Integrating Laser-Induced Breakdown Spectroscopy (LIBS) and Machine Learning for Soil Elemental Analysis: Exploring Atomic Interactions and Environmental Applications

Environmental monitoring plays a vital role in enabling precise and timely assessments of the current situation. Understanding the state of soil and the services provided by sustainable ecosystems is essential for linking terrestrial and marine environments. Laser-Induced Breakdown Spectroscopy (LIBS) is a well-established analytical technique with growing significance in environmental and biodiversity research. Its capacity for real-time, in-situ, multi-elemental analysis with minimal sample preparation presents clear advantages. This research presents the development of a structural design and system architecture for the rapid characterization of environmental samples and their precipitates using LIBS. A key objective is to enable consistent monitoring of soil quality and to support detailed assessments of soil pollution-both qualitatively and semi-quantitatively. LIBS analytical data were employed to identify elemental components in soils treated with various fertilizers under experimental conditions. The study investigated the influence of both organic and inorganic fertilizers—including no phosphorus (P), triple superphosphate (TSP), compost, and compost+TSP—on the availability and distribution of phosphorus in soil. Field experiments in Northern Germany provided data for evaluating the performance of LIBS in this context. In addition, the study explored the classification of agricultural soils versus diesel oilcontaminated soils using Partial Least Squares Discriminant Analysis (PLS-DA) on an available dataset. This pilot investigation underscores the potential of LIBS for supporting sustainable land use management. Moreover, the work aims to strengthen the EU-CONEXUS alliance by fostering transnational research collaboration. The adaptable LIBS framework proposed here can be extended to other environmental matrices, supporting broader goals in sustainability and ecological protection.

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Published
2025-06-27
DOI
https://doi.org/10.1109/i2cacis65476.2025.11100930
Primary Topic
Laser-induced spectroscopy and plasma
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article
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article

Integrating Laser-Induced Breakdown Spectroscopy (LIBS) and Machine Learning for Soil Elemental Analysis: Exploring Atomic Interactions and Environmental Applications

Muhammad Sufi bin Roslan, Josephine Ying Chyi Liew, Zuhaib Haider, Haider Al-Juboori
Laser-induced spectroscopy and plasma
article

Integrating Laser-Induced Breakdown Spectroscopy (LIBS) and Machine Learning for Soil Elemental Analysis: Exploring Atomic Interactions and Environmental Applications

Muhammad Sufi bin Roslan, Josephine Ying Chyi Liew, Zuhaib Haider, Haider Al-Juboori
article en

Abstract

Environmental monitoring plays a vital role in enabling precise and timely assessments of the current situation. Understanding the state of soil and the services provided by sustainable ecosystems is essential for linking terrestrial and marine environments. Laser-Induced Breakdown Spectroscopy (LIBS) is a well-established analytical technique with growing significance in environmental and biodiversity research. Its capacity for real-time, in-situ, multi-elemental analysis with minimal sample preparation presents clear advantages. This research presents the development of a structural design and system architecture for the rapid characterization of environmental samples and their precipitates using LIBS. A key objective is to enable consistent monitoring of soil quality and to support detailed assessments of soil pollution-both qualitatively and semi-quantitatively. LIBS analytical data were employed to identify elemental components in soils treated with various fertilizers under experimental conditions. The study investigated the influence of both organic and inorganic fertilizers—including no phosphorus (P), triple superphosphate (TSP), compost, and compost+TSP—on the availability and distribution of phosphorus in soil. Field experiments in Northern Germany provided data for evaluating the performance of LIBS in this context. In addition, the study explored the classification of agricultural soils versus diesel oilcontaminated soils using Partial Least Squares Discriminant Analysis (PLS-DA) on an available dataset. This pilot investigation underscores the potential of LIBS for supporting sustainable land use management. Moreover, the work aims to strengthen the EU-CONEXUS alliance by fostering transnational research collaboration. The adaptable LIBS framework proposed here can be extended to other environmental matrices, supporting broader goals in sustainability and ecological protection.

Universiti Putra Malaysia (MY), Institute of Technology Carlow (IE), Tun Hussein Onn University of Malaysia (MY)
Openalex Percentile: Top 77%
Laser-induced spectroscopy and plasma
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