| 光谱学与光谱分析 |
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| Application of Wavelet Multi-Scale Orthogonal Signal Correction in Milk Components Measurement Using Near-Infrared Spectroscopy |
| PENG Dan, XU Ke-xin*, LI Chen-xi |
| State Key Laboratory of Precision Measuring and Instruments, Tianjin University, Tianjin 300072, China |
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Abstract Spectral interferences can have a significant impact on the spectral variation and as a consequence can adversely affect the results of calibration model in spectra analysis. Wavelet transform (WT) and orthogonal signal correction (OSC) were both the popular preprocessing algorithms. It was known that the former can effectively eliminate the background and noise and the latter can effectively filter out the interference information irrelevant to analyte concentration during the preprocessing of spectra. According to the different characteristics of analyte information and interference information in near-infrared (NIR) spectra, a new hybrid algorithm (WMOSC) that was the combination of discrete wavelet transform (DWT) and OSC was proposed to eliminate the spectral interferences including background, noise and systemic spectral variation irrelevant to the concentration. First, DWT was used to split the spectral signal into different frequency components, which keep the same data points as the original spectra data, to remove noise and background information by threshold method. Then OSC was applied to each frequency components to remove the information uncorrelated to the concentration independently. Finally, the spectra preprocessed by WMOSC were achieved through the summation of all frequency components. WMOSC was successfully applied to preprocess the NIR spectra data of milk. After elimination of the interference in the NIR spectra data by WMOSC, the partial least squares (PLS) regression was used to develop the calibration models for estimating the contents of main constituents in milk. The prediction ability and robustness of models obtained in subsequent PLS calibration using WMOSC were superior to those obtained using either DWT or OSC alone. The root mean square errors of prediction (RMSEP) of the models for fat, protein and lactose were 0.101 6%, 0.087 1% and 0.110 7%, respectively. The experimental results show that WMOSC is an effective method for eliminating the interferences information in NIR spectra.
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Received: 2007-06-16
Accepted: 2007-09-28
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Corresponding Authors:
XU Ke-xin
E-mail: pengdantju@gmail.com
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[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(近红外光谱分析基怠与应用). Beijing：Light Industry Press of China(北京：中国轻工业出版社)，2005.
[2] Seasholtz M B, Kowalski B R. Anal. Chim. Acta, 1993, 277: 165.
[3] TIAN Gao-you, YUAN Hong-fu, CHU Xiao-li, et al(田高友, 袁洪福, 褚小立, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(4): 516.
[4] Tan H W, Steven D. Brown. J. Chemometrics, 2002, 16: 228.
[5] Mittermary C R, Tan H W, Brown S D. Appl. Spectrosc., 2001, 55: 827.
[6] WU Rong-hui, SHAO Xue-guang(吴荣晖, 邵学广). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(4): 617.
[7] ZHAO Jie-wen, ZHANG Hai-dong, LIU Mu-hua(赵杰文，张海东，刘沐华). Acta Optica Sinica(光学学报), 2006，26: 136.
[8] QU Hai-bin, OU Dan-lin, CHENG Yi-yu. J. Zhejiang Univ. (B Life Science), 2005, 6B(8): 838.
[9] Woody N A, Feudale R N, Myles A J, et al. Anal. Chem. 2004, 76: 2595.
[10] Blanco M, Coello J, Montoliu I. Anal. Chim. Acta, 2001, 434: 125.
[11] LIU Rong, L Li-na, CHEN Wen-liang, et al(刘 蓉，吕丽娜, 陈文亮, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(9): 1042.
[12] HAO Yong, CHEN Bin, ZHU Rui(郝 勇, 陈 斌, 朱 锐). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(10): 1838.
[13] Wise B M, Gallagher N B. www.eigenvector.com/MATLAB/OSC.html.
[14] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福, 陆婉珍). Progress in Chemistry(化学进展), 2004，16: 528.
[15] Wold S, Antti H, Lindgren F, et al. Chemometrics and Intelligent Laboratory Systems, 1998, 44: 175. |
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