Prediction of Soil pH in Ash-Enriched Laboratory Columns Using Portable Near-Infrared Spectroscopy: A Comparison of Analytical Strategies.

IF 2.2 3区 化学 Q2 INSTRUMENTS & INSTRUMENTATION
Edouard J Acuña, Francisco J Calderon, Carlos A Bonilla
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引用次数: 0

Abstract

In the post-fire stage, precipitation and superficial incorporation of ashes alter the chemical properties of the soil. This study evaluated the combined effects of spectral preprocessing methods, data partitioning strategies, and modeling approaches on soil pH prediction using a portable near-infrared (NIR) spectrometer in wildfire ash-enriched soil. A laboratory column experiment was conducted using disturbed sandy loam soil, in which wildfire ashes were incorporated. The experimental design considered five treatments (n = 3) of Eucalyptus globulus and Quillaja saponaria ash incorporations (C: no ash; T1: 2% ash at 2.5  cm; T2: 2% ash at 5  cm; T3: 4% ash at 2.5  cm; T4: 4% ash at 5  cm). After simulating a precipitation of 20  mm h-1 for 6 hours, the soil columns were sampled at 5 depths (D1: 2-3  cm, D2: 7-8  cm, D3: 12-13  cm, D4: 16-17  cm, D5: 20-21  cm). The samples were analyzed using a NIR spectrometer (range: 1350-2550  nm), and the levels of pH (1:2.5) were determined in the laboratory. Eight preprocessing techniques (P0 to P7) were tested, including absorbance conversion, mean centering, trimming, smoothing, standard normal variate (SNV), moving window average (MWA), Savitzky-Golay filtering, and first derivative transformation. Using the Kennard-Stone method, 70% of the data was used for calibration (CAL) and 30% for validation (VAL), considering two partitioning approaches, the same partition by pseudo absorbance values (Scenario A) and different partitions by preprocessing method (Scenario B). Partial least square (PLS) and random forest (RF) models were applied, and performance was assessed using root mean square error (RMSE), coefficient of determination (r2), and ratio of performance to interquartile distance (RPIQ) analyses. The most accurate pH predictions were achieved with RF under Scenario B using trimming + standard normal variate (SNV) + moving weighted average (MWA) preprocessing, yielding r2 values of 0.95 (CAL) and 0.91 (VAL), with RMSEs of 0.23 (CAL) and 0.57 (VAL), and RPIQs of 4.33 (CAL) and 4.61 (VAL). Overall, portable NIR spectroscopy demonstrated strong potential for soil pH prediction in ash-enriched soil, emphasizing the critical role of appropriate spectral preprocessing to avoid overfitting. These findings provide insights into applying portable NIR spectroscopy as a cost-effective tool for monitoring soil pH following wildfires.

使用便携式近红外光谱法预测富灰实验室柱中的土壤pH值:分析策略的比较。
在火灾后阶段,沉淀和灰烬的表面掺入改变了土壤的化学性质。本研究评估了光谱预处理方法、数据划分策略和建模方法在野火灰富集土壤中使用便携式近红外光谱仪预测土壤pH值的综合效果。采用扰动砂质壤土,加入野火灰,进行室内柱状试验。试验设计考虑5个处理(n = 3),分别为:C:无灰分;T1: 2.5%灰分;T2: 2%灰分;T3: 2.5%灰分;T4: 4%灰分;模拟20 mm h-1降水6小时后,在5个深度(D1: 2-3 cm, D2: 7-8 cm, D3: 12-13 cm, D4: 16-17 cm, D5: 20-21 cm)取样土壤柱。样品使用近红外光谱仪(范围:1350-2550 nm)进行分析,并在实验室测定pH值(1:25 .5)。测试了8种预处理技术(P0 ~ P7),包括吸光度转换、均值定心、切边、平滑、标准正态变量(SNV)、移动窗口平均(MWA)、Savitzky-Golay滤波和一阶导数变换。采用Kennard-Stone方法,将70%的数据用于校准(CAL), 30%用于验证(VAL),考虑两种划分方法,即采用伪吸光值进行相同的划分(场景A)和采用预处理方法进行不同的划分(场景B)。应用偏最小二乘(PLS)和随机森林(RF)模型,并使用均方根误差(RMSE)、决定系数(r2)和性能与四分位数距离之比(RPIQ)分析对性能进行评估。采用微调+标准正态变量(SNV) +移动加权平均(MWA)预处理的RF在情景B下获得了最准确的pH预测,r2值为0.95 (CAL)和0.91 (VAL), rmse为0.23 (CAL)和0.57 (VAL), RPIQs为4.33 (CAL)和4.61 (VAL)。总体而言,便携式近红外光谱显示出在灰富集土壤中预测土壤pH值的强大潜力,强调了适当的光谱预处理以避免过拟合的关键作用。这些发现为将便携式近红外光谱作为监测野火后土壤pH值的经济有效工具提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Spectroscopy
Applied Spectroscopy 工程技术-光谱学
CiteScore
6.60
自引率
5.70%
发文量
139
审稿时长
3.5 months
期刊介绍: Applied Spectroscopy is one of the world''s leading spectroscopy journals, publishing high-quality peer-reviewed articles, both fundamental and applied, covering all aspects of spectroscopy. Established in 1951, the journal is owned by the Society for Applied Spectroscopy and is published monthly. The journal is dedicated to fulfilling the mission of the Society to “…advance and disseminate knowledge and information concerning the art and science of spectroscopy and other allied sciences.”
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