global-spectral-deconvolution and peak optimizer

Global Spectra Deconvolution

gsdis using an algorithm that is searching for inflection points to determine the position and width of peaks. The width is defined as the distance between the 2 inflection points. Depending the shape of the peak this width may differ from 'fwhm' (Full Width Half Maximum).

Preprocessing of the data involves the following parameters

maxCriteria: search either for maxima or minima. We will invert the data and the results if searching for a minima
noiseLevel: specifies the noise level. All the peaks bellow this value (or above in case of maxCriteria=false) are ignored. By default the noiseLevel will be set to the median + 3 x sd. This is a good value when not too many peaks are present in the spectrum.
sgOptions: Savitzky-Golay filter that is used to smooth the data for the calculation of the derivatives
smoothY: If this value is true the SG filter is not only applied during the calculation of the derivatives but also on the original data

gsd({x:[], y:[]}, options)

The result of GSD is an array of GSDPeak:

x: position of the peak on the x axis
y: the height of the peak
width: width at the level of the inflection points
index: index in the 'x' and 'y' array of the peak
ddY: second derivative value at the level of the peak. Allows to identify 'large' peaks
inflectionPoints: an object with the position of the inflection points
- from: { x, index }
- to: { x, index }

Parameters

minMaxRatio=0.00025 (0-1)

Threshold to determine if a given peak should be considered as a noise, bases on its relative height compared to the highest peak.

maxCriteria=true [true||false]

Peaks are local maximum(true) or minimum(false)

smoothY=true [true||false]

Select the peak intensities from a smoothed version of the independent variables?

realTopDetection=false [true||false]

Use a quadratic optimizations with the peak and its 3 closest neighbors to determine the true x,y values of the peak?

sgOptions={windowSize: 5, polynomial: 3}

Savitzky-Golay parameters. windowSize should be odd; polynomial is the degree of the polynomial to use in the approximations. It should be bigger than 2.

Post methods

GSD.broadenPeaks(peakList, {factor=2, overlap=false})

We enlarge the peaks and add the properties from and to. By default we enlarge of a factor 2 and we don't allow overlap.

GSD.optimizePeaks(data, peakList, options)

Optimize the position (x), max intensity (y), full width at half maximum (fwhm) and the ratio of gaussian contribution (mu) if it's required. It currently supports three kind of shapes: gaussian, lorentzian and pseudovoigt

Example

import { IsotopicDistribution } from 'mf-global';
import { gsd, optimizePeaks } from 'ml-gsd';

// generate a sample spectrum of the form {x:[], y:[]}
const data = new IsotopicDistribution('C').getGaussian();

let peaks = gsd(data, {
  minMaxRatio: 0.00025, // Threshold to determine if a given peak should be considered as a noise
  realTopDetection: true, // Correction of the x and y coordinates using a quadratic optimizations
  maxCriteria: true, // Are we looking for maxima or minima
  smoothY: false, // should we smooth the spectra and return smoothed peaks ? Default false.
  sgOptions: { windowSize: 7, polynomial: 3 }, // Savitzky-Golay smoothing parameters for first and second derivative calculation
});
console.log(peaks);
/*
  array of peaks containing {x, y, width, ddY, inflectionPoints}
  - width = distance between inflection points
  - ddY = second derivative on the top of the peak
 */

let optimized = optimizePeaks(data, peaks);
console.log(optimized);
/*
[
  {
    x: 11.99999999960885,
    y: 0.9892695646808637,
    shape: { kind: 'gaussian' },
    fwhm: 0.010000209455943584,
    width: 0.008493395898379276
  },
  {
    x: 13.003354834590702,
    y: 0.010699637653261198,
    shape: { kind: 'gaussian' },
    fwhm: 0.010000226962299321,
    width: 0.008493410766908847
  }
]
*/

API documentation

License

MIT