨,铽

本文将偏最小二乘法(PLS)用于荧光光度法同时测定铈、镨、铽。对荧光光度法的测定条件及PLS法中系列校准样品的实验设计及测量波长点的选择等进行了试验和讨论。所建立的方法用于天然混合稀土氧化物中铈、镨、铽的测定,获得较满意的结果     

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.