Results 31 to 40 of about 72,257 (309)
Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data [PDF]
, 2005 Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of high-dimensional genomic data.
Boulesteix, Anne-Laure +1 more
core +2 more sources
Designing algorithms to aid discovery by chemical robots [PDF]
, 2018 Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades ...
Cronin, Leroy +2 more
core +1 more source
Journal of Proteome Research, 2006 We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data.
Johan Trygg +2 more
openaire +3 more sources
Neural Network-Based Active Learning in Multivariate Calibration [PDF]
IEEE Transactions on Systems, Man, Cybernetics-Part C, vol. 42,
issue 6, pp. 1763-1771, 2012, 2015 In chemometrics, data from infrared or near-infrared (NIR) spectroscopy are often used to identify a compound or to analyze the composition of amaterial. This involves the calibration of models that predict the concentration ofmaterial constituents from the measured NIR spectrum.
arxiv +1 more source
The wavelet transform for pre-processing IR spectra in the identification of mono- and di-substituted benzenes [PDF]
, 1994 This paper describes the wavelet transformation of IR spectra with the Daubechies analysing wavelet functions as a feature extracting method that successfully reduces the spectral data more than 20-fold with a significant improvement in the ...
Bos, M., Vrielink, J.A.M.
core +2 more sources
Combination of Raman spectroscopy and chemometrics: A review of recent studies published in the Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy Journal [PDF]
arXiv, 2022 Raman spectroscopy is a promising technique used for noninvasive analysis of samples in various fields of application due to its ability for fingerprint probing of samples at the molecular level. Chemometrics methods are widely used nowadays for better understanding of the recorded spectral fingerprints of samples and differences in their chemical ...
arxiv
Nature Communications, 2023 Functionalizing porous materials with capping agents generates hybrid materials with enhanced properties, while the challenge is how to improve the selectivity and maintain the porosity of the parent framework.
Yu Liang +5 more
doaj +1 more source
Chemometrics and standards [PDF]
Journal of Research of the National Bureau of Standards, 1988 Standards are central to the achievement and maintenance of accuracy in trace analysis. This fact is well-known and well-accepted in the international analytical chemical community, where "standards" are generally considered to be Standard Reference Materials (SRMs) or Certified Reference Materials (CRMs).
openaire +1 more source
Developing Synthetic Spectroscopy Noise and Chemometric Database for Computational Classification [PDF]
arXiv, 2021 There has been little to no work in the area of spectroscopy noise in order to create data sets for analytical algorithms to be challenged on the ability to separate chemicals. We present a framework on how to build off of a sparse about of experimental data in order to expand your chemometric database and create realistic instrumentation noise.
arxiv
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling [PDF]
Chemometrics and Intelligent Laboratory Systems / I Mathematical
Background Chemometrics Intell Lab Syst 80, 2 (2006) 215-226, 2007 Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of parameters, leading to overfitting and poor ...
arxiv +1 more source