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Machine Learning in Green Chemistry

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Dr. Tu Le

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Guest Editor

School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
Interests: machine learning; advanced materials; renewable energy; advanced therapeutics; sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Haoxin Mai

E-Mail Website
Guest Editor Assistant

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: photocatalysis; water splitting; machine learning

Special Issue Information

Dear Colleagues,

Machine learning has emerged as a powerful tool in green chemistry. It is a data-driven approach harnessing data analysis and computational models to revolutionize the design, optimization, and assessment of sustainable chemical processes. Through predictive modeling, pattern recognition, and informed decision making, machine learning techniques contribute to the development of environmentally friendly and economically viable chemical solutions. In chemical synthesis, machine learning models not only facilitate the prediction of reaction outcomes, but also optimize reaction conditions, minimize waste, and reduce energy consumption. In toxicity prediction and assessment, by analyzing chemical structures and their associated toxicity profiles, machine learning helps in identifying safer compounds and ensuring the development of environmentally friendly products. In addition, machine learning contributes to solvent selection, recommending greener alternatives that have lower environmental impact and reduced health hazards. Therefore, the integration of machine learning and green chemistry holds promise for a more sustainable future. By accelerating the development of ecologically sound processes, optimizing resource utilization, and minimizing detrimental effects on the environment, machine learning is driving the paradigm shift towards greener and more sustainable chemical practices.

Dr. Tu Le
Guest Editor

Dr. Haoxin Mai
Guest Editor Assistant

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Keywords

environment
green solvents
sustainability
renewable energy
catalysis
carbon reduction
machine learning
green synthesis

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15 pages, 5076 KiB

Open AccessArticle

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy

by Pengjie Zhang, Bin Du, Jiwei Xu, Jiang Wang, Zhiwei Liu, Bing Liu, Fanhua Meng and Zhaoyang Tong

Molecules 2024, 29(13), 3132; https://doi.org/10.3390/molecules29133132 - 1 Jul 2024

Viewed by 377

Abstract

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky–Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation–emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols. Full article

(This article belongs to the Special Issue Machine Learning in Green Chemistry)

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14 pages, 3304 KiB

Open AccessArticle

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

by Pengjie Zhang, Bing Liu, **hui Mu, Jiwei Xu, Bin Du, Jiang Wang, Zhiwei Liu and Zhaoyang Tong

Molecules 2024, 29(1), 197; https://doi.org/10.3390/molecules29010197 - 29 Dec 2023

Cited by 2 | Viewed by 1232

Abstract

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices. Full article

(This article belongs to the Special Issue Machine Learning in Green Chemistry)

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