Søren Balling Engelsen

Bio: Søren Balling Engelsen is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Chemometrics & Partial least squares regression. The author has an hindex of 59, co-authored 295 publications receiving 13310 citations. Previous affiliations of Søren Balling Engelsen include Centre national de la recherche scientifique & University of Copenhagen Faculty of Life Sciences.

Review of the most common pre-processing techniques for near-infrared spectra

Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy

Abstract: A new graphically oriented local modeling procedure called interval partial least-squares (i PLS) is presented for use on spectral data. The i PLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of i PLS (r=0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by i PLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and i PLS is still able to utilize the first-order advantage. Index Headings: Interval PLS; Variable selection; NIR, Principal variables; Forward stepwise selection; Recursively weighted regression; Beer; Extract.

Light scattering and light absorbance separated by extended multiplicative signal correction. application to near-infrared transmission analysis of powder mixtures.

Abstract: The extended multiplicative signal correction (EMSC) preprocessing method allows a separation of physical light-scattering effects from chemical (vibrational) light absorbance effects in spectra from, for example, powders or turbid solutions. It is here applied to diffuse near infrared transmission (NIT) spectra of mixtures of wheat gluten (protein) and starch (carbohydrate) powders, linearized by conventional log(1/T). Without any correction for uncontrolled light scattering variation between the powder samples, these absorbance spectra could give reasonable predictions of the analyte (gluten), but only when using multivariate calibration with a much more complex model than expected. Standard MSC preprocessing did not work for these data at all; it removed too much analyte information. However, the EMSC preprocessing yielded powder spectra that obeyed Beer's Law more or less as if they had been obtained from transparent liquid solutions, apparently by isolating the chemical light absorption from additive, multiplicative, and wavelength-dependent effects of uncontrolled light-scattering variations. The model-based EMSC and its converse, the extended inverted signal correction (EISC), gave rather complete descriptions of the diffuse absorbance spectra and virtually indistinguishable performance in the calibration set and the test set of samples.

Multivariate autofluorescence of intact food systems.

Abstract: 3. Data Analysis 1985 3.1. Fluorescence Data Structure 1985 3.2. Chemometrics 1985 3.3. Multivariate Analysis of Fluorescence Data 1985 3.4. Multiway Analysis of Fluorescence Data 1985 4. Food Studies 1986 4.1. Meat 1987 4.2. Fish 1989 4.3. Dairy Products 1989 4.4. Edible Oils 1990 4.5. Cereals 1990 4.6. Beer 1991 4.7. Fruit and Vegetables 1991 4.8. Sugar 1991 5. Conclusions and Perspectives 1992 6. Acknowledgments 1992 7. References 1992

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Review of the most common pre-processing techniques for near-infrared spectra

Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

Present and Future of Surface-Enhanced Raman Scattering

Abstract: The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

GLYCAM06: a generalizable biomolecular force field. Carbohydrates.

Abstract: A new derivation of the GLYCAM06 force field, which removes its previous specificity for carbohydrates, and its dependency on the AMBER force field and parameters, is presented. All pertinent force field terms have been explicitly specified and so no default or generic parameters are employed. The new GLYCAM is no longer limited to any particular class of biomolecules, but is extendible to all molecular classes in the spirit of a small-molecule force field. The torsion terms in the present work were all derived from quantum mechanical data from a collection of minimal molecular fragments and related small molecules. For carbohydrates, there is now a single parameter set applicable to both alpha- and beta-anomers and to all monosaccharide ring sizes and conformations. We demonstrate that deriving dihedral parameters by fitting to QM data for internal rotational energy curves for representative small molecules generally leads to correct rotamer populations in molecular dynamics simulations, and that this approach removes the need for phase corrections in the dihedral terms. However, we note that there are cases where this approach is inadequate. Reported here are the basic components of the new force field as well as an illustration of its extension to carbohydrates. In addition to reproducing the gas-phase properties of an array of small test molecules, condensed-phase simulations employing GLYCAM06 are shown to reproduce rotamer populations for key small molecules and representative biopolymer building blocks in explicit water, as well as crystalline lattice properties, such as unit cell dimensions, and vibrational frequencies.