Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Erik Andries"'
Autor:
Erik Andries, Ramin Nikzad‐Langerodi
Publikováno v:
Journal of Chemometrics. 36
Autor:
Ramin Nikzad‐Langerodi, Erik Andries
Publikováno v:
Journal of Chemometrics. 35
Autor:
Sylvie D Dobrota, Robert G. Messerschmidt, Erik Andries, Hershel Macaulay, Thomas Quertermous, Dara Rouholiman
BackgroundAt the present time, optimization of dietary and fitness practices are inadequately addressed in the clinical setting, and poorly documented through research studies investigating response to surrogate disease markers. Identifying and quant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b28ae314e4b8a50d2c867a8199179c25
https://doi.org/10.1101/2021.08.07.21261731
https://doi.org/10.1101/2021.08.07.21261731
Publikováno v:
Journal of Chemometrics. 30:144-152
Tikhonov regularization (TR) has been successfully applied to form spectral multivariate calibration models by augmenting spectroscopic data with a regulation operator matrix. This matrix can be set to the identity matrix I (ridge regression), yieldi
Using the L 1 norm to select basis set vectors for multivariate calibration and calibration updating
Publikováno v:
Journal of Chemometrics. 30:109-120
With projection based calibration approaches, such as partial least squares (PLS) and principal component regression (PCR), the calibration space is spanned by respective basis vectors (latent vectors). Up to rank k basis vectors are formed where k
Publikováno v:
Journal of Chemometrics. 33
Autor:
Erik Andries, John H. Kalivas
Publikováno v:
Journal of Chemometrics. 27:126-140
Orthogonal pre-processing (orthogonal projection) of spectral data is a common approach to generate analyte-specific information for use in multivariate calibration. The goal of this pre-processing is to remove from each spectrum the respective sampl
Autor:
Erik Andries
Publikováno v:
Journal of Chemometrics. 31
Maintaining multivariate calibrations involves keeping models developed on an instrument applicable to predicting new samples over time. Sometimes, a primary instrument model is needed to predict samples measured on secondary instruments. This situat
Publikováno v:
Journal of Chemometrics. 26:66-75
Classifying samples into known categories is a common problem in analytical chemistry and other fields. For example, with spectroscopic data, samples are measured and the corresponding spectra are compared with existing spectral data sets of known cl