How to apply discriminant analysis in ISI Scan 4.xx

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This video is part of a presentation I´m prepairing about the use of discriminant analysis in Win ISI 4, and their aplication in ISI Scan 4.

PLS2 with "R"

I´ve been working these days with PLS2 calibrations with a chemometric software called “Unscrambler” with a data set called “jam”. I said “can I develop PLS2 models with R?”.I look in the book “Introduction to Multivariate Statistical Analysis in Chemometrics”, and I got the response “Yes, we can”.

I have other posts for PLS regressions, but it is PLS1, where we have an X matrix (spectra) and we make a regression for one constituent of the Y matrix at a time. What about to make the regression for all the constituents at the same time using the whole Y matrix?. That is the purpose of PLS2.

PLS2 is recommended when there is a high correlation between the constituents.

library(chemometrics)

data(cereal)

This data is part of a set used by Varmuza et al. 2008, for other papers.

You can get a description for this data in the R help page:

Description

For 15 cereals an X and Y data set, measured on the same objects, is available. The X data are 145 infrared spectra, and the Y data are 6 chemical/technical properties (Heating value, C, H, N, Starch, Ash). Also the scaled Y data are included (mean 0, variance 1 for each column). The cereals come from 5 groups B=Barley, M=Maize, R=Rye, T=Triticale, W=Wheat.

Once loaded, take a look to the data

dim(cereal$X)

dim(cereal$Ysc)

We can have a look to the spectra, (it is already treated with SG and first derivative).

wavelengths<-seq(1126,2278,by=8)

matplot(wavelengths,t(cereal$X),lty=1,xlab="wavelengths(nm)",ylab="log(1/R)")

Now let´s run PLS2, using “mvsr”, with “LOO” (leave one out) cross validation.

cerpls2<-mvr(Ysc~X,data=cereal,method="simpls",validation="LOO")

We can see a summary of the results:

summary(cerpls2)

Now we have to take an important decision, “How many terms to choose?”.

Plots can help us with it:

plot(RMSEP(cerpls2), legendpos = "topright")

We have to select an average, and looking to the plots we can say that 7 is fine, anyway for "starch" less terms would be fine, but for the rest 6 or 7 is the correct number.

Convertir resultados de "tal cual" a "sustancia seca"

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BLOSSOMS - The Quadratic Equation- It's Hip to Be Squared

This is another nice lecture from MIT professor Gilbert Strang. It is good to see this videos from time to time to understand what is behind all the math treatments in chemometrics. Polynomials are used for some math treatments as Savitzky-Golay, where the solution of the polynomial (2º degree, 3º degree,...) is used as the absorbance modified value for the middle point in a moving window of a certain odd segment.

Unscrambler (Jam Exercise) - 004

In the posts:

Unscrambler (Jam Exercise) - 001

Unscrambler (Jam Exercise) - 002

I ´ve been practicing Unscramber with some of the Demo files (Jam), used in the book “Multivariate Data Analysis - in practice” and following the tutorials.

I continue in this post with an important part: Compare the models in order to be sure which one is better, PCR or PLS1, to predict the Y parameter “preference”. For this is clear that we have to look to the residual variance left by the models, taking into account of course the number of terms, over-fitting,…

If we have a look to the plot for the Y residual variance for the PCR, we see an increase in the residual variance for the first PC. That is not good….but think about it.

The PCA does not take into account the Y matrix, so the first PC can be related to some important X structure which cannot be related to the Y parameter. Once extracted, the second PC correlates better with the Y matrix,but still not as good as the first PLS1 term . So this type of plots helps us to understand what is happening.

 Let´s see now the PLS1 residual variance plot for Y, we have a much better prediction with the first term, because the Y matrix was a part of the calculation process in the PLS1.

We have to decide for the model, the best number of terms, and software’s as Unscrambler can decide by you the best option, but you can change the number up or down. You have the control, but we have to check more plots and statistics, before to decide the best option.