Rasmus Bro
University of Copenhagen, Department of Food Science, Faculty Member
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Research Interests: Marketing, Mathematics, Computer Science, Information Retrieval, Statistics, and 12 moreMultivariate Statistics, Principal Component Analysis, Sensory, Pca, Chewing gum, Analytical Method, Partial Least Squares Regression, data analysis methods in fMRI, Sensory properties, Food Sciences, Univariate Analysis, and Partial least square regression
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... doi:10.1016/j.chemolab.2006.03.010 | How to Cite or Link Using DOI Copyright © 2006 Elsevier BV All rights reserved. ... used in this work for creating a blockwise pattern of non-methylated units is a pectin esterase found in a... more
... doi:10.1016/j.chemolab.2006.03.010 | How to Cite or Link Using DOI Copyright © 2006 Elsevier BV All rights reserved. ... used in this work for creating a blockwise pattern of non-methylated units is a pectin esterase found in a commercial enzyme preparation from papaya fruits. ...
Research Interests: Chemistry, Analytical Chemistry, Multivariate Statistics, Chromatography, Process Engineering, and 12 moreAlternating least squares, Calibration, Partial Least Squares Regression, Titration, Continuous stirred tank reactor, Pectin, Elsevier, Binding Site, Reference Value, Partial Least Square, Multivariate Curve Resolution, and Dilution
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Résumé/Abstract An increasing number of quality criteria are involved in the evaluation of the final malt. This implies a comprehensive quality evaluation, normally based on experience and prior knowledge by the maltster/brewer/breeder.... more
Résumé/Abstract An increasing number of quality criteria are involved in the evaluation of the final malt. This implies a comprehensive quality evaluation, normally based on experience and prior knowledge by the maltster/brewer/breeder. This paper describes the principle in, and use of, fuzzy logic for the translation of a complex malt quality profile into a simple univariate overall quality index (OQI). The approach was tested on a data set of 50 malt samples including eleven quality parameters according to the European Brewery ...
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Research Interests: Chemistry, Complementary and Alternative Medicine, Principal Component Analysis, Plant Biology, Metabolomics, and 12 morePharmacognosy, Magnetic Resonance Spectroscopy, Medicine, High Pressure Liquid Chromatography, Plant extracts, Yeast Metabolite Profiling, Hypericum, Metabolite, Metabolome, metabolite profiling, Pharmacology and pharmaceutical sciences, and varimax rotation
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Research Interests: Earth Sciences, Chemistry, Food Science, Biomedical Engineering, Heat Treatment, and 15 moreEnvironmental Sciences, Biosystems engineering, Near Infrared Spectroscopy, Compressive Strength, Model Calibration, Multiple Scattering, Second Order, Partial Least Squares Regression, First Order Logic, Leaf dry matter content, Biosystems, Other Engineering, dry matter content, Root mean square, and Root mean square error(Environmental Sciences, Biosystems engineering, Near Infrared Spectroscopy, Compressive Strength, Model Calibration, Multiple Scattering, Second Order, Partial Least Squares Regression, First Order Logic, Leaf dry matter content, Biosystems, Other Engineering, dry matter content, Root mean square, and Root mean square error)
(Environmental Sciences, Biosystems engineering, Near Infrared Spectroscopy, Compressive Strength, Model Calibration, Multiple Scattering, Second Order, Partial Least Squares Regression, First Order Logic, Leaf dry matter content, Biosystems, Other Engineering, dry matter content, Root mean square, and Root mean square error)
Research Interests: Chemical Engineering, Chemistry, Analytical Chemistry, Chemometrics, Chromatography, and 15 morePrincipal Component Analysis, Mass Spectrometry, Magnetic Resonance Spectroscopy, Comparative Study, Medicine, Multivariate Analysis, Electrospray, Factor analysis, High Performance Liquid Chromatography, High Pressure Liquid Chromatography, Hypericum, Retention Time, Matrix Effect, Elution, and HPLC Chromatography(Principal Component Analysis, Mass Spectrometry, Magnetic Resonance Spectroscopy, Comparative Study, Medicine, Multivariate Analysis, Electrospray, Factor analysis, High Performance Liquid Chromatography, High Pressure Liquid Chromatography, Hypericum, Retention Time, Matrix Effect, Elution, and HPLC Chromatography)
(Principal Component Analysis, Mass Spectrometry, Magnetic Resonance Spectroscopy, Comparative Study, Medicine, Multivariate Analysis, Electrospray, Factor analysis, High Performance Liquid Chromatography, High Pressure Liquid Chromatography, Hypericum, Retention Time, Matrix Effect, Elution, and HPLC Chromatography)
This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics multivariate data analysis it is illustrated how to tackle problems in... more
This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics multivariate data analysis it is illustrated how to tackle problems in food science more efficiently and, moreover, ...
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NMR is one of the most powerful analytical techniques of our time. It allows detailed investigation of qualitative and quantitative characteristics of complex chemical and biological samples. The resulting NMR data provides a wealth of... more
NMR is one of the most powerful analytical techniques of our time. It allows detailed investigation of qualitative and quantitative characteristics of complex chemical and biological samples. The resulting NMR data provides a wealth of information about the samples, but the NMR data analysis has been and still is suffering from oversimplified approaches making it difficult to extract all the information efficiently. For instance, univariate methods that just use one or a few selected variables for the analysis from a whole spectrum lead to a huge loss of information. Such a simplifying approach reduces the chance of discovering new findings and truly learning about complex aspects of the samples investigated. Multivariate data analysis techniques allows for truly exploratory and comprehensive analysis of NMR data. This is particularly advantageous in the investigation of complex biological samples. Chemometrics can be helpful here by providing tools for unsupervised and supervised data exploration, multivariate calibration, classification and discrimination. This chapter presents some important steps in the pre-processing of NMR data as well as some of the most common chemometric techniques for data exploration and analysis. An example of NMR spectra of apple juice samples is given, to illustrate the power of the combination of NMR data and chemometrics. (Less)
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This IUPAC Technical Report describes and compares the currently applied methods for the calibration and standardization of multi-dimensional fluorescence (MDF) spectroscopy data as well as recommendations on the correct use of... more
This IUPAC Technical Report describes and compares the currently applied methods for the calibration and standardization of multi-dimensional fluorescence (MDF) spectroscopy data as well as recommendations on the correct use of chemometric methods for MDF data analysis. The paper starts with a brief description of the measurement principles for the most important MDF techniques and a short introduction to the most important applications. Recommendations are provided for instrument calibration, sample preparation and handling, and data collection, as well as the proper use of chemometric data analysis methods.
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In many areas of science, multiple sets of data are collected pertaining to the same system. Examples are food products that are characterized by different sets of variables, bioprocesses that are online sampled with different... more
In many areas of science, multiple sets of data are collected pertaining to the same system. Examples are food products that are characterized by different sets of variables, bioprocesses that are online sampled with different instruments, or biological systems of which different genomic measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have something in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding of the relationships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology and in methods: There is no common ground that hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of da...
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Multi‐way data analysis is popular in chemometrics for the decomposition of, for example, spectroscopic or chromatographic higher‐order tensor datasets. Parallel factor analysis (PARAFAC) and its extension, PARAFAC2, are extensively... more
Multi‐way data analysis is popular in chemometrics for the decomposition of, for example, spectroscopic or chromatographic higher‐order tensor datasets. Parallel factor analysis (PARAFAC) and its extension, PARAFAC2, are extensively employed methods in chemometrics. Applications of PARAFAC2 for untargeted data analysis of hyphenated gas chromatography coupled with mass spectrometric detection (GC‐MS) have proven to be very successful. This is attributable to the ability of PARAFAC2 to account for retention time shifts and shape changes in chromatographic elution profiles. Despite its usefulness, the most common implementations of PARAFAC2 are considered quite slow. Furthermore, it is difficult to apply constraints (e.g., non‐negativity) to the shifted mode in PARAFAC2 models. Both aspects are addressed by a new shift‐invariant tri‐linearity (SIT) algorithm proposed in this paper. It is shown on simulated and real GC‐MS data that the SIT algorithm is 20–60 times faster than the latest PARAFAC2‐alternating least squares (ALS) implementation and the PARAFAC2‐flexible coupling algorithm. Further, the SIT method allows the implementation of constraints in all modes. Trials on real‐world data indicate that the SIT algorithm compares well with alternatives. The new SIT method achieves better factor resolution than the benchmark in some cases and tends to need fewer latent variables to extract the same chemical information. Although SIT is not capable of modeling shape changes in elution profiles, trials on real‐world data indicate the great robustness of the method even in those cases.
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Research Interests: Mathematics, Algorithms, Artificial Intelligence, Comparative Study, Medicine, and 11 morePharmaceutics, Discriminant Analysis, Linear Discriminant Analysis, Near Infrared Spectroscopy, Classification, Hyperspectral Imaging, Partial Least Squares Regression, Spectrum analysis, Cls, Statistical models, and Pharmacology and pharmaceutical sciences
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Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying... more
Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.
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ABSTRACT
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For many years it has been known that PARAFAC offers a very attractive approach for modeling fluorescence excitation–emission matrices. Due to the uniqueness of the PARAFAC model and analogy between the structure of fluorescence data and... more
For many years it has been known that PARAFAC offers a very attractive approach for modeling fluorescence excitation–emission matrices. Due to the uniqueness of the PARAFAC model and analogy between the structure of fluorescence data and the PARAFAC model, it is apparent that PARAFAC can resolve overlapping signals into pure spectra and relative concentrations under mild conditions. There are hundreds of applications exemplifying this, but still the use of PARAFAC has not spread from chemometrics to more main-stream analytical chemistry. Many reasons can be offered to explain this, but one seems to be that in practice it is difficult for chemometric novices to make use of PARAFAC. Selection of wavelengths, handling of scatter and of outliers are all issues that must be dealt with in order to build a good PARAFAC model. In this paper, a new algorithm called EEMizer is developed that aims to automate the use of PARAFAC. Through several examples it is shown how this algorithm can provide appealing PARAFAC models of data that would otherwise be hard to model.
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An investigation was conducted on whether the fluorescence spectra of the very similar catecholamines adrenaline and noradrenaline could be separated using chemometric methods. The fluorescence landscapes (several excitation and emission... more
An investigation was conducted on whether the fluorescence spectra of the very similar catecholamines adrenaline and noradrenaline could be separated using chemometric methods. The fluorescence landscapes (several excitation and emission spectra were measured) of two data sets with respectively 16 and 6 samples were measured, the smaller data set with higher resolution and i.e. precision. The samples were artificial urine (pH approximately equal to 3) spiked with the catecholamines in the concentration ranges 40--1200 nmol/L and 5.5--18 micromol/L, respectively. Unfold partial least squares regression (Unfold-PLSR) on the larger data set and parallel factor analysis (PARAFAC) of the six samples of the smaller set showed that there was no difference between the fluorescence landscapes of adrenaline and noradrenaline. It can be concluded that chemometric separation of adrenaline and noradrenaline is not obtainable using this type of fluorescence measurement. Raman scatter, which overlaps the catecholamine spectra, was shown not to have any influence on the models calculated.