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.
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
AI, Deep Learning, and Machine Learning in the Dynamic World of Spectroscopy
December 2nd 2024Over the past two years Spectroscopy Magazine has increased our coverage of artificial intelligence (AI), deep learning (DL), and machine learning (ML) and the mathematical approaches relevant to the AI topic. In this article we summarize AI coverage and provide the reference links for a series of selected articles specifically examining these subjects. The resources highlighted in this overview article include those from the Analytically Speaking podcasts, the Chemometrics in Spectroscopy column, and various feature articles and news stories published in Spectroscopy. Here, we provide active links to each of the full articles or podcasts resident on the Spectroscopy website.
Diffuse Reflectance Spectroscopy to Advance Tree-Level NSC Analysis
November 28th 2024Researchers have developed a novel method combining near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy with advanced data fusion techniques to improve the accuracy of non-structural carbohydrate estimation in diverse tree tissues, advancing carbon cycle research.
Young Scientist Awardee Uses Spectrophotometry and AI for Pesticide Detection Tool
November 11th 2024Sirish Subash is the winner of the Young Scientist Award, presented by 3M and Discovery education. His work incorporates spectrophotometry, a nondestructive method that measures the light of various wavelengths that is reflected off fruits and vegetables.
Emerging Leader Highlights Innovations in Machine Learning, Chemometrics at SciX Awards Session
October 23rd 2024Five invited speakers joined Joseph Smith, the 2024 Emerging Leader in Molecular Spectroscopy, on stage to speak about trends in hyperspectral imaging, FT-IR, surface enhanced Raman spectroscopy (SERS), and more during the conference in Raleigh.
Advancing Forensic Science with Chemometrics: New Tools for Objective Evidence Analysis
October 22nd 2024A review by researchers from Curtin University comprehensively explores how chemometrics can revolutionize forensic science by offering objective and statistically validated methods to interpret evidence. The chemometrics approach seeks to enhance the accuracy and reliability of forensic analyses, mitigating human bias and improving courtroom confidence in forensic conclusions.