Comparative study between Partial Least Squares and Rational function Ridge Regression models for the prediction of moisture content of woodchip samples using a handheld spectrophotometer

2021 ◽  
Author(s):  
Manuela Mancini ◽  
Veli‐Matti Taavitsainen ◽  
Giuseppe Toscano
Keyword(s):  
Moisture Content ◽  
Comparative Study ◽  
Least Squares ◽  
Rational Function ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Regression Models

Comparative study of partial least squares and multivariate curve resolution for simultaneous spectrophotometric determination of pharmaceuticals in environmental samples

RSC Advances ◽  
2015 ◽  
Vol 5 (86) ◽  
pp. 70017-70024 ◽  
Author(s):  
Hadi Parastar ◽  
Hamidreza Shaye
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Simultaneous Determination ◽  
Partial Least Squares ◽  
Mefenamic Acid ◽  
Environmental Samples ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Target Analytes

The potentials of PLSR and MCR-ALS are evaluated for the simultaneous determination of diclofenac, naproxen, mefenamic acid and carbamazepine as target analytes and gemfibrozil as interference in synthetic and real environmental samples.

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.

scholarly journals Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015 ◽  
pp. 55 ◽  
Author(s):  
Ll. Pérez-Planells ◽  
J. Delegido ◽  
J. P. Rivera-Caicedo ◽  
J. Verrelst
Keyword(s):  
Linear Regression ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Gaussian Process Regression ◽  
Least Squares Regression ◽  
Kernel Ridge Regression

<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

Prediction of Long-Residue Properties of Potential Blends from Mathematically Mixed Infrared Spectra of Pure Crude Oils by Partial Least-Squares Regression Models

2009 ◽  
Vol 23 (4) ◽  
pp. 2164-2168 ◽  
Author(s):  
Peter de Peinder ◽  
Tom Visser ◽  
Derek D. Petrauskas ◽  
Fabien Salvatori ◽  
Fouad Soulimani ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Crude Oils ◽  
Least Squares Regression
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