Rapid estimation of soil heavy metal nickel content based on optimized screening of near-infrared sp

• 作者: Qian Lu Shijie Wang Xiaoyong Bai Fang Liu Shiqi Tian Mingming Wang Jinfeng Wang
• 学科: 地球科学
• 关键词: Heavy metal Band extraction Partial least squares regression Extreme learning machine Near infrared spectroscopy
• 年，卷（期）: 2022,（1）
• 页码范围: 116-126
• 页数: 10页
• 语种: 英文
• DOI: 10.1007/s11631-019-00357-7

摘要

In near-infrared spectroscopy, the traditional feature band extraction method has certain limitations. Therefore, a band extraction method named the three-step extraction method was proposed. This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables. Partial least squares regression (PLSR) and extreme learning machine (ELM) models were used to verify the effect of the band extraction method. Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel (Ni) content. Most correlation coefficients were above 0.7 and approximately 20% higher than those of other transformation methods. The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form. In five types of spectral transformation, the RPD values of the proposed method were all within the same level. The RPD values of the PLSR model were concentrated between 1.6 and 1.8, and those of the ELM model were between 2.5 and 2.9, indicating that this method is beneficial for extracting more complete spectral features. The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil. The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models. The ELM model of the first-order differential transformation has the best prediction accuracy (R2P = 0.923, RPD = 3.634). The research results provide some technical support for monitoring heavy metal content spectrum in local soils.