| 光谱学与光谱分析 |
|
|
|
|
|
| Detection of Chromium Content in Soybean Oil by Laser Induced Breakdown Spectroscopy and UVE Method |
| SUN Tong1, WU Yi-qing1, LIU Xiu-hong2, MO Xin-xin1, LIU Mu-hua1* |
1. Optics-Electronics Application of Biomaterials Lab，Jiangxi Agricultural University，Nanchang 330045，China
2. Technical Center of Inspection and Quarantine，Jiangxi Entry-Exit Inspection and Quarantine Bureau, Nanchang 330038，China |
|
|
|
|
Abstract In order to monitor chromium (Cr) content in soybean oil, laser induced breakdown spectroscopy (LIBS) was used to detect Cr content in this research. Pine wood chips was used to enrich heavy metal of Cr, and the spectra of pine wood chips were acquired in the wavelength range of 206.28~481.77 nm by a two-channel high-precision spectrometer. Then, uninformative variable elimination (UVE) method was used to select sensitive wavelength variables for heavy metal of Cr, and calibration model of Cr in soybean oil was developed with partial least squares (PLS) regression, the performance of the calibration model was compared to univariate and full PLS calibration models. The results indicate that the performance of UVE-PLS calibration model is better than that of univariate and full PLS calibration models, the correlation coefficient, root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) are 0.990, 0.045 mg·g-1, 0.050 mg·g-1 and 0.054 mg·g-1, respectively. After UVE variable selection, the number of wavelength variables in UVE-PLS calibration model is about 2% of wavelength variables in full PLS calibration model. This means UVE is an effective variable selection method which can select correlative variables for heavy metal of Cr.
|
|
Received: 2015-08-11
Accepted: 2015-12-09
|
|
|
|
Corresponding Authors:
LIU Mu-hua
E-mail: suikelmh@sohu.com
|
|
[1] Gaudiuso R, Dell’Aglio M, Pascale O, et al. Sensors, 2010, 10(8): 7434.
[2] LI Jun-xiang, YANG You-liang, MENG Fan-wei, et al(李俊香, 杨友良, 孟凡伟, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2013, 50: 031406.
[3] Yuan T, Wang Z, Li Z, et al. Analytica Chimica Acta, 2014, 807: 29.
[4] Tawfik W, Bousiakou L G, Qindeel R, et al. Optoelectronics and Advanced Materials-Rapid Communications, 2015, 9(1-2): 185.
[5] Kim G, Kwak J, Kim K R, et al. Journal of Hazardous Materials, 2013, 263(2): 754.
[6] Li W, Huang L, Yao M, et al. Journal of Applied Spectroscopy, 2014, 81(5): 850.
[7] SUN Lan-xiang, YU Hai-bin, CONG Zhi-bo, et al(孙兰香, 于海斌, 丛智博, 等). Chinese Journal of Scientific Instrument(仪器仪表学报), 2011, 32(11): 2602.
[8] Huang L, Yao M, Xu Y, et al. Applied Physics B, 2013, 111(1): 45.
[9] WANG Yin, ZHAO Nan-jing, MA Ming-jun, et al(王 寅, 赵南京, 马明俊, 等). Laser Technology(激光技术), 2013, 37(6): 808.
[10] Yu Y L, Zhou W D, Qian G, et al. Plasma Science & Technology, 2014, 16(7): 683.
[11] Li K, Guo L, Li C, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(7): 1623.
[12] GU Yan-hong, LI Ying, TIAN Ye, et al(谷艳红, 李 颖, 田 野, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(8): 2244.
[13] SHEN Qin-mei, ZHOU Wei-dong, LI Ke-xue(沈沁梅, 周卫东, 李科学). Acta Photonica Sinica(光子学报), 2010, 39(12): 2134.
[14] GUO Rui, SONG Hai-yan(郭 锐, 宋海燕). Journal of Taiyuan University of Technology(太原理工大学学报), 2014, 45(9): 679.
[15] Wen G H, Sun D X, Su M G, et al. Plasma Science & Technology, 2014, 16(6): 598.
[16] ZHANG Da-cheng, MA Xin-wen, ZHAO Dong-mei, et al(张大成, 马新文, 赵冬梅, 等). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2013, 33(12): 2070.
[17] Wu D, Chen X J, Zhu X G, et al. Analytical Methods, 2011, 3(8): 1790.
[18] LIU Xue-mei, LIU Jian-she(刘雪梅, 柳建设). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(3): 88.
[19] Centner V, Massart D L, de Noord O E, et al. Analytical Chemistry, 1996, 68(21): 3851.
|
| [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] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy （LIBS）[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
| [3] |
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. |
| [4] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [5] |
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. |
| [6] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [7] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [8] |
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. |
| [9] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
| [10] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
| [11] |
JIANG Da-peng2, GAO Li-bin2, CHEN Jin-hao2, ZHANG Yi-zhuo1*. Near Infrared Spectroscopy Modeling Method of Wood Tensile Strength Based on MC-UVE-IVSO[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2488-2493. |
| [12] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
| [13] |
LI Chang-ming1, CHEN An-min2*, GAO Xun3*, JIN Ming-xing2. Spatially Resolved Laser-Induced Plasma Spectroscopy Under Different Sample Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2032-2036. |
| [14] |
ZHAO Yang1, ZHANG Lei2, 3*, CHENG Nian-kai4, YIN Wang-bao2, 3*, HOU Jia-jia5, BAI Cheng-hua1. Research on Space-Time Evolutionary Mechanisms of Species Distribution in Laser Induced Binary Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2067-2073. |
| [15] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
|
|
|
|
