Relevant bibliographies by topics / Data processing and Chemometrics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Data processing and Chemometrics'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Data processing and Chemometrics"

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EL-Gindy, Alaa, and Ghada M. Hadad. "Chemometrics in Pharmaceutical Analysis: An Introduction, Review, and Future Perspectives." Journal of AOAC INTERNATIONAL 95, no. 3 (May 1, 2012): 609–23. http://dx.doi.org/10.5740/jaoacint.sge_el-gindy.

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Abstract Chemometrics is the application of statistical and mathematical methods to analytical data to permit maximum collection and extraction of useful information. The utility of chemometric techniques as tools enabling multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. Application of this approach mainly for interpretation of UV-Vis and near-IR (NIR) spectra, as well as for data obtained by other instrumental methods, makes identification and quantitative analysis of active substances in complex mixtures possible, especially in the analysis of pharmaceutical preparations present in the market. Such analytical work is carried out by the use of advanced chemical instruments and data processing, which has led to a need for advanced methods to design experiments, calibrate instruments, and analyze the resulting data. The purpose of this review is to describe various chemometric methods in combination with UV-Vis spectrophotometry, NIR spectroscopy, fluorescence spectroscopy, electroanalysis, chromatographic separation, and flow-injection analysis for the analysis of drugs in pharmaceutical preparations. Theoretical and practical aspects are described with pharmaceutical examples of chemometric applications. This review will concentrate on gaining an understanding of how chemometrics can be useful in the modern analytical laboratory. A selection of the most challenging problems faced in pharmaceutical analysis is presented, the potential for chemometrics is considered, and some consequent implications for utilization are discussed. The reader can refer to the citations wherever appropriate.
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Hibbert, David B. "Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)." Pure and Applied Chemistry 88, no. 4 (April 1, 2016): 407–43. http://dx.doi.org/10.1515/pac-2015-0605.

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AbstractRecommendations are given concerning the terminology relating to chemometrics. Building on ISO definitions of terms for basic concepts in statistics the vocabulary is concerned with mainstream chemometric methods. Where methods are used widely in science, definitions are given that are most useful to chemical applications. Vocabularies are given for general data processing, experimental design, classification, calibration and general multivariate methods.
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Dumancas, Gerard G., Ghalib Bello, Jeff Hughes, Renita Murimi, Lakshmi Viswanath, Casey O. Orndorff, Glenda Fe G. Dumancas, Jacy O'Dell, Prakash Ghimire, and Catherine Setijadi. "Chemometrics." International Journal of Fog Computing 2, no. 1 (January 2019): 1–42. http://dx.doi.org/10.4018/ijfc.2019010101.

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The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.
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Whitfield, Matthew B., and Mari S. Chinn. "Near infrared spectroscopic data handling and chemometric analysis with the R statistical programming language: A practical tutorial." Journal of Near Infrared Spectroscopy 25, no. 6 (November 14, 2017): 363–80. http://dx.doi.org/10.1177/0967033517740768.

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Near infrared spectroscopy is widely used for compositional analysis of bulk materials because it is inexpensive, fast, and non-destructive. However, the chemometric techniques required to produce near infrared calibrations are varied and complex. While there are a number of commercial applications capable of implementing these techniques, there has also been a recent proliferation of R packages for chemometrics. The R programming language has greater capabilities for data processing, automation of multiple analyses, and user development of new techniques than many of the closed-source, graphical user interface-based commercial chemometrics applications do. The R project is thus a powerful, open-source option for generating and testing near infrared calibrations, albeit with a longer learning curve than many of the commercial chemometric applications. The calibration techniques available in R have been widely demonstrated in both the primary literature and introductory texts, but less so the steps between the acquisition of the data and the calibration. This tutorial seeks to bridge that gap by demonstrating a practical approach to data transfer and handling, using R and several packages available on the Comprehensive R Archive Network ( https://cran.r-project.org/ ), and then illustrates the use of the resulting data framework in the generation of near infrared calibrations.
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Artemenko, Vladuslav, and Volodymyr Petrovych. "NEW CONCEPT OF CHEMOMETRICS." Automobile Roads and Road Construction, no. 113.2 (2023): 117–32. http://dx.doi.org/10.33744/0365-8171-2023-113.2-117-132.

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The parametric statistical methods of the research experimental data most often use presently in chemometrics. If experimental data do not correspond to the normal probabilistic distribution that in this case it is impossible prodice adequate data processing. At recently in chemometrics more often started to use the classical nonparametric methods. The classical nonparametric methods do not handle the raw datas probabilistic distribution. But these methods when undertakind the real calculations don’t care use that or other types of the distributions. The classical nonparametric methods under its realization usually requare referencing to corresponding statistical tables. Its own table is used for each such method. Chemometrics to presently presents itself many not bound between itself statistical methods of the investigations. The huge defect of the classical methods of chemometrics (parametric and nonparametric) in absence of the united approach to processing the raw datas. In this article is offered in fundamentally new approach to statistical investigations of the datas. If use the method of nonparametric bootstrap that possible replace many unbound between itself methods of classical chemometrics whole one ore two universal methods. And these universal methods have not what or essential defect. With standpoint of the program realization this means presence one or two universal procedures for decision nearly all practical problems of chemometrics. One of the primary tasks of nonparametric bootstrap as follows problem of the duplication of the sample is considered in article. Herewith simulated that was organized not one series of experiment but well over (for example 1000 … 10000). On example is shown use the method of nonparametric bootstrap with finding confidential interval for average and median for ecological time series. On programming language MATLAB is brought code corresponding bootstrap procedure.
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Pereira da Cunha, Pedro Henrique, Gabriely Silveira Folli, Sara Joaquina Inocencio Dionisio, Amanda Guedes Caldeira, and Paulo Roberto Filgueiras. "Tutorial para aplicação didática de quimiometria em software gratuito – Parte II: Regressão por Mínimos Quadrados Parciais (PLS) em dados de infravermelho médio e próximo para determinação de teor de adulterantes e propriedades físico-químicas." Revista Ifes Ciência 10, no. 4 (June 20, 2024): 01–16. http://dx.doi.org/10.36524/ric.v10i4.2535.

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The constant advance in technology, instrumentation, and the increase in data generation capacity has resulted in a new challenge for chemists: how to deal with complex, extensive, and multidimensional data sets. In this scenario, chemometrics emerged as a branch of chemistry specialized in the application of statistical and mathematical techniques for the analysis of multivariate analytical data, resulting in more precise and reliable scientific conclusions. As it is a relatively new area, there is still a lack of educational resources on the subject, and chemometric approaches are mostly confined to postgraduate courses. Therefore, this article provides a tutorial that can be used in various academic instances, with a focus on undergraduate education, to teach partial least squares regression (PLS). PLS is considered one of the most essential and common methods in chemometrics, and stands out for its ability to efficiently handle complex and highly correlated data sets, which is particularly important in regression analysis when there is significant multicollinearity among independent variables. In addition, PLS is known for its ease of use, making it a valuable tool for professionals seeking an effective approach to complex statistical modeling. We detail all the steps to create a complete PLS model, from installing the free GNU Octave software to producing the final figures. We also provide all the algorithms developed for data reading and processing. Finally, we interpret the obtained results so that the discussions and conclusions are easily understood by everyone.
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Vandeerstraeten, F., C. Wojciechowski, N. Dupuy, and J. P. Huvenne. "Recognition of starch origin and modifications by chemometrics spectral data processing (in French)." Analusis 26, no. 8 (October 1998): 57–62. http://dx.doi.org/10.1051/analusis:199826080057.

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Musa, Ayoko, Ward, Rösch, Brown, and Rainey. "Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity." Cells 8, no. 8 (August 7, 2019): 851. http://dx.doi.org/10.3390/cells8080851.

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Microalgae are swift replicating photosynthetic microorganisms with several applications for food, chemicals, medicine and fuel. Microalgae have been identified to be suitable for biofuels production, due to their high lipid contents. Microalgae-based biofuels have the potential to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. However, the present state of technology does not economically support sustainable large-scale production. The biofuel production process comprises the upstream and downstream processing phases, with several uncertainties involved. This review examines the various production and processing stages, and considers the use of chemometric methods in identifying and understanding relationships from measured study parameters via statistical methods, across microalgae production stages. This approach enables collection of relevant information for system performance assessment. The principal benefit of such analysis is the identification of the key contributing factors, useful for decision makers to improve system design, operation and process economics. Chemometrics proffers options for time saving in data analysis, as well as efficient process optimization, which could be relevant for the continuous growth of the microalgae industry.
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Kebede, Biniam, Pui Lee, Sze Leong, Vidya Kethireddy, Qianli Ma, Kemal Aganovic, Graham Eyres, Nazimah Hamid, and Indrawati Oey. "A Chemometrics Approach Comparing Volatile Changes during the Shelf Life of Apple Juice Processed by Pulsed Electric Fields, High Pressure and Thermal Pasteurization." Foods 7, no. 10 (October 17, 2018): 169. http://dx.doi.org/10.3390/foods7100169.

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High-Pressure Processing (HPP) and Pulsed Electric Fields (PEF) processing technologies are being used increasingly on a commercial basis, with high-quality labelled fruit juices being one of the most important promotion strategies. Quality-related enzymes, which might still be active after HPP and PEF pasteurization, can cause undesirable aroma changes during storage. This study investigated volatile changes during the shelf life of PEF (15.5 kV/cm and specific energy of 158 kJ/L), HPP (600 MPa for 3 min), and thermally (72 °C for 15 s) pasteurized Jazz apple juices—up to five weeks. To have an increased insight into the volatile changes, an integrated instrumental (GC-MS) and data analysis (chemometrics) approach was implemented. Immediately after pasteurization, PEF processing resulted a better retention of odor-active volatiles, such as (E)-2-hexenal and hexyl acetate, whereas thermal processing lowered their amount. During refrigerated storage, these volatiles have gradually decreased in all processed juices. By the end of storage, the amount of these aroma relevant volatiles appears to still be higher in PEF and HPP pasteurized juices compared to their conventional counterparts. This study demonstrated the potential of advanced chemometric approaches to obtain increased insight into complex shelf life changes.
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Stone, David C. "Application of median filtering to noisy data." Canadian Journal of Chemistry 73, no. 10 (October 1, 1995): 1573–81. http://dx.doi.org/10.1139/v95-195.

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The properties and application of the median filter are investigated using both simulated and real data for signals evolving with time. Comparison is made with existing numerical techniques for drift compensation and noise reduction in analytical measurements such as the moving average and Savitzky–Golay digital filters. The median filter provides a means for dealing with "spiky" noise and separating peaks from a slowly changing baseline, even when the exact nature of the drift and noise distribution is not known. Median filtering is a useful and complementary addition to existing digital filtering techniques, being mathematically robust and readily implemented on any computer platform. Keywords: median filtering, signal noise, signal processing, digital filters, chemometrics, data smoothing.
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Dissertations / Theses on the topic "Data processing and Chemometrics"

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Pierce, Karisa M. "Objectively obtaining information from gas chromatographic separations of complex samples using novel data processing and chemometric techniques /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/8575.

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Jonsson, Pär. "Multivariate processing and modelling of hyphenated metabolite data." Doctoral thesis, Umeå universitet, Kemi, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-663.

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One trend in the ‘omics’ sciences is the generation of increasing amounts of data, describing complex biological samples. To cope with this and facilitate progress towards reliable diagnostic tools, it is crucial to develop methods for extracting representative and predictive information. In global metabolite analysis (metabolomics and metabonomics) NMR, GC/MS and LC/MS are the main platforms for data generation. Multivariate projection methods (e.g. PCA, PLS and O-PLS) have been recognized as efficient tools for data analysis within subjects such as biology and chemistry due to their ability to provide interpretable models based on many, correlated variables. In global metabolite analysis, these methods have been successfully applied in areas such as toxicology, disease diagnosis and plant functional genomics. This thesis describes the development of processing methods for the unbiased extraction of representative and predictive information from metabolic GC/MS and LC/MS data characterizing biofluids, e.g. plant extracts, urine and blood plasma. In order to allow the multivariate projections to detect and highlight differences between samples, one requirement of the processing methods is that they must extract a common set of descriptors from all samples and still retain the metabolically relevant information in the data. In Papers I and II this was done by applying a hierarchical multivariate compression approach to both GC/MS and LC/MS data. In the study described in Paper III a hierarchical multivariate curve resolution strategy (H-MCR) was developed for simultaneously resolving multiple GC/MS samples into pure profiles. In Paper IV the H-MCR method was applied to a drug toxicity study in rats, where the method’s potential for biomarker detection and identification was exemplified. Finally, the H-MCR method was extended, as described in Paper V, allowing independent samples to be processed and predicted using a model based on an existing set of representative samples. The fact that these processing methods proved to be valid for predicting the properties of new independent samples indicates that it is now possible for global metabolite analysis to be extended beyond isolated studies. In addition, the results facilitate high through-put analysis, because predicting the nature of samples is rapid compared to the actual processing. In summary this research highlights the possibilities for using global metabolite analysis in diagnosis.
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Vitale, Raffaele. "Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/90442.

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The present Ph.D. thesis, primarily conceived to support and reinforce the relation between academic and industrial worlds, was developed in collaboration with Shell Global Solutions (Amsterdam, The Netherlands) in the endeavour of applying and possibly extending well-established latent variable-based approaches (i.e. Principal Component Analysis - PCA - Partial Least Squares regression - PLS - or Partial Least Squares Discriminant Analysis - PLSDA) for complex problem solving not only in the fields of manufacturing troubleshooting and optimisation, but also in the wider environment of multivariate data analysis. To this end, novel efficient algorithmic solutions are proposed throughout all chapters to address very disparate tasks, from calibration transfer in spectroscopy to real-time modelling of streaming flows of data. The manuscript is divided into the following six parts, focused on various topics of interest: Part I - Preface, where an overview of this research work, its main aims and justification is given together with a brief introduction on PCA, PLS and PLSDA; Part II - On kernel-based extensions of PCA, PLS and PLSDA, where the potential of kernel techniques, possibly coupled to specific variants of the recently rediscovered pseudo-sample projection, formulated by the English statistician John C. Gower, is explored and their performance compared to that of more classical methodologies in four different applications scenarios: segmentation of Red-Green-Blue (RGB) images, discrimination of on-/off-specification batch runs, monitoring of batch processes and analysis of mixture designs of experiments; Part III - On the selection of the number of factors in PCA by permutation testing, where an extensive guideline on how to accomplish the selection of PCA components by permutation testing is provided through the comprehensive illustration of an original algorithmic procedure implemented for such a purpose; Part IV - On modelling common and distinctive sources of variability in multi-set data analysis, where several practical aspects of two-block common and distinctive component analysis (carried out by methods like Simultaneous Component Analysis - SCA - DIStinctive and COmmon Simultaneous Component Analysis - DISCO-SCA - Adapted Generalised Singular Value Decomposition - Adapted GSVD - ECO-POWER, Canonical Correlation Analysis - CCA - and 2-block Orthogonal Projections to Latent Structures - O2PLS) are discussed, a new computational strategy for determining the number of common factors underlying two data matrices sharing the same row- or column-dimension is described, and two innovative approaches for calibration transfer between near-infrared spectrometers are presented; Part V - On the on-the-fly processing and modelling of continuous high-dimensional data streams, where a novel software system for rational handling of multi-channel measurements recorded in real time, the On-The-Fly Processing (OTFP) tool, is designed; Part VI - Epilogue, where final conclusions are drawn, future perspectives are delineated, and annexes are included.
La presente tesis doctoral, concebida principalmente para apoyar y reforzar la relación entre la academia y la industria, se desarrolló en colaboración con Shell Global Solutions (Amsterdam, Países Bajos) en el esfuerzo de aplicar y posiblemente extender los enfoques ya consolidados basados en variables latentes (es decir, Análisis de Componentes Principales - PCA - Regresión en Mínimos Cuadrados Parciales - PLS - o PLS discriminante - PLSDA) para la resolución de problemas complejos no sólo en los campos de mejora y optimización de procesos, sino también en el entorno más amplio del análisis de datos multivariados. Con este fin, en todos los capítulos proponemos nuevas soluciones algorítmicas eficientes para abordar tareas dispares, desde la transferencia de calibración en espectroscopia hasta el modelado en tiempo real de flujos de datos. El manuscrito se divide en las seis partes siguientes, centradas en diversos temas de interés: Parte I - Prefacio, donde presentamos un resumen de este trabajo de investigación, damos sus principales objetivos y justificaciones junto con una breve introducción sobre PCA, PLS y PLSDA; Parte II - Sobre las extensiones basadas en kernels de PCA, PLS y PLSDA, donde presentamos el potencial de las técnicas de kernel, eventualmente acopladas a variantes específicas de la recién redescubierta proyección de pseudo-muestras, formulada por el estadista inglés John C. Gower, y comparamos su rendimiento respecto a metodologías más clásicas en cuatro aplicaciones a escenarios diferentes: segmentación de imágenes Rojo-Verde-Azul (RGB), discriminación y monitorización de procesos por lotes y análisis de diseños de experimentos de mezclas; Parte III - Sobre la selección del número de factores en el PCA por pruebas de permutación, donde aportamos una guía extensa sobre cómo conseguir la selección de componentes de PCA mediante pruebas de permutación y una ilustración completa de un procedimiento algorítmico original implementado para tal fin; Parte IV - Sobre la modelización de fuentes de variabilidad común y distintiva en el análisis de datos multi-conjunto, donde discutimos varios aspectos prácticos del análisis de componentes comunes y distintivos de dos bloques de datos (realizado por métodos como el Análisis Simultáneo de Componentes - SCA - Análisis Simultáneo de Componentes Distintivos y Comunes - DISCO-SCA - Descomposición Adaptada Generalizada de Valores Singulares - Adapted GSVD - ECO-POWER, Análisis de Correlaciones Canónicas - CCA - y Proyecciones Ortogonales de 2 conjuntos a Estructuras Latentes - O2PLS). Presentamos a su vez una nueva estrategia computacional para determinar el número de factores comunes subyacentes a dos matrices de datos que comparten la misma dimensión de fila o columna y dos planteamientos novedosos para la transferencia de calibración entre espectrómetros de infrarrojo cercano; Parte V - Sobre el procesamiento y la modelización en tiempo real de flujos de datos de alta dimensión, donde diseñamos la herramienta de Procesamiento en Tiempo Real (OTFP), un nuevo sistema de manejo racional de mediciones multi-canal registradas en tiempo real; Parte VI - Epílogo, donde presentamos las conclusiones finales, delimitamos las perspectivas futuras, e incluimos los anexos.
La present tesi doctoral, concebuda principalment per a recolzar i reforçar la relació entre l'acadèmia i la indústria, es va desenvolupar en col·laboració amb Shell Global Solutions (Amsterdam, Països Baixos) amb l'esforç d'aplicar i possiblement estendre els enfocaments ja consolidats basats en variables latents (és a dir, Anàlisi de Components Principals - PCA - Regressió en Mínims Quadrats Parcials - PLS - o PLS discriminant - PLSDA) per a la resolució de problemes complexos no solament en els camps de la millora i optimització de processos, sinó també en l'entorn més ampli de l'anàlisi de dades multivariades. A aquest efecte, en tots els capítols proposem noves solucions algorítmiques eficients per a abordar tasques dispars, des de la transferència de calibratge en espectroscopia fins al modelatge en temps real de fluxos de dades. El manuscrit es divideix en les sis parts següents, centrades en diversos temes d'interès: Part I - Prefaci, on presentem un resum d'aquest treball de recerca, es donen els seus principals objectius i justificacions juntament amb una breu introducció sobre PCA, PLS i PLSDA; Part II - Sobre les extensions basades en kernels de PCA, PLS i PLSDA, on presentem el potencial de les tècniques de kernel, eventualment acoblades a variants específiques de la recentment redescoberta projecció de pseudo-mostres, formulada per l'estadista anglés John C. Gower, i comparem el seu rendiment respecte a metodologies més clàssiques en quatre aplicacions a escenaris diferents: segmentació d'imatges Roig-Verd-Blau (RGB), discriminació i monitorització de processos per lots i anàlisi de dissenys d'experiments de mescles; Part III - Sobre la selecció del nombre de factors en el PCA per proves de permutació, on aportem una guia extensa sobre com aconseguir la selecció de components de PCA a través de proves de permutació i una il·lustració completa d'un procediment algorítmic original implementat per a la finalitat esmentada; Part IV - Sobre la modelització de fonts de variabilitat comuna i distintiva en l'anàlisi de dades multi-conjunt, on discutim diversos aspectes pràctics de l'anàlisis de components comuns i distintius de dos blocs de dades (realitzat per mètodes com l'Anàlisi Simultània de Components - SCA - Anàlisi Simultània de Components Distintius i Comuns - DISCO-SCA - Descomposició Adaptada Generalitzada en Valors Singulars - Adapted GSVD - ECO-POWER, Anàlisi de Correlacions Canòniques - CCA - i Projeccions Ortogonals de 2 blocs a Estructures Latents - O2PLS). Presentem al mateix temps una nova estratègia computacional per a determinar el nombre de factors comuns subjacents a dues matrius de dades que comparteixen la mateixa dimensió de fila o columna, i dos plantejaments nous per a la transferència de calibratge entre espectròmetres d'infraroig proper; Part V - Sobre el processament i la modelització en temps real de fluxos de dades d'alta dimensió, on dissenyem l'eina de Processament en Temps Real (OTFP), un nou sistema de tractament racional de mesures multi-canal registrades en temps real; Part VI - Epíleg, on presentem les conclusions finals, delimitem les perspectives futures, i incloem annexos.
Vitale, R. (2017). Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90442
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Pomareda, Sesé Victor. "Signal Processing Approaches to the Detection and Localization of Gas Chemical Sources using Partially Selective Sensors." Doctoral thesis, Universitat de Barcelona, 2013. http://hdl.handle.net/10803/119727.

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Due to recent progress, higher-order chemical instrumentation provides large amounts of data which need automated processing in order to extract relevant information. In most cases, the raw signals or spectra are too complex for manual analysis. The ability to detect, identify and quantitate chemical substances in gas phase in field operations is required in a huge number of applications. Among them, I would like to highlight the need for chemical sensing on diverse humanitarian, safety and security applications. In these cases, it becomes extremely important to continuously monitor the environments where chemicals are spread in order to be ready to act when abnormal events are discovered. In most critical scenarios the sample can not just be taken to the laboratory and analyzed, since an immediate answer is needed. In some other scenarios, the exploration of the area must be performed because the localization of the gas source or material of interest is unknown. This exploration can be performed using multiple mobile sensors in order to localize the chemical source or material. Different sensing technologies have been successfully used to detect and identify different chemical substances (gases or volatile compounds). These compounds could be either toxic or hazardous, or they can be signatures of the materials to be detected, for instance, explosives or drugs. Among these technologies, mobility based analyzers provide fast responses with high sensitivity. However, IMS instruments are not exempt of problems. Typically, they provide moderate selectivity, appearing overlapped peaks in the spectra. Moreover, the presence of humidity makes peaks wider, thus worsening the resolving power and the resolution. Furthermore, the response of IMS is non-linear as substance concentration increases and more than one peak can appear in the spectra due to the same compound. In the present thesis, these problems are addressed and applications using an Ion Mobility Spectrometer (IMS) and a Differential Mobility Analyzer (DMA) are shown. It is demonstrated that multivariate data analysis tools are more effective when dealing with these technologies. For the first time, multivariate data analysis tools have been applied to a novel DMA. It is shown that DMA could be established as a good instrumentation for the detection of explosives and the detection and quantitation of VOCs. Furthermore, Multivariate curve resolution Alternating Least Squares (MCR-ALS) is shown to be suitable to analyze IMS spectra qualitatively when interfering chemicals appear in the spectra and even when their behaviour is non-linear. Partial Least Squares (PLS) methods are demonstrated to work properly for the quantitative analysis of these signals; from this analysis the chemical concentrations of the target substances are obtained. It is also demonstrated in this thesis that the quantitative measurements from these sensors can be integrated in a gas source localization algorithm in order to improve the localization of the source in those scenarios where it is required. It is shown that the new proposal works significantly better in cases where the source strength is weak. This is illustrated presenting results from simulations generated under realistic conditions. Moreover, real-world data were obtained using a mobile robot mounting a photo ionization detector (PID). Experiments were carried out under forced ventilation and turbulences in indoors and outdoors environments. The results obtained validate the simulation results and confirm that the new localization algorithm can effectively operate in real environments.
Debido a los progresos recientes, la instrumentación química genera mayores volúmenes de datos los cuales requieren de un procesado automático con la finalidad de extraer la información relevante, ya que un análisis manual no suele ser viable debido a la elevada complejidad de los datos. La habilidad de detectar, identificar y cuantificar sustancias químicas en fase gas en operaciones de campo es requerida en un gran número de aplicaciones. Entre ellas, aplicaciones humanitarias y de seguridad. En estos casos, la monitorización continua de los entornos es extremadamente importante, ya que se debe estar alerta de eventos anormales. En los escenarios más críticos, debe realizarse una exploración del área porque la posición de la fuente de gas de interés es desconocida. Esta exploración puede realizarse usando múltiples robots. Diferentes tecnologías de sensores se han aplicado con éxito a la detección e identificación de diferentes sustancias químicas (gases o compuestos volátiles). Estos compuestos pueden ser tóxicos, peligrosos, o precursores de explosivos o drogas. De entre estas tecnologías, los analizadores basados en movilidad iónica (IMS) proporcionan rápidas respuestas con gran sensibilidad. Sin embargo, estos instrumentos no están exentos de problemas. Típicamente, proporcionan una moderada selectividad, apareciendo picos solapados en los espectros. Además, la presencia de humedad provoca que los picos se ensanchen, así empeorando la resolución. Además, la respuesta de IMS es no lineal al incrementar la concentración y es posible que más de un pico debido al mismo compuesto aparezca en el espectro. En la presente tesis se trata con estos problemas y se demuestra que las herramientas de análisis de datos multivariantes son más efectivas que las herramientas típicas univariantes al tratar con tecnologías de movilidad iónica (IMS y DMA), especialmente para el análisis cualitativo y cuantitativo de sus espectros. Además, se demuestra que las medidas cuantitativas pueden integrarse de manera efectiva en un algoritmo de localización de fuentes químicas. Los resultados obtenidos (simulaciones realistas y datos reales) muestran que el algoritmo desarrollado durante la tesis puede funcionar especialmente bien en situaciones en las que la potencia de emisión de la fuente a detectar sea débil.
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Jacq, Kévin. "Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core High-resolution grain size distribution of sediment core with 2 hyperspectral imaging Study of pansharpening methods applied to hyperspectral images of sediment cores." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAA024.

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L'évolution de l'environnement et le climat sont, actuellement, au centre de toutes les attentions. Les impacts de l'activité des sociétés actuelles et passées sur l'environnement sont notamment questionnés pour mieux anticiper les implications de nos activités sur le futur. Mieux décrire les environnements passés et leurs évolutions sont possibles grâce à l'étude de nombreux enregistreurs naturels (sédiments, spéléothèmes, cernes, coraux). Grâce à eux, il est possible de caractériser des évolutions bio-physico-chimiques à différentes résolutions temporelles et pour différentes périodes. La haute résolution entendue ici comme la résolution su sante pour l'étude de l'environnement en lien avec l'évolution des sociétés constitue le principal verrou de l'étude de ces archives naturelles notamment en raison de la capacité analytique des appareils qui ne peuvent que rarement voir des structures fines inframillimétriques. Ce travail est bâti autour de l'hypothèse que l'utilisation de caméras hyperspectrales (VNIR, SWIR, LIF) couplée à des méthodes statistiques pertinentes doivent permettre d'accéder aux informations spectrales et donc bio-physico-chimiques contenues dans ces archives naturelles à une résolution spatiale de quelques dizaines de micromètres et, donc, de proposer des méthodes pour atteindre la haute résolution temporelle (saisonnière). De plus, a n d'avoir des estimations ables, plusieurs capteurs d'imageries et de spectroscopies linéaires (XRF, TRES) sont utilisés avec leurs propres caractéristiques (résolutions, gammes spectrales, interactions atomiques/moléculaires). Ces méthodes analytiques sont utilisées pour la caractérisation de la surface des carottes sédimentaires. Ces analyses spectrales micrométriques sont mises en correspondance avec des analyses géochimiques millimétriques usuelles. Optimiser la complémentarité de toutes ces données, implique de développer des méthodes permettant de dépasser la difficulté inhérente au couplage de données considérées par essence dissimilaire (résolutions, décalages spatiaux, non-recouvrement spectral). Ainsi, quatre méthodes ont été développées. La première consiste à associer les méthodes hyperspectrales et usuelles pour la création de modèles prédictifs quantitatifs. La seconde permet le recalage spatial des différentes images hyperspectrales à la plus basse des résolutions. La troisième s'intéresse à la fusion de ces dernières à la plus haute des résolutions. Enfin, la dernière s'intéresse aux dépôts présents dans les sédiments (lamines, crues, tephras) pour ajouter une dimension temporelle à nos études. Grâce à l'ensemble de ces informations et méthodes, des modèles prédictifs multivariés ont été estimés pour l'étude de la matière organique, des paramètres texturaux et de la distribution granulométrique. Les dépôts laminés et instantanés au sein des échantillons ont été caractérisés. Ceci a permis d'estimer des chroniques de crues, ainsi que des variations biophysico-chimiques à l'échelle de la saison. L'imagerie hyperspectrale couplée à des méthodes d'analyse des données sont donc des outils performants pour l'étude des archives naturelles à des résolutions temporelles fines. L'approfondissement des approches proposées dans ces travaux permettra d'étudier de multiples archives pour caractériser des évolutions à l'échelle d'un ou de plusieurs bassin(s) versant(s)
The evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
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6

Chen, Zhaomin. "Human Liver Metastases: Chemometrics of Imaging FTIR Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437662269.

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Gromski, Piotr Sebastian. "Application of chemometrics for the robust analysis of chemical and biochemical data." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/application-of-chemometrics-for-the-robust-analysis-of-chemical-and-biochemical-data(3049006f-e218-4286-83a8-e1fd85004366).html.

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In the last two decades chemometrics has become an essential tool for the experimental biologist and chemist. The level of contribution varies strongly depending on the type of research performed. Therefore, chemometrics may be used to interpret and explain results, to compare experimental data with real-word ‘unseen’ data, to accurately detect certain chemical vapour, to identify cancerous related metabolites, to identify and rank potentially relevant/important variables or simply just for a pictorial interpretation and understanding of the results. Whilst many chemometrics methods are well-established in the area of chemistry and metabolomics many scientists are still using them with what is often referred to as a ‘black-box’ approach, that is without prior knowledge of the methods and well-recognised statistical properties. This lack of knowledge is thanks to the wide availability of powerful computers and – perhaps more notably – up-to-date, easy to use and reliable software. The main aim of this study is to reduce this gap by providing extensive demonstration of several approaches applied at different stages of the data analysis pipeline highlighting the importance of appropriate method selection. The comparisons are based both on chemical and biochemical (metabolomics) data and construct a firm basis for the researchers in terms of understanding of chemometric methods and the influence of parameter selection. Consequently, in this thesis the exploration and comparison of different approaches employed for various statistical steps are investigated. These include pre-treatment steps such as dealing with missing data and scaling. First, different substitution of missing values and their influence on unsupervised and supervised learning have been compared, where it has been shown that metabolites that display skewness in distribution can have a significant impact on the replacement approach. The scaling approaches were compared in terms of effect on classification accuracy for variety of metabolomics data sets. It was shown that the most standard option which is autoscaling is not always the best. In the next step a comparison of various variable selection methods which are commonly used for the analysis of chemical data has been carried out. The results revealed that random forests, with its variable selection techniques, and support vector machines, combined with recursive feature elimination as a variable selection method, displayed the best results in comparison to other approaches. Moreover, in this study a double cross-validation procedure was applied to minimize the consequence of over-fitting. Finally, seven different algorithms and two model validation procedures based on either 10-fold cross-validation or bootstrapping were investigated in order to allow direct comparison between different classification approaches.
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Xu, Yun. "Chemometrics pattern recognition with applications to genetic and metabolomics data." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435733.

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Loades, Victoria Catherine. "The application of chemometrics to spectroscopic and process analytical data." Thesis, University of Hull, 2003. http://hydra.hull.ac.uk/resources/hull:13971.

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The research has included collaboration with number of different companies and consortiums involving spectroscopic measurements with the application of chemometric techniques. For the 'European Framework 5', Standards Measurements and Testing (SMT) chemometrics network consortium a certified reference dataset based on visible metals complex spectra was developed. An inter-laboratory study was carried out which demonstrated the between subject significant difference for chemometric data analysis. An industrial collaboration with BNFL, Springfield's, this work consisted of producing a PLS regression model which could be used to predict levels of uranyl and nitrate in uranyl nitrate liquors samples, which were analysed by Raman spectroscopy which was insensitive to temperature. A substantial amount of work has been in the development of GMS with multivariate calibration for process analysis. The GMS is designed for the analysis of flowing mixtures, slurries and moisture content. The method is currently hindered by the existing calibration method; here PCA, PLS and weighted ridge regression (WRR) have been applied to the broadband, complex spectra to successfully allow measurement of a range of samples including; aqueous, organic, fermentation and non-homogeneous samples.
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Emerton, Guy. "Data-driven methods for exploratory analysis in chemometrics and scientific experimentation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86366.

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Thesis (MSc)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: Background New methods to facilitate exploratory analysis in scientific data are in high demand. There is an abundance of available data used only for confirmatory analysis from which new hypotheses can be drawn. To this end, two new exploratory techniques are developed: one for chemometrics and another for visualisation of fundamental scientific experiments. The former transforms large-scale multiple raw HPLC/UV-vis data into a conserved set of putative features - something not often attempted outside of Mass-Spectrometry. The latter method ('StatNet'), applies network techniques to the results of designed experiments to gain new perspective on variable relations. Results The resultant data format from un-targeted chemometric processing was amenable to both chemical and statistical analysis. It proved to have integrity when machine-learning techniques were applied to infer attributes of the experimental set-up. The visualisation techniques were equally successful in generating hypotheses, and were easily extendible to three different types of experimental results. Conclusion The overall aim was to create useful tools for hypothesis generation in a variety of data. This has been largely reached through a combination of novel and existing techniques. It is hoped that the methods here presented are further applied and developed.
AFRIKAANSE OPSOMMING: Agtergrond Nuwe metodes om ondersoekende ontleding in wetenskaplike data te fasiliteer is in groot aanvraag. Daar is 'n oorvloed van beskikbaar data wat slegs gebruik word vir bevestigende ontleding waaruit nuwe hipoteses opgestel kan word. Vir hierdie doel, word twee nuwe ondersoekende tegnieke ontwikkel: een vir chemometrie en 'n ander vir die visualisering van fundamentele wetenskaplike eksperimente. Die eersgenoemde transformeer grootskaalse veelvoudige rou HPLC / UV-vis data in 'n bewaarde stel putatiewe funksies - iets wat nie gereeld buite Massaspektrometrie aangepak word nie. Die laasgenoemde metode ('StatNet') pas netwerktegnieke tot die resultate van ontwerpte eksperimente toe om sodoende ân nuwe perspektief op veranderlike verhoudings te verkry. Resultate Die gevolglike data formaat van die ongeteikende chemometriese verwerking was in 'n formaat wat vatbaar is vir beide chemiese en statistiese analise. Daar is bewys dat dit integriteit gehad het wanneer masjienleertegnieke toegepas is om eienskappe van die eksperimentele opstelling af te lei. Die visualiseringtegnieke was ewe suksesvol in die generering van hipoteses, en ook maklik uitbreibaar na drie verskillende tipes eksperimentele resultate. Samevatting Die hoofdoel was om nuttige middele vir hipotese generasie in 'n verskeidenheid van data te skep. Dit is grootliks bereik deur 'n kombinasie van oorspronklike en bestaande tegnieke. Hopelik sal die metodes wat hier aangebied is verder toegepas en ontwikkel word.
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Books on the topic "Data processing and Chemometrics"

1

Brereton, Richard G. Chemometrics. New York: John Wiley & Sons, Ltd., 2003.

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1942-, Meloun M., Militký Miří, and Forina Michele, eds. Chemometrics for analytical chemistry. New York: Ellis Horwood, 1992.

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Meloun, M. Chemometrics for analytical chemistry. New York: Ellis Horwood, 1994.

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Brown, S. D. Comprehensive chemometrics: Chemical and biochemical data analysis. Edited by Sarabia L. A, Trygg Johan, and ScienceDirect (Online service). Amsterdam: Elsevier, 2009.

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C, Miller J., ed. Statistics and chemometrics for analytical chemistry. 5th ed. Harlow, England: Pearson Prentice Hall, 2005.

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C, Miller J., and Miller J. C, eds. Statistics and chemometrics for analytical chemistry. 4th ed. Harlow, England: Prentice Hall, 2000.

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A, Gribov L., and Institut geokhimii i analiticheskoĭ khimii im. V.I. Vernadskogo., eds. ĖVM v analiticheskoĭ khimii: Annotat͡s︡ii programm. Moskva: Akademii͡a︡ nauk SSSR, In-t geokhimii i analiticheskoĭ khimii im. V.I. Vernadskogo, 1987.

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A, Gribov L., ed. Matematicheskie metody i ĖVM v analiticheskoĭ khimii: Sbornik nauchnykh trudov. Moskva: "Nauka", 1989.

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G, Brereton Richard, ed. Chemometrics tutorials II: Collected from Chemometrics and intelligent laboratory systems - an international journal, volumes 6-11. Amsterdam: Elsevier, 1992.

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G, Brereton Richard, ed. Multivariate pattern recognition in chemometrics: Illustrated by case studies. Amsterdam: New York, 1992.

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Book chapters on the topic "Data processing and Chemometrics"

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Wehrens, Ron. "Data." In Chemometrics with R, 7–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17841-2_2.

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Roussel, Sylvie, Sébastien Preys, Fabien Chauchard, and Jordane Lallemand. "Multivariate Data Analysis (Chemometrics)." In Food Engineering Series, 7–59. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0311-5_2.

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Smit, Henri Casemirus, and Erik Jan Heuvel. "Signal and data analysis in chromatography." In Chemometrics and Species Identification, 63–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/3-540-17308-0_3.

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He, Ping, Xiaoling Peng, and Qingsong Xu. "From “Clothing Standard” to “Chemometrics”." In Contemporary Experimental Design, Multivariate Analysis and Data Mining, 37–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46161-4_3.

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Balke, Stephen T. "Chemometrics in Size Exclusion Chromatography." In Detection and Data Analysis in Size Exclusion Chromatography, 202–19. Washington, DC: American Chemical Society, 1987. http://dx.doi.org/10.1021/bk-1987-0352.ch012.

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 179–227. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-5126-6_9.

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Raggatt, Peter. "Data Processing." In Principles and Practice of Immunoassay, 190–218. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-11234-0_7.

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Singh, Pramod. "Data Processing." In Learn PySpark, 17–48. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4961-1_2.

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Fuertes, Inmaculada, Maria Vila-Costa, Jana Asselman, Benjamín Piña, and Carlos Barata. "Data Processing for RNA/DNA Sequencing." In Comprehensive Chemometrics, 507–14. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-409547-2.14595-0.

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Sauzier, G., and S. W. Lewis. "Chemometric Methods." In Chemometric Methods in Forensic Science, 10–38. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839166099-00010.

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Chemometrics encompasses a wide range of tools that can provide valuable information in support of forensic investigations. This chapter provides an accessible outline of the use and interpretation of common chemometric methods applicable to the forensic sciences. Guidance is also provided concerning appropriate data collection, data pre-processing, and validation approaches to ensure the validity of the results.
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Conference papers on the topic "Data processing and Chemometrics"

1

He, Sheng, and Gang Zhang. "Geomagnetic Data Processing." In 2024 Photonics & Electromagnetics Research Symposium (PIERS), 1–3. IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618623.

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Calvo-Gomez, Octavio, Akbarali Ruzibayev, Shakhnozakhon Salijonova, Shakhnozakhon Gaipova, Sarvar Khodjaev, Zulfiyakhon Khakimova, and Dilshod Rakhimov. "Chemometrics as an aid to quickly evaluate galactomannans through infrared spectroscopy." In Research for Rural Development 2024 : annual 30th international scientific conference, 79–84. Latvia University of Life Sciences and Technologies, 2024. https://doi.org/10.22616/rrd.30.2024.013.

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Galactomannans, composed of galactose and mannose, may form gels and are considered safe because of their non-toxic, biodegradable, and biocompatible nature. As a result, they are widely utilized in the food industry as stabilizers and thickeners. Among galactomannan producing species, guar gum and locust bean gum are particularly important due to their economical relevance. Guar gum and locust bean gum are often adulterated with cellulose gums like xanthan gum and carboxymethyl cellulose (CMC). Adulteration of galactomannans with other gums may introduce uncertainties regarding functionality and complicate quality control, posing a potential problem for the food industry. Among the different techniques which have been used for determining and characterizing galactomannans, Fourier Transform Infrared Spectroscopy stands out. Especially when coupled to Attenuated Total Reflection (ATR), analyses are performed rapidly, with a minimum sample preparation, and without the need for solvent or previous extraction mechanisms. However, food is a very complex matrix that contains a high number of components which generate a multitude of spectral information and large data sets. Consequently, additional data processing tools such as chemometrics are needed to be able to draw useful information from spectra. Our goal in this work is to show how to optimize conditions for instrumental analysis by infrared spectroscopy of galactomannans and its constituent monomers and create a chemometric model where galactomannans could be differentiated as a single group. We successfully optimized the PCA model obtained after chemometric processing of infrared data through reducing dimensions by loadings selection.
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Živojinović, Dragana Z., Dušan V. Trajković, and Jelena D. Božović. "Application of chemometrics in monitoring of spatial and temporal variations in river water quality and water classification." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.245z.

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The Topčiderska River is the right tributary of the Sava River, with the length of its course of 31 km, and the catchment area of about 147 km2 . It represents one of the most polluted urban waters in the territory of the Republic of Serbia. Soil erosion, as well as the discharge of wastewater from certain parts of the city and from industrial zones located in its catchment, to the greatest extent cause the pollution of this river. Within this work, representative samples of Topčiderka River water sampled in different time periods and from different locations were analyzed. Over ten physico-chemical parameters were selected that best indicate the current quality of the river water. The obtained measurement results were subjected to chemometric analysis, using chemometric methods (HCA and PCA). Water quality was analyzed from two aspects, temporal and locational. Based on the results of statistical data processing, insight was gained into the variation in the water quality of the Topčiderska River and the causes of the resulting variation were revealed. Based on these conclusions, the water of this river was classified in accordance with the Regulation on limit values of pollutants in surface and underground waters and the Regulation on water classification. The obtained results show that the water of the Topčiderska River is highly polluted, and that according to the degree of pollution and purpose, it is classified as IV class.
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Cartellieri, Ansgar, Philipp Schapotschnikow, Wolfgang Weinzierl, Johannes Denninger, and Alina Adams. "Acquiring Accurate Real-Time Formation Fluid Properties to Provide In-Situ Fluid Analysis While Drilling." In SPE Offshore Europe Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215520-ms.

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Abstract The composition of natural gas and crude oils specifies their calorific and commercial value. To determine required processing steps for intermediate and end products of the reservoir fluid and to define the layout of production facilities, an accurate fluid analysis is vital. Currently, the most reliable way of acquiring fluid compositional data is provided by recovering samples from the downhole environment with dedicated fluid sampling services for surface laboratory analysis. Specialized PVT (Pressure-Volume-Temperature) laboratories provide a detailed fluid characterization using advanced techniques such as gas chromatography (GC). This procedure is very costly and time consuming. Therefore, improved measurement systems and alternative methods with lower cost are commonly requested. One way to hasten the analysis of reservoir fluids and to lower the cost is the introduction of sampling while drilling services. Even though, these products have been commercial for nearly a decade, most sampling jobs are still conducted with wireline services after the well has been drilled, delaying the acquisition, and incurring additional rig costs. One reason that has limited the application of logging-while-drilling (LWD) fluid sampling services is the reduced data resolution in real time. Whereas wireline services are able to transmit comprehensive fluid identification data in seconds, it takes minutes for a comparable LWD service to transmit the data even in significantly reduced resolution. The wireline data transmission rate is many thousand times higher than while drilling. A wired pipe application is available that can overcome this constraint, however, at much higher cost. For a representative fluid evaluation and the selection of the optimal fluid samples for characterization in a dedicated PVT lab, an improved prediction of the fluid properties during pump-out, especially for LWD services, is required. Considering the limited real-time bandwidth available, the evaluation must be performed downhole. Therefore, new fluid type algorithms that combine multiple sensor readings into one model have been developed and implemented in the downhole electronics to provide a more sophisticated and thus conclusive in-situ fluid analysis. This paper will discuss and present the most recent enhancements, achieved by adding machine learning algorithms and chemometrics to LWD fluid sampling services. Models are trained on data from multiple field studies and typical deepwater applications to simplify data interpretation and sample selection, resulting in comprehensive fluid type information being available at surface for real-time decisions and accurate in-situ fluid analysis.
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Tang, Fenfen, Emmanuel Hatzakis, Hilary Green, and Selina Wang. "The Analysis and Authentication of Avocado Oil using High Field- & Low Field-NMR." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/hnwv1042.

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The popularity of avocado oil has increased among consumers due to its organoleptic properties and health-promoting effects. Avocado oil in the US market has been found to be adulterated with cheaper oils like other high-value edible oils, such as olive oil, or of poor quality. A variety of analytical methods, including chromatography and spectroscopy, have been used to evaluate the quality and purity of avocado oils. In addition, recently, high-resolution (HR) NMR has been successfully applied to determine fatty acid contents and to discriminate avocado oil from other vegetable oils. Despite their advantages, these methods suffer from several weaknesses. For example, they can be either labor-intensive and time-consuming, or expensive and requiring highly skilled experts. LF-NMR has been utilized for the analysis of many food products. It is more affordable, user-friendly, and fits well in an industrial environment, in addition to being rapid and non-destructive. However, most of the LF-NMR applications involve relaxometry instead of spectroscopy, which limits its potential in food analysis. As LF-NMR has been developed into a more powerful and versatile tool over decades, here we applied LF-NMR with chemometrics to distinguish avocado oil from other vegetable oils, including olive, canola, soybean, high-oleic (HO) safflower and HO sunflower oil, and validated the results by fatty acids and triacylglycerols profiling using GC-FID and HPLS-CAD, respectively. With the exploitation of advanced multivariate data analysis, such as Random Forest, LF-NMR provided comparable discrimination performance of different types of vegetable oils to HR-NMR, despite the challenges of high oleic oils. LF-NMR combined with PLS regression was able to efficiently and rapidly determine fatty acid contents using GC-FID as the reference method for modeling. LF-NMR was shown to have the potential for monitoring avocado oil processing and authentication in many sectors, as an alternative or complementary method to conventional food analysis instrumentations.
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Tranter, Roy L., Brian Davies, Chris M. Harland, and Steve L. Boucher. "From Spectral Data To Useful (Chemical) Information Via Chemometrics." In Recent Developments and Applications if Infrared Analytical Instrumentation, edited by Harry A. Willis. SPIE, 1988. http://dx.doi.org/10.1117/12.945585.

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Hadjiloucas, S., G. C. Walker, J. W. Bowen, and R. K. H. Galvao. "Femto-chemometrics: The signal processing of fast pulse transients." In 2010 35th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz 2010). IEEE, 2010. http://dx.doi.org/10.1109/icimw.2010.5612671.

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Burger, J. E., and A. A. Gowen. "The interplay of chemometrics and hyperspectral chemical imaging." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080856.

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Dutrow, Barbara, Shoshauna Farnsworth-Pinkerton, Darrell Henry, and Nancy McMillan. "Copper-Bearing Tourmaline Provenance from Chemometrics of LIBS and EMP Data." In Goldschmidt2020. Geochemical Society, 2020. http://dx.doi.org/10.46427/gold2020.626.

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Serranti, S., S. Malinconico, I. Lonigro, R. Gasbarrone, G. Bonifazi, and S. Bellagamba. "Chrysotile Detection In Soils With Proximal Hyperspectral Sensing And Chemometrics." In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2022. http://dx.doi.org/10.1109/whispers56178.2022.9955138.

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Reports on the topic "Data processing and Chemometrics"

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Casasent, David. Optical Data Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada174465.

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Conlin, Jeremy L., and Andrej Trkov. Nuclear Data Processing. IAEA Nuclear Data Section, November 2018. http://dx.doi.org/10.61092/iaea.c7t6-j2x8.

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SEA TECHNOLOGY ARLINGTON VA. Communications, Telemetry, Data Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada417821.

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Andrews, Elisabeth. RACORO aerosol data processing. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1028128.

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- UC BERKELEY, M. SHEATS. ADVANCED DATA PROCESSING FOR VOLUMETRIC COMPUTED TOMOGRAPHY DATA. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/784592.

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Feng, Ya-Chien, Alyssa Matthews, Marqi Rocque, Mindy Deng, Timothy Wendler, Karen Johnson, Eddie Schuman, et al. TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report. Office of Scientific and Technical Information (OSTI), March 2024. http://dx.doi.org/10.2172/2326212.

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Paterno, Marc, and Chris Green. Processing Contexts for Experimental HEP Data. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1422188.

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Macduff, M., and D. Egan. ACRF Data Collection and Processing Infrastructure. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/1020559.

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Fields, Erik, Karen Tracey, and D. R. Watts. Inverted Echo Sounder Data Processing Report. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada237576.

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Kennelly, Maureen, Karen Tracey, and D. R. Watts. Inverted Echo Sounder Data Processing Manual. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada477328.

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