Peak Picking and Deconvolution

This example covers the peak picking and peak deconvolution capabilities of MOCCA2.

These can be called directly from the Chromatogram class, or you can get more granular control using the find_peaks and deconvolve_adaptive functions.

First, let’s import the libraries and load the data.

from mocca2 import example_data
from matplotlib import pyplot as plt
import pandas as pd

# Load the chromatogram and correct baseline
chromatogram = example_data.example_2()
chromatogram.correct_baseline()

# Crop out the relevant part of the chromatogram
chromatogram.extract_time(1.5, None, inplace=True)

Picking the peaks is a one liner. Let’s see how good are the default parameters.

# Find the peaks
chromatogram.find_peaks()

# Plot chromatogram with the peaks
chromatogram.plot()
plt.show()

The algorithm did not detect most of the smaller peaks. This can be easily fixed by adjusing the threshold.

# It is possible to adjust the threshold to pick even the smaller peaks
chromatogram.find_peaks(min_height=1)

# Let's see the chromatogram again
chromatogram.plot()
plt.show()

The algorithm now correctly detected all the peaks!

However, some of the peaks overlap - the algorithm merges these into a single peak with multiple maxima. To get the individual components, we need to deconvolve the peaks.

# As you can see, some the algorithm detected that some peaks overlap
# Let's deconvolve them
chromatogram.deconvolve_peaks(
    model="FraserSuzuki",
    min_r2=0.99,
    relaxe_concs=False,
    max_comps=3,
)

# And plot the chromatogram again
chromatogram.plot()
plt.show()

The algorithm was successful and separated the overlapping peaks into individual components!

Let’s integrate the peaks and summarize the results.

# Finally, let's get all the components and calculate their area %
# (the integral is averaged over all wavelengths)
data = pd.DataFrame(
    [
        {
            "Elution Time": chromatogram.time[component.elution_time],
            "Integral": component.integral,
        }
        for component in chromatogram.all_components()
    ]
)
data["Area %"] = data["Integral"] / data["Integral"].sum() * 100
data.sort_values("Elution Time [min]", inplace=True)
print(data.round(2).to_string(index=False))

Elution Time [min]  Integral  Area %
              1.64    375.87    7.51
              1.70     54.88    1.10
              1.74    485.64    9.71
              1.91   1591.49   31.81
              1.95    285.16    5.70
              2.14   1346.80   26.92
              2.23    187.07    3.74
              2.27    125.47    2.51
              2.64     56.99    1.14
              3.50    494.14    9.88

Final note

It is often rather subjective which peaks to pick, which peaks overlap, and where are the peak borders. For this reason, it might be neccessary to tweak the parameters to your needs.