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用于估算农业土壤中有机物、总碳和总氮的近红外光谱(NIRS)技术的发展。

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil.

作者信息

Santasup Natchanon, Theanjumpol Parichat, Santasup Choochard, Kittiwachana Sila, Mawan Nipon, Prantong Lalicha, Khongdee Nuttapon

机构信息

Department of Plant and Soil Science, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand.

Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand.

出版信息

MethodsX. 2024 Jun 15;13:102798. doi: 10.1016/j.mex.2024.102798. eCollection 2024 Dec.

DOI:10.1016/j.mex.2024.102798
PMID:39007027
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11239694/
Abstract

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0-20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky-Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil ( of calibration and validation higher than 0.80).

摘要

使用传统方法分析土壤有机质(OM)、总碳(TC)和总氮(TN)相当耗时,且需要使用危险的化学试剂。吸收光谱法,尤其是近红外(NIR)光谱法,在土壤分析中越来越受欢迎。这种方法几乎不需要样品制备,无需使用化学试剂,通过单次光谱分析即可评估土壤性质。因此,本研究旨在开发一种用于分析农业土壤中OM、TC和TN的近红外光谱法。这些发现可以为使用近红外技术的偏最小二乘回归提供一个很好的概念。方法如下:

•从各个农业田地采集表层土壤(0 - 20厘米)样本。使用传统方法和近红外光谱法分析OM、TC和TN。

•使用傅里叶变换近红外光谱仪获取近红外光谱,原始光谱采用Savitzky - Golay平滑、标准正态变量(SNV)和多元散射校正(MSC)预处理方法,通过偏最小二乘(PLS)回归建立预测模型,其中65%用于校准,其余35%用于验证。

•结果表明,农业土壤中测得的土壤性质(SOM和TC)与近红外吸收光谱之间存在显著关系(校准和验证的 高于0.80)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/57d0799a3f78/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/47331acb68bc/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/feb037100cd6/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/d238c555a44a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/7d27a0f9865b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/57d0799a3f78/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/47331acb68bc/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/feb037100cd6/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/d238c555a44a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/7d27a0f9865b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc0a/11239694/57d0799a3f78/gr4.jpg

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