Rapid Determination of Fatty Acids in Soybean Oils by Transmission Reflection-Near Infrared Spectroscopy
SONG Tao1, ZHANG Feng-ping1, 2*, LIU Yao-min1, WU Zong-wen1, SUO You-rui3
1. Inspection Center of Tongwei Co., Ltd., Chengdu 610041, China 2. College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China 3. Northwest Institute of Plateau Biology，Chinese Academy of Sciences，Xining 810008, China
Abstract：In the present research, a novel method was established for determination of five fatty acids in soybean oil by transmission reflection-near infrared spectroscopy. The optimum conditions of mathematics model of five components(C16∶0, C18∶0 , C18∶1, C18∶2 and C18∶3) were studied, including the sample set selection, chemical value analysis, the detection methods and condition. Chemical value was analyzed by gas chromatography. One hundred fifty eight samples were selected, 138 for modeling set, 10 for testing set and 10 for unknown sample set. All samples were placed in sample pools and scanned by transmission reflection-near infrared spectrum after sonicleaning for 10 minute. The 1 100~2 500 nm spectral region was analyzed. The acquisition interval was 2 nm. Modified partial least square method was chosen for calibration mode creating. Result demonstrated that the 1-VR of five fatty acids between the reference value of the modeling sample set and the near infrared spectrum predictive value were 0.883 9, 0.583 0, 0.900 1, 0.977 6 and 0.959 6, respectively. And the SECV of five fatty acids between the reference value of the modeling sample set and the near infrared spectrum predictive value were 0.42, 0.29, 0.83, 0.46 and 0.21, respectively. The standard error of the calibration (SECV) of five fatty acids between the reference value of testing sample set and the near infrared spectrum predictive value were 0.891, 0.790, 0.900, 0.976 and 0.942, respectively. It was proved that the near infrared spectrum predictive value was linear with chemical value and the mathematical model established for fatty acids of soybean oil was feasible. For validation, 10 unknown samples were selected for analysis by near infrared spectrum. The result demonstrated that the relative standard deviation between predict value and chemical value was less than 5.50%. That was to say that transmission reflection-near infrared spectroscopy had a good veracity in analysis of fatty acids of soybean oil.
Key words：Near-infrared spectroscopy;Transmission reflection;Modified partial least square;Fatty acid;Soybean oil
[1] Barlows S. Advocate, 2000, 3(2): 85. [2] Regost C, Arzel J, Robin J, et al. Aquaculture, 2003, 217 : 465. [3] Peng S M, Chen L Q, Qin J G, et al. Aquaculture, 2008, 276: 154. [4] Grisdale-Helland B, Ruyter B, Rosenlund G, et al. Aquaculture, 2002, 207: 311. [5] ISO 5509-2000. Animal and Vegetable Fats and Oils —Preparation of Methyl Esters of Fatty Acids, International Standard of International Organization for Standardization. [6] ISO 5508-1990. Animal and Vegetable Fats and Oils —Analysis by Gas Chromatography of Methyl Esters of Fatty Acids, International Standard of International Organization for Standardization. [7] Sato T, Kawano S, Lwarnoto M. J. Am. Oil Chem. Soc., 1991, 68(11): 827. [8] Sato T. J. Am. Oil Chem. Soc., 1994, 71(3): 293. [9] Sato T. Biosci. Biotechnol. Biochem, 2002, 66(12): 2543. [10] Azizian H, Kramer J K G, Winsborough S. Eur. J. Lipid Sci. Technol., 2007, 109: 960. [11] Galtier O, Dupuya N, Le Dreau Y, et al. Analytica Chimica Acta, 2007, 595: 136. [12] LI Juan, FAN Lu, DEMG De-wen, et al(李 娟，范 璐，邓德文，等). Journal of Henan University of Technology (Natural Science Edition)(河南工业大学学报·自然科学版), 2008, 29(5): 18. [13] Hormoz Azizian H, Kramer J K G, Ehler S. Eur. J. Lipid Sci. Technol., 2010, 112: 452. [14] Dupuy N, Galtier O, Ollivier D, et al. Analytica Chimica Acta, 2010, 666: 23. [15] Casale M, Zunin P, Cosulich M E, et al. Food Chemistry, 2010, 122: 1261. [16] Maggio R M, Valli E, Bendini A, et al. Food Chemistry, 2011, 127: 216. [17] Xie L J, Ye X Q, Liu D H, et al. Food Chemistry, 2009, 114: 1135. [18] Ferrer-Gallego R, Hernández-Hierro J M, Rivas-Gonzalo J C, et al. LWT - Food Science and Technology, 2011, 44: 847.
