Diagnostics: Some solvent vapors in the noise spectra

Some days ago, Oscar (a follower of this blog) sent me some estrange diagnostics from his NIR Instrument. It was curious that when I plotted every of the 10 noise spectra test (the final diagnostic result is an average on the ten spectra statistics), the peaks increase more and more and in the same direction (in this case negative), and at the same wavelength, it was like if the NIR was measuring something.

 

 

The way that the NIR perform every noise spectra, is measuring first the background in a ceramic plate, and after scans again the ceramic as a sample. So in the ideal case there is no difference, so we should see something similar to a flat line along cero, but if we zoom the spectra, we start to see the noise due to the instrument hardware, if the background is stable. These noise must be random, without special patterns.

There are cases that some mechanical noise give some noise peaks at certain wavelengths, so we can identify an encoder problem, a filter (order shorter) problem,…

Sometimes stray light goes into the detectors, the lamp,the laboratory temperature, or the detectors temperature, can be also unstable. These and other cases are the cause that we see special signatures in the noise spectra.

So coming back to the Oscar spectra, something seems to be in the air of the laboratory which makes those patters. It was not water vapor signature (as we saw in other cases), it was another thing, and it was obvious because of the smell (similar to ammonia).

The noise spectra shows how it was decreasing, and that date (a little bit later) the noise was fine (random), and the smell as well.

Thanks Oscar for the information

Understanding better Hyperspectral Image Analysis

In my last post I recommended the article :
"Near infrared hyperspectral image analysis using R, Part 5", which appears in the NIR News Vol. 25 No. 7 (November 2014).

In the second part of the tutorial, you can develop an animation to see the spectrum of every pixel in a single line of the bread.
We can see the noise spectra of the background, and the spectra of the different pixels in the line of the bread at a certain Y level .

The animation of the first tutorial is in the post: "Animated visualisation of hyperspectral data using R ".

It is really great to see how, how the scientific community is using R, and we hope to see more articles and papers in the future.

Authors:
Y. Dixit, R. Cama,a C. Sullivan, L. Alvarez Jubetea
School of Food Science & Environmental Health, Dublin Institute of Technology, Cathal Brugha Street, Dublin 1, Ireland
A. Ktenioudakib
Department of Food Chemistry & Technology, Teagasc Food Research Center Ashtown, Ashtown, Dublin 15, Ireland

Animated visualisation of hyperspectral data using R

Very useful article in the last issue of NIR News Vol. 25 No. 7 (November 2014), with an amazing tutorial, about how to develop animated vizualizations of hyperspectral NIR images.
See the second part of the tutorial in the post:"Understanding better Hyperspectral Image Analysis".

Authors:
Y. Dixit, R. Cama,a C. Sullivan, L. Alvarez Jubetea
School of Food Science & Environmental Health, Dublin Institute of Technology, Cathal Brugha Street, Dublin 1, Ireland
A. Ktenioudakib
Department of Food Chemistry & Technology, Teagasc Food Research Center Ashtown, Ashtown, Dublin 15, Ireland

 

 

SG 2nd Derivative + MSC

As you know derivatives remove the baseline offset and curvature in the spectra, but the should be combined with anti-scatter math treatments if we want to remove scatter effects which affect the correlation between the constituents of interest and the spectral bands. There are some cases (especially when developing discriminant models), where it is not convenient to apply the anti-scatter math treatments and we just leave the derivatives alone.

Following the Shoot-out tutorial and following the paper "Shoot-out 2002: transfer of calibration for content of active in a pharmaceutical tablet", from David W. Hopkins (NIR news Vol14 No. 5 2003), I tried the math treatment recommended by the author and apply the MSC after the SG2D1104, just to have a look to the spectra:

You can see how the red spectra, has been calculated in the previous post, and for the green one (SG second derivative combined with MSC), I use the following script:

> X1_sg2dmsc<-msc(X1_sg2d_pracma)
> matplot(wavelength2[11:281],t(X1_sg2dmsc[,11:281]),type="l",
+ xlab="Wavelength (nm)",ylab="1/R (SG 2nd der + MSC)",lty=1,
+ col=3,main="SG-2D1104 + MSC")
If we want to see them over-plotted

Max Kuhn Interviewed by DataScience.LA at useR 2014

Learn more about this great R developer (Caret Package) in this link