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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

NIR:

Near-infrared

ARA:

Arachidonic acid

PUFA:

Polyunsaturated fatty acids

FAMEs:

Fatty acid methyl esters

FID:

Flame ionization detector

SNV:

Standard normal variate

MSC:

Multiplicative scatter correction

WT:

Wavelet transforms

OSC:

Orthogonal signal correction

PLS:

Partial Least-Squares

RMSEC:

Root mean square error of calibration

RMSECV:

Root mean square error of cross-validation

RMSEP:

Root mean square error of prediction

References

  1. 1.

    Bouwstra H, Dijck-Brouwer DA, Wildeman JAL, Tjoonk HM, van der Heide JC, Boersma ER, Muskiet FAJ, Hadders-Algra M (2003) Long-chain polyunsaturated fatty acids have a positive effect on the quality of general movements of healthy term infants. Am J Clin Nutr 78:313

    CAS  PubMed  Google Scholar 

  2. 2.

    Larque E, Demmelmair H, Koletzko B (2002) Perinatal supply and metabolism of long-chain polyunsaturated fatty acids. Ann NY Acad Sci 967:299–310

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Uauy R, Hoffman DR, Mena P, Llanos A, Birch EE (2003) Term infant studies of DHA and ARA supplementation on neurodevelopment: results of randomized controlled trials. J Pediatr 143:17–25

    Article  Google Scholar 

  4. 4.

    Carlson SE (2001) Docosahexaenoic acid and arachidonic acid in infant development. Semin Neonatol 6:437–449

    Article  CAS  PubMed  Google Scholar 

  5. 5.

    Putnam JC, Carlson SE, DeVoe PW, Barness LA (1982) The effect of variations in dietary fatty acids on the fatty acid composition of erythrocyte phosphatidylcholine and phosphatidylethanolamine in human infants. Am J Clin Nutr 36:106

    CAS  PubMed  Google Scholar 

  6. 6.

    Sanders TAB, Naismith DJ (2007) A comparison of the influence of breast-feeding and bottle-feeding on the fatty acid composition of the erythrocytes. Brit J Nutr 41:619–623

    Article  Google Scholar 

  7. 7.

    FAO (1994) Fats and oils in human nutrition: report of a joint expert consultation. FAO Food Nutr Pap 57:1–147

    Google Scholar 

  8. 8.

    Blanco M, Villarroya I (2002) NIR spectroscopy: a rapid-response analytical tool. Trends Analyt Chem 21:240–250

    Article  CAS  Google Scholar 

  9. 9.

    Wu D, Feng S, He Y (2007) Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder. J Dairy Sci 90:3613

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    Koprna R, Nerusil P, Kolovrat O, Kucera V, Kohoutek A (2006) Estimation of fatty acid content in intact seeds of oilseed rape (Brassica napus L.) lines using near-infrared spectroscopy. Czech J Genet Plant Breed 42:132

    Google Scholar 

  11. 11.

    Brenna OV, Berardos N (2004) Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize. J Agric Food Chem 52:5577–5582

    Article  CAS  PubMed  Google Scholar 

  12. 12.

    Szlyk E, Szydlwska-Czerniak A, Kowalczyk-Marzec A (2005) NIR spectroscopy and partial least-squares regression for determination of natural alpha-tocopherol in vegetable oils. J Agric Food Chem 53:6980–6987

    Article  CAS  PubMed  Google Scholar 

  13. 13.

    AOCS (2007) Determination of cis-, trans-, saturated, monounsaturated, and poly unsaturated fatty acids in dairy and ruminant fats by capillary GLC. AOCS Press Champaign

  14. 14.

    AOCS (1997) Preparation of methyl ester of fatty acids. AOCS Press Champaign

  15. 15.

    Helland IS, Naes T, Isaksson T (1995) Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data. Chemo Intell Lab Syst 29:233–241

    Article  CAS  Google Scholar 

  16. 16.

    Luypaert J, Heuerding S, Heyden YV, Massart DL (2004) The effect of preprocessing methods in reducing interfering variability from near-infrared measurements of creams. J Pharm Biomed 36:495–503

    Article  CAS  Google Scholar 

  17. 17.

    Gorry PA (1990) General least-squares smoothing and differentiation by the convolution (Savitzky–Golay) method. Anal Chem 62:570–573

    Article  CAS  Google Scholar 

  18. 18.

    Barnes RJ, Dhanoa MS, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777

    Article  CAS  Google Scholar 

  19. 19.

    Isaksson T, Naes T (1988) The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl Spectrosc 42:1273–1284

    Article  CAS  Google Scholar 

  20. 20.

    Fearn T, Naes T, Isaksson T, Davies T (2002) A user-friendly guide to multivariate calibration and classification. In: NIR publications, Chichester, UK

  21. 21.

    Hourant P, Baeten V, Morales MT, Meurens M, Aparicio R (2000) Oil and fat classification by selected bands of near-infrared spectroscopy. Appl Spectrosc 54:1168–1174

    Article  CAS  Google Scholar 

  22. 22.

    Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18:72–83

    Article  CAS  Google Scholar 

Download references

Acknowledgments

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mingyong Xie.

About this article

Cite this article

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). https://doi.org/10.1007/s11745-010-3423-2

Download citation

Keywords

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