Soil clay regressions: Looking for the better accuracy (part 1)

To have a good traceability in our data bases is important to develop accurate calibrations. We have seen with the Soil LUCAS database that we can filter the data by sample origin (Spain for my case) and after that filter it by land type (I choose for this example “Croplands). The samples are split into training and test set randomly.

 After that we must decide if we choose all the wavelength range (VIS + NIR) or the just the NIR. In this case the calibration is for Clay and different test tell me that the complete range is the best option.

 After this is time to check for the best math treatment trying in this case with 2º SG derivative, 1º SG derivative and SNV+Detrend scatter correction. The last two options gave me better validation statistics than the 2º SG derivative when using a PLS regression

 This is the XY plot for the validation set:

Can we improve the results with another type of regression for these cropland samples? This is what we will see in the next coming posts.

Another question can be: May I use this database to predict samples from another database? These can be samples from a different area in the same country, taken with a different instrument and analysed in a different laboratory. Check all this is important to see the robustness of the calibrations.