FOSS CALIBRATOR: Tutorial 005

Time to create the outlier model to predict the Mahalanobis distances in the principal component space.

FOSS CALIBRATOR: Tutorial 004

This is the video number 4 for the Foss Calibrator tutorials in spanish, where a model is developed using the MPLS algorithm. After the model calculation we can see several plots and statistics.

Foss calibrator is very fast for this types of models so we can do several almost at the same time and choose the best one.

Review the statistics and plots trying to finds patterns, outliers,...,etc

Creating a Single Sample Standardization

We create two single sample standardization files, one with the NIR5000 as Master and the DS2500F as Host , and other with the NIR5000 as Host and the DS2500F as Master. 

Depending of the scenario we can use one or the other.

Choosing one sample for the standardization

See first the other three videos:
 Trim Spectra
 RMS of subsamples
 RMS between same samples in different instruments 

In this video we want to select one of the samples to create a standarization file to transfer the data base of soy meal from the NIR5000 to the DS2500.

 One rule of thumb is to select the sample with the lowest GH value looking that the sample is near the average of the spectral population of the soy datababe from which I had developed the calibration in the NIR5000.

RMS between same samples in different instruments

See first the previous two videos:
Trim Spectra
RMS of subsamples
 

Now we average the repacks and compare the RMS between the samples scanned on different instrument not standardized using the Contrast spectra function of Win ISI. 

Obviously the RMS are higher than the repacks of the samples in one instrument, because we are adding the difference between instruments, but the idea is that after the standardization the RMS of the same sample between two instruments be similar or if possible lower than the sampling error  (RMS between repacks on the same instrument).

RMS of Subsamples

 

See first the previous video:

Trim spectra

It is important to know the sampling error, and for this reason the calculation of the RMS of subsamples is very important. 

The RMS is a way to obtain a value for the spectral differences between all the repacks.

For the calculation of the RMS simply scan diiferent repacks of the same sample and homogenize the sample betwee subsamples. Try to do it as best as you can in order to get a low RMS.

Same products are quite heterogeneus and for that reason the RMS can increase. If we grind the sample the RMS wil decrease much more.

The RMS value wil be usefull for some comparisons during the standardization or database transfer.

TRIM SPECTRA

We have a certain number of samples scanned in two instruments (a NIR5000 and a DS2500F). Several repacks of the same sample have been scanned on both instruments, due that they have different sample presentation and different cups.

A sample with a certain ID was well homogenized and the contain was splitted into the two cuvettes, and we repeat the process several times in order to get a higher probability that the same sample has been scanned on both instrument and that will help to see the differences between the instruments.

When we want to compare spectra files from different instruments they must have the same range and the same number of data. In the video we trim the spectra from a DS2500F (850-2500, 0.5) to the range and data points of a NIR5000 (1100-2500, 2nm). 

After that we can overplot or subtract the spectra.

The idea of all this coming videos is to show the process of database transfer from a NIR5000 to a DS2500 or DS2500F instrument.