化学计量在速差动力学分析中的应用及进展

化学计量学在动力学多组分分析中的应用是近年来非常活跃的领域。本文多远元校正、卡尔曼滤波、协同效应校正、人工神经网络及 计算机应用等方面对化学计量学速差动力学分析领域出现的新方法及新动向作一评述,并对今后的工作进行了展望。引用文献９９篇。     

简讯

化学计量学在速差动力学分析中的应用及发展 南昌大学化学系倪永年对化学计量学在速差动力学分析中的应用及进展进行了评述。文章指出,化学计量学作为数学、统计学、计算机科学与化学的接口、应用于速差动力学分析,不仅可扩大速差动力学分析的应用范围,同时化学计量学自身也能得到进一步发展。近年来分析仪器的日趋自动化和多通道检测器的广泛应用,使速差动力学分析呈现许多新的特点。流动注射及停流技术的应用使速差动力学分析的时间精度、实验条件控制和实验自动化程度得以提高;二极管阵列检测器的联用,使采集以时间为变量的二维数据扩展到动力学与光度、荧光或电分析相结合的三维数据成为可能,这些都进一步促进了化学计量学与速差动力学分析的     

化学计量学在过程分析中的应用及进展

评述了化学计量学方法在生产过程分析中各个方面 ,如过程优化、过程模拟、仪器及仪器校正、过程监测等方面的应用 ,并展望了化学计量学在过程分析中的应用前景     

化学计量学在过程分析中应用及进展

评述了化学计量学方法在生产过程分析中各个方面，如过程优化、过程模拟、仪器及仪器校正、过程监测等方面的应用，并展望了化学计量学在过程分析中的应用前景。     

化学计量学在分析化学中的应用

从化学定量构效关系、模式识别法、人工神经网络、波谱化学、多元校正分析法等方面对化学计量学在分析化学中的应用进行了综述。阐明了化学计量学在分析化学中的作用及广阔的应用前景。     

复杂色谱信号自动解析中的化学计量学方法

色谱及其联用技术日趋完善,并向自动化、高通量和快速的方向发展。化学计量学利用"数学分离"手段,可以实现色谱信号的自动化解析,已成为现代色谱分析中非常活跃的研究领域。但以往的化学计量学方法并不能完全有效地实现复杂色谱信号自动化解析。为此,自动化色谱解析算法成为科研工作者关心的重点,众多新型的自动化解析算法被提出。针对复杂一维色谱数据以及联用仪器得到的二维和更高维数据的自动化分析,化学计量学研究主要集中在自动色谱峰识别、背景以及基线漂移校正、色谱谱峰漂移校正以及重叠色谱峰的解析。该文对近十年来发展的复杂体系色谱信号自动化解析中化学计量学方法的原理与应用进行了总结与评述,比较了各类方法的优势与不足。在此基础上,针对当前色谱自动化分析过程中的难题对未来该领域的研究方向进行了展望。     

多元校正-速差动力学分光光度法同时测定三组分有机磷农药

速差动力学分析结合化学计量学对动力学数据进行解析计算, 可扩大动力学分析的范围, 提高分析的准确度. 本文利用对硫磷、甲基对硫磷和杀螟硫磷在碱性介质中能与氧化剂过氧化氢发生氧化反应生成对硝基苯酚的性质, 引入多元校正方法中的经典最小二乘法(CLS)、偏最小二乘法(PLS)和主成分回归法(PCR)对动力学数据进行解析, 实现了三组分人工合成样品的同时测定.     

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...     

化学计量学在电分析化学中的应用

本文对化学计量学各种方法，诸如多元校正，因子分析，信号处理，参数估计，模式识别等电分析化学中的应用作了回顾及评述，指出了化学计量学电分析化学中应用的良好前景。     

现代化学计量学进展

本文介绍化学计量学的概貌及其最近进展。化学计量学是最近十多年来发展起来的化学,主要是分析化学新兴分支学科。化学计量学的研究旨在统计学、数学及发现对分析化学有用的领域中改进分析方法和仪器。因此,现代化学计量学的进展,已使它成为分析测定过程中设计、控制、评价、验证等方面反映的中心。     

Photochemistry and chemometrics—An overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics.To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.     

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of ∼±10% is acceptable.     

Simultaneous determination of iron and aluminium by differential kinetic spectrophotometric method and chemometrics

A differential kinetic spectrophotometric method was researched and developed for the simultaneous determination of iron and aluminium in food samples. It was based on the direct reaction kinetics and spectrophotometry of these two metal ions with Chrome Azurol S (CAS) in ethylenediamine-hydrochloric acid buffer (pH 6.3). The results were interpreted with the use of chemometrics. The kinetic runs and the visible spectra of the complex formation reaction were studied between 540 and 750 nm every 30 s over a total period of 285 s. A set of synthetic metal mixture samples was used to build calibrations models. These were based on the spectral and kinetic two-way data matrices, which were processed separately by the radial basis function-artificial neural network (global RBF-ANN) method. The prediction performance of these models was poorer than that from the combined kinetic-spectral three-way array, which was similarly processed by the same method (% relative prediction error (RPE_T) = 5.6). These results demonstrate that improved predictions can be obtained from the data array, which has more information, and that appropriate chemometrics methods can enhance analytical performance of simple techniques such as spectrophotometry.Other chemometrics models were then applied: N-way partial least squares (NPLS), parallel factor analysis (PARAFAC), back propagation-artificial neural network (BP-ANN), single radial basis function-artificial neural network (RBF-ANN), and principal component neural network (PC-RBF-ANN). There was no substantial difference between the methods with the overall %RPE_T range being 5.0-5.8. These two values corresponded to the NPLS and BP-ANN models, respectively. The proposed method was applied for the determination of iron and aluminium in some commercial food samples with satisfactory results.     

化学计量学在锕系元素分析中的应用

锕系元素的化学性质相似,各元素的分离和分析都很困难,用传统的数据解析手段,难以实现各元素的同时、快速分析。化学计量学是一种高效、功能强大的数据解析方法,对于样品复杂,基体干扰严重以及多组分样品的分析具有独特优势。将化学计量学应用于锕系元素的分析中,利用数学分离代替化学分离,可直接对样品进行测定。化学计量学方法也可用来指导试样的科学采集,进行实验设计、仪器分析操作条件选择等。从吸收光谱、ICP–AES及放射性测量3个方面综述了化学计量学在锕系元素分析中的应用,阐明了化学计量学在锕系元素分析中的应用难点及发展前景。     

Chemometrics in pharmaceutical analysis

Chemometrics is now a well established discipline prompting independent research and development in its own right but, for their continuing success, chemometric developments must be relevant to the real needs of analysis. Pharmaceutical analysis, i.e., physical and chemical assessment of drugs and their dosage forms, needs more formal application of mathematical and statistical methods. The organisation of the laboratory, the development, optimization, assessment and interpretation of methods, and the evaluation of the data produced can all benefit from the application of chemometrics. Here, a selection of the more challenging problems facing the pharmaceutical analyst is presented, the potential for chemometrics is considered and some consequent implications for utilisation, against a background of control and regulation are discussed.     

运用色谱指纹图谱与化学计量学方法对灵芝进行分类

采用95%乙醇为提取溶剂,运用高效液相色谱(HPLC)指纹图谱技术与化学计量学方法,对11个不同灵芝菌株子实体进行分类。通过相似度分析分别获得提取样品指纹图谱的13个共有峰及每个样品之间的相似度;以相对共有峰面积为分析参数,运用化学计量学方法包括聚类分析(HCA)、主成分分析(PCA)及判别分析(DA)对其进行分类,结果分为紫芝、赤芝和美国大灵芝3类。实验结果表明,用化学计量学的方法对灵芝样品的指纹图谱数据进行分析,是一种可用于其分类的科学方法。     

Chemometrics study of the kinetics of the Griess reaction

The kinetics of the Griess reaction in which 3‐nitroaniline acts as a nitrosation agent and 1‐naphtylamine as a coupling reagent was studied by chemometrics methods. The kinetic reaction was investigated under pH 1.0 and 25°C by UV‐vis spectrophotometry. The concentrations of nitrite, 3‐nitroaniline and 1‐naphtylamine were such that a second‐order kinetic reaction took place. Data explorations based on principal component analysis and multivariate curve resolution–alternating least squares were performed to obtain information about the reaction. Calculation of band boundaries of the multivariate curve resolution–alternating least squares solutions showed that the rotational ambiguities associated with the calculation of spectra and concentration profiles have been completely removed. The decrease in the ambiguity of the recovered solutions was closely related to the application of the equality constraint. The results of the exploratory data analysis showed that the kinetic reaction proceeds through a two‐step mechanism. Moreover, the two‐steps are second order. Data analysis approaches based on hard modeling and global hard modeling were used to resolve profiles of the reactants, intermediates and products and to evaluate the rate constants. Copyright © 2013 John Wiley & Sons, Ltd.     

PARAFAC Decomposition of Three‐Way Kinetic‐Spectrophotometric Spectral Matrices Based on Phosphomolymbdenum Blue Complex Chemistry for Nitrite Determination in Water and Meat Samples

Abstract

A three‐way analytical methodology experimentally based on kinetic‐spectrophotometric and parallel factor analysis (PARAFAC) chemometrics analysis was assessed for the quantification of nitrite in water and meat samples. This method is based on the reduction of phosphomolybdic acid to phosphomolymbdenum blue complex by sodium sulfide. The obtained phosphomolymbdenum blue complex is oxidized by the addition of nitrite and this causes a reduction in intensity of the blue color. Three‐way data matrices were generated by acquisition of ultraviolet‐visible (UV‐Vis) spectra (600–900 nm) as a function of the time and of different relative concentration of the nitrite (0.10–2.10 µg mL^?1). The PARAFAC trilinear model, without restrictions, was used in the data analysis. A full decomposition of the data matrices was obtained (spectra, concentration, and time profile). It was shown that kinetic methods coupled to three‐way chemometrics analytical methods can be used for the development of robust sensors for the analysis of nitrite in water and meat samples. The accuracy of the method, evaluated through the root mean square error of prediction (RMSEP), was 0.0515 and 0.1181 for nitrite by PARAFAC and partise least squares (PLS) models respectively. The results with the PARAFAC model are better than those of the PLS model, according to results, it being possible to recover the spectra and kinetic profiles, as well as the initial concentration of nitrite with good accuracy.     

电化学分析在有机农药残留量分析中的应用

对近二十年来电化学分析方法在农药残留量分析中的应用进行了综述，对有机氯农药、有机磷农药、有机氮农药、有机硫农药及有机除草剂展开了评述，并对化学计量学在该领域中的应用作了讨论。     

Increasing the scope and power of flow-injection analysis through chemometric approaches

The objective of this paper is to illustrate how chemometrics can enhance the scope and power of flow injection analysis (FIA) by considering a few simple but representative cases where the ability of chemometrics to improve performance is not readily apparent. In principle, there are two phases when chemometrics can be usefully combined with FIA: first when developing an FIA method and, second, when treating raw data acquired from an FIA detection system. The most obvious application of chemometrics for the FIA practitioner is to use experimental design to replace the obsolete, but too often used one-variable-at-a-time approach when optimising an FIA method. Therefore, methods for screening variables and system optimisation are discussed. Raw data acquired from most FIA systems are first-order data, containing information about the dispersed sample plug. However, the information that is extracted when using FIA for routine purposes is of zero-order: predominantly peak height values. It is shown by a simple example that a chemometric approach in such cases can again provide additional useful information about the sample. First-order spectral data and second-order data more or less require a chemometrics approach for successful analysis, and examples of such applications are briefly discussed.