Peak assignment in automatic data analysis
β Scribed by John Haselgrove; Mark Elliott
- Publisher
- John Wiley and Sons
- Year
- 1991
- Tongue
- English
- Weight
- 645 KB
- Volume
- 17
- Category
- Article
- ISSN
- 0740-3194
No coin nor oath required. For personal study only.
β¦ Synopsis
Linear prediction algorithms are able to identify peaks in an NMR spectrum, but are not able to assign these peaks to components anticipated in the spectrum. We have developed an artificial-intelligence protocol which uses the output parameter list from an LPSVD algorithm, and automatically assigns the peaks on the basis of an anticipated list of components. To overcome the influence of experimental conditions on the absolute values of frequency, integrated area, and linewidth, the assignment routine performs an internal scaling of the data by comparing all possible pairs of peaks in the spectrum. Completely automated analysis of large numbers of in vivo FIDs is now possible.
π SIMILAR VOLUMES
The main objective of machine diagnosis is the early recognition of mechanical defects in a machine, which is often referred to as preventive maintenance. This may result in the reduction of faults and higher machine availability. Preventive maintenance can be performed periodically in fixed time in
## Abstract A highly automated procedure for localising and characterising peaks in the chromatographic time domain of LCβMS data has been developed. The work was initiated by an identified need to facilitate the detection and tracking of chromatographic peaks during method development for the anal
A number of methods have been described for automatic amino acid analysis by ion-exchange chromatography followed by calorimetric determination of the separated amino acids, e.g., Spackman, Stein and Moore (l), Piez and Morris (2), Inglis (3)) and Hamilton (4). It was felt that improvements in the v