document: 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 . Two principal issues were studied : -LRB- a -RRB- the number of varieties that could be classified correctly ; and -LRB- b -RRB- various means of preprocessing 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
doc_bio_tags: B I O O O O O O O O O O O O B I I O O B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O B I I I O O B I O O
extractive_keyphrases: wheat variety classification mass spectrometric data gaussian smoothing normalisation procedures multiplicative scatter correction correctly classified mass spectra correlation coefficient variety identification mass spectra processing neural network analysis
abstractive_keyphrases: matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry pre-processing- method