Tidyverse & Chemometrics (part 2)

In the previous post we have seen the histograms for the 40 fish meal samples, now it is time to look to the spectra.

Spectra can be is a matrix called "nir", as far as we have 40 samples and the spectrum of each sample goes from 1100 to 2498 every 2 nm, we have a matrix with 40 rows and 700 columns.

To use "matplot" is a quick option lo look to the raw spectra:

wavelength<-seq(1100,2498,2)
matplot(wavelength,t(nir),type="l",col="blue",
        xlab="nm",ylab = "log1/R",
        main="Fish1 spectra")

And we get this plot:

To remove the scatter and to improve the bands resolution we combine SNV and Detrend with the first derivative with a gap of 1 and a segment of 4.

library(prospectr)
nir_snvdt<-detrend(nir,wavelength)
nir_1d<-gapDer(nir_snv,m=1,w=1,s=4)
colnames(nir_1d)
wavelength_1d<-seq(1108,2490,2)
matplot(wavelength_1d,t(nir_1d),type="l",col="blue",
        xlab="nm",ylab = "log1/R",
        main="Fish1 1D spectra")

We can see how the scatter is reduced and how the band`s resolution increase.

We can try with the second derivative, with a gap of 1 and a segment of 10.

nir_2d<-gapDer(nir_snv,m=2,w=1,s=10)
colnames(nir_2d)
wavelength_2d<-seq(1130,2466,2)
matplot(wavelength_2d,t(nir_2d),type="l",col="blue",
        xlab="nm",ylab = "log1/R",
        main="Fish1 2D spectra")

We are going to use these math treated spectra to develop the Principal Component Analysis and PLS regressions in the coming posts.