scholarly journals Online Monitoring of Copper Damascene Electroplating Bath by Voltammetry: Selection of Variables for Multiblock and Hierarchical Chemometric Analysis of Voltammetric Data

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Aleksander Jaworski ◽  
Hanna Wikiel ◽  
Kazimierz Wikiel
Keyword(s):  
Least Squares ◽  
Online Monitoring ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Sets ◽  
Least Squares Regression ◽  
Data Set ◽  
Selection Of Variables ◽  
Complete Chemical Analysis

The Real Time Analyzer (RTA) utilizing DC- and AC-voltammetric techniques is an in situ, online monitoring system that provides a complete chemical analysis of different electrochemical deposition solutions. The RTA employs multivariate calibration when predicting concentration parameters from a multivariate data set. Although the hierarchical and multiblock Principal Component Regression- (PCR-) and Partial Least Squares- (PLS-) based methods can handle data sets even when the number of variables significantly exceeds the number of samples, it can be advantageous to reduce the number of variables to obtain improvement of the model predictions and better interpretation. This presentation focuses on the introduction of a multistep, rigorous method of data-selection-based Least Squares Regression, Simple Modeling of Class Analogy modeling power, and, as a novel application in electroanalysis, Uninformative Variable Elimination by PLS and by PCR, Variable Importance in the Projection coupled with PLS, Interval PLS, Interval PCR, and Moving Window PLS. Selection criteria of the optimum decomposition technique for the specific data are also demonstrated. The chief goal of this paper is to introduce to the community of electroanalytical chemists numerous variable selection methods which are well established in spectroscopy and can be successfully applied to voltammetric data analysis.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

The Fault Diagnose of Advanced Amphibious Assault Vehicle Fan Pump Based on Principal Component Regression

2013 ◽  
Vol 860-863 ◽  
pp. 1582-1585
Author(s):  
Huan Zhan ◽  
Yu Cai Dong ◽  
Liang Hai Yi ◽  
Su Na Cao ◽  
Qi Jin ◽  
...  
Keyword(s):  
Least Squares ◽  
Hydraulic System ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Fault Diagnose ◽  
Explanatory Role ◽  
Leakage Model ◽  
Modeling Strategy

The fan pump hydraulic system of amphibious assault vehicle is prone to fault , an pump leakage model is established by principal component regression. This new modeling strategy can effectively extract on the components which have a strong explanatory role of the dependent variable, it achieves regression modeling in multiple arguments correlation conditions, and allows the inclusion of all the original variables. This model get rid of drawback that the least squares regression can not effectively identify and eliminate the influence of the multiple relativity among factors.

scholarly journals PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI KUADRAT TERKECIL PARSIAL PADA INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR

2019 ◽  
Vol 8 (4) ◽  
pp. 496-505
Author(s):  
Vetranella .T.R.A. Sinaga ◽  
Diah Safitri ◽  
Rita Rahmawati
Keyword(s):  
Least Squares ◽  
Human Development ◽  
Partial Least Squares ◽  
Human Development Index ◽  
Partial Least Squares Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Development Index ◽  
Independent Variables

The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new   independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s  is 95,85% is greater than PCR’s  = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS

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