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Standal I.B.,Sintef | Rainuzzo J.,Sintef | Axelson D.E.,MRi Consulting | Valdersnes S.,National Institute of Nutrition And Seafood Research | And 3 more authors.
JAOCS, Journal of the American Oil Chemists' Society | Year: 2012

The aim of this study was to examine Peruvian anchovy oil fatty acid (FA) compositions, and to test the possibility of using the FA data to classify the oils according to geographical origin along the Peruvian coast. The levels of contaminants in a representative set of samples were determined to examine the general levels and investigate if such measurements could aid in future discrimination between oils. The FA results showed that the two known stocks of Peruvian anchovy displayed different levels of docosahexaenoic acid (DHA, 22:6n-3) (southern stock; 14.4 ±003B 0.8% versus central-northern stock; 9.9 ± 1.2%). However, principal component analysis (PCA) of the FA data indicated clusters according to three regions; North, Center and South. Using a data set of 57 anchovy samples and 21 FA as input, a probabilistic neural network (PNN) was constructed. For the validation data sets, ''North'' oils was predicted accurately 100% of the time, ''Center'' oils 100% and ''South'' oils 83% of the time. The levels of contaminants in the oils determined were low in all but one sample. © 2012 AOCS. Source


Standal I.B.,Sintef | Axelson D.E.,MRi Consulting | Aursand M.,Sintef | Aursand M.,Norwegian University of Science and Technology
Lipid Technology | Year: 2011

NMR spectroscopy has been particularly valuable in the study of lipids, since it provides qualititative and quantitative information on chemically diverse compounds in a non-destructive and non-selective fashion. NMR gives a fingerprint of the sample analyzed and may be used as a rapid profiling technique. Combined with chemometrics and databases with relevant authentic samples, 13C NMR is a powerful tool for authentication of marine oils. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Standal I.B.,Sintef | Standal I.B.,Norwegian University of Science and Technology | Axelson D.E.,MRi Consulting | Aursand M.,Sintef
Food Chemistry | Year: 2010

The aim of this study was to evaluate if phospholipid profiles obtained by 13C nuclear magnetic resonance (NMR) spectroscopy is characteristic enough to separate species of lean gadoid fish. 13C NMR data were obtained from muscle lipids of five categories of lean gadoid fish, namely, north-east arctic cod and Norwegian coastal cod (Gadus morhua), haddock (Melanogrammus aeglifinus), saithe (Pollachius virens), and pollack (P. pollachius). A total of 27 fish caught at the same location on the Norwegian coast in the traditional fishing season (March/April) in 2006 were analysed. The sn-2 position specificity of 22:6n-3 (docosahexaenoic acid, DHA) in phosphatidyl choline (PC) and phosphatidyl ethanolamine (PE) for the different species/stocks were investigated, and the full 13C NMR spectra applied in multivariate analysis. Stereospecific distribution calculations showed significant differences among species in the distribution of 22:6n-3 in PC and PE, and the pollack group displayed the lowest values for 22:6n-3 in sn-2 position, both in PC and PE. This first screening showed that by using the 13C NMR fingerprint of muscle lipids, linear discriminant analysis gave a correct classification rate of 78% according to the five categories of lean gadoid fish, while successful classification (100%) was achieved with Bayesian belief networks (BBN) predictions. © 2010 Elsevier Ltd. All rights reserved. Source


Giskeodegard G.F.,Norwegian University of Science and Technology | Grinde M.T.,Norwegian University of Science and Technology | Sitter B.,Norwegian University of Science and Technology | Axelson D.E.,MRi Consulting | And 5 more authors.
Journal of Proteome Research | Year: 2010

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients. © 2010 American Chemical Society. Source

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