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在土壤常规分析实验室中利用紧凑型近红外分光光度计构建光谱土壤库以测定土壤有机碳:全局和局部模型的性能

Building a spectral soil library in a soil routine analysis laboratory to determine soil organic carbon using compact near-infrared spectrophotometers: Performance of global and local models.

作者信息

Fonseca Aymbiré Angeletti da, Pasquini Celio, Fonseca Gustavo Ferrão, Soares Emanuelle Mercês Barros, Hespanhol Maria C

机构信息

Chemistry Institute, State University of Campinas, Campinas, SP, Brazil; Suzano company, Aracruz, ES, Brazil.

Chemistry Institute, State University of Campinas, Campinas, SP, Brazil.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2026 Jan 5;344(Pt 1):126707. doi: 10.1016/j.saa.2025.126707. Epub 2025 Jul 16.

Abstract

Compact near-infrared (NIR) spectrophotometers are low-cost instruments enabling rapid, cheap, non-destructive, and environmentally friendly soil organic carbon (SOC) determination. However, the steps necessary to implement these instruments in routine soil analysis laboratories still need to be studied. The main objectives of this study are to assess the performance of spectral libraries built in a commercial soil analysis laboratory using compact NIR spectrophotometers aiming for SOC determination, compare the accuracy of global and local models, and evaluate strategies to implement local calibration. More than 9000 soil samples were included in this study. The independent validation was performed using 1000 randomly selected samples. This procedure was performed three times. Different modeling strategies were tested: conventional partial least square regression PLSR, global-PLSR (G-PLSR), local-PLSR (L-PLSR) based on the selection of n nearest neighbors samples (L-PLSRn), and local L-PLSR based on imposing Mahalanobis distance limit to select samples (L-PLSRdist). NeoSpectra compact spectrophotometer shows the best performance (root mean square error of the prediction, RMSEP, and R of 9.2 g kg and 0.6, respectively, for the G-PLSR, and of 6.5 g kg and 0.7, for L-PLSRn, were found). Models based on the NanoNIR spectrophotometer presented slightly lower accuracy (RMSEP and R values of, respectively, 11.4 g kg and 0.3, for the G-PLSR models, and of 8.6 g kg 0.3 for L-PLSRn models). Models built using G-PLSR performed worse than models built using local modeling strategies. Improvement in SOC determination of up to 42 % over the G-PLSR is found when L-PLSRn is used. Furthermore, the feasibility of improving the performance of local models by imposing Mahalanobis limit distances to select the calibration samples is proven. The results demonstrate that compact NIR spectrophotometers can be a low-cost alternative to partially replace the wet oxidation method on SOC measurement in routine soil analysis laboratories. For this purpose, building its own spectral library is currently the best strategy.

摘要

紧凑型近红外(NIR)分光光度计是低成本仪器,能够快速、廉价、无损且环保地测定土壤有机碳(SOC)。然而,在常规土壤分析实验室中使用这些仪器所需的步骤仍有待研究。本研究的主要目标是评估在商业土壤分析实验室中使用紧凑型NIR分光光度计建立的用于SOC测定的光谱库的性能,比较全局模型和局部模型的准确性,并评估实施局部校准的策略。本研究纳入了9000多个土壤样本。使用1000个随机选择的样本进行独立验证。此过程进行了三次。测试了不同的建模策略:传统偏最小二乘回归PLSR、基于选择n个最近邻样本的全局PLSR(G-PLSR)、局部PLSR(L-PLSR)(L-PLSRn)以及基于施加马氏距离限制来选择样本的局部L-PLSR(L-PLSRdist)。NeoSpectra紧凑型分光光度计表现出最佳性能(对于G-PLSR,预测均方根误差RMSEP为9.2 g/kg,R为0.6;对于L-PLSRn,RMSEP为6.5 g/kg,R为0.7)。基于NanoNIR分光光度计的模型准确性略低(对于G-PLSR模型,RMSEP和R值分别为11.4 g/kg和0.3;对于L-PLSRn模型,RMSEP为8.6 g/kg,R为0.3)。使用G-PLSR建立的模型比使用局部建模策略建立的模型表现更差。当使用L-PLSRn时,SOC测定的改进比G-PLSR高达42%。此外,证明了通过施加马氏距离限制来选择校准样本可提高局部模型性能的可行性。结果表明,紧凑型NIR分光光度计可以作为一种低成本的替代方法,在常规土壤分析实验室中部分替代湿氧化法进行SOC测量。为此,建立自己的光谱库目前是最佳策略。

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