光谱学与光谱分析 |
|
|
|
|
|
Qualitative-Quantitative Analysis of Rice Bran Oil Adulteration Based on Laser Near Infrared Spectroscopy |
TU Bin1, SONG Zhi-qiang1, ZHENG Xiao1*, ZENG Lu-lu1, YIN Cheng1, HE Dong-ping2, QI Pei-shi3 |
1. School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China 2. College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China 3. Pashun Group, Wuhan 430023, China |
|
|
Abstract The purpose of this study is mainly to have qualitative-quantitative analysis on the adulteration in rice bran oil by near-infrared spectroscopy analytical technology combined with chemo metrics methods. The author configured 189 adulterated oil samples according to the different mass ratios by selecting rice bran oil as base oil and choosing soybean oil, corn oil, colza oil, and waste oil of catering industry as adulterated oil. Then, the spectral data of samples was collected by using near-infrared spectrometer, and it was pre-processed through the following methods, including without processing, Multiplicative Scatter Correction(MSC), Orthogonal Signal Correction(OSC), Standard Normal Variate and Standard Normal Variate transformation De-Trending(SNV_DT). Furthermore, this article extracted characteristic wavelengths of the spectral datum from the pre-processed date by Successive Projections Algorithm(SPA), established qualitatively classified calibration methods of adulterated oil through classification method of Support Vector Machine(SVM), optimized model parameters(C, g) by Mesh Search Algorithm and determined the optimal process condition. In extracting characteristic wavelengths of the spectral datum from pretreatment by Backward interval Partial Least Squares(BiPLS) and SPA, quantitatively classified calibration models of adulterated oil through Partial Least Squares(PLS) and Support Vector Machine Regression(SVR) was established respectively. In the end, the author optimized the combination of model parameters(C, g) by Mesh Search Algorithm and determined the optimal parameter model. According to the analysis, the accuracy of prediction set and calibration set for SVC model reached 95% and 100% respectively. Compared with the prediction of the adulteration oil content of rice bran oil which was established by the PLS model, the SVR model is the better one, although both of them could implement the content prediction. Furthermore, the correlation coefficient R is above 0.99 and the Root Mean Square Error (MSE) is below 5.55×10-4. The results show that the near-infrared spectroscopy technology is effective in qualitative-quantitative analysis on the adulteration of rice bran oil. And the method is applicable to analyze adulteration in other oils.
|
Received: 2014-08-08
Accepted: 2014-12-16
|
|
Corresponding Authors:
ZHENG Xiao
E-mail: zhengxiao@whpu.edu.cn
|
|
[1] WANG Rui-yuan(王瑞元). Cereal and Food Industry(粮食与食品工业),2013,20(2):1. [2] LI Li-te(李里特). Cereal and Food Industry(粮食与食品工业),2012,19(6):3. [3] BAI Juan,FANG Hong(白 娟,方 红). International Journal of Geriatrics(国际老年医学杂志),2013,34(5):222. [4] WU Li-rong(武丽荣). China Oils and Fats(中国油脂),2014,39(6):62. [5] LU Wan-zhen,YUAN Hong-fu,XU Guang-tong(陆婉珍,袁洪福,徐广通). Modern Near infrared Spectroscopy Analytical Technology,Second Edition(现代近红外光谱分析技术,第2版). Beijing:China Petrochemical Press(北京:中国石化出版社),2006. [6] WEN Zhen-cai,SUN Tong,GENG Xiang,et al(温珍才,孙 通,耿 响,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2013,33(9):2354. [7] Xu Jiasheng,Zhang Jie. Chemical Research and Application,2013,25(3):355. [8] XU Guang-tong,LU Wan-zhen(徐广通,陆婉珍). Acta Petrolei Sinica·Petroleum Processing Section(石油学报·石油加工),2001,17(2):91. [9] CHEN Bin,LIU Ge,ZHANG Xian-ming(陈 彬,刘 阁,张贤明). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2013,33(11):2959. [10] Nérgaard L,Saudland A,Wagner J,et al. Applied Spectroscopy,2000,54:413. [11] WANG Li-qi,KONG Qing-ming,LI Gui-bin,et al(王立琦,孔庆明,李贵滨,等). Food Science(食品科学),2011,32(9):97. [12] Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York:Springer-Verlag,1999. [13] WANG Xia,WANG Zhan-qi,JIN Gui,et al(王 霞,王占岐,金 贵,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2014,30(4):204. [14] DONG Hai-sheng,ZHANG Li-fen,ZHONG Yue,et al(董海胜,张丽芬,钟 悦,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2013,33(5):1253. [15] Shen Xiong,Zheng Xiao,Song Zhi-qiang,et al. Springer,2012,(10):11. [16] SONG Zhi-qiang,SHEN Xiong,ZHENG Xiao,et al(宋志强,沈 雄,郑 晓,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2013,33(8):2079.
|
[1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[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] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[4] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[5] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[10] |
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. |
[11] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[12] |
YANG Chun-mei1, ZHU Zan-bin1, 2*, LI Yu-cheng1, MA Yan1, SONG Hai-yang3. Bark Content Determination of Ultra-Thin Fibreboard by
Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3266-3271. |
[13] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
[14] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
[15] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
|
|
|
|