Near Infrared Spectrum Detection Method for Moisture Content of Populus Euphratica Leaf

文献类型： 外文期刊

第一作者： Bai Tie-Cheng

作者： Bai Tie-Cheng;Wang Ya-ming;Zhang Nan-nan;Yao Na;Yu Cai-li;Bai Tie-Cheng;Wang Xing-peng;Wang Xing-peng

作者机构：

关键词： Near infrared spectrum;Multiple scattering correction;Successive projection algorithm;Partial least squares regression;Moisture

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2017 年 37 卷 11 期

页码：

收录情况： SCI

摘要： The moisture content of leaves is an important index to evaluate the health condition of the Euphrates poplar, and spectrum detection method is an effective method. But in the process of near infrared spectrum measurement, spectral data will be affected by instrument noise, morphological differences and environment interference. The paper proposed how to establish spectrum detection method of water content of populus euphratica leaf. In the first place, the influence of scattering, noise and baseline drift of spectral data are reduced by using multiple scattering correction (MSC), and increase signal to noise ratio (SNR) of spectrum data and strengthen band features. The effective spectral information is relatively clear, so the choice of characteristic wavelength becomes easier. Then, in order to reduce the complexity of the model, to prevent overfitting and to reduce the influence of collinearity, the successive projections algorithm (SPA) is used to select the feature variables. And a multiple linear regression model is used to analysis and compare the simulated residual squared of different models, evaluate the contribution of each wavelength and eliminate the wavelengths of small contribution value. Then we obtained the optimal characteristic wavelength to improve the conditions of modeling. Finally, the partial least squares regression method is used to establish the test model. The experimental results show that the successive projections algorithm screens six effective variables on the basis of using the original spectrum data, the prediction accuracy is 90.144%, the correlation coefficient (r) is 0.67424, the root mean square error is 0.021434, and the successive projections algorithm screens five effective variables after using multiple scattering correction algorithm to optimize the original spectrum data, the prediction accuracy is 97.734%, the correlation coefficient (r) is 0.78163, the root mean square error is 0.016776. So the multiple scattering correction algorithm and successive projections algorithm has been successfully applied to eliminate the scattering noise, reduce the total linear interference, simplify the complexity of the model, then increase the accuracy and correlation coefficient, reduce the error. This method can be used for fast nondestructive testing of water content of the Euphrates poplar leaf, besides it also has some reference significance for moisture detection of other crops leaf.

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