Comparative elemental analysis of soil of wheat, corn, rice, and okra cropped field using CF-LIBS

Khoso, M. Aslam; Shaikh, Nek M.; Kalhoro, Muhammad Siddique; Jamali, Saifullah; Ujan, Zaheer Ahmed; Ali, Raheel

doi:10.1016/j.ijleo.2022.169247

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…Elsayed et al 74 proposed the use of CF-LIBS for fast determination of phosphorus concentration in phosphogypsum waste samples, while Khoso et al 75 analysed soils with CF-LIBS to prevent heavy metal contamination of plants used in human alimentation.…”

Section: Environmental Analysismentioning

confidence: 99%

Catching up on calibration-free LIBS

Poggialini

Campanella

Cocciaro

et al. 2023

J. Anal. At. Spectrom.

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Section: Environmental Analysismentioning

confidence: 99%

Catching up on calibration-free LIBS

Poggialini

Campanella

Cocciaro

et al. 2023

J. Anal. At. Spectrom.

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“…At present, many works have also been carried out using LIBS in the rapid detection of soil elements, but there is little research on the detection of cultivation substrate components. For example, Tavares et al [23] used a spectral variable screening algorithm to screen the LIBS characteristics of soil nutrients, and then constructed soil nutrient prediction models by comparing the performance of different modeling algorithms so as to obtain the best model for soil nutrient LIBS detection; Hossen et al [24] used a combination of unmanned aerial vehicles multispectral imaging, LIBS, and artificial intelligence to achieve a rapid and efficient assessment of soil TN; Khoso et al [25] used calibration-free LIBS to rapidly detect heavy metals in soils planted with different crops; Erler et al [26] applied a portable LIBS detection device and the constructed multiple regression model to rapidly detect multiple nutrient elements in soil; and Ding et al [27] enhanced the detection accuracy of heavy metals in oily soil through the combination of interval partial least squares and LIBS. In summary, LIBS had good feasibility in the rapid detection of agricultural substrate elements, and it would provide data support for the decisionmaking of precision agriculture fertilization.…”

Section: Introductionmentioning

confidence: 99%

Rapid and High-Performance Analysis of Total Nitrogen in Coco-Peat Substrate by Coupling Laser-Induced Breakdown Spectroscopy with Multi-Chemometrics

Lu,

Wang,

et al. 2024

Agriculture

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Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient (RC2) and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient (RP2) and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

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