"R" Chemometrics

Savitzky-Golay filters in R

March 17, 2012 | jrcuesta

Derivatives are a good way to extract information to our spectra. As we know NIR contents overlapping bands, and spectra must be treated with math operations in order to extract as much information as possible and to correlate it with the constituent &...
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NIT: Fatty acids study in R – Part 007

March 14, 2012 | jrcuesta

Once we have chosen the model, we can continue acquiring spectra of new samples. Spectra is exported to a txt or csv file and we imported in R to be reprocessed.We use the function “predict” from the PLS package. I have done this with 20 new sample...
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NIT: Fatty acids study in R – Part 006

March 12, 2012 | jrcuesta

In one of the columns, for constituent C16_0, one sample (57) has a value of “zero” (we could see this in the histogram).The reason for that is that the laboratory did not supply this value. The PLS regression will consider the lab value as cero, s...
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NIT: Fatty acids study in R – Part 005

March 9, 2012 | jrcuesta

There are several algorithms to run a PLS regression (I recommend to consult the books: “Introduction to Multivariate Analysis in Chemometrics - Kurt Varmuza & Peter Filzmozer” and “Chemometrics with R – Ron Wehrens”).We are going to use ...
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NIT: Fatty acids study in R – Part 004

March 7, 2012 | jrcuesta

It is clear that MSC does not remove the entire scatter in the raw spectra, so some of the information is hidden by the scatter. Improvement of the sample presentation will help to remove the scatter.We know that the first loading is much related to th...
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NIT: Fatty acids study in R – Part 003

March 5, 2012 | jrcuesta

As I told you I´m a beginner in "R", so I realize that I have to prepare my data a little bit in order to continue from my previous post ( NIT: Fatty acids study in R - Part 002) after getting some errors. Anyway I´m really fascinated ...
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NIT: Fatty acids study in R – Part 001

March 1, 2012 | jrcuesta

This time I´m going to use my own data to develop a model to predict some fatty acid in the solid fat (pork).Samples had been analyzed in a NIT (Near Infrared Transmittance) instrument. The range of the wavelengths is from 850 to 1048 nm (100 data poi...
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PCA for NIR Spectra_part 006: "Mahalanobis"

February 28, 2012 | jrcuesta

Outliers have an important influence over the PCs, for this reason they must be detected and examinee.We have just the spectra without lab data, and we have to check if any of the sample spectra is an outlier ( a noisy spectrum, a sample which belongs ...
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PCA for NIR Spectra_part 005: "Reconstruction"

February 27, 2012 | jrcuesta

We saw how to plot the raw spectra (X), how to calculate the mean spectrum, how to center the sprectra (subtracting the mean spectrum from every spectra of the original matrix X). After that we have developed the PCAs with the NIPALS algorithm, getting...
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PCA for NIR Spectra_part 002: "Score planes"

February 23, 2012 | jrcuesta

The idea of this post is to compare the score plots for the first 3 principal components obtained with the algorithm “svd” with the scores plot of  other chemometric software (Win ISI in this case). Previously I had exported the yarn spectra t...
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