Probabilistic model‐based discriminant analysis and clustering methods in chemometrics

In chemometrics, the supervised and unsupervised classification of high‐dimensional data has become a recurrent problem. Model‐based techniques for discriminant analysis and clustering are popular tools, which are renowned for their probabilistic foundations and their flexibility. However, classical model‐based techniques show a disappointing behaviour in high‐dimensional spaces, which up to now have been limited in their use within chemometrics. The recent developments in model‐based classification overcame these drawbacks and enabled the efficient classification of high‐dimensional data, even in the ‘small n / large p’ condition. This work presents a comprehensive review of these recent approaches, including regularization‐based techniques, parsimonious modelling, subspace classification methods and classification methods based on variable selection. The use of these model‐based methods is also illustrated on real‐world classification problems in chemometrics using R packages. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Metabolite Fingerprinting Based on 1H-NMR Spectroscopy and Liquid Chromatography for the Authentication of Herbal Products

Herbal medicines (HMs) are regarded as one of the traditional medicines in health care to prevent and treat some diseases. Some herbal components such as turmeric and ginger are used as HMs, therefore the identification and confirmation of herbal use are very necessary. In addition, the adulteration practice, mainly motivated to gain economical profits, may occur by substituting the high price of HMs with lower-priced ones or by addition of certain chemical constituents known as Bahan Kimia Obat (chemical drug ingredients) in Indonesia. Some analytical methods based on spectroscopic and chromatographic methods are developed for the authenticity and confirmation of the HMs used. Some approaches are explored during HMs authentication including single-component analysis, fingerprinting profiles, and metabolomics studies. The absence of reference standards for certain chemical markers has led to exploring the fingerprinting approach as a tool for the authentication of HMs. During fingerprinting-based spectroscopic and chromatographic methods, the data obtained were big, therefore the use of chemometrics is a must. This review highlights the application of fingerprinting profiles using variables of spectral and chromatogram data for authentication in HMs. Indeed, some chemometrics techniques, mainly pattern recognition either unsupervised or supervised, were applied for this purpose.     

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.     

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.     

Chromatographic fingerprinting and related chemometric techniques for quality control of traditional Chinese medicines

Development of chromatographic fingerprint (CF) and related chemometric methods and their applications to quality control of traditional Chinese medicines (TCMs) were discussed. CF is essentially a kind of quality control method for TCMs (or Chinese herbal medicines). Also, it is a quality‐relevant‐data high‐throughput and integral tool to explore chemically the complexity of TCMs. With the help of chemometrics, some difficulties in evaluation and analysis of CFs, such as calculation of information content, peak alignment, pattern analysis, deconvolution of overlapping peaks, etc. could be well solved. To further explore TCMs synergic quality, intensive study of CF coupled with chemometrics will create the possibility to achieve the aim to reveal the working mechanisms of TCMs and to further control and strengthen TCMs' intrinsic quality in a comprehensive manner.     

Utilization of spectral vector properties in multivariate chemometrics analysis of hyperspectral infrared imaging data for cellular studies

A suite of numerical techniques was utilized in a concerted fashion for the efficacious multivariate chemometrics analysis of hyperspectral infrared imaging data of exfoliated oral mucosa cells. Based on the vector representation of infrared spectrum a1xnu), spectral vector properties (SVP) are demonstrated to possess underpinning spectral information that was exploited in crucial chemometrics analyses; which include outlier spectra identification, selection for a subset of imaged mid-infrared spectra that contain good oral mucosa cell signals, and, for the first time, obtain major biochemical constituent spectra via the band-target entropy minimization (BTEM) curve resolution algorithm. The relative concentration spatial distribution of the major biochemical constituents observed, namely membrane lipids and various cellular protein structures (alpha-helix, beta-sheet, turns and bends), were subsequently acquired through multi-linear regression and were displayed as chemical contour maps. Amongst the set of numerical algorithms employed, two novel unsupervised clustering algorithms were developed and tested. One is useful for outlier spectra detection, and the other aids the selection of pertinent spatially distributed spectra that possess oral mucosa cell mid-infrared spectra with good signal-to-noise ratio. It is anticipated that this developed numerical suite will serve as an effective multivariate chemometrics protocol for cellular studies and biomedical diagnostics via infrared imaging.     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

Unsupervised 3D ring template searching as an ideas generator for scaffold hopping: use of the LAMDA, RigFit, and field-based similarity search (FBSS) methods

Crystal structures taken from the Cambridge Structural Database were used to build a ring scaffold database containing 19 050 3D structures, with each such scaffold then being used to generate a centroid connecting path (CCP) representation. The CCP is a novel object that connects ring centroids, ring linker atoms, and other important points on the connection path between ring centroids. Unsupervised searching in the scaffold and CCP data sets was carried out using the atom-based LAMDA and RigFit search methods and the field-based similarity search method. The performance of these methods was tested with three different ring scaffold queries. These searches demonstrated that unsupervised 3D scaffold searching methods can find not only the types of ring systems that might be retrieved in carefully defined pharmacophore searches (supervised approach) but also additional, structurally diverse ring systems that could form the starting point for lead discovery programs or other scaffold-hopping applications. Not only are the methods effective but some are sufficiently rapid to permit scaffold searching in large chemical databases on a routine basis.     

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。     

Integrating multiple analytical platforms and chemometrics for comprehensive metabolic profiling: application to meat spoilage detection

Untargeted metabolic profiling has become a common approach to attempt to understand biological systems. However, due to the large chemical diversity in the metabolites it is generally necessary to employ multiple analytical platforms so as to encompass a wide range of metabolites. Thus it is beneficial to find chemometrics approaches which can effectively integrate data generated from multiple platforms and ideally combine the strength of each platform and overcome their inherent weaknesses; most pertinent is with respect to limited chemistries. We have reported a few studies using untargeted metabolic profiling techniques to monitor the natural spoilage process in pork and also to detect specific metabolites associated with contaminations with the pathogen Salmonella typhimurium. One method used was to analyse the volatile organic compounds (VoCs) generated throughout the spoilage process while the other was to analyse the soluble small molecule metabolites (SMM) extracted from the microbial community, as well as from the surface of the spoiled/contaminated meat. In this study, we exploit multi-block principal component analysis (MB-PCA) and multi-block partial least squares (MB-PLS) to combine the VoCs and SMM data together and compare the results obtained by analysing each data set individually. We show that by combining the two data sets and applying appropriate chemometrics, a model with much better prediction and importantly with improved interpretability was obtained. The MB-PCA model was able to combine the strength of both platforms together and generated a model with high consistency with the biological expectations, despite its unsupervised nature. MB-PLS models also achieved the best over-all performance in modelling the spoilage progression and discriminating the naturally spoiled samples and the pathogen contaminated samples. Correlation analysis and Bayesian network analysis were also performed to elucidate which metabolites were correlated strongly in the two data sets and such information could add additional information in understanding the meat spoilage process.     

电化学分析的进展及应用

本文对2005年1月～2007年3月间我国电化学分析的发展进行了评述.文章按照电化学分析的不同领域分为极谱与伏安法,微电极、超微电极和修饰电极,离子选择性电极与传感器,示波分析法,电泳及色谱电化学,光谱电化学、电致发光法,石英晶体微天平,化学计量学方法,其他分析方法和仪器装置及实验技术等几部分.引用文献561篇.     

Resolving NMR signals of short‐chain fatty acid mixtures using unsupervised component analysis

Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.     

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。     

食用植物油中甘油三酯色谱分析方法研究进展

甘油三酯 (TAGs) 是植物油的主要成分(占95%~98%),对植物油的性质有着重要的影响。对其结构组成的分析有助于监控食用植物油质量,保障植物油食用安全。由于甘油骨架上可以结合的脂肪酸很多,导致甘油三酯的种类十分庞大,且一般天然油脂中的甘油三酯不仅物理化学性质非常接近,同时还存在大量的同分异构体和位置异构体。因此甘油三酯的分析是一项非常有挑战性的工作。该文对各类色谱分析技术在食用油中甘油三酯分析方面的应用进行了综述,评述了各方法的优缺点,并对指纹图谱技术和化学计量学方法在植物油鉴定中的应用进行了介绍。     

Sustainable Hues: Exploring the Molecular Palette of Biowaste Dyes through LC-MS Metabolomics

Underutilized biowaste materials are investigated for their potential as sustainable textile colorants through an approach based on mass spectrometry, bioinformatics, and chemometrics. In this study, colorful decoctions were prepared from the outer bark of Eucalyptus deglupta and fruit peels of Syzygium samarangense, Syzygium malaccense, Diospyros discolor, and Dillenia philippinensis. Textile dyeing was performed along with liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics to determine the small molecules responsible for the observed colors. Global Natural Products Social Molecular Networking (GNPS) guided the annotation of black-producing proanthocyanidins in D. philippinensis and E. deglupta through complexation with FeSO₄ mordant. Flavonoids from the yellow-colored D. philippinensis extracts were found to be similar to those in Terminalia catappa, a known traditional dye source. A higher intensity of epicatechin in E. deglupta produced a red-brown color in the presence of Cu²⁺. Furthermore, Syzygium fruit peels have poor wash-fastness in cotton fibers, but bioactive chalcone unique to S. samarangense samples may be a potential nutritional food colorant. Unsupervised PCA and supervised OPLS-DA chemometrics distinguished chemical features that affect dyeing properties beyond the observed color. These findings, along with growing data on natural dyes, could guide future research on sustainable colorants.     

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10–79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.     

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。     

Molecular challenges in modern chemometrics

Since the very beginning of the discipline, chemometrics has mainly focussed on analytical chemical problems such as calibration. With the growing importance of databases and applications in medicinal and computational chemistry, the domains of analytical chemistry and chemometrics have been enlarged significantly in recent years. Especially the relation between molecular structure and function has become of considerable interest. Despite the huge quantities of data that are available nowadays, it is often difficult to recognise and extract relevant chemical information for the problem at hand. One of the main obstacles is the definition of an appropriate representation of a molecule. Although a variety of different representations are used, none are generally applicable.

This paper focuses on the challenges that arise in the chemometrical analysis of molecular structures, the relation between structure and function and the relation between molecular representation and chemometrical modelling. Exciting opportunities for further research are illustrated using an example concerning the prediction of co-crystallisation behaviour for small organic molecules with cephalosporin antibiotics.