Search results for "least squares"
showing 10 items of 268 documents
Modelling the enantioresolution capability of cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions for neutral a…
2018
[EN] To the best of our knowledge, the prediction of the enantioresolution ability of polysaccharides-based stationary phases in liquid chromatography for structurally unrelated compounds has not been previously reported. In this study, structural information of neutral and basic compounds is used to model their enantioresolution levels obtained from an immobilised cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions. Thirty-four structurally unrelated chiral drugs and pesticides, from seven families, are studied. Categorical enantioresolution levels (RsC, 0 = no baseline enantioresolution and 1 = baseline enantioresolution) are established from the expe…
Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints
2022
Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake…
New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in …
2008
A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. H…
Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment
2006
Abstract In this paper different predictive models for nutrient estimation in a sequencing batch reactor (SBR) for wastewater treatment are compared: principal component regression (PCR), partial least squares (PLS), and artificial neural networks (ANNs). Two unfolding procedures were used: batch-wise and variable-wise. For the latter unfolding method, X and Y matrix augmentation with lagged variables were used in some models to incorporate process dynamics. The results have shown that batch-wise unfolding PLS models outperform the other approaches. The ANN models are good predictive models, but in this particular case-study, they do not outperform those multivariate projection models that …
Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.
2019
Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…
Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*
2020
The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …
On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water
2001
Abstract Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …
Multivariate regression analysis applied to the calibration of equipment used in pig meat classification in Romania.
2016
This paper highlights the statistical methodology used in a dissection experiment carried out in Romania to calibrate and standardize two classification devices, OptiGrade PRO (OGP) and Fat-o-Meat'er (FOM). One hundred forty-five carcasses were measured using the two probes and dissected according to the European reference method. To derive prediction formulas for each device, multiple linear regression analysis was performed on the relationship between the reference lean meat percentage and the back fat and muscle thicknesses, using the ordinary least squares technique. The root mean squared error of prediction calculated using the leave-one-out cross validation met European Commission (EC…
Determination of vinegar acidity by attenuated total reflectance infrared measurements through the use of second-order absorbance-pH matrices and par…
2007
Univariate (zero-order), multivariate (first-order) and multiway (second-order) calibrations were assayed for the determination of vinegar acidity using a mechanized procedure based upon vibrational spectroscopy and the emerging multicommutation methodology. The second-order methodology relies on the use of a flow system based on multicommutation and binary sampling. The flow network comprises a set of three-way solenoid valves, computer-controlled to provide facilities to handle the sample and to generate a time-dependent pH gradient using two carrier solutions. The procedure is based on the volumetric fraction variation approach that maintains the same volume of sample solution and dynami…
Estimating brain connectivity when few data points are available: Perspectives and limitations
2017
Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in …