Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

文献类型： 外文期刊

第一作者： Zhang, Guang-Cai

作者： Zhang, Guang-Cai;Yan, Xiang-Mei;Zhou, Ping;Lin, Guo-Lin;Zhou, Chong-Jun;Liu, Ning;Han, Xiao-Ri;Zhang, Guang-Cai;Li, Zhuang;Cheng, Cun-Gang;Yan, Xiang-Mei

作者机构：

关键词： Apple leaf;multiple linear regression;near infrared spectroscopy;nitrogen

期刊名称：COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS （ 影响因子：1.327； 五年影响因子：1.315 ）

ISSN： 0010-3624

年卷期： 2012 年 43 卷 13 期

页码：

收录情况： SCI

摘要： A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (R-v), 1.119 g kg(-1) for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with R-v = 0.952, RMSEP = 0.633 g kg(-1), the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.

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