A recent study from Central South University in China examined how to assess cobalt content in soil.
A new study from Central South University in China showcased the development of a new approach for identifying cobalt (Co) contamination in soil using advanced machine learning (ML) techniques. The findings of this study were published in the Journal of Environmental Chemical Engineering (1).
Co is a transition metal on the periodic table. It is considered an essential micronutrient for human beings and for plants (2). However, the questions that scientists are still pondering is how much Co is good for plants before it becomes toxic (3). There is a need for a nondestructive, inexpensive, and rapid method to help detect Co contamination in plants.
Gardener is digging soil with a shovel at spring green outdoors background. | Image Credit: © vitanovski - stock.adobe.com
Historically, the identification of Co contamination has been limited to small-scale studies and simplistic analytical methods, leaving large areas underassessed (1). Chongchong Qi and his research team at Central South University explored a new method to determine cobalt contamination by leveraging eight different ML algorithms combined with visible and near-infrared (NIR) reflectance spectroscopy to create a robust, large-scale model capable of accurately classifying Co content in soil (1).
Qi and his team referred to data set of 18,675 topsoil samples, which they collected and analyzed to train and validate the models (1). To improve the performance of the tested models, the researchers deployed two methods: principal component analysis (PCA) and rigorous hyper-parameter tuning (1). These improvements were measured using multiple evaluation indicators to ensure the highest accuracy and reliability (1).
Among the various ML models the researchers tested, the eXtreme Gradient Boosting (XGB) algorithm performed the best, with an area under the curve (AUC) value of 0.901 on the training set and 0.904 on the testing set (1). These results indicate a high level of precision in distinguishing between contaminated and non-contaminated samples.
Following the model training and validation, the optimal XGB model was applied to a comprehensive United States soil spectral data set. This application revealed several states, including Utah, Arizona, New Mexico, North Dakota, Arkansas, Mississippi, and Alabama, as having a higher risk of Co contamination (1). These findings are crucial for environmental managers and policymakers because they highlight specific regions where intervention and remediation efforts may be urgently needed (1).
This study also lays the groundwork for future research in this space. The methodology and framework developed by Qi’s team can be adapted for the detection and assessment of other hazardous elements in soil, thereby broadening the scope of environmental protection efforts (1). This study exemplifies how cutting-edge technology can be harnessed to address complex environmental challenges, ultimately contributing to safer and healthier ecosystems (1).
As cobalt is increasingly used in various industrial applications, including rechargeable batteries and aerospace components, understanding its environmental impact becomes ever more important. The ability to accurately detect and map cobalt contamination on a large scale ensures that potential risks to human health and the environment can be managed proactively (1). By combining machine learning algorithms with spectral analysis, they have developed a powerful tool for identifying and managing cobalt contamination in soil.
(1) Zhou, N.; Hu, T.; Wu, M.; et al. Comparative analysis of machine learning algorithms for identifying cobalt contamination in soil using spectroscopy. J. Environ. Chem. Eng. 2024, 12 (5), 113328. DOI: 10.1016/j.jece.2024.113328
(2) Hu, X.; Wei, X.; Ling, J.; Chen, J. Cobalt: An Essential Micronutrient for Plant Growth? Front Plant Sci. 2021, 12, 768523. DOI: 10.3389/fpls.2021.768523
(3) Srivastava, P.; Bolan, N.; Casagrande, V.; et al. In Appraisal of Metal(loids) in the Ecosystem, Chapter 5 - Cobalt in soils: sources, fate, bioavailability, plant uptake, remediation, and management. 2022, 81–104. DOI: 10.1016/B978-0-323-85621-8.00007-8
Get essential updates on the latest spectroscopy technologies, regulatory standards, and best practices—subscribe today to Spectroscopy.
Drone-Mounted Infrared Camera Sees Invisible Methane Leaks in Real Time
July 9th 2025Researchers in Scotland have developed a drone-mounted infrared imaging system that can detect and map methane gas leaks in real time from up to 13.6 meters away. The innovative approach combines laser spectroscopy with infrared imaging, offering a safer and more efficient tool for monitoring pipeline leaks and greenhouse gas emissions.
How Spectroscopy Drones Are Detecting Hidden Crop Threats in China’s Soybean Fields
July 8th 2025Researchers in Northeast China have demonstrated a new approach using drone-mounted multispectral imaging to monitor and predict soybean bacterial blight disease, offering a promising tool for early detection and yield protection.
Radar and Soil Spectroscopy Boost Soil Carbon Predictions in Brazil’s Semi-Arid Regions
July 7th 2025A new study published in Geoderma demonstrates that combining soil spectroscopy with radar-derived vegetation indices and environmental data significantly improves the accuracy of soil organic carbon predictions in Brazil’s semi-arid regions.
Advancing Deep Soil Moisture Monitoring with AI-Powered Spectroscopy Drones
July 7th 2025A Virginia Tech study has combined drone-mounted NIR hyperspectral imaging (400 nm to 1100 nm) and AI to estimate soil moisture at root depths with remarkable accuracy, paving the way for smarter irrigation and resilient farming.
AI Boosts SERS for Next Generation Biomedical Breakthroughs
July 2nd 2025Researchers from Shanghai Jiao Tong University are harnessing artificial intelligence to elevate surface-enhanced Raman spectroscopy (SERS) for highly sensitive, multiplexed biomedical analysis, enabling faster diagnostics, imaging, and personalized treatments.
Artificial Intelligence Accelerates Molecular Vibration Analysis, Study Finds
July 1st 2025A new review led by researchers from MIT and Oak Ridge National Laboratory outlines how artificial intelligence (AI) is transforming the study of molecular vibrations and phonons, making spectroscopic analysis faster, more accurate, and more accessible.