光谱学与光谱分析 |
|
|
|
|
|
Research on the Robustness Improvement of Calibration Model for Measuring the Contents of Components in Milk by Multidimensional Calibration in Near-Infrared Spectroscopy |
PENG Dan1,2,XU Ke-xin1*,SONG Yang1 |
1. State Key Laboratory of Precision Measuring and Instruments, Tianjin University, Tianjin 300072, China 2. College of Grain Oil and Food Science, Henan University of Technology, Zhengzhou 450052, China |
|
|
Abstract A new hybrid algorithm (NOSC-NPLS), the combination of multi-way orthogonal signal correction (N-OSC) algorithm and multi-way partial least squares (N-PLS) algorithm, is proposed. In NOSC-NPLS algorithm, the 3-D spectral matrix was firstly constructed, which is composed of the temperature information and the NIR spectrum. Secondly, the N-OSC algorithm was used as a pretreatment algorithm to remove the interference information irrelevant to analyte in 3-D spectral matrix. Finally, the N-PLS algorithm was applied to develop the calibration model based on the pretreated 3-D transmission spectral matrix and content matrix of major components in milk. In order to evaluate the performances of conventional algorithms and multidimensional calibration algorithms on suppressing the effects of temperature variation, a batch of milk samples at temperature of 25, 30, 35 and 40 ℃ were measured in the wavelength range from 1 100 to 1 700 nm and the experimental results were investigated. It was found that the conventional algorithms, which could not suppress the effects caused by temperature variation, failed to obtain satisfactory results. However, compared with these algorithms, the experimental results showed that the NOSC-NPLS algorithm can effectively eliminate the effects of temperature variation and also can help achieve the analytic models with better prediction ability and robustness.
|
Received: 2007-11-22
Accepted: 2008-03-02
|
|
Corresponding Authors:
XU Ke-xin
E-mail: kexin@tju.edu.cn
|
|
[1] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄,赵龙莲,韩东海,等). Principle and Application of Near Infrared Spectroscopy(近红外光谱分析基础与应用). Beijing: Light Industry Press of China(北京:中国轻工业出版社), 2005. [2] WANG Li-jie, XU Ke-xin, GUO Jian-ying(王丽杰, 徐可欣, 郭建英). Journal of Optoelectronics·Laser(光电子·激光), 2004, 15(4): 468. [3] Sasic S, Ozaki Y. Appl. Spectrosc., 2000, 54: 1327. [4] Thygesen L G J. J. Near Infrared Spectrosc., 2000, 8: 183. [5] Kamal Y T, Vidi A S. Progress Report, 2000, 3: 2. [6] Hazen K H, Arnold M A, Small G W. Applied Spectroscopy, 1994, 48(4): 477. [7] Florian W, Wm Th Kok, Age K S. Anal. Chem.,1998, 70: 1761. [8] CHU Xiao-li, YUAN Hong-fu, WANG Yan-bin, et al(褚小立, 袁洪福, 王艳斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(6): 666. [9] ZHANG Jun, CHEN Hua-cai, CHEN Xing-dan(张 军, 陈华才, 陈星旦). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(6): 890. [10] Venyaminov S Yu, Prendergast F G. Anal. Biochem., 1997, 248: 234. [11] Fornes V, Chaussidon J. J. Chem. Phys., 1978, 68: 4667. [12] CHANG Min, PENG Dan, XU Ke-xin(常 敏, 彭 丹, 徐可欣). Acta Optica Sinica(光学学报), 2007, 27(6): 1080. [13] http: //www.foodsci.uoguelph.ca/dairyedu/chem.html. [14] Andersson C A, Bro R. Chemometrics and Intelligent Laboratory Systems, 1998,42: 93. [15] Bro R. J. Chemometrios, 1996, 10(1): 47. [16] Peinado A C, van den F Berg, Blanco M, et al. Chemometrics and Intelligent Laboratory Systems, 2006,83: 75. |
[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] |
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. |
[4] |
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. |
[5] |
ZHANG Ning-chao1, YE Xin1, LI Duo1, XIE Meng-qi1, WANG Peng1, LIU Fu-sheng2, CHAO Hong-xiao3*. Application of Combinatorial Optimization in Shock Temperature
Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3666-3673. |
[6] |
LIANG Ya-quan1, PENG Wu-di1, LIU Qi1, LIU Qiang2, CHEN Li1, CHEN Zhi-li1*. Analysis of Acetonitrile Pool Fire Combustion Field and Quantitative
Inversion Study of Its Characteristic Product Concentrations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3690-3699. |
[7] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
CHEN Heng-jie, FANG Wang, ZHANG Jia-wei. Accurate Semi-Empirical Potential Energy Function, Ro-Vibrational Spectrum and the Effect of Temperature and Pressure for 12C16O[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3380-3388. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|