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Thomas Skov Lektor Geverifieerd e-mailadres voor food. Colin A. Claus A. Giorgio Tomasi University of Copenhagen Geverifieerd e-mailadres voor plen. Georgios B. Giannakis Endowed Chair Prof. Kathleen Kate R.

## Chemometrics

Murphy Chalmers University of Technology Geverifieerd e-mailadres voor chalmers. Francesco Savorani Assoc. Polytechnic of Turin Geverifieerd e-mailadres voor polito. Tsutomu Ohno University of Maine Geverifieerd e-mailadres voor maine. Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics , applied mathematics , and computer science , in order to address problems in chemistry , biochemistry , medicine , biology and chemical engineering.

In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics. Chemometrics is applied to solve both descriptive and predictive problems in experimental natural sciences, especially in chemistry. In descriptive applications, properties of chemical systems are modeled with the intent of learning the underlying relationships and structure of the system i. In predictive applications, properties of chemical systems are modeled with the intent of predicting new properties or behavior of interest. In both cases, the datasets can be small but are often very large and highly complex, involving hundreds to thousands of variables, and hundreds to thousands of cases or observations.

Chemometric techniques are particularly heavily used in analytical chemistry and metabolomics , and the development of improved chemometric methods of analysis also continues to advance the state of the art in analytical instrumentation and methodology. It is an application-driven discipline, and thus while the standard chemometric methodologies are very widely used industrially, academic groups are dedicated to the continued development of chemometric theory, method and application development.

Although one could argue that even the earliest analytical experiments in chemistry involved a form of chemometrics, the field is generally recognized to have emerged in the s as computers became increasingly exploited for scientific investigation. Many early applications involved multivariate classification, numerous quantitative predictive applications followed, and by the late s and early s a wide variety of data- and computer-driven chemical analyses were occurring. Multivariate analysis was a critical facet even in the earliest applications of chemometrics.

The structure of these data was found to be conducive to using techniques such as principal components analysis PCA , and partial least-squares PLS. This is primarily because, while the datasets may be highly multivariate there is strong and often linear low-rank structure present. Partial least squares in particular was heavily used in chemometric applications for many years before it began to find regular use in other fields.

Through the s three dedicated journals appeared in the field: Journal of Chemometrics , Chemometrics and Intelligent Laboratory Systems , and Journal of Chemical Information and Modeling. These journals continue to cover both fundamental and methodological research in chemometrics. At present, most routine applications of existing chemometric methods are commonly published in application-oriented journals e.

Chemometrics: a textbook , [4] and Multivariate Calibration by Martens and Naes. An account of the early history of chemometrics was published as a series of interviews by Geladi and Esbensen. Many chemical problems and applications of chemometrics involve calibration. The objective is to develop models which can be used to predict properties of interest based on measured properties of the chemical system, such as pressure, flow, temperature, infrared , Raman , NMR spectra and mass spectra. The process requires a calibration or training data set, which includes reference values for the properties of interest for prediction, and the measured attributes believed to correspond to these properties.

For case 1 , for example, one can assemble data from a number of samples, including concentrations for an analyte of interest for each sample the reference and the corresponding infrared spectrum of that sample. Multivariate calibration techniques such as partial-least squares regression, or principal component regression and near countless other methods are then used to construct a mathematical model that relates the multivariate response spectrum to the concentration of the analyte of interest, and such a model can be used to efficiently predict the concentrations of new samples.

Techniques in multivariate calibration are often broadly categorized as classical or inverse methods. The main advantages of the use of multivariate calibration techniques is that fast, cheap, or non-destructive analytical measurements such as optical spectroscopy can be used to estimate sample properties which would otherwise require time-consuming, expensive or destructive testing such as LC-MS.

Equally important is that multivariate calibration allows for accurate quantitative analysis in the presence of heavy interference by other analytes. The selectivity of the analytical method is provided as much by the mathematical calibration, as the analytical measurement modalities.

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For example, near-infrared spectra, which are extremely broad and non-selective compared to other analytical techniques such as infrared or Raman spectra , can often be used successfully in conjunction with carefully developed multivariate calibration methods to predict concentrations of analytes in very complex matrices. Supervised multivariate classification techniques are closely related to multivariate calibration techniques in that a calibration or training set is used to develop a mathematical model capable of classifying future samples.

### Duplikatsitater

The use of rank reduction techniques in conjunction with these conventional classification methods is routine in chemometrics, for example discriminant analysis on principal components or partial least squares scores. A family of techniques, referred to as class-modelling or one-class classifiers , are able to build models for an individual class of interest. Unsupervised classification also termed cluster analysis is also commonly used to discover patterns in complex data sets, and again many of the core techniques used in chemometrics are common to other fields such as machine learning and statistical learning.

In chemometric parlance, multivariate curve resolution seeks to deconstruct data sets with limited or absent reference information and system knowledge. Some of the earliest work on these techniques was done by Lawton and Sylvestre in the early s.

## Multi-way Analysis: Applications in the Chemical Sciences

For example, from a data set comprising fluorescence spectra from a series of samples each containing multiple fluorophores, multivariate curve resolution methods can be used to extract the fluorescence spectra of the individual fluorophores, along with their relative concentrations in each of the samples, essentially unmixing the total fluorescence spectrum into the contributions from the individual components.

The problem is usually ill-determined due to rotational ambiguity many possible solutions can equivalently represent the measured data , so the application of additional constraints is common, such as non-negativity, unimodality, or known interrelationships between the individual components e. Experimental design remains a core area of study in chemometrics and several monographs are specifically devoted to experimental design in chemical applications.

Signal processing is also a critical component of almost all chemometric applications, particularly the use of signal pretreatments to condition data prior to calibration or classification. The techniques employed commonly in chemometrics are often closely related to those used in related fields. A recent report by Olivieri et al. Multivariate statistical process control MSPC , modeling and optimization accounts for a substantial amount of historical chemometric development.

Specifically in terms of MSPC, multiway modeling of batch and continuous processes is increasingly common in industry and remains an active area of research in chemometrics and chemical engineering. Process analytical chemistry as it was originally termed, [26] or the newer term process analytical technology continues to draw heavily on chemometric methods and MSPC. Multiway methods are heavily used in chemometric applications.

For example, while the analysis of a table matrix, or second-order array of data is routine in several fields, multiway methods are applied to data sets that involve 3rd, 4th, or higher-orders. The data across multiple samples thus comprises a data cube. Batch process modeling involves data sets that have time vs. From Wikipedia, the free encyclopedia.

Chemometrics and Intelligent Laboratory Systems. Factor Analysis in Chemistry. New York: Wiley. Chemometrics: a textbook.