近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量

彭海根; 金楹; 詹莜国; 陈雅琼; 封幸兵; 钱发聪; 黄果; 黄天杰; 李杰

doi:doi：10.3969/j.issn.1004-4957.2020.10.019

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近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量

更新时间：2023-04-18

- 近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量
- Quantitative Determination of Hydrolytic Nitrogen Content in Soil by Near Infrared Spectroscopy Combined with Competitive Adaptive Reweighted Sampling Variable Selection Algorithm
- 分析测试学报 2020年39卷第10期页码：1305-1310
- 作者机构：
  
  1. 四川威斯派克科技有限公司
  2. 云南省烟草公司昆明市公司
  3. 中国烟草总公司云南省公司
  4. 昆明市烟草公司嵩明分公司
- 作者简介：
- 基金信息：
  
  中国烟草总公司云南省公司科技计划重点项目(2019530000241013)
- DOI：doi：10.3969/j.issn.1004-4957.2020.10.019
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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彭海根, 金楹, 詹莜国, 陈雅琼, 封幸兵, 钱发聪, 黄果, 黄天杰, 李杰. 近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量[J]. 分析测试学报, 2020,39(10):1305-1310.

彭海根, 金楹, 詹莜国, 陈雅琼, 封幸兵, 钱发聪, 黄果, 黄天杰, 李杰. Quantitative Determination of Hydrolytic Nitrogen Content in Soil by Near Infrared Spectroscopy Combined with Competitive Adaptive Reweighted Sampling Variable Selection Algorithm[J]. 2020, 39(10): 1305-1310.
彭海根, 金楹, 詹莜国, 陈雅琼, 封幸兵, 钱发聪, 黄果, 黄天杰, 李杰. 近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量[J]. 分析测试学报, 2020,39(10):1305-1310. DOI： doi：10.3969/j.issn.1004-4957.2020.10.019.

彭海根, 金楹, 詹莜国, 陈雅琼, 封幸兵, 钱发聪, 黄果, 黄天杰, 李杰. Quantitative Determination of Hydrolytic Nitrogen Content in Soil by Near Infrared Spectroscopy Combined with Competitive Adaptive Reweighted Sampling Variable Selection Algorithm[J]. 2020, 39(10): 1305-1310. DOI： doi：10.3969/j.issn.1004-4957.2020.10.019.

摘要

为了能够快速准确地掌握整个昆明地区土壤水解性氮含量的情况，收集963个不同类型的土壤样品，采用竞争自适应重加权采样(Competitive adaptive reweighted sampling，CARS)变量选择方法筛选波长变量，并建立水解性氮的偏最小二乘法(Partial least squares，PLS)分析模型。结果表明，采用CARS方法优选波长变量后，模型参数有所改善，交互验证标准偏差(Root mean square error of cross validation，RMSECV)由31.63降至25.55，交互验证相关系数(Correlation coefficientof cross validation

Rcv)由078提升至0.84，且模型外部验证结果与内部交叉验证结果基本一致。研究结果表明近红外光谱技术结合CARS分法，在大量代表性样品建模下，能够有效建立昆明地区不同土壤类型的水解性氮含量的近红外数学模型，方法可推广应用于土壤其他组分的近红外检测，具有重要的指导意义。

Abstract

In order to quickly and accurately grasp the hydrolytic nitrogen content of soil in the whole Kunming area

963 soil samples of different types were collected.Partial least squares(PLS) combined with competitive adaptive reweighted sampling(CARS) method used to screen the spectral wavelength variables was adopted to establish an analysis model for hydrolytic nitrogen.Results showed that the parameters for the model were improved after the optimization of wavelength variables by CARS

and the root mean square error of cross validation(RMSECV) was reduced from 31.63 to 25.55

the correlation coefficient of cross validation(Rcv) was increased from 0.78 to 0.84

and the external verification results of the model were basically consistent with the internal cross verification results.Meanwhile

near infrared spectroscopy combined with CARS method could be used to effectively establish a near infrared(NIR) mathematical model for hydrolytic nitrogen content in different soil types in Kunming area under the modeling of a large number of representative samples

which is suitable for the NIR detection of other components in soil and has an important guiding significance.

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