IRSAC-corrected CF-LIBS validated by ICP-OES for soil classification using machine learning

Ahmed Azam; Muhammad Afzaal; Zahid Farooq

doi:10.1039/d6ja00076b

January 1, 2026Journal of Analytical Atomic Spectrometry

IRSAC-corrected CF-LIBS validated by ICP-OES for soil classification using machine learning

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Key Points

To evaluate the effectiveness of IRSAC-corrected CF-LIBS for soil classification using machine learning techniques.
Utilized IRSAC-corrected CF-LIBS for soil analysis in a controlled laboratory setting.
Applied elemental profiling and PCA for data analysis and classification.
Incorporated machine learning algorithms to enhance classification accuracy.
Successfully classified soil types with high accuracy.
Demonstrated significant improvement in classification efficiency compared to traditional methods.
Validated findings through ICP-OES measurements.

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Implication

Validates IRSAC-corrected CF-LIBS for soil classification, indicating effective machine learning application.

Abstract

IRSAC-corrected CF-LIBS combines plasma diagnostics, elemental profiling, PCA, and machine learning for controlled laboratory soil suitability classification.

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Cite This Study

Azam et al. (2026) studied this question.

synapsesocial.com/papers/6a338c62630953a74978dae8 https://doi.org/10.1039/d6ja00076b

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IRSAC-corrected CF-LIBS validated by ICP-OES for soil classification using machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

IRSAC-corrected CF-LIBS validated by ICP-OES for soil classification using machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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