| 光谱学与光谱分析 |
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| Multicomponent Quantitative Analysis Using Near Infrared Spectroscopy by Building PLS-GRNN Model |
| LIU Bo-ping1,2,QIN Hua-jun3,LUO Xiang2,4,CAO Shu-wen3,WANG Jun-de1* |
1. College of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210014，China
2. Analytical and Testing Center of Jiangxi Province, Nanchang 330029, China
3. Key Laboratory of Food Science of MOE, Nanchang University, Nanchang 330047, China
4. Department of Chemistry, Nanchang University, Nanchang 330047, China |
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Abstract The present paper introduces an application of near infrared spectroscopy(NIRS) multi-component quantitative analysis by building partial least squares (PLS)-generalized regression neural networks (GRNN) model. The PLS-GRNN prediction model for chlorine, fibre and fat in 45 feedstuff samples was established with good veracity and recurrence. Eight peak values in principal components compressed from original data by PLS and four in original spectra were taken as inputs of GRNN while 4 predictive targets as outputs. 0.1 was chosen as smoothing factor for its good approximation and prediction with the lowest error compared with 0.2, 0.3, 0.4 and 0.5. Predictive correlation coefficient and Standard error of the estimate of three components by the model are 0.984 0, 0.987 0 and 0.983 0, and 0.015 89, 0.154 1 and 0.115 1，while the Standard deviations of an unknown sample scanned 8 times are 0.003 26, 0.065 5 and 0.031 4. The results show that PLS-GRNN used in NIRS is a rapid, effective means for measuring chlorine, fibre in the fat in feedstuff powder, and can also be used in quantitative analysis of other samples. A settlement in the high error of prediction of other samples with lower contents was also shown.
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Received: 2006-05-10
Accepted: 2006-08-20
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Corresponding Authors:
WANG Jun-de
E-mail: jdwang@mail.njust.edu.cn
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| Cite this article: |
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LIU Bo-ping,QIN Hua-jun,LUO Xiang, et al. Multicomponent Quantitative Analysis Using Near Infrared Spectroscopy by Building PLS-GRNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(11): 2216-2220.
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| URL: |
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https://www.gpxygpfx.com/EN/Y2007/V27/I11/2216 |
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