Renormalise spectral data with a custom reference

Some use cases require more flexibility than the high-level user-friendly functions provides by lightr. For this use case, lightr also exports the low-level individual parsers, which allow the user to code its own custom workflow.

We don't recommend the use of those functions unless you absolutely have to. Most users should use lr_get_spec() and lr_get_metadata() instead.

Here, we take the example of the method presented in @Gruson2019_QuantitativeCharacterizationIridescent where reflectance spectra need to be normalised in an unusual way.

Raw, un-normalised spectral data depends on both the spectrometer and the lamp as well as the conditions during the recording (including ambient light, temperature, etc.). To allow for comparison between studies, it is thus normalised by a white and a dark reference with the following formula:

Processed = Raw-Dark White-Dark

For this example here, we need to normalise the raw data by a white reference contained in another file. This can't be done with with lr_get_spec() because lr_get_spec() returns reflectance spectra that have already been normalised by the white reference contained in the same file.

library(lightr)

Step 1: import un-normalised data

We manually import the data using the appropriate low-level parser:

reflect_data <- lr_parse_procspec(
  system.file("testdata", "procspec_files", "OceanOptics_Linux.ProcSpec",
               package = "lightr")
  )
length(reflect_data)

The result contains 2 elements:

head(reflect_data[[1]])

Step 2: find the matching white reference

We import that white reference in the same way:

white_data <- lr_parse_procspec(
  system.file("testdata", "procspec_files", "whiteref.ProcSpec",
               package = "lightr")
)

Step 3: normalise the reflectance data

We can now normalise the reflectance spectrum with the equation stated at the beginning of this vignette:

Processed = Raw-Dark White-Dark

But first, we verify that the integration times:

We can now get rid of the metadata part and focus on the data only:

reflect_data <- data.frame(reflect_data[[1]])
white_data <- data.frame(white_data[[1]])

As a last step before being able to normalise the data, we also need to check if the reflectance spectrum and the white reference are sampled with the same wavelengths:

all.equal(reflect_data$wl, white_data$wl)
res <- (reflect_data$scope - reflect_data$dark) / 
       (white_data$white - white_data$dark)
head(res)


Try the lightr package in your browser

Any scripts or data that you put into this service are public.

lightr documentation built on June 19, 2021, 5:05 p.m.