MVC1_GUI: A MATLAB graphical user interface for first-order multivariate calibration. An upgrade including artificial neural networks modelling

2020 ◽  
Vol 206 ◽  
pp. 104162
Author(s):  
Fabricio A. Chiappini ◽  
Héctor C. Goicoechea ◽  
Alejandro C. Olivieri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
User Interface ◽  
Graphical User Interface ◽  
Multivariate Calibration ◽  
First Order ◽  
Artificial Neural

Sensitivity for Multivariate Calibration Based on Multilayer Perceptron Artificial Neural Networks

2020 ◽  
Vol 92 (18) ◽  
pp. 12265-12272
Author(s):  
Fabricio A. Chiappini ◽  
Franco Allegrini ◽  
Héctor C. Goicoechea ◽  
Alejandro C. Olivieri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Multivariate Calibration ◽  
Artificial Neural

Artificial Neural Networks in Multivariate Calibration

1993 ◽  
Vol 1 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Tormod Næs ◽  
Knut Kvaal ◽  
Tomas Isaksson ◽  
Charles Miller
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Method ◽  
Linear Calibration ◽  
Feed Forward Network ◽  
Non Linear ◽  
Artificial Neural

This paper is about the use of artificial neural networks for multivariate calibration. We discuss network architecture and estimation as well as the relationship between neural networks and related linear and non-linear techniques. A feed-forward network is tested on two applications of near infrared spectroscopy, both of which have been treated previously and which have indicated non-linear features. In both cases, the network gives more precise prediction results than the linear calibration method of PCR.

Nonlinear Modeling of Chromium Tanning Solution Using Artificial Neural Networks

1994 ◽  
Vol 48 (1) ◽  
pp. 21-26
Author(s):  
M. Marjoniemi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multivariate Calibration ◽  
Nonlinear Modeling ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Nonlinear Fitting ◽  
Artificial Neural ◽  
Multicomponent Solutions

In this article artificial neural networks (ANNs) are applied for multivariate calibration using spectroscopic data and for generation of quantitative estimates of the concentrations of a component (chromium) in solutions. Neural networks are capable of handling nonlinear relationships. Absorbance is nonlinearly dependent on concentration, especially in the case of wide concentration ranges and multicomponent solutions. In addition to the aforementioned reasons, nonlinearities are also caused by aging and by differences in pH and in the temperatures of the chromium-tanning solutions to be modeled. The sigmoid output function was used in the hidden layer to perform nonlinear fitting. The results are compared with the results obtained with principal component regression (PCR) and partial least-squares regression (PLS) methods.

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