Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
권호 표지
DOI QR코드

DOI QR Code

Rancidity Prediction of Soybean Oil by Using Near-Infrared Spectroscopy Techniques

  • Hong, Suk-Ju (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Lee, Ah-Yeong (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Han, Yun-hyeok (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Park, Jongmin (Department of Bio-industrial Machinery Engineering, Pusan National University) ;
  • So, Jung Duck (Department of Mechanical Systems Engineering, Jeonju University) ;
  • Kim, Ghiseok (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University)
  • Received : 2018.07.09
  • Accepted : 2018.08.30
  • Published : 2018.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: This study evaluated the feasibility of a near-infrared spectroscopy technique for the rancidity prediction of soybean oil. Methods: A near-infrared spectroscopy technique was used to evaluate the rancidity of soybean oils which were artificially deteriorated. A soybean oil sample was collected, and the acid values were measured using titrimetric analysis. In addition, the transmission spectra of the samples were obtained for whole test periods. The prediction model for the acid value was constructed by using a partial least-squares regression (PLSR) technique and the appropriate spectrum preprocessing methods. Furthermore, optimal wavelength selection methods such as variable importance in projection (VIP) and bootstrap of beta coefficients were applied to select the most appropriate variables from the preprocessed spectra. Results: There were significantly different increases in the acid values from the sixth days onwards during the 14-day test period. In addition, it was observed that the NIR spectra that exhibited intense absorption at 1,195 nm and 1,410 nm could indicate the degradation of soybean oil. The PLSR model developed using the Savitzky-Golay $2^{nd}$ order derivative method for preprocessing exhibited the highest performance in predicting the acid value of soybean oil samples. onclusions: The study helped establish the feasibility of predicting the rancidity of the soybean oil (using its acid value) by means of a NIR spectroscopy together with optimal variable selection methods successfully. The experimental results suggested that the wavelengths of 1,150 nm and 1,450 nm, which were highly correlated with the largest absorption by the second and first overtone of the C-H, O-H stretch vibrational transition, were caused by the deterioration of soybean oil.

Keywords

References

  1. AOCS. 1977. Official Methods and Recommended Practices of the American Oil Chemists' Society, method Te 1a-64. Champaign, IL, USA: American Oil Chemists' Society.
  2. ASTM International. 2000. American Society for Testing and Materials, E1866-97: Standard Guide for Establishing Spectrophotometer Performance Tests; E1655: Standard Practices for Infrared, Multivariate, Quantitative Analysis. Philadelphia, PA, USA: Official ASTM Publications.
  3. Ben-Dor, E., J. R. Irons and G. F. Epema. 1999. Remote Sensing for the Earth Sciences: Manual of Remote Sensing. 3rd ed. New York: John Wiley & Sons.
  4. Bickel, P. J. and D. A. Freedman. 1981. Some Asymptotic Theory for the Bootstrap. The Annals of Statistics 9(6): 1196-1217. https://projecteuclid.org/euclid.aos/1176345637 https://doi.org/10.1214/aos/1176345637
  5. Choe, E. and D. B. Min. 2006. Mechanisms and factors for edible oil oxidation. Comprehensive reviews in food science and food safety 5(4): 169-186. https://doi.org/10.1111/j.1541-4337.2006.00009.x
  6. Choe, E., F. van der Meer, F. van Ruitenbeek, H. van der Werff, B. de Smeth, and K. W. Kim. 2008. Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: a case study of the Rodalquilar mining area, SE Spain. Remote Sensing of Environment 112(7): 3222-3233. https://doi.org/10.1016/j.rse.2008.03.017
  7. Chung, H.I. and H.J. Kim. 2000. Near-Infrared spectroscopy: principles. Analytical science & technology 13(1): 138-151. (In Korean, with English abstract).
  8. Chung, H. K., C. S. Choe, J. H. Lee, M. J. Chang, and M. H. Kang. 2003. Oxidative stability of the pine needle extracted oils and sensory evaluation of savored laver made by extracted oils. Journal of the Korean Society of Food Culture 18(2): 89-95.
  9. Dull, G.G., R.G. Leffler, G.S. Birth and D.A. Smittle. 1992. Instrument for non-destructive measurement of soluble solids in honeydew melons. Transactions of American Society of Agricultural Engineers 35: 735-737. http://elibrary.asabe.org/azdez.asp?AID=28656&T=2 https://doi.org/10.13031/2013.28656
  10. Geladi, P. and B. R. Kowalski. 1986. Partial least-squares regression: a tutorial. Analytica chimica acta 185: 1-17. https://doi.org/10.1016/0003-2670(86)80028-9
  11. Helland, I. S. 1988. On the structure of partial least squares regression. Communications in statistics-Simulation and Computation 17(2): 581-607. https://doi.org/10.1080/03610918808812681
  12. Joshi, R., C. Mo, W.-H. Lee, S. H. Lee and B.-K. Cho. 2015. Review of rice quality under various growth and storage conditions and its evaluation using spectroscopic technology. Journal of Biosystems Engineering 40(2): 124-136. https://doi.org/10.5307/JBE.2015.40.2.124
  13. Kamruzzaman, M., G. ElMasry, D.-W. Sun and P. Allen. 2013. Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry 141(1): 389-396. https://doi.org/10.1016/j.foodchem.2013.02.094
  14. Kandpal, L.M., S. Lohumi, M.S. Kim, J.-S. Kang and B.-K, Cho. 2016. Near-Infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sensors and Actuators B: Chemical 229: 534-544. https://doi.org/10.1016/j.snb.2016.02.015
  15. Kawano, S., H. Watanabe and M. Iwamoto. 1992. Determination of sugar content in intact peaches by near infrared spectroscopy journal of Japanese society of horticultural science 61(2): 445-451. https://doi.org/10.2503/jjshs.61.445
  16. Kim, Y.T. and S.R. Suh. 2008. Comparison of performance of models to predict hardness of tomato using spectroscopic data of reflectance and transmittance. Journal of Biosystems Engineering 33(1): 63-68. (In Korean, with English abstract) https://doi.org/10.5307/JBE.2008.33.1.063
  17. Kweon, G. Y., E. Lund, C. Maxton, P. Drummond and K. Jensen. 2009. Soil profile measurement of carbon contents using a probe-type VIS-NIR spectrophotometer. Journal of Biosystems Engineering 34(5): 382-389. https://www.e-sciencecentral.org/articles/SC000015422 https://doi.org/10.5307/JBE.2009.34.5.382
  18. Lazraq, A., R. Cleroux and J.-P. Gauchi. 2003. Selecting both latent and explanatory variables in the PLS1 regression model. Chemometrics and Intelligent Laboratory Systems 66(2): 117-126. https://doi.org/10.1016/S0169-7439(03)00027-3
  19. Lee, C.J., H.W. Park, C.O. Huh, G.Y. Kim, M.H. Kang, S.S. Cho and S.J. Kim. 1997. A study on the using frequency of edible oils and food development by using edible oil in food service operation. Food Industry and Nutrition. 2: 33-40. (In Korean, with English abstract).
  20. Lee, K.J., W. R. Hruschka, J. A. Abbott, S. H. Noh and B. S. Park. 1998, Predicting the soluble solids of apples by near infrared spectroscopy (II) -PLS and ANN Models-. Journal of Biosystems Engineering 23(6): 571-582. (In Korean, with English abstract). http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=NOGGB5_1998_v23n6_571
  21. May, W. A., R. J. Peterson, and S. S. Chang. 1983. Chemical reactions involved in the deep-fat frying of foods: IX. Identification of the volatile decomposition products of triolein. Journal of the American Oil Chemists' Society 60(5): 990-995. https://link.springer.com/article/10.1007/BF02660214
  22. Mehmood, T., K. H. Liland, L. Snipen and S. Saebo. 2012. A review of variable selection methods in partial least squares regression. Chemometrics and Intelligent Laboratory Systems 118: 62-69. https://doi.org/10.1016/j.chemolab.2012.07.010
  23. Park, G.Y., A.K. Kim, K.A. Park, B.K. Jung, C.H. Bae and M.H. Kim. 2003. Acidification of frying oil used for chicken. Journal of Food Hygiene and Safety 18(1): 36-41. (In Korean, with English abstract). http://db.koreascholar.com/article.aspx?code=344259
  24. Porep, J. U., D. R. Kammerer and R, Carle. 2015. On-line application of near infrared (NIR) spectroscopy in food production. Trends in Food Science & Technology 46(2): 211-230. https://doi.org/10.1016/j.tifs.2015.10.002
  25. Rinnan, A., F. van den Berg and S.B. Engelsen. 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28(10): 1201-1222. https://doi.org/10.1016/j.trac.2009.07.007
  26. Sarathjith, M.C., B.S. Das, S. P. Wani and K.L. Sahrawat. 2016. Variable indicators for optimum wavelength selection in diffuse reflectance spectroscopy of soils. Geoderma 267(1): 1-9. https://doi.org/10.1016/j.geoderma.2015.12.031
  27. Summerfield, F. W and A. L. Tappel, "Detection and measurement by high-performance liquid chromatography of malondialdehyde crosslinks in DNA," Analytical Biochemistry, vol. 143, no. 2, pp. 265-271, 1984. https://doi.org/10.1016/0003-2697(84)90662-6
  28. Wold, S., M. Sjostrom and L. Eriksson. 2001. PLSregression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems 58(2): 109-130. https://doi.org/10.1016/S0169-7439(01)00155-1
  29. Zhang, C., H. Jiang, F. Liu and Y. He. 2017. Application of near-infrared hyperspectral imaging with variable selection methods to determine and visualize caffeine content of coffee beans. Food and Bioprocess Technology 10(1): 213-221. https://link.springer.com/article/10.1007/s11947-016-1809-8