Fourier transform sparse inverse regression estimators for sufficient variable selection

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

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Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data

Analytica Chimica Acta ◽

10.1016/s0003-2670(02)00272-6 ◽

2002 ◽

Vol 461 (2) ◽

pp. 189-200 ◽

Cited By ~ 158

Author(s):

Riccardo Leardi ◽

Mary Beth Seasholtz ◽

Randy J. Pell

Keyword(s):

Genetic Algorithm ◽

Fourier Transform ◽

Variable Selection ◽

Spectral Data ◽

Polymer Films ◽

Multivariate Calibration ◽

Fourier Transform Infrared ◽

Infrared Spectral ◽

Selection For

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Optimal variable selection for Fourier transform infrared spectroscopic analysis of starch-adulterated garlic powder

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2015.04.060 ◽

2015 ◽

Vol 216 ◽

pp. 622-628 ◽

Cited By ~ 21

Author(s):

Santosh Lohumi ◽

Sangdae Lee ◽

Byoung-Kwan Cho

Keyword(s):

Fourier Transform ◽

Variable Selection ◽

Spectroscopic Analysis ◽

Fourier Transform Infrared ◽

Garlic Powder ◽

Infrared Spectroscopic Analysis ◽

Infrared Spectroscopic ◽

Selection For

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Determination of sulfur in diesel using ATR/ FTIR and multivariate calibration

Eclética Química Journal ◽

10.26850/1678-4618eqj.v35.2.2010.p71-78 ◽

2018 ◽

Vol 35 (2) ◽

pp. 71

Author(s):

Itânia Pinheiro Soares ◽

Thais F. Rezende ◽

Isabel Cristina Pereira Fortes

Keyword(s):

Fourier Transform ◽

Variable Selection ◽

Sulfur Content ◽

Multivariate Calibration ◽

Infrared Spectrometry ◽

Fourier Transform Infrared Spectrometry ◽

Standard Methods ◽

X Ray ◽

Calibration Models

The aim of this present work was to provide a more fast, simple and less expensive to analyze sulfur content in diesel samples than by the standard methods currently used. Thus, samples of diesel fuel with sulfur concentrations varying from 400 and 2500 mgkg-1 were analyzed by two methodologies: X-ray fluorescence, according to ASTM D4294 and by Fourier transform infrared spectrometry (FTIR). The spectral data obtained from FTIR were used to build multivariate calibration models by partial least squares (PLS). Four models were built in three different ways: 1) a model using the full spectra (665 to 4000 cm-1), 2) two models using some specific spectrum regions and 3) a model with variable selected by classic method of variable selection stepwise. The model obtained by variable selection stepwise and the model built with region spectra between 665 and 856 cm-1 and 1145 and 2717 cm-1 showed better results in the determination of sulfur content.

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