Analysis of Moisture Content, Total Oil and Fatty Acid Composition by NIR Reflectance Spectroscopy: A Review
Abstract
Near Infrared (NIR) Reflectance spectroscopy has established itself as an important analytical technique in the field of food and agriculture. It is quicker and easier to use, and does not require processing the samples with corrosive chemicals such as acids or hydroxides. However, in earlier times, the samples had to be ground into powder form before making any measurements. Thanks to the development of new soft ware packages for use with NIR instruments, NIR techniques could be used in the analysis of intact grains and seeds. While most of the commercial instruments presently available work well with small grain size materials such as wheat and corn, they were found to be unsuitable for large kernel size products such as shelled or in-shell peanuts. In this chapter, principles of NIR Reflectance spectroscopy were reviewed, in particular reference to the water and oil bands. Also presented are some recent applications of NIR for the rapid and nondestructive measurement of moisture and total oil contents in shelled and in-shell peanuts. Applicability, and limitations of NIR reflectance method in the analysis of fatty acid composition of different varieties of peanuts while they are in their shells was also discussed. Ability to rapidly and nondestructively measure the water and total oil content, and analyze the fatty acid composition, will be immensely useful in the grading process of grains and nuts.
Keywords
Fatty Acid Composition Partial Less Square Partial Little Square Regression Peanut Kernel Residual Predictive DeviationPreview
Unable to display preview. Download preview PDF.
References
- 1.Seigel, R., Howell, J.R.: Thermal Radiation Heat Transfer, 2nd edn., p. 5. Hemisphere Publishing Corp., Washington (1981)Google Scholar
- 2.Bellamy, L.J.: The Infra-red spectra of complex molecules, 3rd edn. Chapman and Hill, London (1975)Google Scholar
- 3.Wetzel, D.L.: In: Charalambus, G., Inglett, G. (eds.) Instrument Analysis of Foods—Recent Progress, vol. 1, pp. 183–202. Academic Press, USA (1983)Google Scholar
- 4.Kawamura, S., Tsukahara, M., Natsuga, M., Itoh, K.: Transaction of the ASABE. Paper Number: 033026 (2003)Google Scholar
- 5.Savenije, B., Geesink, G.H., Van der Palen, J.G.P., Hemke, G.: Meat Sci. 73, 181–184 (2006), doi:10.1016/j.meatsci.2005.11.006CrossRefGoogle Scholar
- 6.Isaksson, T., Nilsen, B.N., Øgersen, G.T., Hammond, R.P., Hildrum, K.I.: Meat Sci. 43, 245–253 (1996), doi:10.1016/S0309-1740(96)00016-2CrossRefGoogle Scholar
- 7.Abdi, H.H.: In: Lewis-Beck, M., Bryman, A., Futing, T. (eds.) Encyclopedia of Social Sciences Research Methods. Sage, Thousand Oaks (2003)Google Scholar
- 8.Falk Frank, R., Miller, B.N.: A Primer for Soft Modeling. University of Akron, Akron (1992)Google Scholar
- 9.Geladi, P., Kowlaski, B.: Anal. Chem. Acta 35, 1–17 (1986)CrossRefGoogle Scholar
- 10.Aydin, C.: J. Food Eng. 79(3), 810–816 (2006), doi:10.1016/j.jfoodeng.2006.02.045MathSciNetCrossRefGoogle Scholar
- 11.Kandala, C.V.K., Konda Naganathan, G., Subbiah, J.: Paper #7065-28 SPIE Optics. In: Photonics Conference, San Diego, California, USA, pp. 10–14 (August 2008)Google Scholar
- 12.Barry Tillman, L., Daniel Gorbet, W., Person, G.: Crop Sci. 46, 2121–2126 (2006), doi:10.2135/cropsci2006.01.0031CrossRefGoogle Scholar
- 13.Misra, B.J., Mathur, R.S., Bhatt, D.M.: J. Sci. Food Agric. 80, 237–240 (2000), doi:10.1002/(SICI)1097-0010(20000115)80:2237:AID-JSFA523[3.0.CO;29Google Scholar
- 14.Rao, Y., Xiang, B., Zhou, X., Wang, Z., Xie, S., Xu, J.: J. Food Eng. 93(2), 249–252 (2009), doi:10.1016/j.jfoodeng.2009.01.023CrossRefGoogle Scholar
- 15.Norris, K.H.: Trans. ASAE 7, 240 (1964)Google Scholar
- 16.Norris, K.H., Hart, J.R.: In: Wexler, A. (ed.) Principles and Methods of Measuring Moisture in Liquids and Solids, Reinhold, New York, vol. 4, pp. 19–25 (1965)Google Scholar
- 17.Rosenthal, R.D.: The Grain Quality Analyzer. In: 3rd Pacific Northwest Grain Inspection Workshop, Portland, OR (1973)Google Scholar
- 18.Govindarajan, K.N., Kandala, C.V.K., Subbiah, J.: NIR reflectance spectroscopy for nondestructive moisture content determination in peanut kernels. Trans. of the ASABE 52(5) (2009)Google Scholar
- 19.ASAE Standards. S410.1: Moisture measurement —Peanuts. St. Joseph, Mich.: ASAE (1982)Google Scholar
- 20.Sundaram, J., Kandala, C.V.K., Butts, C.L., Windham, W.R.: Application of NIR reflectance spectroscopy for the determination of moisture content of in-shell peanuts: a nondestructive analysis. Trans. of the ASABE 53(1), 183–189 (2009)Google Scholar
- 21.Sundaram, J., Kandala, C.V.K., Holser, R.A., Windham, W.R., Butts, C.L.: Determination of in-shell peanut moisture, oil and fatty acid composition using Near Infrared Reflectance Spectroscopy. J. American Oil Society (2010), doi:10.1007/s11746-010-1589-7Google Scholar
- 22.Osborne, B.G., Fearn, T.: Applications of near-infrared spectroscopy in food analysis. In: Osborne, B.G., Fearn, T., Hindle, P.H. (eds.) Near-Infrared Spectroscopy in Food Analysis, 2nd edn. Longman, New York (1993)Google Scholar
- 23.Sato, T.: New estimation method for fatty acid composition in oil using near infrared spectroscopy. Biosci. Biotechnol. Biochem. 66, 2453–2458 (2002)Google Scholar
- 24.Williams, P., Norris, K.: Near-infrared technology in the agricultural and food industries, 2nd edn. AACC, St. Paul (2001)Google Scholar
- 25.Pazdernik, D.L., Killam, A.S., Orf, J.H.: Analysis of amino and fatty acid composition in soybean seed, using near infrared reflectance spectroscopy. Agron. J. 89, 679–685 (1997)CrossRefGoogle Scholar