| 1. |
- Champagne, M, et al.
(author)
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The use of orthogonal signal correction to improve NIR readings of pulp fibre properties
- 2001
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In: Pulp & Paper-Canada. - 0316-4004. ; 102:4, s. 41-3
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Journal article (peer-reviewed)abstract
- In 1999 Tembec Industries and the National Renewal Energy Laboratories worked together in developing a methodology to use Near-infrared (NIR). Technology of in-house pulp fibre quality properties Q99 and Q97. The initial results with dry samples of pulp were encouraging. the wet samples results were initially disappointing using the standard chemometric techniques. Svante Wold developed a new chemometric method called Orthogonal Signal correction (OSC), which was used to obtain a good correction of Q99 in the wet pulp samples.
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| 2. |
- Eriksson, Lennart, et al.
(author)
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Multi- and Megavariate Data Analysis : Part II: Advanced Applications and Method Extensions
- 2006
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Book (peer-reviewed)abstract
- This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely:QSAR (quantitative structure-activity relationships) describes how series of molecular structures can be translated to quantitative data and how these data then are used to model and predict biological activity measurements made on the corresponding molecules. Chapters on how the QSAR concept applies in peptide QSAR, lead finding and optimization, combinatorial chemistry, and chem-and bio-informatics, are included.The multi- and megavariate analysis of “omics” data, has a special chapter, i.e., data from metabonomics, proteomics, genomics and other areas.Then follow six chapters on extensions of the basic projection methods (PCA and PLS):Orthogonal PLS (OPLS) showing how a PLS model can be “rotated” so that all y-related information appears in the first component, which facilitates the model interpretation.Hierarchical modeling, both PC and PLS, allowing variables of different types to be handled in separate blocks, which greatly simplifies the handling of datasets with very many variables.Non-linear PLS describes various approaches to the modeling of non-linear relationships between predictors X and responses Y.The Image Analysis chapter shows how multivariate analysis applies to the analysis of series of digital images.Data Mining and Integration has a discussion of how to get useful information out of large and complicated data sets, and how to manage and organize data in complex investigations.The second volume ends with a chapter on preference and sensory data, followed by an appendix summarizing the multivariate approach, statistical notes, and references.
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| 3. |
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| 4. |
- Wikström, Conny, et al.
(author)
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Multivariate process and quality monitoring applied to an electrolysis process. : Part II - Multivariate time-series analysis of lagged latent variables
- 1998
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In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42:1-2, s. 233-240
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Journal article (peer-reviewed)abstract
- Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.
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| 5. |
- Wikström, Conny, et al.
(author)
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Multivariate process and quality monitoring applied to an electrolysis process. : Part I - Process supervision with multivariate control charts
- 1998
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In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42, s. 221-231
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Journal article (peer-reviewed)abstract
- Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.
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| 6. |
- Berglund, Anders, 1970-, et al.
(author)
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The GIFI approach to non-linear PLS modeling
- 2001
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In: Journal of Chemometrics. - : Wiley Inter Science. - 0886-9383 .- 1099-128X. ; 15:4, s. 321-36
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Journal article (peer-reviewed)abstract
- The GIFI approach to non-linear modeling involves the transformation of quantitative variables to a set of 1/0 dummies in a similar manner to the way qualitative variables are coded. This is followed by analyzing the sets of 1/0 dummies by principal component analysis, multiple regression or, as discussed here, PLS. The patterns of the resulting coefficients indicate the nature of the non-linearities in the data. Here the potential uses and limitations of PLS regression, in combination with four variants of GIFI coding, are investigated using both simulated and empirical data sets.
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| 7. |
- Kettaneh, Nouna, et al.
(author)
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PCA and PLS with very large data sets
- 2005
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In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473. ; 48:1, s. 69-85
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Journal article (peer-reviewed)abstract
- Chemometrics was started around 30 years ago to cope with the rapidly increasing volumes of data produced in chemical laboratories. A multivariate approach based on projections—PCA and PLS—was developed that adequately solved many of the problems at hand. However, with the further increase in the size of our data sets seen today in all fields of science and technology, we start to see inadequacies in our multivariate methods, both in their efficiency and interpretability.Starting from a few examples of complicated problems seen in RD&P (research, development, and production), possible extensions and generalizations of the existing multivariate projection methods—PCA and PLS—will be discussed. Criteria such as scalability of methods to increasing size of problems and data, increasing sophistication in the handling of noise and non-linearities, interpretability of results, and relative simplicity of use, will be held as important. The discussion will be made from a perspective of the evolution of scientific methodology as (a) driven by new technology, e.g., computers and graphical displays, and the need to answer some always reoccurring and basic questions, and (b) constrained by the limitations of the human brain, i.e., our ability to understand and interpret scientific and data analytic results.
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| 8. |
- Trygg, Johan, et al.
(author)
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2D wavelet analysis and compression of on-line industrial process data
- 2001
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In: Journal of Chemometrics: SPECIAL ISSUE: Dedicated to Harald Martens-The Third Recipient of the Herman Wold Medal. Issue Edited by Lennart Eriksson, Torbjörn Lundstedt. - : Wiley. ; 15:4, s. 299-319
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Journal article (peer-reviewed)abstract
- In recent years the wavelet transform (WT) has interested a large number of scientists from many different fields. Pattern recognition, signal processing, signal compression, process monitoring and control, and image analysis are some areas where wavelets have shown promising results. In this paper, 2D wavelet analysis and compression of near-infrared spectra for on-line monitoring of wood chips is reviewed. We introduce a new parameter for outlier detection, distance to model in wavelet space (DModW), which is analogous to the residual parameter (DModX) used in principal component analysis (PCA) and partial least squares analysis (PLS). Additionally, we describe the wavelet power spectrum (WPS), the wavelet analogue of the power spectrum. The WPS gives an overview of the time-frequency content in a signal. In the example given, wavelets improved the detection of spectral shift and compressed data 1000-fold without degrading the quality of the 2D wavelet-compressed PCA model. The example concerned an industrial process-monitoring situation where near-infrared spectra are measured on-line on top of a conveyer belt filled with wood chips at a Swedish pulp plant.
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| 9. |
- Wold, Svante, et al.
(author)
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New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production) - with examples from pharmaceutical research and process modeling
- 2002
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In: Journal of Chemometrics: Special Issue: Proceedings of the 7th Scandinavian Symposium on Chemometrics . Issue Edited by Lars Nørgaard. - : Wiley. ; 16:8-10, s. 377-86
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Journal article (peer-reviewed)abstract
- Chemometrics was started around 30 years ago to cope with and utilize the rapidly increasing volumes of data produced in chemical laboratories. The methods of early chemometrics were mainly focused on the analysis of data, but slowly we came to realize that it is equally important to make the data contain reliable information, and methods for design of experiments (DOE) were added to the chemometrics toolbox. This toolbox is now fairly adequate for solving most R&D problems of today in both academia and industry, as will be illustrated with a few examples. However, with the further increase in the size of our data sets, we start to see inadequacies in our multivariate methods, both in their efficiency and interpretability. Drift and non-linearities occur with time or in other directions in data space, and models with masses of coefficients become increasingly difficult to interpret and use. Starting from a few examples of some very complicated problems confronting chemical researchers today, possible extensions and generalizations of the existing chemometrics methods, as well as more appropriate preprocessing of the data before the analysis, will be discussed. Criteria such as scalability of methods to increasing size of problems and data, increasing sophistication in the handling of noise and non-linearities, interpretability of results, and relative simplicity of use will be held as important. The discussion will be made from a perspective of the evolution of the scientific methodology as driven by new technology, e.g. computers, and constrained by the limitations of the human brain, i.e. our ability to understand and interpret scientific and data analytical results. Quilt-PCA and Quilt-PLS presented here address and offer a possible solution to these problems.
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| 10. |
- Wold, Svante, et al.
(author)
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The chemometric analysis of point and dynamic data in pharmaceutical and biotech production (PAT) — some objectives and approaches
- 2006
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 84:1-2, s. 159-63
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Journal article (peer-reviewed)abstract
- Checking that a process is doing what it is supposed to is of critical importance in manufacturing, economics, environmental monitoring, patient monitoring, and more. Given sufficient and adequate analytical and process measurements made during the history of the well functioning process, a multivariate model of the process variation around a multivariate dynamic trajectory will, in principle, form a good basis for this checking. Such systems are often labeled process monitoring, real-time quality control (RTQC), PAT level 4, and advanced process control/fault detection and classification (APC/FDC). Here PAT stands for process analytical technology indicating the reliance on adequate and multiple data for this checking.In practice, there are many difficulties in making an RTQC/PAT-4 system work well. Starting from an industrial example, the problems of constructing and implementing a well working checking system are discussed in relation to its different parts — analytical and process data, chemometrical and other methods for their modeling and analysis, and various forms of data management to handle the data flow and synchronization, as well as storage and retrieval. The display and interpretability of diagnostics and results are emphasized.
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