Low-field H-1 nuclear magnetic resonance and chemometrics combined for simultaneous determination of water, oil, and protein contents in oilseeds

Prediction of the content of water, oil, and protein in rape and mustard se ed was examined by a combination of low-field H-1 nuclear magnetic resonanc e (LF-NMR) and chemometrics, enabling utilization of the entire relaxation curves in the data evaluation. To increase the range of relative contents, the untreated seeds were wetted and dried; each treatment was followed by N MR analysis, The chemometric results are compared to traditional evaluation by multiexponential fitting of the relaxation curves. For this purpose, a new JackKnife validation procedure was developed to evaluate the number of exponential components objectively. Classification of the two kinds of seed s was easily performed by LF-NMR. Partial least squares regression to oil c ontent in untreated rape and mustard seed yielded models with correlation c oefficients of r = 0.88 and 0.89 with root mean square error of cross-valid ation (RMSECV) of 0.84 and 0.45, respectively. The rapeseed model was based on one component, whereas the mustard seed model was based on two componen ts. If the seeds were dried, the predictive performance improved to r = 0.9 8 and RMSECV = 0.36 for rapeseed and to r = 0.95 and RMSECV = 0.38 for must ard seed. Upon drying, prediction of protein content in mustard seed improv ed, whereas the prediction of protein for rapeseed deteriorated. Global mod els, including the combination of untreated, wet, and dry seeds, all result ed in a robust and good predictive performance with RMSECV in the range 0.8 -1.3% to water, oil, and protein content. It was demonstrated that drying t he seeds to simultaneously determine water and oil content was not necessar y when chemometrics was applied on the relaxation curves.