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  1. Article ; Online: GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry.

    Guo, Zixuan / Fan, Yingjie / Yu, Chuanxiu / Lu, Hongmei / Zhang, Zhimin

    Analytical chemistry

    2024  Volume 96, Issue 15, Page(s) 5878–5886

    Abstract: Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting ... ...

    Abstract Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.
    Language English
    Publishing date 2024-04-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.3c05772
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Lewis Acidic Metal-Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue.

    Lv, Yuanxia / Zhao, Zhihao / Long, Zheng / Yu, Chuanxiu / Lu, Hongmei / Wu, Qian

    Analytical chemistry

    2024  Volume 96, Issue 3, Page(s) 1073–1083

    Abstract: The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or ... ...

    Abstract The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids. Here, a metal-organic framework (MOF) Zr
    MeSH term(s) Metal-Organic Frameworks ; Mass Spectrometry/methods ; Phospholipids ; Diagnostic Imaging ; Brain ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
    Chemical Substances Metal-Organic Frameworks ; Phospholipids
    Language English
    Publishing date 2024-01-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.3c03690
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Rare Benzothiazole Glucoside as a Derivative of 'Albedo Bluing' Substance in Citrus Fruit and Its Antioxidant Activity.

    Yang, Chao / Yu, Chuanxiu / Li, Qiang / Peng, Liangzhi / Chun, Changpin / Tang, Xiaolong / Liu, Song / Hu, Chengbo / Ling, Lili

    Molecules (Basel, Switzerland)

    2024  Volume 29, Issue 2

    Abstract: Albedo bluing' of fruits occurs in many varieties of citrus, resulting in a significant reduction in their commercial value. We first presented a breakthrough method for successfully extracting and purifying the 'albedo bluing' substance (ABS) from ... ...

    Abstract 'Albedo bluing' of fruits occurs in many varieties of citrus, resulting in a significant reduction in their commercial value. We first presented a breakthrough method for successfully extracting and purifying the 'albedo bluing' substance (ABS) from citrus fruits, resulting in the attainment of highly purified ABS. Then, HPLC and UPLC-QTOF-MS were used to prove that ABS in the fruits of three citrus varieties (
    MeSH term(s) Antioxidants ; Citrus ; Benzothiazoles ; Chromatography, High Pressure Liquid ; Glucosides
    Chemical Substances Antioxidants ; Benzothiazoles ; Glucosides
    Language English
    Publishing date 2024-01-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1413402-0
    ISSN 1420-3049 ; 1431-5165 ; 1420-3049
    ISSN (online) 1420-3049
    ISSN 1431-5165 ; 1420-3049
    DOI 10.3390/molecules29020302
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

    Fan, Xiaqiong / Wang, Yue / Yu, Chuanxiu / Lv, Yuanxia / Zhang, Hailiang / Yang, Qiong / Wen, Ming / Lü, Hongmei / Zhang, Zhimin

    Analytical Chemistry. 2023 Mar. 13, v. 95, no. 11 p.4863-4870

    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 ... ...

    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.
    Keywords Japan ; Raman spectroscopy ; analytical chemistry ; models
    Language English
    Dates of publication 2023-0313
    Size p. 4863-4870.
    Publishing place American Chemical Society
    Document type Article ; Online
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.2c03853
    Database NAL-Catalogue (AGRICOLA)

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  5. Article ; Online: Machine learning: Next promising trend for microplastics study.

    Su, Jiming / Zhang, Fupeng / Yu, Chuanxiu / Zhang, Yingshuang / Wang, Jianchao / Wang, Chongqing / Wang, Hui / Jiang, Hongru

    Journal of environmental management

    2023  Volume 344, Page(s) 118756

    Abstract: Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a ... ...

    Abstract Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.
    MeSH term(s) Humans ; Microplastics ; Plastics ; Ecosystem ; Environmental Pollution/analysis ; Environmental Pollutants/analysis ; Water Pollutants, Chemical/analysis
    Chemical Substances Microplastics ; Plastics ; Environmental Pollutants ; Water Pollutants, Chemical
    Language English
    Publishing date 2023-08-11
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 184882-3
    ISSN 1095-8630 ; 0301-4797
    ISSN (online) 1095-8630
    ISSN 0301-4797
    DOI 10.1016/j.jenvman.2023.118756
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning.

    Fan, Xiaqiong / Wang, Yue / Yu, Chuanxiu / Lv, Yuanxia / Zhang, Hailiang / Yang, Qiong / Wen, Ming / Lu, Hongmei / Zhang, Zhimin

    Analytical chemistry

    2023  Volume 95, Issue 11, Page(s) 4863–4870

    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 ... ...

    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.
    Language English
    Publishing date 2023-03-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.2c03853
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples.

    Fan, Yingjie / Yu, Chuanxiu / Lu, Hongmei / Chen, Yi / Hu, Binbin / Zhang, Xingren / Su, Jiaen / Zhang, Zhimin

    Journal of chromatography. A

    2022  Volume 1690, Page(s) 463768

    Abstract: Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to distill ... ...

    Abstract Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to distill chemical information from raw GC-MS data efficiently. In this study, we proposed an Automatic Resolution method (AutoRes) based on pseudo-Siamese convolutional neural networks (pSCNN) to extract the meaningful features swamped by the noises, baseline drifts, retention time shifts, and overlapped peaks. Two pSCNN models were trained with 400,000 augmented spectral pairs, respectively. They can predict the selective region (pSCNN1) and elution region (pSCNN2) of compounds in an untargeted manner. The accuracies of the pSCNN1 model and the pSCNN2 model on their test sets are 99.9% and 92.6%, respectively. Then, the chromatographic profile of each component was automatically resolved by full rank resolution (FRR) based on the predicted regions by these models. The performance of AutoRes was evaluated on the simulated and plant essential oil datasets. Compared to AMDIS and MZmine, AutoRes resolves more reasonable mass spectra, chromatograms, and peak areas to identify and quantify compounds. The average match scores of AutoRes (925 and 936) outperformed AMDIS (909 and 925) and MZmine (888 and 916) when resolving mass spectra from overlapped peaks on the Set Ⅰ and Set Ⅱ of plant essential oil dataset and matching them against the NIST17 library. It extracted peak areas and mass spectra automatically from 10 GC-MS files of plant essential oils, and the entire process was completed in 8 min without any prior information or manual intervention. It is implemented in Python and is available as an open-source package at https://github.com/dyjfan/AutoRes.
    MeSH term(s) Gas Chromatography-Mass Spectrometry/methods ; Deep Learning ; Mass Spectrometry ; Neural Networks, Computer ; Oils, Volatile ; Plant Oils
    Chemical Substances Oils, Volatile ; Plant Oils
    Language English
    Publishing date 2022-12-29
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1171488-8
    ISSN 1873-3778 ; 0021-9673
    ISSN (online) 1873-3778
    ISSN 0021-9673
    DOI 10.1016/j.chroma.2022.463768
    Database MEDical Literature Analysis and Retrieval System OnLINE

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