食品分析

本文对１９９２￣１９９３年间的国内食品分析进展作了僮介。内容包括光度分析（分光光度法、发光光度法、原子吸收光谱法、原子荧光光谱法、原子发射光谱法、近红外光谱法等），色谱分析（气相色谱法、液相色谱法、离子色谱法、簿层色谱法、电泳分析等）及电化学、化学计量学等，引用文献５８４篇。     

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     

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。     

北欧化学计量学发展动态——第二届斯堪地那维亚化学计量学学术会议简况

第二届斯堪地那维亚化学计量学学术会议于1991年5月28—31日在挪威卑尔根市举行。斯堪地那维亚是化学计量学的发祥地,chemometrics一词源出于此地区,因此,斯堪地那维亚学派在化学计量学发展中占有重要地位,在该地区举办的化学计量学学术活动,为各国化学计量研究者所嘱日。来自斯堪地那维亚地区各国及美国、中国、英国、法国、加拿大、意大利、奥地利等共17个国家的一百多位研究与应用化学计量学方法的化学及其他科学工作者参加了会议。挪威化学会的化学计量学委员会及卑尔根大学是这次会议     

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。     

化学计量学在电分析化学中的新进展

评述了化学计量学的各种方法,如主成分分析、偏最小二乘、小波分析、人工神经网络等在电分析化学中的进展,主要介绍了这些方法在电分析化学中的应用,并展望了化学计量学在电分析化学中的应用前景。     

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。     

人工神经网络-近红外光谱法非破坏监测芦丁药品的质量

用近红外漫反射光谱法非破坏监测芦丁药品的质量。利用人工神经网络化学计量学分类技术，建立三层神经元的神经网络，对网络参数进行优化选择以建立最佳网络模型。根据芦丁药品的近红外漫反射光谱，成功地分辨出合格药品和不合格药品。     

MATLAB在化学计量学中的应用

化学计量学是数学和统计学，化学及计算机科学相互交叉形成的一门新的化学分支学科，是解决化学问题的强有力工具，目前运用于化学计量学的商品软件有MATLAB、Maple,MathCAD,EXCEL,SPSS等。MATLAB是一种高性能的数值计算的科学计算语言，具有程序开发环境简洁直观，数值稳定性好，函数资源丰富的特点，本文以几种常用的化学计量学方法为例，讨论了MATLAB在化学中的应用。     

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.     

信息论与化学计量学

本文讨论了信息论在发展分析化学计量学中的意义和作用以及信息论在化学计量学中应用的一般规律和关系，总结了信息科学对化学计量学发展的贡献。     

Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks

Metal ions such as Co(II), Ni(II), Cu(II), Fe(III) and Cr(III), which are commonly present in electroplating baths at high concentrations, were analysed simultaneously by a spectrophotometric method modified by the inclusion of the ethylenediaminetetraacetate (EDTA) solution as a chromogenic reagent. The prediction of the metal ion concentrations was facilitated by the use of an orthogonal array design to build a calibration data set consisting of absorption spectra collected in the 370-760 nm range from solution mixtures containing the five metal ions earlier. With the aid of this data set, calibration models were built based on 10 different chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), artificial neural networks (ANN) and others. These were tested with the use of a validation data set constructed from synthetic solutions of the five metal ions. The analytical performance of these chemometrics methods were characterized by relative prediction errors and recoveries (%). On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). PLS and PCR models applied to the spectral data matrix that used the first derivative pre-treatment were the preferred methods. They together with ANN-radial basis function (RBF) and PLS were applied for analysis of results from some typical industrial samples analysed by the EDTA-spectrophotometric method described. DPLS, DPCR and the ANN-RBF chemometrics methods performed particularly well especially when compared with some target values provided by industry.     

Multivariate calibration: What is in chemometrics for the analytical chemist?

Chemometrics has been used for some 30 years but there is still need for disseminating the potential benefits to a wider audience. In this paper, we claim that proper analytical chemistry (1) must in fact incorporate a chemometric approach and (2) that there are several significant advantages of doing so. In order to explain this, an indirect route will be taken, where the most important benefits of chemometric methods are discussed using small illustrative examples. Emphasis will be on multivariate data analysis (for example calibration), whereas other parts of chemometrics such as experimental design will not be treated here. Four distinct aspects are treated in detail: noise reduction; handling of interferents; the exploratory aspect and the possible outlier control. Additionally, some new developments in chemometrics are described.     

化学计量学用于有机合成分析：Ⅱ.呋喃催化加氢

报道了将化学计量学用于有机合成分析，研究了从有机合成实验设计，条件优化，反应建模及机理剖析等问题。以呋喷 催化加氢合成四氢呋喃为例说明了方法的可行性性和的有效性。     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

化学计量学在海洛因来源判别中的应用

介绍化学计量学在非法海洛因来源判别中的意义和需求,并结合国际上关于非法海洛因来源判别的应用现状和技术特点讲述了化学计量方法的应用种类和特点。虽然各国所采用的化学计量学方法各异,但基本上可以划分为表观判别法和数理统计法两大类。     

电化学发光传感器在农业传感领域中的研究进展

基于电化学发光(ECL)分析法构建的电化学发光传感器具有灵敏度高、背景信号低、操作简单的优点,因此,它在农业、工业、环境、临床和食品等领域具有广泛的应用前景。 本文主要综述了电化学发光传感器检测农药残留和真菌毒素的应用及其相应的检测性能,分析了电化学发光传感器在农业传感领域的研究现状,并阐述了未来电化学发光传感器的发展趋势。     

计量合成学的产生与发展

对计量合成学的产生与发展及其研究与应用进行了综论，强调了计量化学在合成中的应用，集中在开发有机合成反应的决策及战术方面，通过试验设计对反应体系深入探索及通过自适应建模对试验条件的系统优化和通过综合评价对合成产物的全面考察，将有机反应开发为合成方法，参考文献３５篇。     

Classification and Prediction of Geological Samples by Principal Component Analysis

The principal component analysis is an ancient multivariate statistical method[1]. It is extensively used in spectrometry with the popularization of computer and development of the method of chemometrics. It is regarded as an effective method of multivariate statistical analysis. The principal component analysis is universally included in common program package of multivariate statistical analysis. The method, as well as other multivariate calibration methods, combined with artificial neural networks forms the foundation of the chemometrics.