| 光谱学与光谱分析 |
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| Detection of Firmness and Surface Color of Pear by Near Infrared Spectroscopy Based on Monte Carlo Uninformative Variables Elimination Method |
| HAO Yong, SUN Xu-dong, PAN Yuan-yuan, GAO Rong-jie, LIU Yan-de* |
| College of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang 330013, China |
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Abstract In the present study, NIRS was applied to nondestructive and rapid measurement of firmness and surface color of pear. In order to improve the prediction precision and eliminate the influence of uninformative variables on model robustness, Monte Carlo uninformative variables elimination (MC-UVE) and Monte Carlo uninformative variables elimination based on wavelet transform (WT-MC-UVE) methods were proposed for variable selection in firmness and surface color NIR spectral modeling. Results show that WT-MC-UVE can reduce the modeling variables from 1 451 to 210, and get similar prediction results for firmness. WT-MC-UVE improved the prediction precision for surface color, the root mean square error of prediction (RMSEP) and calibration variables were reduced from 1.06 and 1 451 to 0.90 and 220 respectively, and the correlation coefficient (r) was improved from 0.975 to 0.981. The proposed method is able to select important wavelength from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis of firmness and surface color.
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Received: 2010-05-16
Accepted: 2010-08-20
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Corresponding Authors:
LIU Yan-de
E-mail: jxliuyd@163.com
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[1] FU Xia-ping, YING Yi-bin, LIU Yan-de, et al(傅霞萍，应义斌，刘燕德，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(6): 1038.
[2] LIU Yan-de, CHEN Xing-miao, OUYANG Ai-guo(刘燕德，陈兴苗，欧阳爱国). Journal of Infrared & Millimeter Waves(红外与毫米波学报)，2008, 27(4): 266.
[3] LIU Yan-de, LUO Ji, CHEN Xing-miao(刘燕德, 罗 吉, 陈兴苗). J. Infrared Millin. Waves(红外与毫米波学报)，2008, 27(2): 119.
[4] WU Di, HE Yong, FENG Shui-juan, et al(吴 迪, 何 勇, 冯水娟，等). J. Infrared Millin. Waves(红外与毫米波学报)，2008, 27(3): 180.
[5] Centner V, Massart D L. Analytical Chemistry, 1996, 68: 3851.
[6] Chen D, Shao X G, Hu B, et al. Analytica Chimica Acta, 2004, 511(1): 37.
[7] Put R, Daszykowski M, Baczek T, et al. Journal of Proteome Research, 2006, 5(7): 1618.
[8] Polanski J, Gieleciak R. Journal of Chemical Information and Computer Sciences, 2003, 43(2): 656.
[9] Cai W S, Li Y K, Shao X G. Chemometrics and Intelligent Laboratory Systems, 2008, 90(2): 188. |
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