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Vosough M, Schmidt TC, Renner G. Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives. Anal Bioanal Chem 2024; 416:2125-2136. [PMID: 38300263 PMCID: PMC10951028 DOI: 10.1007/s00216-024-05153-8] [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: 10/02/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/02/2024]
Abstract
This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.
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Affiliation(s)
- Maryam Vosough
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany.
- Department of Clean Technologies, Chemistry and Chemical Engineering Research Center of Iran, P.O. Box 14335-186, Tehran, Iran.
| | - Torsten C Schmidt
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany
- IWW Water Centre, Moritzstr. 26, Mülheim an der Ruhr, 45476, North Rhine-Westphalia, Germany
| | - Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany.
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Wang X, Li F, Wei L, Huang Y, Wen X, Wang D, Cheng G, Zhao R, Lin Y, Yang H, Fan M. Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting. Anal Chem 2024; 96:4682-4692. [PMID: 38450485 DOI: 10.1021/acs.analchem.4c00064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.
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Affiliation(s)
- Xueqing Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Fan Li
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Lan Wei
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Yun Huang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Xiang Wen
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Dongmei Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Guiguang Cheng
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Ruijuan Zhao
- Guizhou Academy of Tobacco Science, Guiyang 550081, China
| | - Yechun Lin
- Guizhou Academy of Tobacco Science, Guiyang 550081, China
| | - Hui Yang
- Guizhou Academy of Tobacco Science, Guiyang 550081, China
| | - Meikun Fan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
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Kang D, Yun D, Cho KH, Baek SS, Jeon J. Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry. CHEMOSPHERE 2024; 352:141402. [PMID: 38346509 DOI: 10.1016/j.chemosphere.2024.141402] [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: 10/24/2023] [Revised: 01/31/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024]
Abstract
Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.
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Affiliation(s)
- Daeho Kang
- Department of Environmental Engineering, Changwon National University, Changwon, Gyeongsangnamdo, 51140, South Korea
| | - Daeun Yun
- Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea
| | - Kyung Hwa Cho
- School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea
| | - Sang-Soo Baek
- Department of Environmental Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan-Si, Gyeongbuk, 38541, South Korea
| | - Junho Jeon
- Department of Environmental Engineering, Changwon National University, Changwon, Gyeongsangnamdo, 51140, South Korea; School of Smart and Green Engineering, Changwon National University, Changwon, Gyeongsangnamdo, 51140, South Korea.
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Xia D, Liu L, Zhao B, Xie D, Lu G, Wang R. Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:727-738. [PMID: 38100713 DOI: 10.1021/acs.est.3c06688] [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: 12/17/2023]
Abstract
High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.
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Affiliation(s)
- Di Xia
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Lijun Liu
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Bo Zhao
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Danping Xie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Guining Lu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Rui Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
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Joseph NT, Schwichtenberg T, Cao D, Jones GD, Rodowa AE, Barlaz MA, Charbonnet JA, Higgins CP, Field JA, Helbling DE. Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:14351-14362. [PMID: 37696050 DOI: 10.1021/acs.est.3c03770] [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: 09/13/2023]
Abstract
This study elucidates per- and polyfluoroalkyl substance (PFAS) fingerprints for specific PFAS source types. Ninety-two samples were collected from aqueous film-forming foam impacted groundwater (AFFF-GW), landfill leachate, biosolids leachate, municipal wastewater treatment plant effluent (WWTP), and wastewater effluent from the pulp and paper and power generation industries. High-resolution mass spectrometry operated with electrospray ionization in negative mode was used to quantify up to 50 target PFASs and screen and semi-quantify up to 2,266 suspect PFASs in each sample. Machine learning classifiers were used to identify PFASs that were diagnostic of each source type. Four C5-C7 perfluoroalkyl acids and one suspect PFAS (trihydrogen-substituted fluoroethernonanoic acid) were diagnostic of AFFF-GW. Two target PFASs (5:3 and 6:2 fluorotelomer carboxylic acids) and two suspect PFASs (4:2 fluorotelomer-thia-acetic acid and N-methylperfluoropropane sulfonamido acetic acid) were diagnostic of landfill leachate. Biosolids leachates were best classified along with landfill leachates and N-methyl and N-ethyl perfluorooctane sulfonamido acetic acid assisted in that classification. WWTP, pulp and paper, and power generation samples contained few target PFASs, but fipronil (a fluorinated insecticide) was diagnostic of WWTP samples. Our results provide PFAS fingerprints for known sources and identify target and suspect PFASs that can be used for source allocation.
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Affiliation(s)
- Nayantara T Joseph
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Trever Schwichtenberg
- Chemistry Department, Oregon State University, Corvallis, Oregon 97331, United States
| | - Dunping Cao
- Chemistry Department, Oregon State University, Corvallis, Oregon 97331, United States
| | - Gerrad D Jones
- Department of Biological & Ecological Engineering, Oregon State University, Corvallis, Oregon 97331, United States
| | - Alix E Rodowa
- National Institutes of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Morton A Barlaz
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Joseph A Charbonnet
- Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, Iowa 50011, United States
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado 80401, United States
| | - Christopher P Higgins
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado 80401, United States
| | - Jennifer A Field
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon 97331, United States
| | - Damian E Helbling
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States
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Li F, Huang Y, Wang X, Wang D, Fan M. Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification. LUMINESCENCE 2023; 38:302-307. [PMID: 36702476 DOI: 10.1002/bio.4449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/28/2023]
Abstract
The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.
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Affiliation(s)
- Fan Li
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Yuting Huang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Xueqing Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Dongmei Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Meikun Fan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
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Kawakami J, Piccolo SR, Kauwe JK, Graves SW. Gender differences contribute to variability of serum lipid biomarkers for Alzheimer's disease. Biomark Med 2022; 16:1089-1100. [PMID: 36625236 DOI: 10.2217/bmm-2022-0462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Background: Alzheimer's disease (AD) cannot currently be diagnosed by a blood test. One reason may be gender differences. Another may be the statistical methods used. The authors evaluate these possibilities. Objective: The authors applied serum lipidomics to find AD biomarkers in men and women. They hypothesized that AD biomarkers would differ between genders and that machine-learning algorithms would improve diagnostic performance. Methods: Serum lipids were analyzed by mass spectrometry for a training set of AD cases and controls and in a blinded test set. Statistical analyses considered gender differences. Results: Lipids best classifying AD subjects differed significantly between men and women. Robust statistical algorithms did not improve diagnostic performance. Conclusion: Poor performance of AD biomarkers appears to be due primarily to inherent variability in AD patients.
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Affiliation(s)
- Jie Kawakami
- Department of Chemistry & Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Stephen R Piccolo
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - John Ks Kauwe
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Steven W Graves
- Department of Chemistry & Biochemistry, Brigham Young University, Provo, UT 84602, USA
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