Sundekilde UK
1
, Clausen MR
1
, Larsen LB
2
& Bertram HC
1
1 Department of Food Science, Årslev, Science and Technology, Aarhus University, Denmark
2 Department of Food Science, Foulum, Science and Technology, Aarhus University, Denmark
The use of NMR metabonomics in assessing milk quality is an attractive approach as it is rapid, non-destructive and gives reproducible results. However, whole milk is an emulsion of high and low molecular weight constituents some of which give rise to broad NMR peaks, thus care must be taken in order to get high quality data, which in turn shows promising possibilities for the prediction of technological and nutritional quality of the milk.
In the joint Danish-Swedish Milk Genomics Initiative milk from 1200 cows originating from different farms in Denmark and Sweden comprising three different breeds have been sampled and screened. This large-scale screening of milk phenotypes include many different analyses including metabolic profiling, mineral and fatty acid composition and assessment of functional properties of the milk. The present study encompasses skimmed milk samples from 400 Danish Holstein-Friesian cows. Proton nuclear magnetic resonance (NMR) is applied for metabonomic profiling of these individual milk samples using a high-resolution 600 MHz NMR spectrometer. The present study aims at elucidating whether centrifugation or filtration of samples selectively removes important metabolites from the NMR profile and if different preprocessing steps are able to highlight interesting metabolites.
Following NMR measurements the spectra must be phase and baseline corrected and a number of preprocessing steps must be performed in order to obtain reliable data. Alignment can either be done by shifting the entire spectrum ( 1 ) or by interval Correlation Optimized shifting ( 2 ). Small shifts can also be dealt with by data reduction methods such as binning. Thus, we have evaluated the use of the entire resolution i.e. no binning, using fixed width bins of varying size (typically
0.01-0.1 ppm; ( 3 ) and by using adaptive, intelligent binning algorithms ( 4 ). Normalization is another crucial preprocessing step. In the present study we have tested absence of normalization, normalization to TSP, integral normalization, and probabilistic quotient normalization ( 5 ). The final step before multivariate data analysis is the data scaling, which enables non dominant metabolites to influence the model behavior. Hence, we also test pareto scaling, unit variance scaling, range scaling ( 6 ), VAST scaling ( 7 ), and log or power transformations ( 8 ). Furthermore, useful techniques for variable selection are discussed.
In summary, the present study aims at maximizing the output of a single set of milk samples using different sample preparation and preprocessing techniques as outlined above.
Acknowledgements
The present Ph.d.-project is part of a joint Swedish/Danish ”Milk Genomics Initiative” funded by FØSU, Danish Cattle
Federation and Faculty of Agricultural Sciences, Aarhus University.

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