| 1. |
- Karlsson, Peter S., 1968-, et al.
(författare)
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A Liu estimator for the beta regression model and its application to chemical data
- 2020
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 34:10, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Abstract Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers.
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| 2. |
- Lukmanov, Rustam A., et al.
(författare)
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Chemical identification of microfossils from the 1.88-Ga Gunflint chert : Towards empirical biosignatures using laser ablation ionization mass spectrometer
- 2021
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 35:10
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Tidskriftsartikel (refereegranskat)abstract
- In this contribution, we investigated the chemical composition of Precambrian microfossils from the Gunflint chert (1.88 Ga) using a miniature laser ablation ionization mass spectrometer (LIMS) developed for in situ space applications. Spatially resolved mass spectrometric imaging (MSI) and depth profiling resulted in the acquisition of 68,500 mass spectra. Using single mass unit spectral decomposition and multivariate data analysis techniques, we identified the location of aggregations of microfossils and surrounding inorganic host mineral. Our results show that microfossils have unique chemical compositions that can be distinguished from the inorganic chert with high fidelity. Chemical depth profiling results also show that with LIMS microprobe data, it is possible to identify chemical differences between individual microfossils, thereby providing new insights about nature of early life. Analysis of LIMS spectra acquired from the individual microfossils reveals complex mineralization, which can reflect the metabolic diversity of the Gunflint microbiome. An intensity-based machine learning model trained on LIMS Gunflint data might be applied for the future investigations of putative microfossils from silicified matrices, where morphological integrity of investigated structures is lost, and potentially in the investigation of rocks acquired from the Martian surface.
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| 3. |
- Sjögren, Rickard, et al.
(författare)
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Multivariate patent analysis : using chemometrics to analyze collections of chemical and pharmaceutical patents
- 2020
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 34:1
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Tidskriftsartikel (refereegranskat)abstract
- Patents are an important source of technological knowledge, but the amount of existing patents is vast and quickly growing. This makes development of tools and methodologies for quickly revealing patterns in patent collections important. In this paper, we describe how structured chemometric principles of multivariate data analysis can be applied in the context of text analysis in a novel combination with common machine learning preprocessing methodologies. We demonstrate our methodology in 2 case studies. Using principal component analysis (PCA) on a collection of 12338 patent abstracts from 25 companies in big pharma revealed sub-fields which the companies are active in. Using PCA on a smaller collection of patents retrieved by searching for a specific term proved useful to quickly understand how patent classifications relate to the search term. By using orthogonal projections to latent structures (O-PLS) on patent classification schemes, we were able to separate patents on a more detailed level than using PCA. Lastly, we performed multi-block modeling using OnPLS on bag-of-words representations of abstracts, claims, and detailed descriptions, respectively, showing that semantic variation relating to patent classification is consistent across multiple text blocks, represented as globally joint variation. We conclude that using machine learning to transform unstructured data into structured data provide a good preprocessing tool for subsequent chemometric multivariate data analysis and provides an easily interpretable and novel workflow to understand large collections of patents. We demonstrate this on collections of chemical and pharmaceutical patents.
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| 4. |
- Skotare, Tomas, et al.
(författare)
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Visualization of descriptive multiblock analysis
- 2020
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 34:1
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Tidskriftsartikel (refereegranskat)abstract
- Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.
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| 5. |
- Varmuza, Kurt, et al.
(författare)
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Composition of cometary particles collected during two periods of the Rosetta mission : multivariate evaluation of mass spectral data
- 2020
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Ingår i: Journal of Chemometrics. - : John Wiley and Sons Ltd. - 0886-9383 .- 1099-128X.
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Tidskriftsartikel (refereegranskat)abstract
- The instrument COSIMA (COmetary Secondary Ion Mass Analyzer) onboard of the European Space Agency mission Rosetta collected and analyzed dust particles in the neighborhood of comet 67P/Churyumov-Gerasimenko. The chemical composition of the particle surfaces was characterized by time-of-flight secondary ion mass spectrometry. A set of 2213 spectra has been selected, and relative abundances for CH-containing positive ions as well as positive elemental ions define a set of multivariate data with nine variables. Evaluation by complementary chemometric techniques shows different compositions of sample groups collected during two periods of the mission. The first period was August to November 2014 (far from the Sun); the second period was January 2015 to February 2016 (nearer to the Sun). The applied data evaluation methods consider the compositional nature of the mass spectral data and comprise robust principal component analysis as well as classification with discriminant partial least squares regression, k-nearest neighbor search, and random forest decision trees. The results indicate a high importance of the relative abundances of the secondary ions C+ and Fe+ for the group separation and demonstrate an enhanced content of carbon-containing substances in samples collected in the period with smaller distances to the Sun. © 2020 The Authors.
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| 6. |
- Walsh, Alexandra, 1989, et al.
(författare)
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Method development for in situ study of marine vanadium peroxidase based on SERS and chemometrics
- 2020
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 34:9
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Tidskriftsartikel (refereegranskat)abstract
- Vanadium peroxidases from marine algae are responsible for the production of ozone-depleting compounds, volatile halogenated organic compounds (VHOC). Due to the impact the release of these compounds has on the global atmospheric and biogeochemical processes, there is an interest within marine sciences in developing analytical methods for studying the various aspects of the VHOC production, particularly in situ. This study aimed to provide new methods towards the development of in situ methods within marine sciences. We demonstrate the use of design of experiments together with orthogonal partial least squares (OPLS) and transposed orthogonal partial least squares (T-OPLS) to address the qualitative spectral analysis of an enzyme-buffer system. The measurements were performed with surface-enhanced Raman spectroscopy (SERS) on vanadium bromoperoxisase from the red algaeCorallina officinalis. The chemometric tools used aimed to provide greater insights into how factors such as time, amount of gold nanoparticles and enzyme concentration influence the spectral responses and whether there was any synergy between those factors. The results acquired in this report aim to support future method development of chemometrics within in situ applications in marine sciences.
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