It is amazing the quantity of graphics you can develop with R, and how you can show and manage  these graphics. Here in the same plot we compare the raw demo spectra, treated with MSC, SNV and with the first derivative (differences between consecu...

We have in the Demo sample set “66” samples.  In this post we´ll see one way to divide the set in two parts: one for “Validation” and another for Training or Calibration.The selection will be random. And we are going to use the command:

########### RAW NIR Demo Data##################################demo

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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 &...

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...

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...

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 ...

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...

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 ...

__ library(chemometrics)__ fatmsc_nipals CPs matplot(CPs,t(fatmsc_nipals$T),lty=1,pch=21,  + xlab="PC_number",ylab="Explained_Var")In the 2D plot, we can see that with 3 or 4 principal...

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...

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 ...

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...

This plot in 2D, help us to decide the number of PCs, it is easy to create in R, once we have discompose the X matrix into a P matrix (loadings) and a T matrix (scores).For this plot, we just need the T matrix.__ CPs  matp...

__ X X_nipals wavelengths matplot(w...

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...