| 光谱学与光谱分析 |
|
|
|
|
|
| Identification of Adulterants in Adulterated Milks by Near Infrared Spectroscopy Combined with Non-Linear Pattern Recognition Methods |
| NI Li-jun1, ZHONG Lin1, ZHANG Xin2, ZHANG Li-guo1*, HUANG Shi-xin2 |
1. Institute of Chemical and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
2. Institute of Veterinary Drug Detection, Shanghai Center of Animal Disease Control and Prevention, Shanghai 201103, China |
|
|
|
|
Abstract In the present work, two hundred and eighty seven raw milks collected from pastures in Shanghai and surrounding areas of Shanghai were used as true milk samples and divided into three true milk sets. Five hundred and twenty six adulterated milk samples, which contained dextrin (or starch) mixed with melamine (or urea, or ammonium nitrate), were prepared as six different adulterated milk sets. The concentrations of these adulterants in the adulterated milks were designed to be 0.15%~0.45% (starch or dextrin), 700~2 100 mg·kg-1 (ammonium nitrate), 524~1 572 mg·kg-1 (urea), and 365.5~1 096.5 mg·kg-1 (melamine) to guarantee the protein content of adulterated milks detected by Kjeldahl method not lower than 3%. All the near infrared spectra (NIR) of the samples should have a pretreatment of normal variable transformation (SNV) before they were used to build discriminating models. The three true milk sets and six adulterated milk sets were combined in different ways in order to build NIR models for discriminating different kinds of adulterants (i. e., dextrin, starch, melamine, urea and ammonium nitrate) based on simplified K-nearest neighbor classification algorithm (IS-KNN) and an improved and simplified of support vector machine (ν-SVM) method. The relationship between mass concentration of the adulterants and the rate of correct discrimination was also investigated. The results show that the average discrimination accuracy of IS-KNN and ν-SVM for identifying melamine, urea and ammonium nitrate were in the region of 49.55% to 51.01%, 61.78% to 68.79% and 68.25% to 73.51%, respectively. Therefore within the concentration regions designed in this study, it is difficult to distinguish different kinds of pseudo proteins by NIR spectroscopy. However, the average accuracy of IS-KNN and ν-SVM for identifying starch and dextrin are 92.33% and 93.66%, 77.29% and 85.08%, respectively. Most discrimination results of ν-SVM are better than those of IS-KNN. The correlative analysis between the discrimination accuracy rate and the content levels of the adulterants indicated that near infrared spectroscopy combined with non-linear pattern recognition methods can distinguish dextrin and starch in milks with higher concentration levels (>0.15%), but do not work well on identifying the adulterants with lower concentrations such as melamine (365.5 to 1 096.5 mg·kg-1), urea (524 to 1 572 mg·kg-1), ammonium nitrate (700 to 2 100 mg·kg-1). Therefore near Infrared Spectroscopy is not suitable for identifying the adulterants with concentrations are below 0.1%.
|
|
Received: 2014-05-20
Accepted: 2014-07-28
|
|
|
|
Corresponding Authors:
ZHANG Li-guo
E-mail: hardtimes@ecust.edu.cn
|
|
[1] Balabin R M，Smirnov S V. Talanta，2011，85(1)：562.
[2] Kasemsumran S, Thanapase W, Kiatsoonthon A. Anal. Sci., 2007, 23(7): 907.
[3] XU Yun，WANG Yi-ming，WU Jing-zhu，et al(徐 云，王一鸣，吴静珠，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2010，29(1)：53.
[4] Yang R, Liu R, Kexin X. Food Bioscience, 2013, 2: 61.
[5] Santos P M，Pereira-Filho E R，Rodriguez-Saona L E. Journal of Agricultural and Food Chemistry，2013，61(6)：1205.
[6] Santos P M，Pereira-Filho E R，Rodriguez-Saona L E. Food Chemistry，2013，138(1)：19.
[7] ZHONG Zhen-zhen，ZHANG Li-guo，ZHANG Xin，et al(钟珍珍，张立国，张 鑫，等). Computers and Applied Chemistry(计算机与应用化学)，2010，27(12)：1691.
[8] Ni L J，Zhang L G，Xie J，et al. Analytica Chimica Acta，2009，633(1): 43.
[9] Zhang L G，Zhang X，Ni L J，et al. Food Chemistry，2014，145：342.
[10] Chan E Y Y，Griffiths S M，Chan C W. Lancet，2008，372(9648)：1444.
[11] Schlkopf B，Smola A J，Williamson R C，et al. Neural Computation，2000，12(5)：1207. |
| [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. |
|
|
|
|
