光谱学与光谱分析 |
|
|
|
|
|
Fast Discrimination of Varieties of Infant Milk Powder Using Near Infrared Spectra |
HUANG Min1,HE Yong1,CEN Hai-yan1,HU Xing-yue2* |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 2. Sir Rum Run Shaw Hospital, Zhejiang University, Hangzhou 310016,China |
|
|
Abstract A new method for discrimination of varieties of infant milk powder by means of visible/near infrared spectroscopy (Vis/NIRS) (325-1 075 nm) was developed. Partial least square (PLS) was used to analyze the characteristics of the pattern. PLS compressed thousands of spectral data into a small quantity of principal components and described the body of spectra. The first seven principal components were confirmed as the best number of principal components. Then, these seven principal components were applied as the input to a back propagation neural network with one hidden layer. The infant milk powder varieties data were applied as the output of BP neural network. One hundred eighty samples containing nine typical varieties of infant milk powder were selected randomly, and they were used as a training set of the BP neural network model, and the remainder samples (total 90 samples) formed the prediction set. With a proper network training parameter, the recognition accuracy of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of infant milk powder.
|
Received: 2006-03-16
Accepted: 2006-07-28
|
|
Corresponding Authors:
HU Xing-yue
E-mail: yhe@zju.edu.cn
|
|
Cite this article: |
HUANG Min,HE Yong,CEN Hai-yan, et al. Fast Discrimination of Varieties of Infant Milk Powder Using Near Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(05): 916-919.
|
|
|
|
URL: |
https://www.gpxygpfx.com/EN/Y2007/V27/I05/916 |
[1] HOU Dong-yan, HUI Rui-hua, LI Tie-chun, et al(侯冬岩,回瑞华,李铁纯,等). Journal of Anshan Normal University(鞍山师范学院学报), 2004, 6(4): 37. [2] GU Ri-xu, ZHANG Jian-guo(谷日旭, 张建国). Shanxi Journal of Preventive Medicine(山西预防医学), 1997, 6(3): 198. [3] LE Jun-ming, CHEN Ying, DING Ying(乐俊明,陈 鹰,丁 映). Guizhou Agricultural Sciences(贵州农业科学), 2005, 33(3): 62. [4] He Yong, Li Xiao-li, Shao Yong-ni. Lecture Notes in Artificial Intelligence, 2005, 3809: 1053. [5] Esteban-Diez I, Gonzalez-Saiz J M, Pizarro C. Analytica Chimica Acta, 2004, 514:57. [6] Zsolt Seregely, Tamas Deak, Gyorgy Denes Bisztray. Chemometrics and Intelligent Laboratory Systems, 2004, 72: 195. [7] Turza S, Toth A, Varadi M. Near Infrared Spectroscopy: Proceedings of the 8th International Conference, Chichester, UK: NIR Publications, 1998. 183. [8] SUN Su-qin, TANG Jun-ming, YUAN Zi-min, et al(孙素琴,汤俊明,袁子民, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003, 23(2): 258. [9] YU Wei, YONG Ke-lan(俞 蔚, 雍克岚). Food Science(食品科学), 2003, 24(11): 105. [10] BAO Yi-dan, WU Yan-ping, HE Yong(鲍一丹,吴燕萍,何 勇). Journal of Agricultural Mechanization Research(农机化研究), 2004, 3: 162. [11] He Yong, Song Hai-yan, Pereira A G, et al. Lecture Notes in Computer Science, 2005, 3644: 859. [12] TANG Qi-yi, FENG Ming-guang(唐启义,冯明光). DPS Data Processing System for Practical Statistics(实用统计分析及其DPS数据处理系统). Beijing: Science Press(北京:科学出版社),2002. [13] QI Xiao-ming,ZHANG Lu-da,DU Xiao-lin,et al(齐小明,张录达,杜晓林,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2003,23(5):870. [14] YIN Qiu,SHU Xiao-zhou,XU Zhao-an,et al(尹 球,疏小舟,徐兆安,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2004,23(6):427.
|
[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] |
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. |
[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] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|