光谱学与光谱分析 |
|
|
|
|
|
The Rapid Analysis of Fatty Acids in Vegetable Oils by Near Infrared Spectrum |
YU Yan-bo1,ZANG Peng1,FU Yuan-hua1,ZHANG Lu-da2,YAN Yan-lu3,CHEN Bin1* |
1. China Astronaut Research and Training Center, Beijing 100094, China 2. College of Science, China Agricultural University, Beijing 100094, China 3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China |
|
|
Abstract In this research, The functional components of vegetable oils were analyzed by near infrared(NIR) spectral technology. The optimum conditions of mathematics model of four components(C16:0,C18:0,C18:1,C18:2)were studied, including the sample set selection, chemical value analysis, the detection methods and condition. Chemical value was analyzed by HPLC. 52 samples were selected, 41 for modeling set and 11 for testing set. All samples were placed in 5mm thick sample pools and swept by near infrared(NIR) with discrimination factor 8 cm-1 without any other disposal. Using PLS methods sated model. Data were processed by first derivative method and centering method. 5 000-9 000 cm-1 spectral region was analyzed. Correlating index (r), RMSECV and RMSEP were chose as evaluation index. The result demonstrated that the correlation between the reference value of the modeling sample set and the near infrared predictive value were r(C16:0)=0.891, r(C18:0)=0.837, r(C18:1)=0.982, r(C18:2)=0.971, respectively. And the correlation between the reference value of the testing sample set and the near infrared predictive value were 0.921, 0.891, 0.946 and 0.949, respectively. It proved that the near infrared predictive value was linear with chemical value and the mathematical model established for components of vegetable oils was feasible. For validation, 8 unknown samples were selected to be analysis by infrared(NIR). The result demonstrated that error between predict value and chemical value was less than 10%. That was to say infrared (NIR) had a good veracity in analysis components of vegetable oil. Because infrared(NIR) spectral technology is convenient, rapid than HPLC in oil components analysis, moreover, infrared(NIR) can analyze many components at the same time. It must have great application prospect in vegetable oil components analysis.
|
Received: 2007-03-12
Accepted: 2007-06-16
|
|
Corresponding Authors:
CHEN Bin
E-mail: chenb12@yahoo.com.cn
|
|
[1] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai,et al(严衍禄,赵龙莲,韩东海,等). Basis and Aapplication of Near Infrared Reflectance Spectroscopy(近红外光谱分析基础与应用). Beijing:China Light Industry Press(北京:中国轻工业出版社), 2005. 12. [2] LIU Ai-qiu, DENG Xiao-jian, WANG Ping-rong, et al(刘爱秋,邓晓建,王平荣,等). Southwest China Journal of Agriculture Science(西南农业学报),2003, 16(20):98. [3] YAN Yan-lu, ZHAO Long-lian, LI Jun-hui, et al(严衍禄,赵龙莲,李军会,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(6):777. [4] Cozzolino D, Murray I, Chree A, et al. LWT-Food Science and Technology, 2005, 38(8):821. [5] Man Y B Che, Moh M H. Journal of American Oil Chemists Society, 1998, 75(5):557. [6] Sato T. Lipid- Technology,1997,9(2):486. [7] Li Hui, van de Voort F R, Ismail A A, et al. Journal of American Oil Chemists Society, 2000, 77(1):29. [8] ZHAO Wu-shan, CHEN Yun-bo, LI Xiang-yang, et al (赵武善,陈云波,李向阳, 等). China Oils and Fats(中国油脂), 2003, 28(9):38. [9] Li Hui, van de Voort F R, Ismail A A, et al. Journal of American Oil Chemists Society, 2000, 77(2):137. [10] Voort F R vandc, Memon K. P. Journal of the American Oil Chemists Society, 1996, 73(4):411. [11] WU Jian-guo, SHI Chun-hai, ZHANG Hai-zhen(吴建国,石春海,张海珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(2):259. [12] YANG Cui-ling, CHEN Wen-jie, ZHANG Wen-xue, et al(杨翠玲,陈文杰,张文学,等). Acta Agriculturae Boreali-Occidentalis Sinica(西北农业学报), 2005, 14(6):72. [13] Tetsuo Sa To. Bioscience, Biotechnology and Biochemistry, 2002, 66(12):2543. [14] XU Yong-qun, TANG Jun-ming(徐永群, 汤俊明). Henan Sciences(河南科学), 2002, 20(3):245. [15] SU Yue, GUO Yin-long(苏 越,郭寅龙). Computers and Applied Chemistry(计算机与应用化学),2001, 18(3):237. [16] CHEN Bin, ZHAO Long-lian, LI Jun-hui, et al(陈 斌,赵龙莲,李军会,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2002, 22(16):976. [17] MIN Shun-geng, LI Ning, ZHANG Ming-xiang (闵顺耕,李 宁,张明祥). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(10):1205. [18] WANG Tao, ZHANG Lu-da, LAO Cai-lian, et al(王 韬,张录达,劳采莲,等). Journal of China Agricultural University(中国农业大学学报), 2004, 9(6):76. [19] ZHU Shi-ping, WANG Yi-ming, ZHANG Xiao-chao, et al(祝诗平,王一鸣,张小超,等). Transaction of the Chinese Society for Agriculture Machinery(农业机械学报), 2004, 35(4):115. [20] ZHANG Jun, ZHENG Yong-mei, WANG Fang-rong, et al(张 军,郑咏梅,王芳荣,等). Journal of Jilin University(Information Science Edication)(吉林大学学报·信息科学版), 2003, 21(1):4. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|