评估手持式 LIBS 在预测欧洲土壤有机碳和质地方面的性能†。

IF 3.1 2区 化学 Q2 CHEMISTRY, ANALYTICAL
Alex Wangeci, Maria Knadel, Olga De Pascale, Mogens H. Greve and Giorgio S. Senesi
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引用次数: 0

摘要

激光诱导击穿光谱(LIBS)有助于先进、快速地测定土壤特性,包括土壤有机碳(SOC)和质地。最近开发的商用手持式激光诱导击穿光谱仪(hLIBS)可以直接在野外使用该技术。然而,迄今为止,还没有人对 hLIBS 在不同类型、广泛地理分布的土壤上的性能进行过评估。在本研究中,共使用了 305 个覆盖整个大陆的土壤样本,以评估使用市售 hLIBS 仪器获取的 LIBS 数据的可重复性和再现性。此外,还根据预测误差评估了 SOC 和质地预测模型的性能。根据在相似和不同环境条件(温度和湿度)下进行测量的相对标准偏差(RSD),对 LIBS 数据的重复性和再现性进行了评估。首先,计算信号比的 RSD 和所研究土壤性质的预测值。然后,根据预测的标准化均值误差(SRMSEP)和性能与四分位距之比(RPIQ)比较各种土壤特性的预测精度。使用 C、Si、Ca 和 K LIBS 发射线评估的信号比的重复性为 4-9%,重现性为 7-10%,而预测 SOC 和质地的重复性和重现性为 25%。含沙量的预测误差最小(SRMSEP = 0.14),其次是粘土和粉土(SRMSEP = 0.15),然后是 SOC(SRMSEP = 0.16)。这项工作的结果凸显了 hLIBS 在大规模 SOC 和质地测定方面的巨大潜力,在应用建模过程之前,可通过整合土壤矿物学信息进行土壤分类,从而提高预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Assessing the performance of handheld LIBS for predicting soil organic carbon and texture in European soils†

Assessing the performance of handheld LIBS for predicting soil organic carbon and texture in European soils†

Laser-induced breakdown spectroscopy (LIBS) has contributed to the advanced and rapid determination of soil properties including soil organic carbon (SOC) and texture. Recent developments of commercial handheld LIBS (hLIBS) have allowed the use of the technique directly in the field. However, to date, the performance of hLIBS on different types of soils covering wide geographical distributions has not been evaluated. In this study, a total of 305 soil samples covering a continental scale were used to assess the repeatability and reproducibility of LIBS data acquired using a commercially available hLIBS instrument. Furthermore, the performance of the prediction models for SOC and texture was evaluated based on the prediction error. The repeatability and reproducibility of LIBS data were evaluated based on the relative standard deviation (RSD) for measurements performed under similar and different environmental conditions (temperature and humidity). First, the RSD of the signal ratios and the predicted values for soil properties under investigation were calculated. Then, the prediction accuracy of the various soil properties was compared based on the standardized root mean error of prediction (SRMSEP) and the ratio of performance to interquartile distance (RPIQ). The signal ratios assessed using the C, Si, Ca, and K LIBS emission lines achieved a repeatability of 4–9% and a reproducibility of 7–10%, whereas the repeatability and reproducibility for predicting SOC and texture were <25%. The prediction of sand content exhibited the lowest error (SRMSEP = 0.14) followed by clay and silt (SRMSEP = 0.15), and then SOC (SRMSEP = 0.16). The results of this work underscore the promising potential of hLIBS for large-scale SOC and texture determination, with the opportunity to enhance the prediction accuracy by integrating soil mineralogy information for soil classification before applying the modeling process.

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来源期刊
CiteScore
6.20
自引率
26.50%
发文量
228
审稿时长
1.7 months
期刊介绍: Innovative research on the fundamental theory and application of spectrometric techniques.
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