化学多维校正的若干新进展

化学多维校正方法,作为化学计量学的重要组成部分,已在分析化学领域引起越来越广泛的重视。其中二维校正（常规多元校正）包括主成分回归（PCR）和偏最小二乘（PLS）等方法与近红外光谱等技术联用,在现场实时无损分析、在线快速监控等方面已发挥重要作用,获得较高认同;而三维校正（二阶校正）方法因其具有突出优势正在获得越来越快的发...     

径向基函数网络在近红外人体无创伤血糖浓度检测基础研究中的应用

在近红外无创伤血糖浓度检测的基础研究中，对于多组分的混合物的分析，常因光谱与样品浓度之间呈现非线性响应，使得基于线性模型的校正方法失效。本文讨论了非线性校正方法径向基函数神经网络( RBFN )的有效性，并与线性校正方法中的主成分分析和偏最小二乘法作了对比研究。验证实验所用样品为①葡萄糖水溶液②包含牛血红蛋白和白蛋白的葡萄糖水溶液，结果表明：在①实验中PLS模型和RBFN预测标准偏差分别为8．2、8．9；在②实验中分别为15．6、8．8。可见在样品组分增多时，RBFN算法较线性PLS方法建立的模型预测能力强。     

用近红外透射光谱技术测定精米蛋白质含量研究

应用近红外透射光谱技术．采用3种不同回归统计分析方法建立业米蛋白质含量(PC)定量回归方程。结果表明,用改进的最小二乘法(MPLS)、偏最小二乘法(PLS)和主成分回归法(PCR)进行饺正时．校正标准误差(SEC)、交叉检验标准误差(SECV)分别为0．1258、0．134O(MPLS)．0．1177、0．1175(PLS)．0．1207、0．1275(PCR)校正相关系数(RSQ)和交叉验证相关系数(1-VR)分别为0．9941、0．9931(MPLS)．0．9950、0．9942(PLS)．0．9947、0．9942(PCR)。由此可见,3种回归统计方法在建立业米蛋白质含量回归方程时差异不明显．都具有较好的预测效果。近红外透射光谱法作为一种快速而准确的定量分析手段,在稻米加工企业品质管理、大米品质分析和大米贸易检测上有广阔的应用前景。     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

双模型结合进一步降低预测均方根误差和均方根相对误差的方法

前期研究工作提出了以预测均方根相对误差最小为回归目标的方法(Minimization of prediction relative error,MPRE),它能使得预测结果的均方根相对误差更小.偏最小二乘法(Partial least squares,PLS)是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小.基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法.本方法步骤为:(1)分别采用MPRE与PLS法对校正集建模;(2)计算阈值;(3)分别采用已建立好的MPRE与PLS模型进行预测;(4)将预测结果与阈值进行比较,得到预测结果.通过对酒精的近红外光谱与汽油紫外光谱进行定量分析结果表明,本方法可进一步减小预测均方根误差与相对误差.     

偏最小二乘法测定复方乙酰水杨酸片中的有效成分

将偏最小二乘法(PLS)与近红外漫反射光谱法相结合,对复方乙酰水杨酸片进行无损非破坏定量分析．建立了最佳的数学校正模型,比较了样品中3种有效成分(乙酰水杨酸、非那西丁和咖啡因)同时测定和单独测定时的主成分数对PLS定量预测能力的影响,预测了未知样品。3种有效成分同时测定和单独测定建立的PLS模型具有相同的主成分数,PLS预报浓度与参考浓度具有相近的标准偏差,说明用PLS法同时测定3种组分的含量是可行的。     

白色体系化学计量学方法研究-MLR、Kalman滤波法、PCR、PLS和褶合光谱法的比较

对白色体系的化学计量学方法进行了研究,着重比较了多元线性回归法（MLR、Kalman滤波法（KF）、主成分回归法（PCR）、偏最小二乘法（PLS）和褶合光谱法等方法的原理,并用上述方法进行复方磺胺甲E唑片剂的不分离分析,表明间接校正法（PCR、PLS等）比直接校正法（MLR、KF等）得出的结果更准确。而以既包容了导数光谱法和正交函数法、又与PLS相结合的褶合光谱法结果最好。     

主成分回归一紫外光度法同时测定盐酸西替利嗪和苯甲酸钠

提出了一种用紫外光度法同时测定盐酸西替利嗪和苯甲酸钠的方法.在pH 4.56的B-R缓冲溶液中对盐酸西替利嗪和苯甲酸钠两组分混合溶液进行吸光度测定,所得的重叠光谱数据用主成分回归法(PCR)和偏最小二乘法(PLS)等化学计量学方法进行处理,结果表明主成分回归(PCR)的预报误差最小.对实际样品进行测定,回收率为88.8...     

乙醇混合燃料近红外定量分析

采用近红外光谱(NIR)透射法对乙醇混合燃料各成分进行定量分析；其中乙醇体积分数为84．5％～98．2％，汽油体积分数0～15％；通过偏最小二乘法(PLS)建立模型，乙醇含量NIR模型校正集测定系数(R^2)为0．9969，模型校正集标准差(SEE)和预测集标准差(SEP)分别为0．23和0．38，汽油含量NIR模型校正集测定系数为0．9939，模型校正集标准差和预测集标准差分别为0．38和0．39，对含量较小的干扰物质丙酮预测结果也理想；近红外和多元校正技术可作为乙醇混合燃料中成分含量测定简单、快速方法之一。     

小波变换-主成分回归分光光度法同时测定邻苯二酚、间苯二酚和对苯二酚三组分

根据小波变换具有将信号分频的特点,本文提出了将小波变换与主成分回归(PCR)相结合的一种多元校正算法。该法能更有效地去除噪声,提取有用信息,并将其用于分析邻苯二酚、间苯二酚、对苯二酚三组分体系。实验结果表明,本法比直接用主成分回归处理效果好,得到的平均相对误差从2．24％降低到1．19％。     

Multivariate versus univariate calibration for nonlinear chemiluminescence data: Application to chromium determination by luminol-hydrogen peroxide reaction

Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested.

The use of a double logarithmic transformation previous to the linear regression has been also evaluated. A new double logarithmic transformation previous to the linear regression is proposed in order to avoid the effect of the noise in the calibration model. Pre-processing, optimization and prediction ability of the multivariate calibration models has been studied at nine different experimental conditions including batch and FIA measurements. Box-plots, PCA and cluster analysis have been employed to test the prediction ability of the different models tested. Nonlinear PCR and nonlinear PLS provide the best results. Real samples have been analyzed and compared with the reference method. The results confirm the successful use of the proposed methodology.     

Multicomponent Determination of Organophosphorus Pesticides in Grain Samples by Linear Sweep Stripping Voltammetry and Multivariate Calibration

Abstract

Organophosphorus pesticides, such as parathion methyl (PTM), fenitrothion (FT), parathion (PT), and isocarbophos (ICP), have sensitive but overlapped voltammetric peaks with peak potentials ?309, ?364, ?317, and ?480 mV, respectively, in Britton‐Robinson buffer of pH 4.8 by application of linear sweep stripping voltammetry (LSSV). In this work, two multivariate calibration methods, partial least squares (both PLS‐1 and PLS‐2), and principal component regression (PCR), were applied to quantitatively resolve the overlapping voltammogram of the mixtures of these four pesticides. The prediction results obtained from a set of independent test samples showed that PLS‐1 method performed better prediction ability than PLS‐2 and PCR methods. The proposed method was successfully applied to the determination of these four pesticides in grain samples after a pre‐extraction step with a solvent of acetone.     

Construction of stable multivariate calibration models using unsupervised segmented principal component regression

In multivariate spectral calibration by principal component regression (PCR), the principal components (PCs) are calculated from the response data measured at all employed instrument channels; however some channels are redundant and their responses do not possess useful information. Thus, the extracted PCs possess mixed information from both useful and redundant channels. In this work, we propose a segmentation approach based on unsupervised pattern recognition to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. In this method, the instrument channels are clustered into different segments via Kohonen self‐organization map. The spectral data of each segment are then subjected to PCA and the derived PCs are used as input variables for an inverse least square (ILS) regression model employing stepwise selection of the informative PCs. The proposed method was evaluated by the analysis of four simulated and six experimental data sets. It was found that our proposed method can model the above data sets with prediction errors lower than conventional partial least squares (PLS) and PCR methods. In addition, the prediction ability of our method was better than the previously reported models for these data sets. Copyright © 2011 John Wiley & Sons, Ltd.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks

Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

近红外光谱法测定端羟基环氧乙烷-四氢呋喃共聚醚的羟值

提出了用近红外光谱测定端羟基环氧乙烷-四氢呋喃共聚醚(PET)的羟值,结合主成分回归和偏最小二乘法建立了PET羟值与其近红外光谱之间的关联模型。结果表明,近红外光谱法与化学分析法的测定结果一致;近红外光谱法测定PET羟值的相对误差在5%以内;利用遗传算法选择部分波长建立校正可以降低模型的预测误差。     

偏最小二乘法—流动注射pH梯度技术用于同时测定铜和钴

以PAR作显色剂,用流动注射pH梯度技术测定多个不同pH下的吸光度,以偏最小二乘法建立校正模型并预测,对Cu~(2+)、Co~(2+)二元素进行了同时测定,其计算结果优于主成分回归及多元线性回归法。     

Development and Application of Chemometric Methods in Near Infrared Spectral Analysis

In recent 10 years, like other disciplines influenced by the fast development of PC technique, chemometrics has been used in many analytical methods, especially in instrumental analysis. This article describes applications and comparison of multivariate linear regression (MLR), principal component analysis (PCA), principal component regression (PCR), partial least square (PLS), neural network (ANN), fuzzy and model recognition. A better calibration method can be a great help to improve the efficiency of the routine analytical work.     

用局部拟合主成分回归计算光度分析法测定黄连生物碱

针对具有样本数据非无匀分布和非线性特点的光度分析问题，提聘种局部拟合 主成分回归法，用于中药多组分计算测定。该方法根据待测样本与各已知样本光度 分析数据的欧式距离确定相应的权值，将部分权值较大的样本组成校正集，并用分 段线性拟合算法建立待测样本的校正预测模型，将其用于分析黄连的药根碱、巴巴 亭和小檗碱等三种生物碱，所得预测均方根误差分别为0.023,0.0400和0.052，优 于主成分回归法、偏最小二乘法以及人工神经元网络法所得结果。这表明，本方法 用于中药光度分析能获得较为准确的计算分析结果。