1
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Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe M, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures. BIOMEDICAL OPTICS EXPRESS 2024; 15:4220-4236. [PMID: 39022543 PMCID: PMC11249694 DOI: 10.1364/boe.522376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 07/20/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.
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Affiliation(s)
- Mohammadrahim Kazemzadeh
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
| | | | - Robert Otupiri
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Anastasiia Artuyants
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
| | - MoiMoi Lowe
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Eduardo Reategui
- Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Zachary D Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA
| | - Weiliang Xu
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
| | - Cherie Blenkiron
- Auckland Cancer Society Research Centre, Auckland 1023, New Zealand
| | - Lawrence W Chamley
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland 1023, New Zealand
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
| | - Neil G R Broderick
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Colin L Hisey
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland 1023, New Zealand
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
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2
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Guo P, Wang Y, Moghaddamfard P, Meng W, Wu S, Bao Y. Artificial intelligence-empowered collection and characterization of microplastics: A review. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134405. [PMID: 38678715 DOI: 10.1016/j.jhazmat.2024.134405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.
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Affiliation(s)
- Pengwei Guo
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Yuhuan Wang
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Parastoo Moghaddamfard
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Weina Meng
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shenghua Wu
- Department of Civil, Coastal, and Environmental Engineering, University of South Alabama, Mobile, AL 36688, United States
| | - Yi Bao
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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3
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Yu K, Wu H, Xiong H, Wang G, Wei X, Liang X, Chen R, Zhang Y, Zhang K, Wang Z. Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association. APPLIED SPECTROSCOPY 2024; 78:605-615. [PMID: 38404185 DOI: 10.1177/00037028241231994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.
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Affiliation(s)
- Kai Yu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Hao Wu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Hongli Xiong
- Department of Forensic Medicine, Faculty of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Gongji Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Xin Wei
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Xinggong Liang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Run Chen
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | | | - Kai Zhang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
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4
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Gicquel C, Bruzaud S, Kedzierski M. Generation of synthetic FTIR spectra to facilitate chemical identification of microplastics. MARINE POLLUTION BULLETIN 2024; 202:116295. [PMID: 38537498 DOI: 10.1016/j.marpolbul.2024.116295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 05/08/2024]
Abstract
In a context where learning databases of microplastic FTIR spectra are often incomplete, the objective of our work was to test whether a synthetic data generation method could be relevant to fill the gaps. To this end, synthetic spectra were generated to create new databases. The effectiveness of machine learning from these databases was then tested and compared with previous results. The results showed that the creation of synthetic learning databases could avoid, to a certain extent, the need for learning databases of environmental microplastics FTIR spectra. However, some limitations were encountered, for example, when two different chemical classes had very similar reference spectra or when the intensities of the bands associated with fouling became too intense. The FTIR study of the ageing and fouling of microplastics in the natural environment is one of the identified ways that could further improve this approach.
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Affiliation(s)
- Chloé Gicquel
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France
| | - Stéphane Bruzaud
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France
| | - Mikaël Kedzierski
- Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France.
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5
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Seggio M, Arcadio F, Radicchi E, Cennamo N, Zeni L, Bossi AM. Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor. ACS OMEGA 2024; 9:18984-18994. [PMID: 38708270 PMCID: PMC11064004 DOI: 10.1021/acsomega.3c09485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 05/07/2024]
Abstract
Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.
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Affiliation(s)
- Mimimorena Seggio
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Francesco Arcadio
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Eros Radicchi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Nunzio Cennamo
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Luigi Zeni
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Alessandra Maria Bossi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
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6
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Liu K, Zhu L, Wei N, Li D. Underappreciated microplastic galaxy biases the filter-based quantification. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132897. [PMID: 37935065 DOI: 10.1016/j.jhazmat.2023.132897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/18/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023]
Abstract
Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.
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Affiliation(s)
- Kai Liu
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China.
| | - Lixin Zhu
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China; Marine and Environmental Sciences, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
| | - Nian Wei
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China; Norwegian Institute for Water Research, 94 Økernveien, Oslo 0579, Norway
| | - Daoji Li
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
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7
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Li Y, Zhu Y, Huang J, Ho YW, Fang JKH, Lam EY. High-throughput microplastic assessment using polarization holographic imaging. Sci Rep 2024; 14:2355. [PMID: 38287056 PMCID: PMC10824714 DOI: 10.1038/s41598-024-52762-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/22/2024] [Indexed: 01/31/2024] Open
Abstract
Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.
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Affiliation(s)
- Yuxing Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Yanmin Zhu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jianqing Huang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Key Lab of Education Ministry for Power Machinery and Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Yuen-Wa Ho
- Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
| | - James Kar-Hei Fang
- Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
- State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
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8
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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9
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Xie L, Luo S, Liu Y, Ruan X, Gong K, Ge Q, Li K, Valev VK, Liu G, Zhang L. Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18203-18214. [PMID: 37399235 DOI: 10.1021/acs.est.3c03210] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.
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Affiliation(s)
- Lifang Xie
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Siheng Luo
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yangyang Liu
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Xuejun Ruan
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Kedong Gong
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Qiuyue Ge
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Kejian Li
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Ventsislav Kolev Valev
- Centre for Photonics and Photonic Materials and Centre for Nanoscience and Nanotechnology, Department of Physics, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Guokun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Liwu Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
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10
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Mosquera-Ortega M, Rodrigues de Sousa L, Susmel S, Cortón E, Figueredo F. When microplastics meet electroanalysis: future analytical trends for an emerging threat. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5978-5999. [PMID: 37921647 DOI: 10.1039/d3ay01448g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Microplastics are a major modern challenge that must be addressed to protect the environment, particularly the marine environment. Microplastics, defined as particles ≤5 mm, are ubiquitous in the environment. Their small size for a relatively large surface area, high persistence and easy distribution in water, soil and air require the development of new analytical methods to monitor their presence. At present, the availability of analytical techniques that are easy to use, automated, inexpensive and based on new approaches to improve detection remains an open challenge. This review aims to outline the evolution and novelties of classical and advanced methods, in particular the recently reported electroanalytical detectors, methods and devices. Among all the studies reviewed here, we highlight the great advantages of electroanalytical tools over spectroscopic and thermal analysis, especially for the rapid and accurate detection of microplastics in the sub-micron range. Finally, the challenges faced in the development of automated analytical methods are discussed, highlighting recent trends in artificial intelligence (AI) in microplastics analysis.
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Affiliation(s)
- Mónica Mosquera-Ortega
- Laboratory of Biosensors and Bioanalysis (LABB), Department of Biological Chemistry and IQUIBICEN, Faculty of Sciences, University of Buenos Aires and CONICET, Ciudad Universitaria, Buenos Aires (1428), Argentina.
- Basic Science Department, Faculty Regional General Pacheco, National Technological University, Argentina
| | - Lucas Rodrigues de Sousa
- Laboratory of Biosensors and Bioanalysis (LABB), Department of Biological Chemistry and IQUIBICEN, Faculty of Sciences, University of Buenos Aires and CONICET, Ciudad Universitaria, Buenos Aires (1428), Argentina.
- Chemistry Institute, Federal University of Goias, Campus Samambaia, Goiania, Brazil
| | - Sabina Susmel
- Department of Agricultural, Food, Environmental and Animal Sciences (Di4A), University of Udine, Via Sondrio 2/A, 33100 Udine, Italy
| | - Eduardo Cortón
- Laboratory of Biosensors and Bioanalysis (LABB), Department of Biological Chemistry and IQUIBICEN, Faculty of Sciences, University of Buenos Aires and CONICET, Ciudad Universitaria, Buenos Aires (1428), Argentina.
- Department of Biosciences and Bioengineering, Indian Institute of Technology at Guwahati, Assam, India
| | - Federico Figueredo
- Laboratory of Biosensors and Bioanalysis (LABB), Department of Biological Chemistry and IQUIBICEN, Faculty of Sciences, University of Buenos Aires and CONICET, Ciudad Universitaria, Buenos Aires (1428), Argentina.
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11
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Chen Q, Wang J, Yao F, Zhang W, Qi X, Gao X, Liu Y, Wang J, Zou M, Liang P. A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives. Mikrochim Acta 2023; 190:465. [PMID: 37953347 DOI: 10.1007/s00604-023-06044-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023]
Abstract
The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.
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Affiliation(s)
- Qiang Chen
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jiamiao Wang
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Fuqi Yao
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Wei Zhang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Xiaohua Qi
- Chinese Academy of Inspection and Quarantine (CAIQ), Beijing, 100123, China
| | - Xia Gao
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Yan Liu
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Jiamin Wang
- Institute of Analysis and Testing, Beijing Research Institute of Science and Technology, Beijing, 100089, China
| | - Mingqiang Zou
- Chinese Academy of Inspection and Quarantine (CAIQ), Beijing, 100123, China.
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China.
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12
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Yan X, Cao Z, Murphy A, Ye Y, Wang X, Qiao Y. FRDA: Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165340. [PMID: 37414174 DOI: 10.1016/j.scitotenv.2023.165340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.
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Affiliation(s)
- Xinyu Yan
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland; Luoyang Institute of Science and Technology, China.
| | - Zhi Cao
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Alan Murphy
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Yuhang Ye
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Xinwu Wang
- International Union Laboratory of New Civil Engineering Structure of Henan Province, China.
| | - Yuansong Qiao
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
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13
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Minho LAC, Cardeal ZDL, Menezes HC. A deep learning-based simulator for comprehensive two-dimensional GC applications. J Sep Sci 2023; 46:e2300187. [PMID: 37525343 DOI: 10.1002/jssc.202300187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/02/2023]
Abstract
Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R2 = 0.9202) and 0.014 (relative average deviation = 1.67%, R2 = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.
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Affiliation(s)
- Lucas Almir Cavalcante Minho
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
| | - Zenilda de Lourdes Cardeal
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
| | - Helvécio Costa Menezes
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
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14
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Yao J, Li H, Yang HY. Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning. JOURNAL OF HAZARDOUS MATERIALS 2023; 458:131963. [PMID: 37406525 DOI: 10.1016/j.jhazmat.2023.131963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/13/2023] [Accepted: 06/27/2023] [Indexed: 07/07/2023]
Abstract
We investigated the adsorption mechanism of 66 coexisting pharmaceuticals and personal care products (PPCPs) on microplastics treated with potassium persulfate, potassium hydroxide, and Fenton reagent for 54, 110, and 500 days. The total adsorption capacity (qe) of 66 PPCPs on 15 original microplastics was 171.8 - 1043.7 μg/g, far below that of 177 long-term aged microplastics (7114.0 - 13,114.4 μg/g). Around 69.8% of qe was primarily influenced by the total energy, energy of the highest occupied molecular orbital, and energy gap of PPCPs, calculated using the B3LYP/6-31 G* level. Furthermore, 111 aged microplastics exhibited similar total qe values. Additionally, we developed predictive models based on attenuated total reflectance Fourier transform infrared spectroscopy to predict the individual and total qe on 192 microplastics. These models, including the maximal information coefficient and gradient boosting decision tree regression, exhibited high accuracy with Rtraining2 values of 0.9772 and 0.9661, respectively, and p-values below 0.001. Spectroscopic analysis and machine learning models highlighted surface functional group alterations and the importance of the 1528-1700 cm-1 spectral region and carbon skeleton in the adsorption process. In summary, our findings contribute to understanding the adsorption of PPCPs on microplastics, particularly in the context of long-term aging effects.
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Affiliation(s)
- Jingjing Yao
- Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China; Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.
| | - Haipu Li
- Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China.
| | - Hui Ying Yang
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.
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15
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Valls-Conesa J, Winterauer DJ, Kröger-Lui N, Roth S, Liu F, Lüttjohann S, Harig R, Vollertsen J. Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2226-2233. [PMID: 37114762 DOI: 10.1039/d3ay00514c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
In this work, a random decision forest model is built for fast identification of Fourier-transform infrared spectra of the eleven most common types of microplastics in the environment. The random decision forest input data is reduced to a combination of highly discriminative single wavenumbers selected using a machine learning classifier. This dimension reduction allows input from systems with individual wavenumber measurements, and decreases prediction time. The training and testing spectra are extracted from Fourier-transform infrared hyperspectral images of pure-type microplastic samples, automatizing the process with reference spectra and a fast background correction and identification algorithm. Random decision forest classification results are validated using procedurally generated ground truth. The classification accuracy achieved on said ground truths are not expected to carry over to environmental samples as those usually contain a broader variety of materials.
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Affiliation(s)
- Jordi Valls-Conesa
- Bruker Optics GmbH & Co. KG, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany.
- Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark
| | | | - Niels Kröger-Lui
- Bruker Optics GmbH & Co. KG, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany.
| | - Sascha Roth
- Bruker Optics GmbH & Co. KG, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany.
| | - Fan Liu
- Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark
| | - Stephan Lüttjohann
- Bruker Optics GmbH & Co. KG, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany.
| | - Roland Harig
- Bruker Optics GmbH & Co. KG, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany.
| | - Jes Vollertsen
- Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark
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16
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Tang PM, Habib S, Shukor MYA, Alias SA, Smykla J, Yasid NA. Evaluation of the Deterioration of Untreated Commercial Polystyrene by Psychrotrophic Antarctic Bacterium. Polymers (Basel) 2023; 15:polym15081841. [PMID: 37111988 PMCID: PMC10144070 DOI: 10.3390/polym15081841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
Polystyrene (PS) and microplastic production pose persistent threats to the ecosystem. Even the pristine Antarctic, which is widely believed to be pollution-free, was also affected by the presence of microplastics. Therefore, it is important to comprehend the extent to which biological agents such as bacteria utilise PS microplastics as a carbon source. In this study, four soil bacteria from Greenwich Island, Antarctica, were isolated. A preliminary screening of the isolates for PS microplastics utilisation in the Bushnell Haas broth was conducted with the shake-flask method. The isolate AYDL1 identified as Brevundimonas sp. was found to be the most efficient in utilising PS microplastics. An assay on PS microplastics utilisation showed that the strain AYDL1 tolerated PS microplastics well under prolonged exposure with a weight loss percentage of 19.3% after the first interval (10 days of incubation). Infrared spectroscopy showed that the bacteria altered the chemical structure of PS while a deformation of the surface morphology of PS microplastics was observed via scanning electron microscopy after being incubated for 40 days. The obtained results may essentially indicate the utilisation of liable polymer additives or "leachates" and thus, validate the mechanistic approach for a typical initiation process of PS microplastics biodeterioration by the bacteria (AYDL1)-the biotic process.
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Affiliation(s)
- Pui Mun Tang
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Syahir Habib
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Mohd Yunus Abd Shukor
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Siti Aisyah Alias
- Institute of Ocean and Earth Sciences, C308 Institute of Postgraduate Studies, University of Malaya, Kuala Lumpur 50603, Malaysia
- National Antarctic Research Centre, B303 Institute of Postgraduate Studies, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Jerzy Smykla
- Institute of Nature Conservation, Polish Academy of Sciences, Mickiewicza 33, 31-120 Kraków, Poland
| | - Nur Adeela Yasid
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
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17
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Weng S, Tang L, Wang J, Zhu R, Wang C, Sha W, Zheng L, Huang L, Liang D, Hu Y, Chu Z. Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122311. [PMID: 36608516 DOI: 10.1016/j.saa.2022.122311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/19/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Rui Zhu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Cong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Wen Sha
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Yimin Hu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, China
| | - Zhaojie Chu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China.
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18
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Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe MM, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533481. [PMID: 36993759 PMCID: PMC10055277 DOI: 10.1101/2023.03.20.533481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.
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19
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Fan X, Wang Y, Yu C, Lv Y, Zhang H, Yang Q, Wen M, Lu H, Zhang Z. A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning. Anal Chem 2023; 95:4863-4870. [PMID: 36908216 DOI: 10.1021/acs.analchem.2c03853] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.
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Affiliation(s)
- Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Yue Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Chuanxiu Yu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Yuanxia Lv
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Hailiang Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Qiong Yang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Ming Wen
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
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20
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Yang H, Li X, Zhang S, Li Y, Zhu Z, Shen J, Dai N, Zhou F. A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122210. [PMID: 36508904 DOI: 10.1016/j.saa.2022.122210] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/08/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues. Based on 6,352 raw micro-FTIR spectra, a one-dimensional convolutional neural network (1D-CNN) model was constructed to classify-five stages. Based on the established model, more than 93% accuracy was achieved at each stage, and the accuracy of identifying proliferation, low grade neoplasia, and ESCC cancer groups was achieved 99% for the test dataset. In this proof-of-concept study, the developed method can be applied to other diseases in order to promote the use of FTIR spectroscopy in cancer pathology.
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Affiliation(s)
- Haijun Yang
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Xianchang Li
- Huzhou College, Huzhou 313000, Zhejiang Province, China; Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China.
| | - Shiding Zhang
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Yuan Li
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Zunwei Zhu
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Jingwei Shen
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Ningtao Dai
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Fuyou Zhou
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China.
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21
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Zhang Y, Zhang D, Zhang Z. A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1150. [PMID: 36673905 PMCID: PMC9859244 DOI: 10.3390/ijerph20021150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/25/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.
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Affiliation(s)
- Yan Zhang
- School of Materials and Environmental Engineering, Fujian Polytechnic Normal University, Fuzhou 350300, China
| | - Dan Zhang
- School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University, Fuzhou 350300, China
- Fujian Provincial Key Laboratory of Coastal Basin Environment, Fujian Polytechnic Normal University, Fuzhou 350300, China
| | - Zhenchang Zhang
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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22
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Ranasinghe JC, Wang Z, Huang S. Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications. BIOSENSORS 2022; 13:27. [PMID: 36671862 PMCID: PMC9855372 DOI: 10.3390/bios13010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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23
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Lei B, Bissonnette JR, Hogan ÚE, Bec AE, Feng X, Smith RDL. Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy. Anal Chem 2022; 94:17011-17019. [PMID: 36445839 DOI: 10.1021/acs.analchem.2c02451] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily created─a feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm-1 spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.
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Affiliation(s)
- Benjamin Lei
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
| | - Justine R Bissonnette
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
| | - Úna E Hogan
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
| | - Avery E Bec
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
| | - Xinyi Feng
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
| | - Rodney D L Smith
- Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada.,Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada.,Waterloo Artificial Intelligence Institute, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada
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24
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Kazemzadeh M, Martinez-Calderon M, Xu W, Chamley LW, Hisey CL, Broderick NGR. Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data. Anal Chem 2022; 94:12907-12918. [PMID: 36067379 DOI: 10.1021/acs.analchem.2c03082] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning has had a significant impact on the value of spectroscopic characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to an unintentional bias during classification. To address this, we developed two deep learning methods capable of fully preprocessing raw Raman spectroscopy data without any human input. First, cascaded deep convolutional neural networks (CNN) based on either ResNet or U-Net architectures were trained on randomly generated spectra with augmented defects. Then, they were tested using simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, low resolution Raman spectra of human bladder cancer tissue, and finally, classification of SERS spectra from human placental extracellular vesicles (EVs). Both approaches resulted in faster training and complete spectral preprocessing in a single step, with more speed, defect tolerance, and classification accuracy compared to conventional methods. These findings indicate that cascaded CNN preprocessing is ideal for biomedical Raman spectroscopy applications in which large numbers of heterogeneous spectra with diverse defects need to be automatically, rapidly, and reproducibly preprocessed.
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Affiliation(s)
- Mohammadrahim Kazemzadeh
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand.,Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand
| | | | - Weiliang Xu
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand
| | - Lawrence W Chamley
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand
| | - Colin L Hisey
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand.,Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio43210, United States
| | - Neil G R Broderick
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand.,Department of Physics, University of Auckland, Auckland1061, New Zealand
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25
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Ourgaud M, Phuong NN, Papillon L, Panagiotopoulos C, Galgani F, Schmidt N, Fauvelle V, Brach-Papa C, Sempéré R. Identification and Quantification of Microplastics in the Marine Environment Using the Laser Direct Infrared (LDIR) Technique. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9999-10009. [PMID: 35749650 DOI: 10.1021/acs.est.1c08870] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Here, we evaluate for the first time the performances of the newly developed laser direct infrared (LDIR) technique and propose an optimization of the initial protocol for marine microplastics (MPs) analysis. Our results show that an 8 μm porosity polycarbonate filter placed on a Kevley slide enables preconcentration and efficient quantification of MPs, as well as polymer and size determination of reference plastic pellets of polypropylene (PP), polyethylene (PE), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET), with recoveries ranging from 80-100% and negligible blank values for particle sizes ranging from 200 to 500 μm. A spiked experiment using seawater, sediment, mussels, and fish stomach samples showed that the method responded linearly with significant slopes (R2 ranging from 0.93-1.0; p < 0.001, p < 0.01). Overall, 11 polymer types were identified with limited handling and an analysis time of ca. 3 h for most samples and 6 h for complex samples. Application of this technique to Mediterranean marine samples (seawater, sediment, fish stomachs and mussels) indicated MP concentrations and size distribution consistent with the literature. A high predominance of PVC (sediment, fish stomachs) and PE and PP (seawater, mussels) was observed in the analyzed samples.
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Affiliation(s)
- Mélanie Ourgaud
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
| | - Nam Ngoc Phuong
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
- PhuTho College of Medicine and Pharmacy, 2201 Hung Vuong Boulevard, Viettri City, PhuTho Province 290000, Viet Nam
| | - Laure Papillon
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
| | | | - François Galgani
- Laboratoire Environnement Ressources, Provence-Azur-Corse, IFREMER, Centre Méditerranée, Zone Portuaire de Brégaillon, CS20 330, 83507, La Seyne-sur-Mer Cedex, France
| | - Natascha Schmidt
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
| | - Vincent Fauvelle
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
| | - Christophe Brach-Papa
- Laboratoire Environnement Ressources, Provence-Azur-Corse, IFREMER, Centre Méditerranée, Zone Portuaire de Brégaillon, CS20 330, 83507, La Seyne-sur-Mer Cedex, France
| | - Richard Sempéré
- Aix-Marseille University, Toulon University, CNRS, IRD, M I O, Marseille 13007, France
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26
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Ly NH, Kim MK, Lee H, Lee C, Son SJ, Zoh KD, Vasseghian Y, Joo SW. Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates. JOURNAL OF NANOSTRUCTURE IN CHEMISTRY 2022; 12:865-888. [PMID: 35757049 PMCID: PMC9206222 DOI: 10.1007/s40097-022-00506-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 05/27/2022] [Indexed: 06/07/2023]
Abstract
Micro(nano)plastic (MNP) pollutants have not only impacted human health directly, but are also associated with numerous chemical contaminants that increase toxicity in the natural environment. Most recent research about increasing plastic pollutants in natural environments have focused on the toxic effects of MNPs in water, the atmosphere, and soil. The methodologies of MNP identification have been extensively developed for actual applications, but they still require further study, including on-site detection. This review article provides a comprehensive update on the facile detection of MNPs by Raman spectroscopy, which aims at early diagnosis of potential risks and human health impacts. In particular, Raman imaging and nanostructure-enhanced Raman scattering have emerged as effective analytical technologies for identifying MNPs in an environment. Here, the authors give an update on the latest advances in plasmonic nanostructured materials-assisted SERS substrates utilized for the detection of MNP particles present in environmental samples. Moreover, this work describes different plasmonic materials-including pure noble metal nanostructured materials and hybrid nanomaterials-that have been used to fabricate and develop SERS platforms to obtain the identifying MNP particles at low concentrations. Plasmonic nanostructure-enhanced materials consisting of pure noble metals and hybrid nanomaterials can significantly enhance the surface-enhanced Raman scattering (SERS) spectra signals of pollutant analytes due to their localized hot spots. This concise topical review also provides updates on recent developments and trends in MNP detection by means of SERS using a variety of unique materials, along with three-dimensional (3D) SERS substrates, nanopipettes, and microfluidic chips. A novel material-assisted spectral Raman technique and its effective application are also introduced for selective monitoring and trace detection of MNPs in indoor and outdoor environments.
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Affiliation(s)
- Nguyễn Hoàng Ly
- Department of Chemistry, Gachon University, Seongnam, 13120 Republic of Korea
| | - Moon-Kyung Kim
- Department of Environmental Health Sciences, School of Public Health, Seoul National University, Seoul, 08826 Republic of Korea
| | - Hyewon Lee
- Department of Chemical and Biological Engineering, Seokyeong University, Seoul, 02713 Republic of Korea
| | - Cheolmin Lee
- Department of Chemical and Biological Engineering, Seokyeong University, Seoul, 02713 Republic of Korea
| | - Sang Jun Son
- Department of Chemistry, Gachon University, Seongnam, 13120 Republic of Korea
| | - Kyung-Duk Zoh
- Department of Environmental Health Sciences, School of Public Health, Seoul National University, Seoul, 08826 Republic of Korea
| | - Yasser Vasseghian
- Department of Chemistry, Soongsil University, Seoul, 06978 Republic of Korea
| | - Sang-Woo Joo
- Department of Chemistry, Soongsil University, Seoul, 06978 Republic of Korea
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27
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Xue W, Chen B, Hong D, Yu J, Liu G. Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint. Anal Chem 2022; 94:7628-7636. [PMID: 35584207 DOI: 10.1021/acs.analchem.2c00852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.
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Affiliation(s)
- Wendong Xue
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Benneng Chen
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Deming Hong
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Jiahan Yu
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Guokun Liu
- College of the Environment and Ecology, Xiamen University, Xiamen 361005, Fujian, China
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28
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He C, Zhu S, Wu X, Zhou J, Chen Y, Qian X, Ye J. Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning. ACS OMEGA 2022; 7:10458-10468. [PMID: 35382336 PMCID: PMC8973095 DOI: 10.1021/acsomega.1c07263] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/09/2022] [Indexed: 05/04/2023]
Abstract
Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.
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Affiliation(s)
- Chang He
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Shuo Zhu
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Xiaorong Wu
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Jiale Zhou
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Yonghui Chen
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Xiaohua Qian
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Jian Ye
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Shanghai
Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of
Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
- Institute
of Medical Robotics, Shanghai Jiao Tong
University, Shanghai 200240, P.R. China
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29
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Luo Y, Zhang X, Zhang Z, Naidu R, Fang C. Dual-Principal Component Analysis of the Raman Spectrum Matrix to Automatically Identify and Visualize Microplastics and Nanoplastics. Anal Chem 2022; 94:3150-3157. [PMID: 35109647 PMCID: PMC9620979 DOI: 10.1021/acs.analchem.1c04498] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.
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Affiliation(s)
- Yunlong Luo
- Global
Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, New South Wales 2308, Australia,Cooperative
Research Centre for Contamination Assessment and Remediation of the
Environment (CRC CARE), University of Newcastle, Callaghan, New South Wales 2308, Australia
| | - Xian Zhang
- Key
Lab of Urban Environment and Health, Institute
of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Zixing Zhang
- Key
Lab of Urban Environment and Health, Institute
of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Ravi Naidu
- Global
Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, New South Wales 2308, Australia,Cooperative
Research Centre for Contamination Assessment and Remediation of the
Environment (CRC CARE), University of Newcastle, Callaghan, New South Wales 2308, Australia
| | - Cheng Fang
- Global
Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, New South Wales 2308, Australia,Cooperative
Research Centre for Contamination Assessment and Remediation of the
Environment (CRC CARE), University of Newcastle, Callaghan, New South Wales 2308, Australia,
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