Customize function options

You can change the considered polymers as well as size classess to your desire.

Change evaluated polymers

At the current state, the function evaluates up to 22 polymers (see example). If you would like to add polymers, or just evaluate some of them, you can overwrite the default setting by simply change the content of the c(...) accordingly (make sure, that the content is not ending with a , like (... ,). Just delete or add the (un)desired polymers.

WARNING: If you have named the polymers differently in your Purency software, you should set these accordingly here.

easyCHAMP(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
            polymers = c("PU", "EVAc", "PA", "PAN", "PBT", "PET", "PE", "PMMA", "PP", 
                         "POM", "PS", "PVC", "PC", "ABS", "PPSU", "CA", "PEEK", "EVOH", 
                         "PSU", "SILICONE", "PLA", "PLAPBAT"))

Change evaluated size classes

The size classes that should be evaluated by the function can be set manually. The function will dynamically switch to the desired size classes. It will sum the numbers from 0 to the first number (e.g., <=10. Then it will always exclude the lower number and include the upper number (e.g., >10 to <= 20, >20 to <= 50, …). Finally, the highest number to infinite (e.g., >500).

easyCHAMP(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
            sizeclasses = c(10, 20, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500))

Not interested in shape?

If you are not interested in the shape of your particles and the summaries should be performed without considering the particle shapes, the parameter colShape can be set to none:

easyCHAMP(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
            colShape = "none")

You analysed only parts of a sample? - integrate division factors

In many cases it can happen, that only a part of a sample is measured, if there are too many particles. This might be different between sample and blanks (e.g., it might be that a filter breaks appart). Therefore, you can provide a division factor for each sample and blank separately, or even for each filter separately.

However, to keep it simple and prevent mistakes, you only set setDivFactor when calling the function. There are three options available: setDivFactor = FALSE will ignore this parameter (default). If setDivFactor = "samplewise", this will cause the function to request the required values during the processing for each sample (i.e., all filters of one sample have the same factor). If set to setDivFactor = "filterwise", a division factor will be requested for each filter separately.

Just watch your R console and follow the instructions.

easyCHAMP(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
            setDivFactor = "samplewise")

If you only use a quarter of your sample the factor you should provide here is 0.25. If you did not divide a sample it is 1.

We implemented these two ways, to reduce the effort and possibility of mistakes in typing in the factors, if they are similar for all filters of a sample, but still provide the opportunity to choose the factors for each filter, in case there have been issues during the sample preparation, causing different factors for each filter.

WARNING: Make sure to exactly follow the instructions. Keep the order similar to the provided order of the samples/blanks.

WARNING: If you use several blanks summed for all samples (like example 3 in the beginning) you can only chose a common division factor.

NOTE: this option is not available for easyCHAMP.particles(), since it is not reasonable to multiply particles with similar properties in a particle-wise processing.

One colour of your particles should be separated from the rest? (`easyCHAMP.particles()` only)

In our labwork it can happen, that one likes to separate a certain colour of particles from the others in the evaluation. A usecase could be in the investigation of plastic particles on agricultural fields. There, black mulching films are used to cover the ground. However, if you’re only interested in plastic that does not originate from the mulching film, or you want to be able to distinguish between those, you have to consider the colour of the particles. However, in our practice, this was only of interest for a particle-wise evaluation. Therefore, we implemented another parameter colourSep for the function easyCHAMP.particles(). You can provide a colour as stated in your data, which will be then separately evaluated:

easyCHAMP.particles(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
                      colourSep = "black")

You will receive all blank-corrected particles for the chosen colour and all other colours separately.

NOTE: this option is not available for easyCHAMP, since colours are not considered in the summary function (it doesn’t make sense to create even more groups as there are anyway)!

Automatically fill missing values with provided default

With filters of hundreds of particles it can be exhausting to set every shape and colour in the Purency software manually, especially if it is all the same. Therefore, you can provide default values to the parameters formFillDefault and colourFillDefault . If there is a plastic particle in the list with NA in the respective field, it will be set with the default, that you provided. This could be done in excel, of course, but with this, you can do it just on-the-flow for all files you have.

easyCHAMP.particles(path="C:/users/MYNAME/Desktop/MYFILESTOBEPROCESSED/", 
                      formFillDefault = "fragment",
                      colourFillDefault = "black")