This baseline correction routine iteratively finds the baseline of a spectrum using polynomial fitting methods or accepts a manual baseline.
Usage
subtr_baseline(x, ...)
# Default S3 method
subtr_baseline(
x,
y,
type = "polynomial",
degree = 8,
raw = FALSE,
full = T,
remove_peaks = T,
refit_at_end = F,
crop_boundaries = F,
iterations = 10,
peak_width_mult = 3,
termination_diff = 0.05,
degree_part = 2,
bl_x = NULL,
bl_y = NULL,
make_rel = TRUE,
...
)
# S3 method for class 'OpenSpecy'
subtr_baseline(
x,
type = "polynomial",
degree = 8,
raw = FALSE,
full = T,
remove_peaks = T,
refit_at_end = F,
crop_boundaries = F,
iterations = 10,
peak_width_mult = 3,
termination_diff = 0.05,
degree_part = 2,
baseline = list(wavenumber = NULL, spectra = NULL),
make_rel = TRUE,
...
)Arguments
- x
a list object of class
OpenSpecyor a vector of wavenumbers.- y
a vector of spectral intensities.
- type
one of
"polynomial"or"manual"depending on the desired baseline correction method.- degree
the degree of the full spectrum polynomial. Must be less than the number of unique points when
rawisFALSE. Typically, a good fit can be found with an 8th order polynomial.- raw
if
TRUE, use raw and not orthogonal polynomials.- full
logical, whether to use the full spectrum as in
"imodpoly"or to partition as in"smodpoly".- remove_peaks
logical, whether to remove peak regions during first iteration.
- refit_at_end
logical, whether to refit a polynomial to the end result (TRUE) or to use linear approximation.
- crop_boundaries
logical, whether to smartly crop the boundaries to match the spectra based on peak proximity.
- iterations
the number of iterations for automated baseline correction.
- peak_width_mult
scaling factor for the width of peak detection regions.
- termination_diff
scaling factor for the ratio of difference in residual standard deviation to terminate iterative fitting with.
- degree_part
the degree of the polynomial for
"smodpoly". Must be less than the number of unique points.- bl_x
a vector of wavenumbers for the baseline.
- bl_y
a vector of spectral intensities for the baseline.
- make_rel
logical; if
TRUE, spectra are automatically normalized withmake_rel().- baseline
an
OpenSpecyobject containing the baseline data to be subtracted (only for"manual").- ...
further arguments passed to
poly()or"smodpoly"parameters.
Value
subtr_baseline() returns a data frame containing two columns named
"wavenumber" and "intensity".
Details
This function supports two types of "polynomial" automated baseline correction with options.
Default settings are closest to "imodpoly" for iterative polynomial
fitting based on Zhao et al. (2007). Additionally options recommended by
"smodpoly" for segmented iterative polynomial fitting with enhanced peak detection
from the S-Modpoly algorithm (https://github.com/jackma123-rgb/S-Modpoly),
and "manual" for applying a user-provided baseline.
References
Chen MS (2020). Michaelstchen/ModPolyFit. MATLAB. Retrieved from https://github.com/michaelstchen/modPolyFit (Original work published July 28, 2015)
Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi:10.1366/000370207782597003 .
Jackma123 (2023). S-Modpoly: Segmented modified polynomial fitting for spectral baseline correction. GitHub Repository. Retrieved from https://github.com/jackma123-rgb/S-Modpoly.
See also
poly();
smooth_intens()
Examples
data("raman_hdpe")
# Use polynomial
subtr_baseline(raman_hdpe, type = "polynomial", degree = 8)
#> wavenumber intensity
#> <num> <num>
#> 1: 301.040 0.000000000
#> 2: 304.632 0.001328324
#> 3: 308.221 0.000000000
#> 4: 311.810 0.000000000
#> 5: 315.398 0.000000000
#> ---
#> 960: 3187.990 0.016928702
#> 961: 3190.520 0.016928702
#> 962: 3193.060 0.022547154
#> 963: 3195.590 0.022547154
#> 964: 3198.120 0.011310249
#>
#> $metadata
#> x y user_name spectrum_type spectrum_identity organization
#> <int> <int> <char> <char> <char> <char>
#> 1: 1 1 Win Cowger Raman HDPE Horiba Scientific
#> license session_id
#> <char> <char>
#> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf
#> file_id
#> <char>
#> 1: cb06ce2846b119d932fb6696479a445b
subtr_baseline(raman_hdpe, type = "polynomial", iterations = 5)
#> wavenumber intensity
#> <num> <num>
#> 1: 301.040 0.000000000
#> 2: 304.632 0.001328324
#> 3: 308.221 0.000000000
#> 4: 311.810 0.000000000
#> 5: 315.398 0.000000000
#> ---
#> 960: 3187.990 0.016928702
#> 961: 3190.520 0.016928702
#> 962: 3193.060 0.022547154
#> 963: 3195.590 0.022547154
#> 964: 3198.120 0.011310249
#>
#> $metadata
#> x y user_name spectrum_type spectrum_identity organization
#> <int> <int> <char> <char> <char> <char>
#> 1: 1 1 Win Cowger Raman HDPE Horiba Scientific
#> license session_id
#> <char> <char>
#> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf
#> file_id
#> <char>
#> 1: cb06ce2846b119d932fb6696479a445b
# Use manual
bl <- raman_hdpe
bl$spectra$intensity <- bl$spectra$intensity / 2
subtr_baseline(raman_hdpe, type = "manual", baseline = bl)
#> wavenumber intensity
#> <num> <num>
#> 1: 301.040 0.00000000
#> 2: 304.632 0.03037975
#> 3: 308.221 0.02784810
#> 4: 311.810 0.02405063
#> 5: 315.398 0.02531646
#> ---
#> 960: 3187.990 0.05696203
#> 961: 3190.520 0.05696203
#> 962: 3193.060 0.06202532
#> 963: 3195.590 0.06202532
#> 964: 3198.120 0.05189873
#>
#> $metadata
#> x y user_name spectrum_type spectrum_identity organization
#> <int> <int> <char> <char> <char> <char>
#> 1: 1 1 Win Cowger Raman HDPE Horiba Scientific
#> license session_id
#> <char> <char>
#> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf
#> file_id
#> <char>
#> 1: cb06ce2846b119d932fb6696479a445b