| 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
-
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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| 11. |
- Aftab, Obaid, 1984-, et al.
(författare)
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Label free quantification of time evolving morphologies using time-lapse video microscopy enables identity control of cell lines and discovery of chemically induced differential activity in iso-genic cell line pairs
- 2015
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 141, s. 24-32
-
Tidskriftsartikel (refereegranskat)abstract
- Label free time-lapse video microscopy based monitoring of time evolving cell population morphology has potential to offer a simple and cost effective method for identity control of cell lines. Such morphology monitoring also has potential to offer discovery of chemically induced differential changes between pairs of cell lines of interest, for example where one in a pair of cell lines is normal/sensitive and the other malignant/resistant. A new simple algorithm, pixel histogram hierarchy comparison (PHHC), for comparison of time evolving morphologies (TEM) in phase contrast time-lapse microscopy movies was applied to a set of 10 different cell lines and three different iso-genic colon cancer cell line pairs, each pair being genetically identical except for a single mutation. PHHC quantifies differences in morphology by comparing pixel histogram intensities at six different resolutions. Unsupervised clustering and machine learning based classification methods were found to accurately identify cell lines, including their respective iso-genic variants, through time-evolving morphology. Using this experimental setting, drugs with differential activity in iso-genic cell line pairs were likewise identified. Thus, this is a cost effective and expedient alternative to conventional molecular profiling techniques and might be useful as part of the quality control in research incorporating cell line models, e.g. in any cell/tumor biology or toxicology project involving drug/agent differential activity in pairs of cell line models.
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| 12. |
- Andersson, M, et al.
(författare)
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NIR spectroscopy on moving solids using a scanning grating spectrometer - impact on multivariate process analysis
- 2005
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 75:1, s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- The effect of sample movement on spectral response during fiber probe diffuse reflectance near-infrared spectrometry (NIR) sampling was characterized. This is of central importance in Process Analytical Chemistry (PAC) and Process Analytical Technology (PAT). The incitement to this study was the observation of spectral artifacts during measurements of powder samples in process streams when using a mechanically scanning spectrometer. Artifacts appeared as momentary changes in the spectral response during acquisition of a scan. These transitions emanate from continuous replacement of the sample subfraction seen by the probe and are typical for turbid media where sample properties may vary locally with respect to scattering and/or absorption. The impact on qualitative and quantitative analysis using chemometric methods such as principal component analysis (PCA) and partial least squares (PLS) regression was evaluated through experimental and theoretical simulations. It was generally found that spectra with the smallest residuals after projection onto the models came from non-moving samples or samples moving only slowly. It is shown that the magnitude of the spectral residuals is directly connected to the effective sample size, which relates both to sample speed as well as to the sample area presented to the probe. Implications for in-line/on-line process analysis of solids are discussed. (C) 2004 Elsevier B.V. All rights reserved.
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| 13. |
- Antti, Henrik, et al.
(författare)
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Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects
- 2004
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 139-49
-
Tidskriftsartikel (refereegranskat)abstract
- Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. 1H NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) 1H NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose×time.
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| 14. |
- Björk, Anders, et al.
(författare)
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Modeling of pulp quality parameters from distribution curves extracted from process acoustic measurements on a thermo mechanical pulp (TMP) process
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 85:1, s. 63-69
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper the feasibility of modeling strength and optical pulp properties from length distribution curves extracted from acoustic data using continuous wavelet transform-fiber length extraction, CWT-FLE (A Björk and L-G Danielsson, 'Extraction of Distribution Curves from Process Acoustic Measurements on a TMP-Process', Pulp and Paper Canada 105 No. 11 (2004), T260-T264) by use of Partial Least Squares (PLS) have been tested. The curves used have earlier been validated against length distribution curves obtained by analyzing pulp samples with a commercial analyzer (FiberMaster). The curves were extracted from acoustic data without any "calibration" against fiber length analyses. The acoustic measurements were performed using an accelerometer affixed to the refiner blow-line during a full-scale trial with a Sunds Defibrator double disc refiner at SCA Ortviken, Sweden. Pulp samples were collected concurrently with the acoustic measurements and extensive physical testing has been made on these samples. For each trial point three pulp samples were collected. PLS1 and PLS2 models were successfully made linking the distribution curves obtained using CWT-FLE to pulp tensile strength properties as well as optical properties. The resulting Root Mean Square Error of Prediction (RMSEP) for all parameters is comparable to what can be obtained by pooling the standard deviations of reference measurements from the different trial points. The results obtained are compared to FiberMaster data modeled in the same fashion, yielding lower prediction errors than the CWT-FLE data. However, this can be partly due to the five-year storage of pulp samples between pulp sampling/acoustic measurement and FiberMaster analyses/sheet testing. The acoustic method is fast and produces results without dead time and could constitute a new tool for improving process control and optimizing the fiber characteristics in a specific process and for a specific purpose. The technique could be implemented in a PC-environment at a fairly low cost.
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| 15. |
- Bouveresse, D. Jouan-Rimbaud, et al.
(författare)
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Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
- 2011
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 106:2, s. 173-182
-
Tidskriftsartikel (refereegranskat)abstract
- A modification of the ANOVA-PCA method, proposed by Harrington et al. to identify significant factors and interactions in an experimental design, is presented in this article. The modified method uses the idea of multiple table analysis, and looks for the common dimensions underlying the different data tables, or data blocks, generated by the "ANOVA-step" of the ANOVA-PCA method, in order to identify the significant factors. In this paper, the "Common Component and Specific Weights Analysis" method is used to analyse the calculated multi-block data set. This new method, called AComDim, was compared to the standard ANOVA-PCA method, by analysing four real data sets. Parameters computed during the AComDim procedure enable the computation of F-values to check whether the variability of each original data block is significantly greater than that of the noise.
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| 16. |
- Brink, Mattias, et al.
(författare)
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On-line predictions of the aspen fibre and birch bark content in unbleached hardwood pulp, using NIR spectroscopy and multivariate data analysis
- 2010
-
Ingår i: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - : Elsevier Science B.V., Amsterdam.. - 0169-7439. ; 103:1, s. 53-58
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Tidskriftsartikel (refereegranskat)abstract
- An on-line fibre-based near-infrared (NIR) spectrometric analyser was adapted for on-site process analysis at an integrated paperboard mill. The analyser uses multivariate techniques for the quantitative predication of the aspen fibre (aspen) and the birch bark contents of sheets of unbleached hardwood pulp. The NIR analyser is a prototype constructed from standard NIR components. The spectroscopic data was processed by using principal component analysis (PCA) and partial least square (PLS) regression. Three sample sets were collected from three experimental designs, each composed of known pulp contents of birch, aspen and birch bark. Sets I and 2 were used for model calibration and set 3 was used to validate the models. The PLS model that produced the best predictions gave an error of prediction (RMSEP) of 13% for aspen and less than 2% for birch bark. Eight components resulted in an (RX)-X-2 of 99.3%, (RY)-Y-2 of 99.6%. and Q(2) of 95.3%. For additional validation of aspen, three unbleached hardwood samples from the mills production were calculated to lie between -7% and +6%, regarding to the PIS model. When vessel cells were counted under a light microscope a value for the aspen content of 4.7% was obtained. The predictive models evaluated were suitable for quality assessments rather than quantitative determination.
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| 17. |
- Brydegaard, Mikkel, et al.
(författare)
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Complete parameterization of temporally and spectrally resolved laser induced fluorescence data with applications in bio-photonics
- 2015
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 142, s. 95-106
-
Tidskriftsartikel (refereegranskat)abstract
- We present a set of spectrally and temporally resolved clinical fluorescence data-with two separate excitation wavelengths-that was recorded in vivo. We demonstrate that data in the spectral and temporal domains are in certain ways coupled and provide a method for integrated and effective parameterization of spectrally and temporally resolved fluorescence (i.e., time-resolved emission spectra). This parameterization is based on linear algebra, matrix formulation and system identification. We demonstrate how to empirically extract single exponentially decaying components and provide rectified emission spectra without prior knowledge. We investigate the potential for improved cancer diagnostics according to the reduced parameters along the various domains. In this case, in terms of cancer diagnostics, we were unable to identify any benefits of simultaneously measuring both the temporal and spectral properties of the observed fluorescence. However, we note that this may be explained by an important experimental bias present in many studies of optical cancer diagnostics, namely, that, in general, suspected lesions always differ visually from the neighboring healthy tissue. (C) 2015 Elsevier B.V. All rights reserved.
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| 18. |
- Carlson, Johan E., et al.
(författare)
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Estimation of dielectric properties of crude oils based on IR spectroscopy
- 2014
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 139, s. 1-5
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Tidskriftsartikel (refereegranskat)abstract
- Dielectric properties of crude oils play an important role in characterization and quality control. Measuring permittivity accurately over a wide range of frequencies is, however, a time-consuming task and existing measurement methods are not easily adapted for real-time diagnostics. IR spectroscopy, on the other hand, provides rapid measurements of fundamental molecular properties.In this paper we show that by using multivariate calibration tools such as PLS regression, it is possible to extract dielectric properties of crude oils directly from IR spectra, in addition to conventional interpretation of the spectra, hence reducing the need for direct electrical measurements. Results on 16 different oil samples show that the dielectric parameters obtained with the proposed method agree well with those obtained using direct permittivity measurements. The PLS regression method has also been extended with Monte-Carlo simulation capabilities to account for uncertainties in the data
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| 19. |
- Carlson, Johan E., et al.
(författare)
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Extracting homologous series from mass spectrometry data by projection on predefined vectors
- 2012
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 114, s. 36-43
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical methods, such as Principal Component Analysis (PCA), have been used extensively over the past decades as tools for extracting significant information from complex data sets. As such they are very powerful and in combination with an understanding of underlying chemical principles, they have enabled researchers to develop useful models. A drawback with the methods is that they do not have the ability to incorporate any physical / chemical model of the system being studied during the statistical analysis. In this paper we present a method that can be used as a complement to traditional chemometric tools in finding patterns in mass spectrometry data. The method uses a pre-defined set of equally spaced sequences that are assumed to be present in the data. Allowing for some uncertainty in the peak locations due to the uncertainties for the measurement instrumentation, the measured spectra are then projected onto this set. It is shown that the resulting scores can be used to identify homologous series in measured mass spectra that differ significantly between different measured samples. As opposed to PCA, the loading vectors, in this case the pre-defined homologous series, are readily interpretable.
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| 20. |
- Chauchard, Fablen, et al.
(författare)
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Localization of embedded inclusions using detection of fluorescence: Feasibility study based on simulation data, LS-SVM modeling and EPO pre-processing
- 2008
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 91:1, s. 34-42
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Tidskriftsartikel (refereegranskat)abstract
- Fluorescence spectroscopy is a useful technique for tissue diagnostics and is also a promising tool in the characterization of embedded structures in tissue. The emitted fluorescence from an embedded inclusion, marked with a fluorescent compound, is affected by several factors as the light propagates through the medium to the tissue boundary, where the fluorescence light is detected. Tissue absorption, scattering and autofluorescence, as well as the size and depth of the inclusion, affect the detected fluorescence light. The aim of this study is to investigate if the size and location of a fluorescent inclusion could be determined using models based a combination of External Parameter Orthogonalisation (EPO) and Least Squares Support Vector Machine (LS-SVM). This can be very useful for data pre-processing before a full fluorescence tomography reconstruction. The data set consisted of simulated multispectral fluorescence, where depth and radius of a spherical fluorescent inclusion were varied as well as the fluorescence contrast and optical properties of the surrounding tissue. The results showed that the non-linear models based on LS-SVM can simultaneously predict both radius and depth. It was observed that EPO acts as a useful pre-processing tool on spectra for this nonlinear model and that it was necessary to perform EPO to be able to predict the depth with the LS-SVM model. (C) 2007 Elsevier B.V. All rights reserved.
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| 21. |
- Danielsson, Rolf, et al.
(författare)
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Exploring liquid chromatography-mass spectrometry fingerprints of urine samples from patients with prostate or urinary bladder cancer
- 2011
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 108:1, s. 33-48
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Tidskriftsartikel (refereegranskat)abstract
- Data processing and analysis have become true rate and success limiting factors for molecular research where a large number of samples of high complexity are included in the data set. In general rather complicated methodologies are needed for the combination and comparison of information as obtained from selected analytical platforms. Although commercial as well as freely accessible software for high-throughput data processing are available for most platforms, tailored in-house solutions for data management and analysis can provide the versatility and transparency eligible for e.g. method development and pilot studies. This paper describes a procedure for exploring metabolic fingerprints in urine samples from prostate and bladder cancer patients with a set of in-house developed Matlab tools. In spite of the immense amount of data produced by the LC-MS platform, in this study more than 1010 data points, it is shown that the data processing tasks can be handled with reasonable computer resources. The preprocessing steps include baseline subtraction and noise reduction, followed by an initial time alignment. In the data analysis the fingerprints are treated as 2-D images, i.e. pixel by pixel, in contrast to the more common list-based approach after peak or feature detection. Although the latter approach greatly reduces the data complexity, it also involves a critical step that may obscure essential information due to undetected or misaligned peaks. The effects of remaining time shifts after the initial alignment are reduced by a binning and [‘]blurring’ procedure prior to the comparative multivariate and univariate data analyses. Other factors than cancer assignment were taken into account by ANOVA applied to the PCA scores as well as to the individual variables (pixels). It was found that the analytical day-to-day variations in our study had a large confounding effect on the cancer related differences, which emphasizes the role of proper normalization and/or experimental design. While PCA could not establish significant cancer related patterns, the pixel-wise univariate analysis could provide a list of about a hundred [‘]hotspots’ indicating possible biomarkers. This was also the limited goal for this study, with focus on the exploration of a really huge and complex data set. True biomarker identification, however, needs thorough validation and verification in separate patient sets.
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| 22. |
- Danielsson, Rolf, et al.
(författare)
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Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 33-39
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Tidskriftsartikel (refereegranskat)abstract
- One- or two-dimensional data obtained with LC/GC/CE and single or multiple channel detection (MS, UV/VIS) are often used as 'fingerprints' in order to characterize complex samples. The relation between samples is then explored by multivariate data analysis (PCA, hierarchical clustering), but inevitable more or less random variation in separation conditions obstructs the analysis. Several methods for peak alignment have been developed, with more or less increase of time and efforts for computations. In this work another approach is presented, based on a correlation measure less sensitive for variations in retention/migration time. The merits of the method as a fast initial data exploration tool are demonstrated for a case study of urine profiling with CE/MS.
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| 23. |
- dos Santos, Victor Hugo J. M., et al.
(författare)
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Discriminant analysis of biodiesel fuel blends based on combined data from Fourier Transform Infrared Spectroscopy and stable carbon isotope analysis
- 2017
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 161, s. 70-78
-
Tidskriftsartikel (refereegranskat)abstract
- A multivariate approach was used for classification of fuel blends using the combined information from Fourier Transform Infrared Spectroscopy (FTIR) and stable carbon isotopes analysis by Isotope Ratio Mass Spectrometry (IRMS). Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to the classification of biodiesel/diesel fuel blends containing 0-100% (v/v) of biodiesel. The LDA and PLS-DA methods were able to discriminate samples ranging from 10% to 100% biodiesel (v/v) using the combined information from FTIR and IRMS. Since the global trend is toward a gradual increase in the percentage of biodiesel in fuel blends, the technique presented in this paper could be an important development in improving the traceability and identification of different raw materials used in biodiesel production.
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| 24. |
- Eliasson, Charlotte, 1973, et al.
(författare)
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Multivariate methodology for surface enhanced Raman chemical imaging of lymphocytes
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 81:1, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- Surface enhanced Raman spectroscopy (SERS) was used to study the uptake of rhodamine 6G in human lymphocytes. In total four Raman images of lymphocytes were used. The aim was to find a multivariate methodology capable of separating spectra with chemical information from those that mainly contained the surface enhanced background, in order to create chemical images. The standard PCA procedure was compared with PCA of standard normal variate (SNV) corrected spectra, spectra baseline corrected in the wavelet domain, and variable trimming before PCA, to isolate unique spectra. It was not straightforward to perform a standard PCA for overview, since the small background variation in many variables dominated over the Raman band variation that only occur in few variables. It was shown that wavelet filtering could remove background variations and that variable trimming followed by PCA modelling left the unique Raman spectra as outliers, which facilitated interpretation of the Raman score images.
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| 25. |
- Eriksson, Lennart, et al.
(författare)
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Time-resolved QSAR: an approach to PLS modelling of three-way biological data
- 2004
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 73-84
-
Tidskriftsartikel (refereegranskat)abstract
- This paper outlines a novel approach to the analysis of three-way Y-data in quantitative structure–activity relationship (QSAR) modelling. The new method represents a modification of an existing approach for multivariate modelling of batch process data. It is based on unfolding the three-way Y-matrix into a two-way matrix according to a sequential order of an external variable. In QSAR, time, pH, or temperature at which the biological data were gathered, are conceivably such external variables. Thus, unfolding can be done differently depending on the objective of the investigation, thereby shifting the focus of the QSAR analysis. The ensuing multivariate data analysis uses two levels of modelling. (1) On the lower (observation) level a projections to latent structures (PLS) model is developed between the unfolded biological data and the external variable. This model will identify compounds with biological data being sensitive to changes in the external variable (like time, pH, or temperature). (2) The scores of the lower level model are then re-arranged to enable the upper (QSAR) level model. In this model, a battery of structure descriptors (X) is related to the Y-matrix of scores of the lower level model. As an example, a series of 35 compounds and their anti-microbial activity towards the bacterial strain Escherichia coli CCM2260 is used. This biological activity has been determined at different times (2 to 10 h) and pH-values (pH 5.6 to 8.0).
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| 26. |
- Forshed, Jenny, et al.
(författare)
-
Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
- 2007
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V. - 0169-7439 .- 1873-3239. ; 85:2, s. 179-185
-
Tidskriftsartikel (refereegranskat)abstract
- A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1
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| 27. |
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| 28. |
- Forshed, Jenny, et al.
(författare)
-
Evaluation of different techniques for fusion of LC/MS and 1HNMR data
- 2007
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 85:1, s. 102-109
-
Tidskriftsartikel (refereegranskat)abstract
- In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LUMS and H-1-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern). In this work, both LC/MS and H-1-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.(2)
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| 29. |
- Gabrielsson, Jon, et al.
(författare)
-
OPLS methodology for analysis of pre-processing effects on spectroscopic data
- 2006
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 84:1-2, s. 153-8
-
Tidskriftsartikel (refereegranskat)abstract
- Pre-processing of spectroscopic data is commonly applied to remove unwanted systematic variation. Possible loss of information and ambiguity regarding discarded variation are issues that complicate pre-treatment of data. In this paper, OPLS methodology is applied to evaluate different techniques for pre-processing of spectroscopic data gathered from a batch process. The objective is to present a rational scheme for analysis of pre-processing in order to understand the influence and effect of pre-treatment.O2PLS uses linear regression to divide the systematic variation in X and Y into three parts; one part with joint X–Y covariation, i.e. related to both X and Y, one part of X with Y-orthogonal variation and one part of Y with X-orthogonal variation.All of the investigated pre-treatment methods removed an additive baseline as expected. In the analysis of raw and differentiated data variation associated with the baseline was found in the Y-orthogonal part of X. Orthogonal information was also found in Y, which suggests that this pre-processing procedure not only removed variation. This would have been more difficult to detect without the O2PLS model since both raw and differentiated data must be analysed simultaneously.Development of a knowledge based strategy with OPLS methodology is an important step towards eliminating trial and error approaches to pre-processing.
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| 30. |
- Gajjar, Shriram, et al.
(författare)
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Selection of Non-zero Loadings in Sparse Principal Component Analysis
- 2017
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 162, s. 160-171
-
Tidskriftsartikel (refereegranskat)abstract
- Principal component analysis (PCA) is a widely accepted procedure for summarizing data through dimensional reduction. In PCA, the selection of the appropriate number of components and the interpretation of those components have been the key challenging features. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing principal components with sparse loadings via the variance-sparsity trade-off. Although several techniques for deriving sparse loadings have been offered, no detailed guidelines for choosing the penalty parameters to obtain a desired level of sparsity are provided. In this paper, we propose the use of a genetic algorithm (GA) to select the number of non-zero loadings (NNZL) in each principal component while using SPCA. The proposed approach considerably improves the interpretability of principal components and addresses the difficulty in the selection of NNZL in SPCA. Furthermore, we compare the performance of PCA and SPCA in uncovering the underlying latent structure of the data. The key features of the methodology are assessed through a synthetic example, pitprops data and a comparative study of the benchmark Tennessee Eastman process.
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| 31. |
- Galindo-Prieto, Beatriz, et al.
(författare)
-
A new approach for variable influence on projection (VIP) in O2PLS models
- 2017
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 160, s. 110-124
-
Tidskriftsartikel (refereegranskat)abstract
- A novel variable influence on projection approach for O2PLS® models, named VIPO2PLS, is presented in this paper. VIPO2PLS is a model-based method for judging the importance of variables. Its cornerstone is the 2-way formalism of the O2PLS models; i.e. the use of both predictive and orthogonal normalized loadings of the two modelled data matrices, and also a new weighting system based on the sum of squares of both data blocks (X, Y). The VIPO2PLS algorithm has been tested in one synthetic data set and two real cases, and the outcomes have been compared to the PLS-VIP, VIPOPLS, and i-PLS methods. The purpose is to achieve a sharper and enhanced model interpretation of O2PLS models by using the new VIPO2PLS method for assessing the importance of both X- and Y- variables.
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| 32. |
- Galindo-Prieto, Beatriz, et al.
(författare)
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Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis
- 2015
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 146, s. 297-304
-
Tidskriftsartikel (refereegranskat)abstract
- Abstract Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.
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| 33. |
- Gottfries, Johan, et al.
(författare)
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Proteomics for drug target discovery
- 2004
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V.. - 0169-7439. ; 73:1, s. 47-53
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Tidskriftsartikel (refereegranskat)abstract
- Proteomics, genomics and metabonomics have, during the last decade, provided researchers with huge amounts of data. The choice of transformation of such data into useful information is dependent on the study aims and objectives. In the present study, projection methods (i.e., Principal Components Analysis [PCA] and Partial List Squares-Discriminant Analysis [PLS-DA]) were used to overview results from two-dimensional (2D) protein gel separations. The aim was to unravel possibilities for target discovery options via an in-depth understanding of quantified alterations in tissue or body fluid sample protein levels related to diseases. Two examples will be included comprising (1) data measured in cerebrospinal fluid (CSF) samples from diagnosed dementia patients and healthy volunteers, and (2) data from liver samples of drug-treated animals (i.e., Rosigltazone and Wy14643). The examples reveal clear clustering, using the protein levels as input, coinciding with the clinical diagnoses in example 1 and by treatment group in example 2.
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| 34. |
- Gut, Luiza, et al.
(författare)
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Assessment of a two-step partial nitritation/Anammox system with implementation of multivariate data analysis
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 86:1, s. 26-34
-
Tidskriftsartikel (refereegranskat)abstract
- Complexity of biological wastewater treatment, in which a variety of physical and (bio)chemical processes concurrently take place, demands appropriate approach in the data analysis. In this study, the data set collected during a 20 month operation of a two-step partial nitritation/Anammox system for nitrogen removal from wastewater (semi-industrial pilot plant scale) are subjected to Principal Component Analysis (PCA) and Partial Least Squares projections to latent structures (PLS) analysis. Interpretation of PCA- and PLS models enable to discern relationships between different factors for the start-up period and stable operation of the pilot plant. Variables like conductivity, pH value, dissolved oxygen concentration and nitrite-to-ammonium ratio (NAR) appear to be the key factors in the process control and monitoring. Extension of the Anammox reactor capacity demands accurate monitoring, principally by scrutinizing nitrite nitrogen concentration in the reactor. These findings suggest that the two methods complement each other in assessing the partial nitritation/Anammox system. This study demonstrated that multivariate data analysis provides the powerful implement in the field of wastewater treatment, especially in investigating novel systems.
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| 35. |
- Hedenström, Mattias, et al.
(författare)
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Visualization and interpretation of OPLS models based on 2D NMR Data
- 2008
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 92:2, s. 110-117
-
Tidskriftsartikel (refereegranskat)abstract
- Multivariate analysis on spectroscopic 1H NMR data is well established in metabolomics and other fields where the composition of complex samples is studied. However, biomarker identification can be hampered by overlapping resonances. 2D NMR data provides a more detailed “fingerprint” of the chemical structure and composition of the sample with greatly improved spectral resolution compared to 1H NMR data. In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra. This method is based on the analysis of raw spectral data without any need for peak picking or integration prior to analysis. Spectral features such as line widths and peak positions are thus retained. Hence, the loadings can be visualized and interpreted on a molecular level as pseudo 2D spectra in order to identify potential biomarkers. To demonstrate this strategy we have analyzed HSQC spectra acquired from populus phloem plant extracts originating from a set of designed experiments with OPLS regression.
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| 36. |
- Krupinska, Karolina, et al.
(författare)
-
Detection of low levels of Escherichia coli by electrochemical impedance spectroscopy and singular value decomposition
- 2017
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 163, s. 49-54
-
Tidskriftsartikel (refereegranskat)abstract
- The first steps are reported in the development of a new ultrasensitive electrochemical biosensor for detection of Escherichia coli (E. coli) in water. Two gold electrodes in a sandwich flow-cell were modified with E. coli polyclonal antibody and exposed to three different concentrations of E. coli. Electrochemical Impedance Spectroscopy was used in combination with Singular Value Decompostion of the complex numbers to monitor the interactions at the electrode surfaces. A linear regression line in the concentration range 10-1000 CFU⁎ml-1 was obtained without use of redox probes or metal nanoparticles for signal amplification.
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| 37. |
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| 38. |
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| 39. |
- Lindström, Anton, et al.
(författare)
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Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters
- 2013
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier Science. - 0169-7439 .- 1873-3239. ; 125, s. 33-39
-
Tidskriftsartikel (refereegranskat)abstract
- For the purpose of improved planning and treatment by radiation of tumours, we present work exploring the effect of controllable ultra-short echo-time (UTE) sequence settings on the bone contrast in magnetic resonance (MR) imaging, using design of experiments (DoE). Images were collected using UTE sequences from MR imaging and from standard computed tomography (CT). CT was used for determining the spatial position of the bony structures in an animal sample and co-registered with the MR images. The effect of the UTE sequence parameter flip angle (Flip), repetition time (T-R), echo time (T-E), image matrix size (Vox) and number of radial sampling spokes (Samp) were studied. The parameters were also investigated in a healthy voluntary and it was determined that the optimal UTE settings for high bone contrast in a clinically relevant set up were: Flip similar to 9 degrees and T-E = 0.07 ms, while T-R was kept at 8 ms, Vox at 192 and Samp at 30,000. The use of response surface maps, describing the modelled relation between bone contrast and UTE settings, founded in the DoE, may provide information and be a tool to more appropriately select suitable UTE sequence settings.
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| 40. |
- Lindström, Anton, et al.
(författare)
-
Quantitative protein descriptors for secondary structure characterization and protein classification
- 2009
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 95:1, s. 74-85
-
Tidskriftsartikel (refereegranskat)abstract
- In this study protein chains were characterized based on alignment-independent protein descriptors using three types of structural and sequence data; (i) C-α atom Euclidean distances, (ii) protein backbone ψ and φ angles and (iii) amino acid physicochemical properties (zz-scales). The descriptors were analyzed using principal component analysis (PCA) and further elucidated using the multivariate methods partial least-squares projections to latent structures discriminant-analysis (PLS-DA) and hierarchical-PLS-DA. The descriptors were applied to three protein chain datasets: (i) 82 chains classified, according to the structural classification of proteins (SCOP) scheme, as either all-α or all-β; (ii) 96 chains classified as either α + β or α/β and (iii) 6590 chains of all aforementioned classes selected from the PDB-select database. Results showed that the descriptors related to the secondary structure of the chains. The C-α Euclidean distances, and as expected, the protein backbone angles were found to be most important for the characterization and classification of chains. Assignment of SCOP classes using PLS-DA based on all descriptor types was satisfactory for all-α and all-β chains with more than 93% correct classifications of a large external test set, while the protein chains of types α/β and α + β was harder to discriminate between, resulting in 74% and 54% correct classifications, respectively.
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| 41. |
- Lundstedt-Enkel, Katrin, et al.
(författare)
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Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring
- 2006
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 201-207
-
Tidskriftsartikel (refereegranskat)abstract
- The aim with the present paper is to illustrate the use of multivariate strategies (i.e. integration of different multivariate methods) with five examples, four from the pharmaceutical industry and one from environmental research. In the first part, two examples wherein hierarchical models are applied to quality control (QC) and process control are discussed. In the second part a more complex problem and a strategy for material discovery/development are presented wherein a combination of multivariate calibration, multivariate analysis and multivariate design is needed. In the third part, a process analytical/optimization problem is illustrated with a two-step process, demanding that different multivariate tools are combined in a sequential way so that a useful model can be established and the process can be understood. In the final part the usefulness of principal component analysis followed by soft independent modelling of class analogy is illustrated with an example from environmental process monitoring. The five examples from quite different areas show that the chemometric tools are even more powerful if used integrated. However, different strategies and combinations of the tools have to be applied, depending on the problem and the aim.
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| 42. |
- Lundstedt, Torbjörn, et al.
(författare)
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Dynamic modelling of time series data in nutritional metabonomics : A powerful complement to randomized clinical trials in functional food studies
- 2010
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B V. - 0169-7439 .- 1873-3239. ; 104:1, s. 112-120
-
Tidskriftsartikel (refereegranskat)abstract
- Functional foods are foods or dietary ingredients that provide a health benefit beyond basic nutrition. A new legislation, known as the Nutrition and Health Claims Regulation, defines the legal framework for such claims within the European Union. Any claim about the nutritional or physiological effects of a product must be scientifically demonstrated. In this study, we have focused on the exploration of metabonomics as a complementary profiling technology to establish monitoring/data analysis procedures of randomized nutritional trials. More specifically, a combined intake of soybean and grapefruit in a human intervention study was analyzed with respect to both pharmacological and physiological effects. Resulting multivariate models showed a diet-induced decrease of lactate, cholesterols and triglycerides. The most drastically elevated metabolite, myo-inositol, was found to accompany a marked reduction of triglyceride levels. Suggestively, this is due to the biotransformation of myo-inositol to phosphatidylinositol, which results in a decrease of available precursors to form triglycerides. Strong inter-subject variation was present that required special attention. Dynamic modelling of collected time series data that provided the opportunity to identify slow, medium or fast responders as well as groups of subjects showing different response profiles, was also highlighted in the study. The applied strategy of time series data has proven to be a powerful complement to randomized nutritional studies adopting a clinical trial design.
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| 43. |
- Löfstedt, Tommy, et al.
(författare)
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OnPLS path modelling
- 2012
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 118, s. 139-149
-
Tidskriftsartikel (refereegranskat)abstract
- OnPLS was recently presented as a general extension of O2PLS to the multiblock case. OnPLS is equivalent to O2PLS in the case of two matrices, but generalises symmetrically to cases with more than two matrices, i.e. without giving preference to any one of the matrices.This article presents a straight-forward extension to this method and thereby also introduces the OPLS framework to the field of PLS path modelling. Path modelling links a number of data blocks to each other, thereby establishing a set of paths along which information is considered to flow between blocks, representing for instance a known time sequence, an assumed causality order, or some other chosen organising principle. Compared to existing methods for path analysis, OnPLS path modelling extracts a minimum number of predictive components that are maximally covarying with maximised correlation. This is a significant contribution to path modelling, because other methods may yield score vectors with variation that obstructs the interpretation. The method achieves this by extracting a set of "orthogonal" components that capture local phenomena orthogonal to the variation shared with all the connected blocks.Two applications will be used to illustrate the method. The first is based on a simulated dataset that show how the interpretation is improved by removing orthogonal variation and the second on a real data process for monitoring of protein structure changes during cheese ripening by analysing infrared data.
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| 44. |
- Martyna, Agnieszka, et al.
(författare)
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Likelihood ratio-based probabilistic classifier
- 2023
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : ELSEVIER. - 0169-7439 .- 1873-3239. ; 240
-
Tidskriftsartikel (refereegranskat)abstract
- Modern classification methods are likely to misclassify samples with rare but class-specific data that are more similar (less distant) to the data of another than the original class. This is because they tend to focus on the majority of data, leaving the information provided by the rare data practically ignored. Nevertheless, it is an invaluable source of information that should support classification of samples with such data, despite their low frequency. Current solutions considering the rarity information involve likelihood ratio models (LR). We intend to modify the existing LR models to establish the class membership for the analysed samples by comparing them with the samples of known class label. If two compared samples show similarities of rare but class-specific features it makes the analysed sample much more likely to be a member of this class than any other class, even when its features are less distant to the features of most samples from other classes. The fundamental advantage of the developed methodology is inclusion of information about rare, class-specific features, which is neglected by ordinary classifiers. Converting LR values into probabilities with which a sample belongs to the classes under consideration, generates a powerful tool within the concept of probabilistic classification.
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| 45. |
- Murphy, Kathleen, 1972, et al.
(författare)
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Characterizing odorous emissions using new software for identifying peaks in chemometric models of gas chromatography-mass spectrometry datasets
- 2012
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 118, s. 41-50
-
Tidskriftsartikel (refereegranskat)abstract
- The task of identifying individual compounds within complex gas chromatography - mass spectrometry (GC-MS) chromatograms is made more difficult by interferences between peaks with similar mass spectra eluting at the same time, typically against a background of chemical and electronic noise. Although chemometric techniques like parallel factor analysis and multivariate curve resolution can help to purify spectra and improve correlations with reference compounds, file incompatibilities between GC-MS acquisition software and modeling software prevent the modeled spectra from being easily compared to spectra in reference libraries. In this paper we present an enhancement to OpenChrom, an open-source software for chromatography and mass spectrometry, which implements the automated cross-matching of modeled spectra to NIST08 and NIST11 mass spectral databases. The benefits of this approach are demonstrated using a complex environmental dataset consisting of non-methane volatile organic compound emissions sampled on an Australian poultry farm. © 2012 Elsevier B.V.
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| 46. |
- Nyström, Josefina, et al.
(författare)
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Objective measurement of Radiation Induced Erythema by nonparametric hypothesis testing on indices from multivariate data
- 2008
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 90:1, s. 43-8
-
Tidskriftsartikel (refereegranskat)abstract
- Three instrumental measurement techniques: Laser-Doppler Imaging (LDI), Digital Colour Photography (DCP) and Near InfraRed (NIR) spectroscopy were tested for their potential to objectively measure radiation-based erythema in breast cancer patients. The irradiation dose intervals were 0, 8-16, 18-26, 28-34, 36-44 and 46-50 Gy. In addition, two types of skin lotion for reducing erythema were tested on the patients and these were compared to using no lotion. The measured results had very skew distributions for all three techniques making nonparametric testing necessary. The Wilcoxon Signed Rank Sum Test (WSRST) was used for this purpose. LDI was performed to produce univariate average perfusion values leading to a perfusion increment ratio. These ratios showed a good sensitivity to erythema, with a median detection limit of 18 Gy. DCP was used to extract average red-green-blue (RGB) values that were used in multivariate models. Results for a combination of principal component score values showed a marked increase in median erythema from 8 Gy on. The Multivariate data from NIR spectroscopy were data-reduced to principal component scores and combinations of these were tested. The score combinations were used to show median detection limits down to 8 Gy. The difference between the lotions and using no lotion gave no significant result for the WSRST paired comparison for any used measurement technique.
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| 47. |
- Olsson, Ing-Marie, et al.
(författare)
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D-optimal onion designs in statistical molecular design
- 2004
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 37-46
-
Tidskriftsartikel (refereegranskat)abstract
- Statistical molecular design (SMD) is a technique for selecting a representative (diverse) set of substances in combinatorial chemistry and QSAR, as well as other areas depending on optimising chemical structure. Two approaches often used in SMD are space filling (SF) and D-optimal (DO) designs.Space-filling designs provide good coverage of the physicochemical space but are not explicitly based on a model. For small design sizes, they perform similar to D-optimal designs, which maximize the determinant of the variance–covariance matrix. This leads to selection of the most extreme points of the candidate set and gives a minimal set of selected compounds with maximal diversity. However, the inner regions of the experimental domain are not well sampled by DO or small SF designs.We have developed and evaluated an approach to remedy the shortcomings of SF and DO designs in SMD. This new approach divides the candidate set into a number of subsets (“shells” or “layers”), and a D-optimal selection is made from each layer. This makes it possible to select representative sets of molecular structures throughout any property space, e.g., the physicochemical space, with reasonable design sizes. The number of selected molecules is easily controlled by varying (a) the number of layers and (b) the model on which the design is based.We outline here this new approach, the D-optimal onion design (DOOD). It is tested on two molecular data sets with varying size and compared with SF designs and ordinary DO designs. The designs have been evaluated with parameters, such as condition number, determinant, Tanimoto coefficients and Euclidean distances, as well as external evaluation of the resulting projection to latent structures (PLS) model.
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| 48. |
- Olsson, Ing-Marie, et al.
(författare)
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Rational DOE-protocols for 96-well plates
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 83:1, s. 66-74
-
Tidskriftsartikel (refereegranskat)abstract
- The use of 96-well plates for chemical and biological applications has rapidly increased as new applicable domains have been discovered and new laboratory instruments developed. There are 96, 384, 1536, etc. plates customized for diverse applications such as biological assays, sample preparation, solid-phase extraction and crystallization. Multi-pipettes as well as automated pipette systems accelerate the preparation of plates resulting in even faster evaluation systems. A bottleneck in the use of multi-unit plates is method development and optimization. By applying rational experimental design, the optimization could be made more efficient and less time-consuming. Unfortunately, the workload related to manual preparation of multi-unit plates according to an experimental design is often considered overwhelming. The present study introduces a new approach for experimental design in 96-well plates that minimizes the manual workload without compromising the quality of the experimental design. This approach is scalable to larger rectangular formats such as 384- and 1536-well plates. The optimal combinations will be delineated and applied experimentally to a reporter-gene assay.
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| 49. |
- Rotari, Marta, et al.
(författare)
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An extension of PARAFAC to analyze multi-group three-way data
- 2024
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 246
-
Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of data for multiple variables and process stages are recorded by sensors installed on the production line for each batch. For these three-way arrays, the link between the final product and the observations creates a grouping structure in the observations. This grouping may hamper gaining insight into the process if only some of the groups dominate the controlled variability of the products. In this study, we develop an extension of the PARAFAC model that takes into account the grouping structure of three-way data sets. With this extension, it is possible to estimate a model that is representative of all the groups simultaneously by finding their common structure. The proposed model has been applied to three simulation data sets and a real manufacturing case study. The capability to find the common structure of the groups is compared to PARAFAC and the insights into the importance of variables delivered by the models are discussed.
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| 50. |
- Shoombuatong, Watshara, et al.
(författare)
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Extending proteochemometric modeling for unraveling the sorption behavior of compound-soil interaction
- 2016
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 151, s. 219-227
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Tidskriftsartikel (refereegranskat)abstract
- Contamination of ground water by industrial chemicals presents a major environmental and health problem. Soil sorption plays an important role in the transport and movement of such pollutant chemicals. In this study, proteochemometric (PCM) modeling was used to unravel the origins of interactions of 17 phthalic acid esters (PAEs) against 3 soil types by predicting the organic carbon content normalized sorption coefficient (logK(oc)) values as a function of fingerprint descriptors of 17 PAEs and physical and textural properties of 3 soils. The results showed that PCM models provided excellent predictivity (R-2 = 0.94, Q(2) = 0.89,Q(Ext)(2) = 0.85). In further validation of the model, our proposed PCM model was assessed by leave-one-compound-out (Q(LOCO)(2) = 0.86) and leave-one-soil-out (Q(LOCO)(2) = 0.86) cross-validations. The transparency of the PCM model allowed interpretation of the underlying importance of descriptors, which potentially contributes to a better understanding on the outcome of PAEs in the environment. A thorough analysis of descriptor importance revealed the contribution of secondary carbon atoms on the hydrophobicity and flexibility of PAEs as significant properties in influencing the soil sorption capacity.
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