Important: phase correction is no longer allowed after baseline correction.
Right click to set/unset pivot point for 1st order phase correction.
Hold shift (1 degree per step) or control (0.1 per step) key, then use mouse wheel to adjust phase.
Deep Neural Network and Voigt Fitter for Accurate Deconvolution of Complex 1D NMR Spectra
Step 1 option 1 Time domain data uploading
Bruker fid data format information
- You can find raw fid data in the experimental folder, such as your_exp_name/2, where 2 is EXP ID. The file is usually named fid (or ser)
- The acquisition status file is usually called acqus (not acqu) in the same folder as the FID data file.
- You can also drag and drop fid and/or acqus file, or their parent folder to the box above.
- The server will run automatic phase correction, after ZF and FFT.
Step 1 option 2, Processed ( frequency domain) data uploading
Spectrum processing requirements, please read carefully.
- Please use the 2PI-Kaiser window function for apodization in the NMRPipe software with the command “SP -off 0.5 -end 0.896 -pow 3.684” to achieve desired peak shape (Voigt).
- Beside nmrPipe .ft1 format, you can also use Topspin text file generated using command "totxt" or CSV format from some software (two columns by space, ppm and amplitude). IMPORTANT: In the following database query, COLMAR1d needs to know the number of acquisitions and the spectral width. Therefore, please retain the full spectral length and apply 2× zero filling when running the 'totxt' command.
- You need to keep the imaginary part if you want to run manual phase correction.
Step 2,3 Manual phase and/or baseline correction (optional)
Using the plot to run manual phase correction.
A appreciate baseline correction can only be defined by the user, based on the experimental conditions. That is, the user need to define the smoothness parameter and positive spectrum parameter then apply the baseline correction after satisfying result.
The smoothness parameter is the weight of the smoothness constraint in the baseline estimation. The larger the value, the smoother the baseline.
The positive spectrum parameter is the weight of the constraint that the baseline should be below the spectrum. The larger the value, the less the baseline will be allowed above the spectrum (to accommodate noise).
Step 4 Spectrum deconvolution
DEEP Picker model selection
DEEP Picker model selection
- Model 1 was optimized for peak width (defined as Full Width as Half Height) from 8 to 20, with 12 points as optimal value
- Model 2 was optimized for peak width from 4 to 12, with 8 points as optimal value
- The default model, DEEP Auto, will estimate median peak width, then run cubic spline interpolation to adjust it to 6.0 and apply model 2.
- For spectrum with very heterogeneous peak width, set a compromise interpolation step (for model 2). A step of 0.5 means spectral size will be doubled.
(optional) Pseudo 2D fitting of multiple spectra
pivot: Not set
Phase correction at left end: 0 , at right end: 0
.
Reference
Questions or comments? Please feel free to contact us.
Acknowledgement
This web server and the research that led to it is supported by the National Institutes of Health (Grant R01 GM 066041).*This information is only used for the compilation of our webserver usage data.
This server is last updated at October 18 2025 06:33:45.
For questions, please contact li.4644@osu.edu