Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable for bread-making purposes) were investigated. The samples were collected from grains from 15 until 45 days post-anthesis (dpa). Gluten proteins from wheat grains were extracted and subsequently analysed by mass spectrometry. Discrimination partial least-squares regression and soft independent modelling of class analogy were used to determine the quality of new and unknown wheat samples. With these methods, we were able to predict correctly the end-use qualities at every stage investigated. This new fast technique, based on the rapidity of mass spectrometry combined with the objectivity of multivariate data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later.

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.

Rapid methods for the identification of wheat varieties and their end-use quality have been developed. The methods combine the analysis of wheat protein extracts by mass spectrometry with partial least-squares regression in order to predict the variety or end-use quality of unknown wheat samples. The whole process takes approximately 30 min. Extracts of alcohol-soluble storage proteins (gliadins) from wheat were analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Partial least-squares regression was subsequently applied using these mass spectra for making models that could predict the wheat variety or end-use quality. Previously, an artificial neural network was used to identify wheat varieties based on their protein mass spectra profiles. The present study showed that partial least-squares regression is at least as useful as neural networks for this identification. Furthermore, it was demonstrated that partial least-squares regression could be used to predict wheat end-use quality, which has not been possible using neural networks.