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Oregano is one of the most used culinary herb and it is often adulterated with cheaper plants. In this study, GC–MS was used for identification and quantification of metabolites from 104 samples of oregano (Origanum vulgare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggygria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the regression models on the basis of the oregano-to-adulterants mixing ratio. Orthogonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled discrimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite.

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together., Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together., Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together., Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

Oregano is one of the most used culinary herb and it is often adulterated with cheaper plants. In this study, GC–MS was used for identification and quantification of metabolites from 104 samples of oregano (Origanum vulgare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggygria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the regression models on the basis of the oregano-to-adulterants mixing ratio. Orthogonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled discrimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite.

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together., Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together., Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

This research provides an accurate description of the origin for fruit spirits. In total, 63 samples of various kinds of fruit spirits (especially from apples, pears, plums, apricots and mirabelle) were analysed using headspace-solid phase microextraction and gas chromatography with flame-ionization detector. Obtained volatile profiles were treated and analysed by multivariate regression with a reduction of dimensionality-orthogonal projections to latent structure for the classification of fruit spirits according to their fruit of origin. Basic result of statistical analysis was the differentiation of spirits to groups with respect to fruit kind. Tested kinds of fruit spirits were strictly separated from each other. The selection was achieved with a specificity of 1.000 and a sensitivity of 1.000 for each kind of spirit. The statistical model was verified by an external validation. Hierarchical cluster analysis (calculation of distances by Ward’s method) showed a similarity of volatile profiles of pome fruit spirits (apple and pear brandies) and stone fruit spirits (especially mirabelle and plum brandies)., Tento výzkum poskytuje přesný popis původu ovocných destilátů. Celkem bylo analyzováno 63 vzorků různých druhů ovocných destilátů (zejména z jablek, hrušek, švestek, meruněk a mirabel) pomocí metod mikroextrakce tuhou fází z parního prostoru a plynové chromatografie s plamenovým ionizačním detektorem. Získané profily těkavých látek byly zpracovány a analyzovány metodou ortogonální projekce do latentních struktur s cílem klasifikace ovocných destilátů, přičemž základním výsledkem statistické analýzy bylo rozlišení destilátů na skupiny s ohledem na ovocné druhy. Testované druhy ovocných destilátů byly od sebe odděleny, klasifikace byla dosažena se stoprocentní specificitou a senzitivitou pro každý druh destilátu. Statistický model byl ověřen externí validací. Hierarchická shluková analýza (výpočet vzdáleností Wardovou metodou) ukázala podobnost těkavých profilů destilátů z jádřincových plodů (pálenky z jablek a hrušek) a destilátů z peckovin (zejména pálenky ze švestek a mirabelek).

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Many of the technologies used in metabolomics have method-specific advantages and drawbacks in terms of diversity of metabolites detected, sensitivity, or resolution. We will describe the use of metabolomic methods for: • Correlation of propolis composition to altitude of collection and revealing its major botanical origin. • revealing cytotoxic metabolites from Mahonia aquifolium stem-bark • application for differentiation of the ambiguous taxonomy of the genus Amphoricarpos Vis. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR spectroscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. The utilization of various NMR experiments, in combination with sophisticated multivariate analysis methods, was demonstrated to be a powerful tool to correlate propolis composition to altitude of collection and reveal its major botanical origin. OPLS methods were used to identify changes in the chemical composition of propolis, while O2PLS methods enabled the identification of the botanical origin of propolis.[1] A 1H NMR-based metabolomics method was used to reveal cytotoxic metabolites from Mahonia aquifolium stem-bark. Primary and secondary metabolites in the Mahonia aquifolium extracts were identified by thorough analysis of 1H and 2D NMR spectra, without prior isolation. An OPLS multivariate analysis method was used to correlate the chemical composition of the plant extracts with the results of cytotoxic activity against Human cervical adenocarcinoma cell line.[2] Metabolomic methods were used to get more insight into the ambiguous taxonomy o

Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure-activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (M-w > 500). For the first time, we show that five levels of intracellular lysosomotropic accumulation (as measured by liquid chromatography coupled to tandem mass spectrometry-LC-MS/MS), from low/no to extremely high, can be predicted with 60% balanced accuracy solely from the compound's structure. Although largely built on macrocycles, the eight non-macrocyclic compounds that were added to the set were found to be well incorporated by the models, indicating their possible broader application. By uncovering the link between the molecular structure and cellular accumulation as the key process in tissue distribution of lysosomotropic compounds, these models are applicable for directing the drug discovery process and prioritizing the compounds for synthesis with fine-tuned accumulation properties, according to the desired pharmacokinetic profile.

Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure-activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (M-w > 500). For the first time, we show that five levels of intracellular lysosomotropic accumulation (as measured by liquid chromatography coupled to tandem mass spectrometry-LC-MS/MS), from low/no to extremely high, can be predicted with 60% balanced accuracy solely from the compound's structure. Although largely built on macrocycles, the eight non-macrocyclic compounds that were added to the set were found to be well incorporated by the models, indicating their possible broader application. By uncovering the link between the molecular structure and cellular accumulation as the key process in tissue distribution of lysosomotropic compounds, these models are applicable for directing the drug discovery process and prioritizing the compounds for synthesis with fine-tuned accumulation properties, according to the desired pharmacokinetic profile.

Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure-activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (M-w > 500). For the first time, we show that five levels of intracellular lysosomotropic accumulation (as measured by liquid chromatography coupled to tandem mass spectrometry-LC-MS/MS), from low/no to extremely high, can be predicted with 60% balanced accuracy solely from the compound's structure. Although largely built on macrocycles, the eight non-macrocyclic compounds that were added to the set were found to be well incorporated by the models, indicating their possible broader application. By uncovering the link between the molecular structure and cellular accumulation as the key process in tissue distribution of lysosomotropic compounds, these models are applicable for directing the drug discovery process and prioritizing the compounds for synthesis with fine-tuned accumulation properties, according to the desired pharmacokinetic profile.

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnP

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnP

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation., Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation., Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation., Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and DV spec-troscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500 m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400 m, originating from P. nigra and P.x euramericana buds. Samples collected at 400-500 m were of mixed origin, with variable amounts of all detected metabolites.

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnP

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnP

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation., Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation., Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnP

Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples., Electronic supplementary material The online version of this article (doi:10.1007/s11306-015-0933-1) contains supplementary material, which is available to authorized users.This research was supported by the Swedish Research Council Grant No. 2011-6044 (to JT), the Biology of Liver and Pancreatic Development and Disease (BOLD) Marie Curie Initial Training Network (MCITN) within EU’s FP7 programme (to TL, JT, KB, FT, SC, CH, TM) and the CNRS and Universite´ Pierre et Marie Curie (to SC, CH), the Institut National de la Sante´ et de la Recherche Me´dicale, INSERM (to SC), the Socie´te´ Francophone du Diabe`te and Emergence UPMC (to CH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Conflict of interest: JT, TM and TL are shareholders of AcureOmics AB. No financing has been received from this company.

Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeao University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

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.

A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real-world cases., Additional supporting information may be found in the online version of this article at the publisher’s web site., Innovative Multivariate Model Based Approaches For Industry.

A personal view is given about the gradual development of projection methods-also called bilinear, latent variable, and more-and their use in chemometrics. We start with the principal components analysis (PCA) being the basis for more elaborate methods for more complex problems such as soft independent modeling of class analogy, partial least squares (PLS), hierarchical PCA and PLS, PLS-discriminant analysis, Orthogonal projection to latent structures (OPLS), OPLS-discriminant analysis and more. From its start around 1970, this development was strongly influenced by Bruce Kowalski and his group in Seattle, and his realization that the multidimensional data profiles emerging from spectrometers, chromatographs, and other electronic instruments, contained interesting information that was not recognized by the current one variable at a time approaches to chemical data analysis. This led to the adoption of what in statistics is called the data analytical approach, often called also the data driven approach, soft modeling, and more. This approach combined with PCA and later PLS, turned out to work very well in the analysis of chemical data. This because of the close correspondence between, on the one hand, the matrix decomposition at the heart of PCA and PLS and, on the other hand, the analogy concept on which so much of chemical theory and experimentation are based. This extends to numerical and conceptual stability and good approximation properties of these models. The development is informally summarized and described and illustrated by a few examples and anecdotes.