| 光谱学与光谱分析 |
|
|
|
|
|
| Feasibility of Using NIR Spectroscopy to Detect Melamine in Milk |
| DONG Yi-wei1, TU Zhen-hua2, ZHU Da-zhou2, LIU Ya-wei1, WANG Ya-nan1, HUANG Jin-li1, SUN Bao-li1, FAN Zhong-nan1* |
1. Institute of Environment and Sustainable Development in Agriculture, The Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Environment & Climate Change, Ministry of Agriculture, Beijing 100081, China
2. College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract In the present study, 22 certified milk samples without melamine were collected, then 50 adulterated milk samples with added different content of melamine (0.1-1 500 mg·kg-1) were prepared. The near-infrared (NIR) spectra of these milk samples were measured. The possibility of using NIR spectra to detect melamine in milk was studied. Partial least square regression (PLSR) was applied to construct the calibration model between NIR spectra and the content of melamine. The results showed that NIR spectroscopy can not accurately predict the content of melamine because of its poor detection limit. However, the combination of NIR spectra and partial least square-discriminate analysis (PLS-DA) was applied to differentiate the certified milk samples and the adulterated milk sample. The classification accuracy was 100%. Therefore, NIR spectra could be used to preliminarily detect whether the milk was adulterated with melamine. As a complementary detecting method to the high performance liquid chromatography (HPLC), NIR spectra could improve the detecting efficiency of milk.
|
|
Received: 2008-10-22
Accepted: 2009-01-26
|
|
|
|
Corresponding Authors:
FAN Zhong-nan
E-mail: fanzhn@cjac.org.cn
|
|
[1] 三聚氰胺, 百度百科.[EB/OL](2008-09-27)[2008-10-14]. http://baike.baidu.com/view/298398.htm
[2] Tsenkova R, Atanassova S, Itoh K, et al. Journal of Animal Science, 2000, 78: 515.
[3] Lobodan, Yukihiro Ozaki. Analytical Chemistry, 2001, 73(1): 64.
[4] LI Qing-bo, WANG Yan, XU Ke-xin(李庆波, 汪 曣, 徐可欣). Food Science(食品科学), 2002, 23(6): 122.
[5] PENG Dan, XU Ke-xin, LI Chen-xi(彭 丹, 徐可欣, 李晨曦). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(4): 825.
[6] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Elements and Application of Near-Infrared Spectra Analysis(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. 1.
[7] Otto M. Chemometrics: Statistics and Computer Application in Analytical Chemistry(化学计量学：统计学与计算机在分析化学中的应用). Translated by SHAO Xue-guang, CAI Wen-sheng, XU Xiao-jie(邵学广, 蔡文生, 徐筱杰，译). Beijing: Science Press(北京：科学出版社), 2003. 39.
[8] Kennard R W, Stone L A. Technometrics. 1969, 11: 137.
[9] XU Guang-tong, YUAN Hong-fu, LU Wan-zhen(徐广通，袁洪福，陆婉珍). Chinese Journal of Analytical Chemistry(分析化学), 1999, 27(1): 30.
|
| [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] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [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] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
