Investigation of the Advantages and Limitations of Forward Linear Prediction for Processing 2D Data Sets

✍ Scribed by William F. Reynolds; Margaret Yu; Raul G. Enriquez; Ismael Leon

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 693 KB
• Volume: 35
• Category: Article
• ISSN: 0749-1581
• DOI: 10.1002/(sici)1097-458x(199708)35:8<505::aid-omr130>3.0.co;2-k

✦ Synopsis

Although the potential advantages of forward linear prediction for the processing of multi-dimensional NMR f 1 spectra are well established, this method is surprisingly little used for 2D spectra used for organic structure determination. A detailed investigation of the advantages and limitations of forward linear prediction for this purpose f 1 is reported. This is a reliable technique which is particularly useful for 1H-detected 13C-1H shift correlation spectra, allowing up to 16-fold linear prediction of the 13C axis of HSQC spectra. In general, the use of linear prediction allows one to obtain comparable 2D spectra in one quarter of the time or double the sensitivity in a comparable time relative to similar spectra without linear prediction. The one exception is the absolute value COSY spectrum, where linear prediction beyond a factor of two gives poor results. Linear prediction is generally superior to zero Ðlling as a time-saving technique, although the di †erence between the two approaches disappears as the data point resolution approaches the natural linewidth. By contrast, forward linear prediction is not f 1 f 2 recommended.