Algorithms for spectrum peaks?

[rachmaninoff]

I'm trying to write an algorithm to identify peaks in various UV/vis spectra (as opposed to measuring them myself). I've seen software which does this - for example an FT-IR spectrometer here has software which goes through and does exactly this, labelling the wavelengths of everything. It's a real time-saver.

What I have right now is an long string of AND's and OR's of various ad-hoc conditions, which is perhaps right 90% of the time. Does anyone know of the standardized way to do this? Especially which can handle problems like low resolution, and single-pixel errors?

I'm implementing this in Mathematica, btw.

 

[Aaron Barnard]

One approach I've seen is to use a rolling window technique. You set a window size and shift it along the spectrum. At each point, you look at the data in the window and calculate the slope or curvature (or both). If the slope or curvature is above a certain threshold, you have a peak. This works better for higher resolution spectra but also can work with lower resolution spectra.

Another approach is to use a filter-based approach. Here, you design a band-pass filter with two poles and apply it to the spectrum. The peaks will appear as local maxima in the filtered spectrum. This is usually more reliable than the rolling window technique since it is less affected by noise and resolution.

Finally, there are peak-detection algorithms such as the "double-thresholding" algorithm which use a combination of slope, curvature and noise thresholds to identify peaks.

In the end, the best approach will depend on the nature of your data and the application.

 

[ravenprp]

There are several approaches to identifying peaks in UV/vis spectra through algorithms. One common method is to use a peak finding algorithm, such as the Savitzky-Golay filter, which smooths the spectrum and then identifies peaks based on the local maxima. This approach can handle low resolution and single-pixel errors by taking into account neighboring data points.

Another approach is to use a peak fitting algorithm, such as the Gaussian or Lorentzian fitting, which models the spectrum as a sum of peaks and then determines the parameters of each peak. This approach is more accurate but may require more computational power.

In terms of a standardized way to do this, there are several software packages and libraries available that have pre-written algorithms for peak identification and fitting. For example, the Python library SciPy has functions for peak finding and fitting, and the R programming language has packages specifically designed for analyzing spectral data.

In terms of handling low resolution and single-pixel errors, it may be helpful to pre-process the data by applying a smoothing function or interpolating missing data points. Additionally, incorporating statistical methods, such as peak height and peak width measurements, can help improve the accuracy of peak identification.

Overall, it is important to carefully consider the specific characteristics of your spectra and choose an appropriate algorithm or combination of algorithms that best suits your needs. It may also be helpful to consult with experts in the field or seek out resources and tutorials on spectral data analysis. Good luck with your implementation in Mathematica!