| 1. |
- Abrahamsson, Christoffer, et al.
(författare)
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Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets
- 2003
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 69:1-2, s. 3-12
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Tidskriftsartikel (refereegranskat)abstract
- Near infrared (NIR) transmission spectroscopy is a promising method for fast quantitative measurements on pharmaceutical tablets, but there are still some problems to overcome in order to incorporate the technique as a control tool in tablet production. The main problem is the limited precision for multivariate calibrations based on NIR transmission data. The precision is affected by several factors, where one of the most important is which variable to include in the multivariate calibration model. In this work, four different methods for variable selection in partial least square (PLS) regression were studied and compared to a calibration made with manually selected wavelengths. The methods used were genetic algorithm (GA), iterative PLS (IPLS), uninformative variable elimination by PLS (UVE-PLS) and interactive variable selection for PLS (IVS-PLS). All methods improved the predictive abilities of the model compared to the model where the wavelengths were selected manually. For the data set used in this work, IVS-PLS and GA achieved the best results with improvements in prediction error by 20%, but further measurements and investigations have to be made before any general conclusion can be drawn.
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| 2. |
- Kinser, J. M., et al.
(författare)
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Multidimensional pulse image processing of chemical structure data
- 2000
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 51:1, s. 115-124
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Tidskriftsartikel (refereegranskat)abstract
- Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17 beta-estradiol.
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| 3. |
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| 4. |
- Verikas, Antanas, et al.
(författare)
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Using artificial neural networks for process and system modelling
- 2003
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 67:2, s. 187-191
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Tidskriftsartikel (refereegranskat)abstract
- This letter concerns several papers, devoted to neural network-based process and system modelling, recently published in the Chemometrics and Intelligent Laboratory Systems journal. Artificial neural networks have proved themselves to be very useful in various modelling applications, because they can represent complex mapping functions and discover the representations using powerful learning algorithms. An optimal set of parameters for defining the functions is learned from examples by minimizing an error functional. In various practical applications, the number of examples available for estimating parameters of the models is rather limited. Moreover, to discover the best model, numerous candidate models must be trained and evaluated. In such thin-data situations, special precautions are to be taken to avoid erroneous conclusions. In this letter, we discuss three important issues, namely network initialization, over-fitting, and model selection, the right consideration of which can be of tremendous help in successful network design and can make neural modelling results more valuable.
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| 5. |
- Wikström, Conny, et al.
(författare)
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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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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42:1-2, s. 233-240
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Wikström, Conny, et al.
(författare)
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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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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42, s. 221-231
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Tidskriftsartikel (refereegranskat)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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| 7. |
- Wold, Svante, et al.
(författare)
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Modelling and diagnostics of batch processes and analogous kinetic experiments.
- 1998
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 44:1/2, s. 331-340
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Tidskriftsartikel (refereegranskat)abstract
- In chemical kinetics and batch processes K variables are measured on the batches at regular time intervals. This gives a J×K matrix for each batch (J time points times K variables). Consequently, a set of N normal batches gives a three-way matrix of dimension (N×J×K). The case when batches have different length is also discussed. In a typical industrial application of batch modelling, the purpose is to diagnose an evolving batch as normal or not, and to obtain indications of variables that together behave abnormally in batch process upsets. Other applications giving the same form of data include pharmaco-kinetics, clinical and pharmacological trials where patients (or mice) are followed over time, material stability testing and other kinetic investigations. A new approach to the multivariate modelling of three-way kinetic and batch process data is presented. This approach is based on an initial PLS analysis of the ((N×J)×K) unfolded matrix ((batch×time)×variables) with ‘local time' used as a single y-variable. This is followed by a simple statistical analysis of the resulting scores and results in multivariate control charts suitable for monitoring the kinetics of new experiments or batches. ‘Upsets' are effectively diagnosed in these charts, and variables contributing to the upsets are indicated in contribution plots. In addition, the degree of ‘maturity' of the batch can be as predicted vs. observed local time. The analysis of batch data with respect to various questions is discussed with respect to typical objectives, overview and summary, classification, and quantitative modelling. This is illustrated by an industrial example of yeast production.
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| 8. |
- Öberg, Tomas, 1956-
(författare)
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Importance of the first design matrix in experimental simplex optimization
- 1998
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elevier. - 0169-7439 .- 1873-3239. ; 44:1-2, s. 147-151
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Tidskriftsartikel (refereegranskat)abstract
- The basic and modified simplex methods are efficient optimization techniques applied in many fields of chemistry and engineering. Various first design matrices were evaluated on polynomial models with added noise. D-optimal linear design matrices performed better than regular or cornered first simplices in the normal experimental situation. These findings have been implemented in a new experimental design an optimization software, the MultiSimplex(R).
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| 9. |
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| 10. |
- Abbas, Aamer, 1973, et al.
(författare)
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Characterization and mapping of carotenoids in the algae Dunaliella and Phaeodactylum using Raman and target orthogonal partial least squares
- 2011
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 107:1, s. 174-177
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Tidskriftsartikel (refereegranskat)abstract
- A method was developed for the characterisation of carotenoid pigments in algal species using Raman spectroscopy in combination with multivariate hyperspectral analysis. Target orthogonal partial least squares (T-OPLS) operates by designating one known reference spectrum as the target. The target spectrum is put as the single y column in an OPLS regression model where the X matrix consists of the unfolded image spectra as variables in its columns. The spectral shape of the OPLS first orthogonal target score enabled us to verify the peak positions of the standard, and detect new peaks, not present in the reference standard. It was shown that the mixture of carotenoids present in the algae did not fully match the reference spectrum, however, the method provided enough information to make an analysis possible also in this case. The image results were constructed from the OPLS loading vectors that were showing a correlation map for the reference spectrum from the predictive loadings and maps of the occurrence of deviations from the orthogonal loadings.
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