一个多元校正的稳健诊断新方法

提出一种新的稳健诊断方法，与最小二乘估计结合进行混合物光谱中非线性点的诊断。文中探讨了该方法的性能，用计算机数字模拟及实际多组分光谱体系夺其进行检验，展示了此诊断方法在分析化学计量学中实际应用的可行性。     

稳健Madaline网络算法及其在复方制剂分析中的应用

根据常用稳健回归方法的原理,对神经网络算法之一Ｍａｄａｌｉｎｅ网络算法作了改进,建立了稳健Ｍａｄａｌｉｎｅ网络算法。甲硝唑和氯霉素组成的两组分复方制剂的分析结果表明：该法具有较好的稳健性能,其最大奇异点数约占总样本数的５０％,优于Ｍ估计的稳健性能。     

M—估计用于食用合成色素混合体系的同时测定

利用具有抗异常点干扰能力的Ａｎｄｒｅｗｓ型稳健Ｍ－估计，研究了在正常和异常情况下对色素二组分，三组分，四组分混合体系中各组分的同时测定。结果表明无论是否存在异常值，Ｍ－估计都能得到可信的结果，而传统的最小二乘法在异常值时其结果将是毫无意义的。     

稳健统计技术及其在实验室能力验证数据处理中的应用

介绍了传统统计方法存在的问题及稳健统计技术的发展历史、稳健统计技术的属性、基本假设模型、稳健统计量及在实验室能力验证数据处理中的具体应用,提出了实验室能力验证的评价标准。     

矿产品水分含量代表值Bootstrap模拟取样估计

在对矿产品水分含量基础统计学特征描述的基础上,采用内核密度估计对水分含量数据多态性进行了分析,根据双态分布的特点,使用Bootstrap模拟取样方法对试验样本值模拟重复取样,以多次Bootstrap模拟取样的均值与标准偏差作为矿产品水分含量有限样本代表值及标准偏差的稳健估计,实践证明Bootstrap模拟取样估计对矿产品水分含量代表值的估计是有效的,该项研究为矿产品水分含量代表值的准确评估提供了一种新方法.     

农残水平测试中数据多态性分析及指定值估计研究

对全国农残水平测试中毒死蜱、氯氰菊酯数据进行统计分析,在数据统计分布特征研究基础上,使用内核密度估计进行数据多态性分析,使用bootstrap模拟取样法对数据样本值重复取样,以获得稳健的水平测试样品待测物含量代表值估计、标准误差及置信区间描述,证明以bootstrap模拟取样法获取的均值与标准偏差作为有限单次样本代表值是合理、有效的,解决了四分位稳健统计方法对非正态多态分布代表值估计不稳定问题及取样理论中取样样本数限制的瓶颈,为能力验证计划指定值的获取提供了一种新方法。     

稳健偏最小二乘类模型及其在清真香肠红外质量控制中的应用

针对在实际多元质量控制中经常遇到的奇异样品问题,本研究提出了一种稳健偏最小二乘类模型.本方法基于Stahel-Donoho奇异度和样品重加权策略,用稳健的类中心和模型误差构造稳健的决策区间.将本方法用于清真香肠的红外分析,建立了稳健的质量控制方法.在香肠样品的不同部位进行取样,充分研磨后制备溴化钾压片,以空气为背景,测量4000～400 cm-1范围的红外透射光谱.基于73个清真香肠样品和78个非清真样品的光谱数据,研究了新提出的稳健类模型的统计效率和稳健性.在有奇异样品存在的情况下,本方法能有效检出奇异样品,为新样品的预测提供稳健的决策区间.排除奇异样品后,基于原始光谱的模型灵敏性为0.846,特异性为0.936;基于标准正态变量法的模型灵敏性为0.923,特异性为0.974.     

重整汽油近红外光谱的稳健偏最小二乘解析

近红外光谱(ＮＩＲ)光谱复杂,组分间光谱重叠严重,目前,多元线性回归(ＭｕｌｔｉｐｌｅＬｉｎｅａｒＲｅ ｇｒｅｓｓｉｏｎ,ＭＬＲ)和偏最小二乘法(ＰａｒｔｉａｌＬｅａｓｔ ｓｑｕａｒｅｓ,ＰＬＳ)是近红外光谱分析中使用最多和效果较好的方法[1]。稳健偏最小二乘(ＲｏｂｕｓｔＰａｒｔｉａｌＬｅａｓｔ Ｓｑｕａｒｅｓ,ＲＰＬＳ)是由稳健统计学构造的具有稳健性能的多元校正方法。当化学测量中引入随机异常点或误差的内在分布偏离正态分布时,它仍能给予接近最优性能的校正,确保分析结果的准确性,是消除奇异点的非常有效的方法[2-4],…     

铁酸镁系列超微粒子催化剂的氧化脱氢反应行为:乙苯和环己烷的氧化脱氢反应

采用四种不同的方法制备了系列铁酸镁超微粒子催化剂，考察了其对乙苯和环已烷的氧化脱氢反应性能，结果表明，（Ｄ）样品具有最佳的乙苯氧化脱氢反应活性：４００℃，Ｏ２／Ｃ６Ｈ５Ｃ２Ｈ５（ｍｏｌ）＝３．０时，乙苯转化率为５０％，苯乙烯选择性为８０％，苯乙烯单收达４０％，催化剂对乙苯氧化脱氢反应的活性随样品的粒径变小而提高；对环已烷氧化脱氢反应则恰好相反，即活性随粒径变小而降低，这种差别归因于反应物分子结构与     

硅橡胶弹性行为的研究进展

硅橡胶特别是补强硅橡胶因其良好的抗疲劳性、高阻抗性以及宽的使用温度域而广泛地应用于工业的诸多方面.能够从网络结构上对硅橡胶材料的性能进行合理的解释、模拟以及预测是多年来很多研究者的追求.本文概述了部分过去的相关理论研究,即基于设计一定的理论模型对硅橡胶网络结构进行模拟并预测其性能的模拟理论,重点介绍了具有阶段性意义的经典模拟模型并说明其基本假设、实际应用范围以及缺陷所在,阐述了近些年来新的一些从分子角度计算模拟的研究成果,并与经典的网络模型进行比较,以找到一个到目前为止,综合模拟性能好、适用性强的模拟研究方法.     

Review of robust multivariate statistical methods in high dimension

General ideas of robust statistics, and specifically robust statistical methods for calibration and dimension reduction are discussed. The emphasis is on analyzing high-dimensional data. The discussed methods are applied using the packages chemometrics and rrcov of the statistical software environment R. It is demonstrated how the functions can be applied to real high-dimensional data from chemometrics, and how the results can be interpreted.     

Chemometrics comes to court: evidence evaluation of chem–bio threat agent attacks

Forensic statistics is a well‐established scientific field whose purpose is to statistically analyze evidence in order to support legal decisions. It traditionally relies on methods that assume small numbers of independent variables and multiple samples. Unfortunately, such methods are less applicable when dealing with highly correlated multivariate data sets such as those generated by emerging high throughput analytical technologies. Chemometrics is a field that has a wealth of methods for the analysis of such complex data sets, so it would be desirable to combine the two fields in order to identify best practices for forensic statistics in the future. This paper provides a brief introduction to forensic statistics and describes how chemometrics could be integrated with its established methods to improve the evaluation of evidence in court. The paper describes how statistics and chemometrics can be integrated, by analyzing a previous know forensic data set composed of bacterial communities from fingerprints. The presented strategy can be applied in cases where chemical and biological threat agents have been illegally disposed. Copyright © 2015 John Wiley & Sons, Ltd.     

化学计量学在我国的发展

评述了化学计量学在我国近年来的发展,包括多元校正与分辨、稳健方法、化学模式识别、化学定量构效关系、分子模拟与优化、化学专家系统与库主要分支,其中特别对近年来得到飞速发展的新技术如高维数据解析方法,人工神经网络、小波变换给出了较详细的介绍。     

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.     

Robust cross-validation of linear regression QSAR models

A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.     

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.     

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.     

Unsupervised random forest: a tutorial with case studies

Unsupervised methods, such as principal component analysis, have gained popularity and wide‐spread acceptance in the chemometrics and applied statistics communities. Unsupervised random forest is an additional method capable of discovering underlying patterns in the data. However, the number of applications of unsupervised random forest in chemometrics has been limited. One possible cause for this is the belief that random forest can only be used in a supervised analysis setting. This tutorial introduces the basic concepts of unsupervised random forest and illustrates several applications in chemometrics through worked examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Analytical Method Performance Evaluation (AMPE)--a software tool for analytical method validation

A Windows-based software tool [Analytical Method Performance Evaluation (AMPE)] was developed to support the validation of analytical methods. The software implements standard statistical approaches commonly adopted in validation studies to estimate analytical method performance (limits of detection and quantitation, accuracy, specificity, working range, and linearity of responses) according to ISO 5725. In addition, AMPE proposes the application of innovative and unique approaches for the assessment of analytical method performance. Specifically, AMPE proposes the use of difference-based indexes to quantify the agreement between measurements and reference values, the use of pattern indexes to quantify methods bias with respect to specific external variables, and the application of fuzzy logic to aggregate into synthetic indicators the information collected independently via the different performance statistics traditionally estimated in validation studies. Aggregated measures are particularly useful for methods comparison, when more than one method is available for a specific analysis and it may be of interest to identify the best performing one taking into account, simultaneously, the information available from different performance statistics. Illustrative examples of the type of outputs expected from AMPE-based validation sessions are given. The extensive data handling capabilities and the wide range of statistics supplied in the software package makes AMPE suitable for specific needs that may arise in different validation studies. The installation package, complete with a fully documented help file, is distributed free of charge to interested users along with input files exemplary of the type of entry data required to run validation data analyses.     

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.