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1.
  • Kinser, J. M., et al. (author)
  • Multidimensional pulse image processing of chemical structure data
  • 2000
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 51:1, s. 115-124
  • Journal article (peer-reviewed)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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2.
  • Verikas, Antanas, et al. (author)
  • Using artificial neural networks for process and system modelling
  • 2003
  • In: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 67:2, s. 187-191
  • Journal article (peer-reviewed)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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3.
  • Öberg, Tomas, 1956- (author)
  • Importance of the first design matrix in experimental simplex optimization
  • 1998
  • In: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elevier. - 0169-7439 .- 1873-3239. ; 44:1-2, s. 147-151
  • Journal article (peer-reviewed)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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4.
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5.
  • Abbas, Aamer, 1973, et al. (author)
  • Characterization and mapping of carotenoids in the algae Dunaliella and Phaeodactylum using Raman and target orthogonal partial least squares
  • 2011
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 107:1, s. 174-177
  • Journal article (peer-reviewed)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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6.
  • Aftab, Obaid, 1984-, et al. (author)
  • 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
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 141, s. 24-32
  • Journal article (peer-reviewed)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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7.
  • Björk, Anders, et al. (author)
  • Modeling of pulp quality parameters from distribution curves extracted from process acoustic measurements on a thermo mechanical pulp (TMP) process
  • 2007
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 85:1, s. 63-69
  • Journal article (peer-reviewed)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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8.
  • Bouveresse, D. Jouan-Rimbaud, et al. (author)
  • Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
  • 2011
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 106:2, s. 173-182
  • Journal article (peer-reviewed)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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9.
  • Carlson, Johan E., et al. (author)
  • Estimation of dielectric properties of crude oils based on IR spectroscopy
  • 2014
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 139, s. 1-5
  • Journal article (peer-reviewed)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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10.
  • Carlson, Johan E., et al. (author)
  • Extracting homologous series from mass spectrometry data by projection on predefined vectors
  • 2012
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 114, s. 36-43
  • Journal article (peer-reviewed)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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11.
  • Danielsson, Rolf, et al. (author)
  • Exploring liquid chromatography-mass spectrometry fingerprints of urine samples from patients with prostate or urinary bladder cancer
  • 2011
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 108:1, s. 33-48
  • Journal article (peer-reviewed)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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12.
  • Danielsson, Rolf, et al. (author)
  • Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment
  • 2006
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 33-39
  • Journal article (peer-reviewed)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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13.
  • dos Santos, Victor Hugo J. M., et al. (author)
  • Discriminant analysis of biodiesel fuel blends based on combined data from Fourier Transform Infrared Spectroscopy and stable carbon isotope analysis
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 161, s. 70-78
  • Journal article (peer-reviewed)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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14.
  • Eliasson, Charlotte, 1973, et al. (author)
  • Multivariate methodology for surface enhanced Raman chemical imaging of lymphocytes
  • 2006
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 81:1, s. 13-20
  • Journal article (peer-reviewed)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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15.
  • Forshed, Jenny, et al. (author)
  • Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
  • 2007
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V. - 0169-7439 .- 1873-3239. ; 85:2, s. 179-185
  • Journal article (peer-reviewed)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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16.
  • Forshed, Jenny, et al. (author)
  • Evaluation of different techniques for fusion of LC/MS and 1HNMR data
  • 2007
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 85:1, s. 102-109
  • Journal article (peer-reviewed)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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17.
  • Gajjar, Shriram, et al. (author)
  • Selection of Non-zero Loadings in Sparse Principal Component Analysis
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 162, s. 160-171
  • Journal article (peer-reviewed)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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18.
  • Galindo-Prieto, Beatriz, et al. (author)
  • A new approach for variable influence on projection (VIP) in O2PLS models
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 160, s. 110-124
  • Journal article (peer-reviewed)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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19.
  • Galindo-Prieto, Beatriz, et al. (author)
  • Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis
  • 2015
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 146, s. 297-304
  • Journal article (peer-reviewed)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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20.
  • Gut, Luiza, et al. (author)
  • Assessment of a two-step partial nitritation/Anammox system with implementation of multivariate data analysis
  • 2007
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 86:1, s. 26-34
  • Journal article (peer-reviewed)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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21.
  • Hedenström, Mattias, et al. (author)
  • Visualization and interpretation of OPLS models based on 2D NMR Data
  • 2008
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 92:2, s. 110-117
  • Journal article (peer-reviewed)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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22.
  • Krupinska, Karolina, et al. (author)
  • Detection of low levels of Escherichia coli by electrochemical impedance spectroscopy and singular value decomposition
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 163, s. 49-54
  • Journal article (peer-reviewed)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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23.
  • Lindström, Anton, et al. (author)
  • Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters
  • 2013
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier Science. - 0169-7439 .- 1873-3239. ; 125, s. 33-39
  • Journal article (peer-reviewed)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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24.
  • Lindström, Anton, et al. (author)
  • Quantitative protein descriptors for secondary structure characterization and protein classification
  • 2009
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 95:1, s. 74-85
  • Journal article (peer-reviewed)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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25.
  • Lundstedt-Enkel, Katrin, et al. (author)
  • Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring
  • 2006
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 201-207
  • Journal article (peer-reviewed)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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26.
  • Lundstedt, Torbjörn, et al. (author)
  • Dynamic modelling of time series data in nutritional metabonomics : A powerful complement to randomized clinical trials in functional food studies
  • 2010
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B V. - 0169-7439 .- 1873-3239. ; 104:1, s. 112-120
  • Journal article (peer-reviewed)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.
  •  
27.
  • Löfstedt, Tommy, et al. (author)
  • OnPLS path modelling
  • 2012
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 118, s. 139-149
  • Journal article (peer-reviewed)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.
  •  
28.
  • Martyna, Agnieszka, et al. (author)
  • Likelihood ratio-based probabilistic classifier
  • 2023
  • In: Chemometrics and Intelligent Laboratory Systems. - : ELSEVIER. - 0169-7439 .- 1873-3239. ; 240
  • Journal article (peer-reviewed)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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29.
  • Murphy, Kathleen, 1972, et al. (author)
  • Characterizing odorous emissions using new software for identifying peaks in chemometric models of gas chromatography-mass spectrometry datasets
  • 2012
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 118, s. 41-50
  • Journal article (peer-reviewed)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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30.
  • Nyström, Josefina, et al. (author)
  • Objective measurement of Radiation Induced Erythema by nonparametric hypothesis testing on indices from multivariate data
  • 2008
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 90:1, s. 43-8
  • Journal article (peer-reviewed)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.
  •  
31.
  • Olsson, Ing-Marie, et al. (author)
  • Rational DOE-protocols for 96-well plates
  • 2006
  • In: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 83:1, s. 66-74
  • Journal article (peer-reviewed)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.
  •  
32.
  • Rotari, Marta, et al. (author)
  • An extension of PARAFAC to analyze multi-group three-way data
  • 2024
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 246
  • Journal article (peer-reviewed)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.
  •  
33.
  • Shoombuatong, Watshara, et al. (author)
  • Extending proteochemometric modeling for unraveling the sorption behavior of compound-soil interaction
  • 2016
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 151, s. 219-227
  • Journal article (peer-reviewed)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.
  •  
34.
  • Skantze, Viktor, 1992, et al. (author)
  • Identification of metabotypes in complex biological data using tensor decomposition
  • 2023
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 233
  • Journal article (peer-reviewed)abstract
    • Differences in the physiological response to treatment, such as dietary intervention, has led to the development of precision approaches in nutrition and medicine to tailor treatment for improved benefits to the individual. One such approach is to identify metabotypes, i.e., groups of individuals with similar metabolic profiles and/or regulation. Metabotyping has previously been performed using e.g., principal component analysis (PCA) on matrix data. However, metabotyping methods suitable for more complex experimental designs such as repeated measures or cross-over studies are needed. We have developed a metabotyping method for tensor data, based on CANDECOMP/PARAFAC (CP) tensor decomposition. Metabotypes are inferred from CP scores using k-means clustering, and robustness is evaluated using bootstrapping of metabolites. As a proof-of-concept, we identified metabotypes from metabolomics data where 79 metabolites were analyzed in 8 time points postprandially in 17 overweight men that underwent a three-arm dietary crossover intervention. Two metabotypes were found, characterized by differences in amino acid metabolite concentration, that were differentially associated with baseline plasma creatinine (p = 0.007) and with the baseline metabolome (p = 0.004). These results suggest that CP decomposition provides a viable approach for metabotype identification directly from complex, high-dimensional data with improved biological interpretation compared to the more simplistic PCA approach. A simulation study together with results from measured data concluded that several preprocessing methods should be taken into consideration for CP-based metabotyping on complex tensor data.
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35.
  •  
36.
  •  
37.
  • Spooner, Max, et al. (author)
  • Monitoring batch processes with dynamic time warping and k-nearest neighbours
  • 2018
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 183, s. 102-112
  • Journal article (peer-reviewed)abstract
    • A novel data driven approach to batch process monitoring is presented, which combines the k-Nearest Neighbour rule with the dynamic time warping (DTW) distance. This online method (DTW-NN) calculates the DTW distance between an ongoing batch, and each batch in a reference database of batches produced under normal operating conditions (NOC). The sum of the k smallest DTW distances is monitored. If a fault occurs in the ongoing batch, then this distance increases and an alarm is generated. The monitoring statistic is easy to interpret, being a direct measure of similarity of the ongoing batch to its nearest NOC predecessors and the method makes no distributional assumptions regarding normal operating conditions. DTW-NN is applied to four extensive datasets from simulated batch production of penicillin, and tested on a wide variety of fault types, magnitudes and onset times. Performance of DTW-NN is contrasted with a benchmark multiway PCA approach, and DTW-NN is shown to perform particularly well when there is clustering of batches under NOC.
  •  
38.
  • Spooner, Max, et al. (author)
  • Selecting local constraint for alignment of batch process data with dynamic time warping
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 167, s. 161-170
  • Journal article (peer-reviewed)abstract
    • There are two key reasons for aligning batch process data. The first is to obtain same-length batches so that standard methods of analysis may be applied, whilst the second reason is to synchronise events that take place during each batch so that the same event is associated with the same observation number for every batch. Dynamic time warping has been shown to be an effective method for meeting these objectives. This is based on a dynamic programming algorithm that aligns a batch to a reference batch, by stretching and compressing its local time dimension. The resulting ”warping function” may be interpreted as a progress signature of the batch which may be appended to the aligned data for further analysis. For the warping function to be a realistic reflection of the progress of a batch, it is necessary to impose some constraints on the dynamic time warping algorithm, to avoid an alignment which is too aggressive and which contains pathological warping. Previous work has focused on addressing this issue using global constraints. In this work, we investigate the use of local constraints in dynamic time warping and define criteria for evaluating the degree of time distortion and variable synchronisation obtained. A local constraint scheme is extended to include constraints not previously considered, and a novel method for selecting the optimal local constraint with respect to the two criteria is proposed. For illustration, the method is applied to real data from an industrial bacteria fermentation process.
  •  
39.
  • Stehlik, M., et al. (author)
  • On robust testing for normality in chemometrics
  • 2014
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 130, s. 98-108
  • Journal article (peer-reviewed)abstract
    • The assumption that the data has been generated by a normal distribution underlies many statistical methods used in chemometrics. While such methods can be quite robust to small deviations from normality, for instance caused by a small number of outliers, common tests for normality are not and will often needlessly reject normality. It is therefore better to use tests from the little-known class of robust tests for normality. We illustrate the need for robust normality testing in chemometrics with several examples, review a class of robustified omnibus Jarque-Bera tests and propose a new class of robustified directed Lin-Mudholkar tests. The robustness and power of several tests for normality are compared in a large simulation study. The new tests are robust and have high power in comparison with both classic tests and other robust tests. A new graphical method for assessing normality is also introduced.
  •  
40.
  • Stenlund, Hans, et al. (author)
  • Monitoring kidney-transplant patients using metabolomics and dynamic modeling
  • 2009
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V.. - 0169-7439 .- 1873-3239. ; 98:1, s. 45-50
  • Journal article (peer-reviewed)abstract
    • A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.
  •  
41.
  • Svensson, Olof, et al. (author)
  • An evaluation of 2D-wavelet filters for estimation of differences in textures of pharmaceutical tablets
  • 2006
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 3-8
  • Journal article (peer-reviewed)abstract
    • In chemical imaging spectra are acquired over a surface with one spectrum for each pixel of the image. The obtained spectra usually carry a mixture of chemical and physical information. One may view the properties that vary over the image, the mean spectral magnitude from separate wavelength intervals, or better, PCA scores may be shown as images.In this way a multitude of images are compressed to a few images that in the PCA case are representative for the main variation in the sample images. These images may be viewed manually and deductions as to e.g. differences in homogeneity can be made. At an increased rate of samples, the observer will have difficulties coping with the repetitive work and different observers will most likely have slightly different interpretations. In order to automate the process of estimation of e.g. homogeneity and particle density, image filters can be used to calculate a small set of texture descriptors for each image. Calculations based on the 2D versions of the discrete wavelet transform (DWT) using Daubechies 14 and the dual tree complex wavelet transform (DT-CWT) using near-symmetric 13, 19 tap filters in combination with q-shift 14, 14 tap filters were evaluated for this purpose.The aim with this work is to evaluate texture descriptors based on a combination of 2D-wavelet filters and energy, i.e. l(1)-norm, calculations for each wavelet scale. These descriptors are then used as observations for overview in e.g. PCA. In this way the texture differences can be ranked by ordinary use of PCA or PLS.This method is tested on multivariate near infrared images of pharmaceutical tablets. Score images are selected to represent variations of the aggregate density and sizes in the compressed tablets. Images are shifted and rotated to compare shift and rotational independence of the texture descriptors.
  •  
42.
  • Vaiciukynas, Evaldas, et al. (author)
  • Exploiting statistical energy test for comparison of multiple groups in morphometric and chemometric data
  • 2015
  • In: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 146, s. 10-23
  • Journal article (peer-reviewed)abstract
    • Multivariate permutation-based energy test of equal distributions is considered here. Approach is attributable to the emerging field of ε-statistics and uses natural logarithm of Euclidean distance for within-sample and between-sample components. Result from permutations is enhanced by a tail approximation through generalized Pareto distribution to boost precision of obtained p-values. Generalization from two-sample case to multiple samples is achieved by combining p-values through meta-analysis. Several strategies of varied statistical power are possible, while a maximum of all pairwise p-values is chosen here. Proposed approach is tested on several morphometric and chemometric data sets. Each data set is additionally transformed by principal component analysis for the purpose of dimensionality reduction and visualization in 2D space. Variable selection, namely, sequential search and multi-cluster feature selection, is applied to reveal in what aspects the groups differ most.Morphometric data sets used: 1) survival data of house sparrows Passer domesticus; 2) orange and blue varieties of rock crabs Leptograpsus variegatus; 3) ontogenetic stages of trilobite species Trimerocephalus lelievrei; 4) marine phytoplankton species Prorocentrum minimum.Chemometric data sets used: 1) essential oils composition of medicinal plant Hyptis suaveolensspecimens; 2) chemical information of olive oil samples; 3) elemental composition of biomass ash; 4) exchangeable cations of earth metals in forest soil samples.Statistically significant differences between groups were successfully indicated, but the selection of variables had a profound effect on the result. Permutation-based energy test and it’s multi-sample generalization through meta-analysis proved useful as an unbalanced non-parametric MANOVA approach. Introduced solution is simple, yet flexible and powerful, and by no means is confined to morphometrics or chemometrics alone, but has a wide range of potential applications. Copyright © 2015 Elsevier B.V.
  •  
43.
  • Vanhatalo, Erik, et al. (author)
  • On the structure of dynamic principal component analysis used in statistical process monitoring
  • 2017
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 167, s. 1-11
  • Journal article (peer-reviewed)abstract
    • When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time-dependent data. In DPCA the input matrix is augmented by adding time-lagged values of the variables. In building a DPCA model the analyst needs to decide on (1) the number of lags to add, and (2) given a specific lag structure, how many principal components to retain. In this article we propose a new analyst driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using simulated vector autoregressive and moving average data, and tested on Tennessee Eastman process data.
  •  
44.
  • Wallbäcks, Lars (author)
  • Multivariate data analysis of multivariate populations
  • 2007
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 86:1, s. 10-16
  • Journal article (peer-reviewed)abstract
    • For data that can be arranged in populations, multivariate data analysis of digitalized distributions is suggested as an alternative method to detect variations. This approach includes a representation of the data that avoids information destroying pre-processing such as averaging. The method is exemplified and discussed for both single variable distributions and multivariable distributions. A theoretical discussion is presented on its use with data from fibre measuring systems, time series and image analysis data. The method is suggested as either a complement or alternative to other types of data analysis and opens new possibilities for variation detection.
  •  
45.
  • Öberg, Tomas, et al. (author)
  • Extension of a prediction model to estimate vapor pressures of perfluorinated compounds (PFCs)
  • 2011
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 107:1, s. 59-64
  • Journal article (peer-reviewed)abstract
    • Perfluorinated compounds (PFCs) are persistent and have been found globally as environmental contaminants. Release into the environment can occur from manufacturing, industrial and consumer uses. The vapor pressure is an important physical property influencing both the release and the environmental partitioning, but few reliable experimental determinations are available. Here we update a previous PLS regression model to cover also this compound class, using only a few calibration compounds. The recalibration is accomplished by applying a leverage-based weighting scheme that is generally applicable in updating structure–property relationships. The predictive performance is validated with an external validation set and is considerably better than for other standard estimation software, both with regard to accuracy and precision. The model can be given a chemical interpretation and the prediction error for the liquid vapor pressure is within 0.2 log units of Pa. Finally, the model is applied and vapor pressure estimates are reported for more than 200 PFCs where no reliable experimental data are available.
  •  
46.
  • Öberg, Tomas, 1956- (author)
  • Indicator parameters for PCDD/PCDF from electric arc furnaces
  • 2004
  • In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 73:1, s. 29-35
  • Journal article (peer-reviewed)abstract
    • The unintentional formation and release of persistent organic pollutants (POP) from industrial sources is of environmental concern and efforts are now made to reduce these emissions [The Stockholm Convention on persistent organic pollutants; United Nations Environment Programme: Geneva, 2001]. The emissions of chlorinated trace organics from electric arc furnaces (EAF) have been monitored on a regular basis in Sweden since the 1980s. Most analyses have encompassed not only polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDF), but also chlorinated benzenes and phenols. Emissions of 2,3,7,8-substituted PCDD/PCDF from municipal solid waste incinerators (MSWI) can be modelled and predicted from analyses of chlorinated benzenes and phenols, which are suspected to be precursors in the formation process. The purpose of this investigation was to extend and update previously reported models with new samples from EAF, to describe the main sources of variation and to compare multivariate calibration with univariate regression. The measurement data consisted of 27 samples collected between 1987 and 2002 and analysed by two different laboratories. A general multivariate calibration model was able to describe 96% of the variation in the toxic equivalent quantity (TEQ) value over five orders of magnitude. Univariate regression models cannot account for changes in the congener pattern and thus gave a poorer performance. In plant-specific applications, the univariate approach did, however, perform equally well. It was therefore concluded that both multivariate and univariate regression models can be used in process optimisation studies, but that multivariate models are better suited for emission monitoring and evaluation of removal efficiencies in the off-gas cleaning systems.
  •  
47.
  • Abrahamsson, Christoffer, et al. (author)
  • Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets
  • 2003
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 69:1-2, s. 3-12
  • Journal article (peer-reviewed)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.
  •  
48.
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49.
  • Wikström, Conny, et al. (author)
  • Multivariate process and quality monitoring applied to an electrolysis process. : Part II - Multivariate time-series analysis of lagged latent variables
  • 1998
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42:1-2, s. 233-240
  • 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.
  •  
50.
  • Wikström, Conny, et al. (author)
  • Multivariate process and quality monitoring applied to an electrolysis process. : Part I - Process supervision with multivariate control charts
  • 1998
  • In: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42, s. 221-231
  • 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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