Near-Infrared Spectroscopy and Partial Least-Squares Regression for Determination of Arachidonic Acid in Powdered Oil


Near-infrared (NIR) spectroscopy was evaluated as a rapid method of predicting arachidonic acid content in powdered oil without the need for oil extraction. NIR spectra of powdered oil samples were obtained with an NIR spectrometer and correlated with arachidonic acid content determined by a modification of the AOCS Method. Partial Least-Squares regression was applied to calculate models for the prediction of arachidonic acid. The model developed with the raw spectra had the best performance in cross-validation (n = 72) and validation (n = 21) with a correlation coefficient of 0.965, and the root mean square error of cross-validation and prediction were both 0.50. The results show that NIR, a well-established and widely applied technique, can be applied to determine the arachidonic acid content in powdered oil.

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Arachidonic acid


Polyunsaturated fatty acids


Fatty acid methyl esters


Flame ionization detector


Standard normal variate


Multiplicative scatter correction


Wavelet transforms


Orthogonal signal correction


Partial Least-Squares


Root mean square error of calibration


Root mean square error of cross-validation


Root mean square error of prediction


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The authors gratefully acknowledge the financial support provided by National Key Technology R&D Program (No. 2006BAD27B04), Changjiang Scholars and Innovative Research Team in the University (No: IRT0540), and Nanchang University Testing Fund (No.2008034).

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Correspondence to Mingyong Xie.

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Yang, M., Nie, S., Li, J. et al. Near-Infrared Spectroscopy and Partial Least-Squares Regression for Determination of Arachidonic Acid in Powdered Oil. Lipids 45, 559–565 (2010).

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  • Arachidonic acid
  • Near-infrared spectroscopy
  • Partial least-squares regression