Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis.
The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.
Sørensen
Helle Aagaard
HA
BioCentrum-DTU, Biochemistry and Nutrition, Søltofts Plads, Technical University of Denmark, Building 224, DK-2800 Kgs. Lyngby, Denmark.
Sperotto
Maria Maddalena
MM
Petersen
Marianne
M
Keşmir
Can
C
Radzikowski
Louise
L
Jacobsen
Susanne
S
Søndergaard
Ib
I