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Sagrado S, Pardo-Cortina C, Escuder-Gilabert L, Medina-Hernández MJ, Martín-Biosca Y. Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems? Anal Chem 2024; 96:12205-12212. [PMID: 38982948 PMCID: PMC11270524 DOI: 10.1021/acs.analchem.4c02656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/11/2024]
Abstract
The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.
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Affiliation(s)
- Salvador Sagrado
- Departamento
de Química Analítica, Universitat
de València, Burjassot, E- 46100 Valencia, Spain
- Instituto
Interuniversitario de Investigación de Reconocimiento Molecular
y Desarrollo Tecnológico (IDM), Universitat Politècnica
de València, Universitat de València, E-46100 Valencia, Spain
| | - Carlos Pardo-Cortina
- Departamento
de Química Analítica, Universitat
de València, Burjassot, E- 46100 Valencia, Spain
| | - Laura Escuder-Gilabert
- Departamento
de Química Analítica, Universitat
de València, Burjassot, E- 46100 Valencia, Spain
| | | | - Yolanda Martín-Biosca
- Departamento
de Química Analítica, Universitat
de València, Burjassot, E- 46100 Valencia, Spain
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2
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Wahab MF, Handlovic TT, Roy S, Burk RJ, Armstrong DW. Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI. Anal Chem 2024. [PMID: 39017630 DOI: 10.1021/acs.analchem.4c01734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.
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Affiliation(s)
- M Farooq Wahab
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Troy T Handlovic
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Souvik Roy
- Department of Mathematics, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Ryan Jacob Burk
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Daniel W Armstrong
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
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3
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Cardoso Rial R. AI in analytical chemistry: Advancements, challenges, and future directions. Talanta 2024; 274:125949. [PMID: 38569367 DOI: 10.1016/j.talanta.2024.125949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024]
Abstract
This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.
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Affiliation(s)
- Rafael Cardoso Rial
- Federal Institute of Mato Grosso do Sul, 79750-000, Nova Andradina, MS, Brazil.
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4
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Moulahoum H, Ghorbanizamani F. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chem 2024; 445:138800. [PMID: 38382253 DOI: 10.1016/j.foodchem.2024.138800] [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: 12/08/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.
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Affiliation(s)
- Hichem Moulahoum
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
| | - Faezeh Ghorbanizamani
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
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5
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Kang M, Kim D, Kim J, Kim N, Lee S. Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:3855. [PMID: 38931635 PMCID: PMC11207790 DOI: 10.3390/s24123855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/01/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.
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Affiliation(s)
- Mijeong Kang
- Department of Optics and Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Republic of Korea
- Department of Cogno-Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Republic of Korea
| | - Donghyeon Kim
- Department of Cogno-Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Republic of Korea
| | - Jihee Kim
- Department of Optics and Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Republic of Korea
| | - Nakyung Kim
- Department of Optics and Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Republic of Korea
| | - Seunghun Lee
- Department of Physics, Pukyong National University, Busan 48513, Republic of Korea
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6
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Banerjee S, Mandal S, Jesubalan NG, Jain R, Rathore AS. NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation. Biotechnol Bioeng 2024; 121:1803-1819. [PMID: 38390805 DOI: 10.1002/bit.28681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.
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Affiliation(s)
- Shantanu Banerjee
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Shyamapada Mandal
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Naveen G Jesubalan
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Rijul Jain
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, Delhi, India
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7
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Kishimoto A, Wu D, O'Shea DF. Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions. Sci Rep 2024; 14:9591. [PMID: 38719814 PMCID: PMC11079048 DOI: 10.1038/s41598-024-59619-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Vaping involves the heating of chemical solutions (e-liquids) to high temperatures prior to lung inhalation. A risk exists that these chemicals undergo thermal decomposition to new chemical entities, the composition and health implications of which are largely unknown. To address this concern, a graph-convolutional neural network (NN) model was used to predict pyrolysis reactivity of 180 e-liquid chemical flavours. The output of this supervised machine learning approach was a dataset of probability ranked pyrolysis transformations and their associated 7307 products. To refine this dataset, the molecular weight of each NN predicted product was automatically correlated with experimental mass spectrometry (MS) fragmentation data for each flavour chemical. This blending of deep learning methods with experimental MS data identified 1169 molecular weight matches that prioritized these compounds for further analysis. The average number of discrete matches per flavour between NN predictions and MS fragmentation was 6.4 with 92.8% of flavours having at least one match. Globally harmonized system classifications for NN/MS matches were extracted from PubChem, revealing that 127 acute toxic, 153 health hazard and 225 irritant classifications were predicted. This approach may reveal the longer-term health risks of vaping in advance of clinical diseases emerging in the general population.
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Affiliation(s)
| | - Dan Wu
- Department of Chemistry, Royal College of Surgeons in Ireland (RCSI), Dublin 2, Ireland.
| | - Donal F O'Shea
- Department of Chemistry, Royal College of Surgeons in Ireland (RCSI), Dublin 2, Ireland.
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8
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Khrisanfov MD, Matyushin DD, Samokhin AS. A general procedure for finding potentially erroneous entries in the database of retention indices. Anal Chim Acta 2024; 1297:342375. [PMID: 38438243 DOI: 10.1016/j.aca.2024.342375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND The NIST retention index database is one the most widely used sources of retention indices. In both untargeted analysis and machine learning studies filtering for potential errors is rather lacking or nonexistent. According to our estimates about 80% of the compounds from both NIST 17 and NIST 20 retention index databases have only one RI value per stationary phase, which makes searching for erroneous values with statistical methods impossible. Manual inspection is also impractical because the database contains more than 300 000 entries. RESULTS We suggest a two-step procedure to find potentially erroneous retention indices based on machine learning. The first step is to use five predictive models to obtain predicted retention index values for the whole database. The second one is to compare these predicted values against the experimental ones. We consider a retention index erroneous if its accuracy (the difference between predicted and experimental value) is in the bottom 5% for each of the five models simultaneously. Using this method, we were able to detect 2093 outlier entries for standard and semi-standard non-polar stationary phases in the NIST 17 retention index database, 566 of those were corrected or removed by the developers in the NIST 20. SIGNIFICANCE This is a novel approach to find potentially erroneous entries in a large-scale database with mostly unique entries, which can be applied not only to retention indices. The procedure can help filter and report mishandled data to improve the quality of the dataset for machine learning applications and experimental use.
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Affiliation(s)
- Mikhail D Khrisanfov
- Chemistry Department, Lomonosov Moscow State University, Leninskie Gory 1-3, 119991, Moscow, Russia; A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, 119071, Moscow, Russia.
| | - Dmitriy D Matyushin
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, 119071, Moscow, Russia.
| | - Andrey S Samokhin
- Chemistry Department, Lomonosov Moscow State University, Leninskie Gory 1-3, 119991, Moscow, Russia.
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9
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Ayres LB, Gomez FJV, Silva MF, Linton JR, Garcia CD. Predicting the formation of NADES using a transformer-based model. Sci Rep 2024; 14:2715. [PMID: 38388549 PMCID: PMC10883925 DOI: 10.1038/s41598-022-27106-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/26/2022] [Indexed: 02/24/2024] Open
Abstract
The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.
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Affiliation(s)
- Lucas B Ayres
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
| | - Federico J V Gomez
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Maria Fernanda Silva
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Jeb R Linton
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
- IBM Cloud, Armonk, NY, 10504, USA
| | - Carlos D Garcia
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA.
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10
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Montone CM, Giannelli Moneta B, Laganà A, Piovesana S, Taglioni E, Cavaliere C. Transformation products of antibacterial drugs in environmental water: Identification approaches based on liquid chromatography-high resolution mass spectrometry. J Pharm Biomed Anal 2024; 238:115818. [PMID: 37944459 DOI: 10.1016/j.jpba.2023.115818] [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: 08/05/2023] [Revised: 10/11/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
In recent years, the presence of antibiotics in the aquatic environment has caused increasing concern for the possible consequences on human health and ecosystems, including the development of antibiotic-resistant bacteria. However, once antibiotics enter the environment, mainly through hospital and municipal discharges and the effluents of wastewater treatment plants, they can be subject to transformation reactions, driven by both biotic (e.g. microorganism and mammalian metabolisms) and abiotic factors (e.g. oxidation, photodegradation, and hydrolysis). The resulting transformation products (TPs) can be less or more active than their parent compounds, therefore the inclusion of TPs in monitoring programs should be mandatory. However, only the reference standards of a few known TPs are available, whereas many other TPs are still unknown, due to the high diversity of possible transformation reactions in the environment. Modern high-resolution mass spectrometry (HRMS) instrumentation is now ready to tackle this problem through suspect and untargeted screening approaches. However, for handling the large amount of data typically encountered in the analysis of environmental samples, these approaches also require suitable processing workflows and accurate tandem mass spectra interpretation. The compilation of a suspect list containing the possible monoisotopic masses of TPs retrieved from the literature and/or from laboratory simulated degradation experiments showed unique advantages. However, the employment of in silico prediction tools could improve the identification reliability. In this review, the most recent strategies relying on liquid chromatography-HRMS for the analysis of environmental TPs of the main antibiotic classes were examined, whereas TPs formed during water treatments or disinfection were not included.
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Affiliation(s)
- Carmela Maria Montone
- Department of Chemistry, Sapienza University of Rome, p.le Aldo Moro 5, 00185 Rome, Italy
| | | | - Aldo Laganà
- Department of Chemistry, Sapienza University of Rome, p.le Aldo Moro 5, 00185 Rome, Italy
| | - Susy Piovesana
- Department of Chemistry, Sapienza University of Rome, p.le Aldo Moro 5, 00185 Rome, Italy
| | - Enrico Taglioni
- Department of Chemistry, Sapienza University of Rome, p.le Aldo Moro 5, 00185 Rome, Italy
| | - Chiara Cavaliere
- Department of Chemistry, Sapienza University of Rome, p.le Aldo Moro 5, 00185 Rome, Italy.
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11
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Vitali C, Peters RJB, Janssen HG, Undas AK, Munniks S, Ruggeri FS, Nielen MWF. Quantitative image analysis of microplastics in bottled water using artificial intelligence. Talanta 2024; 266:124965. [PMID: 37487270 DOI: 10.1016/j.talanta.2023.124965] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/04/2023] [Accepted: 07/18/2023] [Indexed: 07/26/2023]
Abstract
The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 μm, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient (R2) of 0.99. Method precision was demonstrated by a repeatability of 9-16% RSD (n = 7) and within-laboratory reproducibility of 15-27% RSD (n = 21). Accuracy based on recovery was 92 ± 15% and 98 ± 23% at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.
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Affiliation(s)
- Clementina Vitali
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands; Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
| | - Ruud J B Peters
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Hans-Gerd Janssen
- Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands; Unilever Foods Innovation Centre - Hive, Bronland 14, 6708 WH, Wageningen, the Netherlands
| | - Anna K Undas
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Sandra Munniks
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Francesco Simone Ruggeri
- Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands; Wageningen University, Physical Chemistry and Soft Matter, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
| | - Michel W F Nielen
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands; Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands
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12
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Gozdzialski L, Hutchison A, Wallace B, Gill C, Hore D. Toward automated infrared spectral analysis in community drug checking. Drug Test Anal 2024; 16:83-92. [PMID: 37248686 DOI: 10.1002/dta.3520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 05/31/2023]
Abstract
The body of knowledge surrounding infrared spectral analysis of drug mixtures continues to grow alongside the physical expansion of drug checking services. Technicians trained in the analysis of spectroscopic data are essential for reasons that go beyond the accuracy of the analytical results. Significant barriers faced by people who use drugs in engaging with drug checking services include the speed and accuracy of the results, and the availability and accessibility of the service. These barriers can be overcome by the automation of interpretations. A random forest model for the detection of two compounds, MDA and fluorofentanyl, was trained and optimized with drug samples acquired at a community drug checking site. This resulted in a 79% true positive and 100% true negative rate for MDA, and 61% true positive and 97% true negative rate for fluorofentanyl. The trained models were applied to selected drug samples to demonstrate a proposed workflow for interpreting and validating model predictions. The detection of MDA was demonstrated on three mixtures: (1) MDMA and MDA, (2) MDA and dimethylsulfone, and (3) fentanyl, etizolam, and benzocaine. The classification of fluorofentanyl was applied to a drug mixture containing fentanyl, fluorofentanyl, 4-anilino-N-phenethylpiperidine, caffeine, and mannitol. Feature importance was calculated using shapely additive explanations to better explain the model predictions and k-nearest neighbors was used for visual comparison to labelled training data. This is a step toward building appropriate trust in computer-assisted interpretations in order to promote their use in a harm reduction context.
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Affiliation(s)
- Lea Gozdzialski
- Department of Chemistry, University of Victoria, Victoria, British Columbia, Canada
| | - Abby Hutchison
- Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
- School of Public Health and Social Policy, University of Victoria, Victoria, British Columbia, Canada
| | - Bruce Wallace
- Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
- School of Social Work, University of Victoria, Victoria, British Columbia, Canada
| | - Chris Gill
- Department of Chemistry, University of Victoria, Victoria, British Columbia, Canada
- Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
- Department of Chemistry, Applied Environmental Research Laboratories (AERL), Vancouver Island University, Nanaimo, British Columbia, Canada
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Dennis Hore
- Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
- Department of Computer Science, University of Victoria, Victoria, British Columbia, Canada
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13
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Clark KM, Ray TR. Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics. ACS Sens 2023; 8:3606-3622. [PMID: 37747817 PMCID: PMC11211071 DOI: 10.1021/acssensors.3c01512] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.
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Affiliation(s)
- Kaylee M. Clark
- Department of Mechanical Engineering, University of Hawai’i at Mãnoa, Honolulu, HI 96822, USA
| | - Tyler R. Ray
- Department of Mechanical Engineering, University of Hawai’i at Mãnoa, Honolulu, HI 96822, USA
- Department of Cell and Molecular Biology, John. A. Burns School of Medicine, University of Hawai’i at Mãnoa, Honolulu, HI 96813, USA
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14
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Ayres L, Benavidez T, Varillas A, Linton J, Whitehead DC, Garcia CD. Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:15644-15655. [PMID: 37796649 DOI: 10.1021/acs.jafc.3c05462] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components, leading to the formation of low molecular weight species with diverse functional groups that impart off-odors and off-flavors. Aiming to control this process, antioxidants are commonly added to these products, often deployed as combinations of two or more compounds, a strategy that allows for lowering the amount used while boosting the total antioxidant capacity of the formulation. While this approach allows for minimizing the potential organoleptic and toxic effects of these compounds, predicting how these mixtures of antioxidants will behave has traditionally been one of the most challenging tasks, often leading to simple additive, antagonistic, or synergistic effects. Approaches to understanding these interactions have been predominantly empirically driven but thus far, inefficient and unable to account for the complexity and multifaceted nature of antioxidant responses. To address this current gap in knowledge, we describe the use of an artificial intelligence model based on deep learning architecture to predict the type of interaction (synergistic, additive, and antagonistic) of antioxidant combinations. Here, each mixture was associated with a combination index value (CI) and used as input for our model, which was challenged against a test (n = 140) data set. Despite the encouraging preliminary results, this algorithm failed to provide accurate predictions of oxidation experiments performed in-house using binary mixtures of phenolic antioxidants and a lard sample. To overcome this problem, the AI algorithm was then enhanced with various amounts of experimental data (antioxidant power data assessed by the TBARS assay), demonstrating the importance of having chemically relevant experimental data to enhance the model's performance and provide suitable predictions with statistical relevance. We believe the proposed method could be used as an auxiliary tool in benchmark analysis routines, offering a novel strategy to enable broader and more rational predictions related to the behavior of antioxidant mixtures.
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Affiliation(s)
- Lucas Ayres
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
| | - Tomás Benavidez
- INFIQC-CONICET, Department of Physical Chemistry, National University of Córdoba, Cordoba 5000, Argentina
| | - Armelle Varillas
- South Carolina Governor's School for Science and Mathematics, Hartsville, South Carolina 29550, United States
| | - Jeb Linton
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
| | - Daniel C Whitehead
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
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15
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Jobst S, Recum P, Écija-Arenas Á, Moser E, Bierl R, Hirsch T. Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis. ACS Sens 2023; 8:3530-3537. [PMID: 37505186 DOI: 10.1021/acssensors.3c01114] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures.
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Affiliation(s)
- Simon Jobst
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Patrick Recum
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
| | - Ángela Écija-Arenas
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
| | - Elisabeth Moser
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Rudolf Bierl
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Thomas Hirsch
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
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16
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Zhu J, Jiang X, Rong Y, Wei W, Wu S, Jiao T, Chen Q. Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models. Food Chem 2023; 414:135705. [PMID: 36808025 DOI: 10.1016/j.foodchem.2023.135705] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/02/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (RP2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10-4 and 7.24 × 10-4 μg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.
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Affiliation(s)
- Jiaji Zhu
- School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, PR China
| | - Xin Jiang
- School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, PR China
| | - Yawen Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Shengde Wu
- Yancheng Products Quality Supervision and Inspection Institute, Yancheng 224056, PR China
| | - Tianhui Jiao
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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17
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Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0. Food Chem 2023; 409:135303. [PMID: 36586255 DOI: 10.1016/j.foodchem.2022.135303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/29/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Abstract
Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.
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18
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Cai P, Liu S, Zhang D, Xing H, Han M, Liu D, Gong L, Hu QN. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics 2023; 24:152. [PMID: 37069545 PMCID: PMC10111727 DOI: 10.1186/s12859-023-05281-5] [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: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.
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Affiliation(s)
- Pengli Cai
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linlin Gong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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19
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Zaid A, Hassan NH, Marriott PJ, Wong YF. Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis. Pharmaceutics 2023; 15:1121. [PMID: 37111606 PMCID: PMC10140985 DOI: 10.3390/pharmaceutics15041121] [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: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/05/2023] Open
Abstract
Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.
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Affiliation(s)
- Atiqah Zaid
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Norfarizah Hanim Hassan
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Philip J. Marriott
- Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Melbourne, VIC 3800, Australia
| | - Yong Foo Wong
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
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20
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Tan X, Tang Y, Yang T, Dai G, Ye C, Meng J, Li F. Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification. Anal Chem 2023; 95:3309-3316. [PMID: 36716054 DOI: 10.1021/acs.analchem.2c04346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for β-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six β-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.
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Affiliation(s)
- Xiaoqing Tan
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yongtao Tang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Tingting Yang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Guoliang Dai
- Research Center for Green Printing Nanophotonic Materials, Jiangsu Key Laboratory for Environmental Functional Materials, School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Changqing Ye
- Research Center for Green Printing Nanophotonic Materials, Jiangsu Key Laboratory for Environmental Functional Materials, School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Jianxin Meng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China.,College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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21
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Fan Y, Yu C, Lu H, Chen Y, Hu B, Zhang X, Su J, Zhang Z. Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples. J Chromatogr A 2023; 1690:463768. [PMID: 36641940 DOI: 10.1016/j.chroma.2022.463768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
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.
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Affiliation(s)
- Yingjie Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Chuanxiu Yu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Yi Chen
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China
| | - Binbin Hu
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China
| | - Xingren Zhang
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China; Baoshan City Branch of Yunnan Tobacco Company, Baoshan 678000, Yunnan, China
| | - Jiaen Su
- Dali Prefecture Branch of Yunnan Tobacco Company, Dali 671000, Yunnan, China.
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China.
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22
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Giordano GF, Ferreira LF, Bezerra ÍRS, Barbosa JA, Costa JNY, Pimentel GJC, Lima RS. Machine learning toward high-performance electrochemical sensors. Anal Bioanal Chem 2023:10.1007/s00216-023-04514-z. [PMID: 36637495 PMCID: PMC9838410 DOI: 10.1007/s00216-023-04514-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/14/2023]
Abstract
The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.
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Affiliation(s)
- Gabriela F. Giordano
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil
| | - Larissa F. Ferreira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Júlia A. Barbosa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
| | - Juliana N. Y. Costa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Gabriel J. C. Pimentel
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,School of Sciences, São Paulo State University, Bauru, São Paulo 17033-360 Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
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23
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Nicoliche CYN, da Silva GS, Gomes-de-Pontes L, Schleder GR, Lima RS. Single-Response Electronic Tongue and Machine Learning Enable the Multidetermination of Extracellular Vesicle Biomarkers for Cancer Diagnostics Without Recognition Elements. Methods Mol Biol 2023; 2679:83-94. [PMID: 37300610 DOI: 10.1007/978-1-0716-3271-0_6] [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] [Indexed: 06/12/2023]
Abstract
Platforms based on impedimetric electronic tongue (nonselective sensor) and machine learning are promising to bring disease screening biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point-of-care, thus contributing to rationalize and decentralize laboratory tests with social and economic impacts being achieved. By combining a low-cost and scalable electronic tongue with machine learning, in this chapter, we describe the simultaneous determination of two extracellular vesicle (EV) biomarkers, i.e., the concentrations of EV and carried proteins, in mice blood with Ehrlich tumor from a single impedance spectrum without using biorecognizing elements. This tumor shows primary features of mammary tumor cells. Pencil HB core electrodes are integrated into polydimethylsiloxane (PDMS) microfluidic chip. The platform shows the highest throughput in comparison with the methods addressed in the literature to determine EV biomarkers.
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Affiliation(s)
- Caroline Y N Nicoliche
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
| | | | - Leticia Gomes-de-Pontes
- Department of Immunology, Institute of Biomedical Sciences, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Gabriel R Schleder
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Renato S Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil.
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, SP, Brazil.
- Federal University of ABC, Santo André, SP, Brazil.
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24
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Davies JC, Pattison D, Hirst JD. Machine learning for yield prediction for chemical reactions using in situ sensors. J Mol Graph Model 2023; 118:108356. [PMID: 36272195 DOI: 10.1016/j.jmgm.2022.108356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 11/28/2022]
Abstract
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including temperature, pressure, and colour. Colour was a good predictor of product formation for this reaction and machine learning models were able to learn which of the properties were important. Predictions for the current product formation (in terms of % yield) had a mean absolute error of 1.2%. For predicting 30, 60 and 120 min ahead the error rose to 3.4, 4.1 and 4.6%, respectively. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.
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Affiliation(s)
- Joseph C Davies
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | | | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
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25
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Carneiro CN, Gomez FJV, Spisso A, Silva MF, Santos JLO, Dias FDS. Exploratory Analysis of South American Wines Using Artificial Intelligence. Biol Trace Elem Res 2022:10.1007/s12011-022-03529-4. [PMID: 36550265 DOI: 10.1007/s12011-022-03529-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
In this work, microwave-induced plasma optical emission spectrometry was applied for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentinian wines. Then, logistic regression, support vector machine, and decision tree for exploratory analysis and comparison of these algorithms in differentiating red wine samples by region of origin were carried out. All wine samples were classified according to their geographical origin. The quantification limits (mg L-1) were P: 0.06, B: 0.08, K: 0.17, Mn: 0.002, Cr: 0.002, and Al: 0.02. The accuracy of the method was evaluated by analyzing the wine samples by ICP OES for results' comparison. The concentrations in mg L-1 found for each element in wine samples were as follows: Al (< 0.02-1.82), Cr (0.15-0.50), Mn (< 0.002-0.8), P (97-277), B (1.7-11.6), Pb (< 0.06-0.3), Na (8.84-41.57), and K (604-1701), in mg L-1.
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Affiliation(s)
- Candice N Carneiro
- Centro de Ciências Exatas E Tecnológicas, Universidade Federal Do Recôncavo da Bahia, Campus Universitário de Cruz das Almas, Cruz das Almas, Bahia, 44380-000, Brazil
| | - Federico J V Gomez
- Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Adrian Spisso
- Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Maria Fernanda Silva
- Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Jorge L O Santos
- Centro Multidisciplinar de Bom Jesus da Lapa, Universidade Federal Do Oeste da Bahia, Bom Jesus da Lapa, Bahia, 47600-000, Brazil
| | - Fabio de S Dias
- Instituto de Química, Universidade Federal da Bahia, Campus Universitário de Ondina, Salvador, Bahia, Brazil.
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26
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Strocchi G, Bagnulo E, Ruosi MR, Ravaioli G, Trapani F, Bicchi C, Pellegrino G, Liberto E. Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning. Foods 2022; 11:foods11244083. [PMID: 36553825 PMCID: PMC9778272 DOI: 10.3390/foods11244083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.
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Affiliation(s)
- Giulia Strocchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy
| | - Eloisa Bagnulo
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy
| | | | | | | | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy
| | | | - Erica Liberto
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy
- Correspondence: ; Tel.: +39-01-1670-7134
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27
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A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies. Metabolites 2022; 12:metabo12121168. [PMID: 36557207 PMCID: PMC9782571 DOI: 10.3390/metabo12121168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.
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28
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Machine learning–based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe. Anal Bioanal Chem 2022; 414:8365-8378. [DOI: 10.1007/s00216-022-04372-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/25/2022] [Accepted: 10/06/2022] [Indexed: 11/01/2022]
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29
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Castro ACH, Bezerra ÍRS, Pascon AM, da Silva GH, Philot EA, de Oliveira VL, Mancini RSN, Schleder GR, Castro CE, de Carvalho LRS, Fernandes BHV, Cilli EM, Sanches PRS, Santhiago M, Charlie-Silva I, Martinez DST, Scott AL, Alves WA, Lima RS. Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests. ACS NANO 2022; 16:14239-14253. [PMID: 35969505 PMCID: PMC9397565 DOI: 10.1021/acsnano.2c04364] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/11/2022] [Indexed: 05/16/2023]
Abstract
Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.
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Affiliation(s)
- Ana C. H. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Aline M. Pascon
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriela H. da Silva
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Eric A. Philot
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Vivian L. de Oliveira
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Laboratory of Immunology, Heart Institute,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Rodrigo S. N. Mancini
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriel R. Schleder
- John A. Paulson School of Engineering and Applied
Sciences, Harvard University, Cambridge, Massachusetts 02138,
United States
| | - Carlos E. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | | | | | - Eduardo M. Cilli
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Paulo R. S. Sanches
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ives Charlie-Silva
- Institute of Biomedical Sciences,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Diego S. T. Martinez
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Ana L. Scott
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Wendel A. Alves
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Institute of Chemistry, University of
Campinas, Campinas, São Paulo 13083-970,
Brazil
- São Carlos Institute of Chemistry,
University of São Paulo, São Carlos, São
Paulo 09210-580, Brazil
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30
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A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva. Anal Chim Acta 2022; 1232:340447. [DOI: 10.1016/j.aca.2022.340447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/19/2022] [Accepted: 09/23/2022] [Indexed: 11/20/2022]
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31
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [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] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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32
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Grinias JP. Navigating the Future of Separation Science Education: A Perspective. Chromatographia 2022; 85:681-688. [PMID: 35875830 PMCID: PMC9295876 DOI: 10.1007/s10337-022-04182-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 11/03/2022]
Abstract
A number of recommendations on how to improve the education and training of separation scientists were recently made by the National Academies of Sciences, Engineering, and Mathematics in their report, A Research Agenda for Transforming Separation Science. This perspective outlines how some of these recommendations may be fulfilled by examining trends in potential curriculum topics related to the field and new technological platforms for interactive content delivery. Identifying and adopting the best practices within these emerging educational directions will ensure the future success of the field.
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Affiliation(s)
- James P. Grinias
- Department of Chemistry & Biochemistry, Rowan University, 201 Mullica Hill Rd, Glassboro, NJ 08028 USA
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33
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Noreldeen HAA, Huang KY, Wu GW, Peng HP, Deng HH, Chen W. Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B 6 Derivatives. Anal Chem 2022; 94:9287-9296. [PMID: 35723526 DOI: 10.1021/acs.analchem.2c00655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Vitamin B6 derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 μM). The determination coefficient (R2) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry.
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Affiliation(s)
- Hamada A A Noreldeen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China.,National Institute of Oceanography and Fisheries, NIOF, Cairo 4262110, Egypt
| | - Kai-Yuan Huang
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Gang-Wei Wu
- Department of Pharmacy, Fujian Provincial Hospital, Fuzhou 350001, China
| | - Hua-Ping Peng
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Hao-Hua Deng
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Wei Chen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
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34
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Ferreira LF, Giordano GF, Gobbi AL, Piazzetta MHO, Schleder GR, Lima RS. Real-Time and In Situ Monitoring of the Synthesis of Silica Nanoparticles. ACS Sens 2022; 7:1045-1057. [PMID: 35417147 DOI: 10.1021/acssensors.1c02697] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The real-time and in situ monitoring of the synthesis of nanomaterials (NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providing optimized quality control. In fact, the lack of reproducibility in the synthesis of NMs is a bottleneck against the translation of nanotechnologies into the market toward daily practice. Here, we address an impedimetric millifluidic sensor with data processing by machine learning (ML) as a sensing platform to monitor silica nanoparticles (SiO2NPs) over a 24 h synthesis from a single measurement. The SiO2NPs were selected as a model NM because of their extensive applications. Impressively, simple ML-fitted descriptors were capable of overcoming interferences derived from SiO2NP adsorption over the signals of polarizable Au interdigitate electrodes to assure the determination of the size and concentration of nanoparticles over synthesis while meeting the trade-off between accuracy and speed/simplicity of computation. The root-mean-square errors were calculated as ∼2.0 nm (size) and 2.6 × 1010 nanoparticles mL-1 (concentration). Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses using different devices, with the global average accuracy being 103.7 ± 1.9%. Our work advances the developments required to transform a closed flow system basically encompassing the reactional flask and an impedimetric sensor into a scalable and user-friendly platform to assess the in situ synthesis of SiO2NPs. Since the sensor presents a universal response principle, the method is expected to enable the monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology.
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Affiliation(s)
- Larissa F. Ferreira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriela F. Giordano
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Angelo L. Gobbi
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Maria H. O. Piazzetta
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R. Schleder
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil
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35
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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36
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Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model. Sci Rep 2022; 12:6350. [PMID: 35428810 PMCID: PMC9012820 DOI: 10.1038/s41598-022-10406-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/01/2022] [Indexed: 01/31/2023] Open
Abstract
This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.
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37
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Zhang J, Xin PL, Wang XY, Chen HY, Li DW. Deep Learning-Based Spectral Extraction for Improving the Performance of Surface-Enhanced Raman Spectroscopy Analysis on Multiplexed Identification and Quantitation. J Phys Chem A 2022; 126:2278-2285. [PMID: 35380835 DOI: 10.1021/acs.jpca.1c10681] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has been recognized as a promising analytical technique for its capability of providing molecular fingerprint information and avoiding interference of water. Nevertheless, direct SERS detection of complicated samples without pretreatment to achieve the high-efficiency identification and quantitation in a multiplexed way is still a challenge. In this study, a novel spectral extraction neural network (SENN) model was proposed for synchronous SERS detection of each component in mixed solutions using a demonstration sample containing diquat dibromide (DDM), methyl viologen dichloride (MVD), and tetramethylthiuram disulfide (TMTD). A SERS spectra dataset including 3600 spectra of DDM, MVD, TMTD, and their mixtures was first constructed to train the SENN model. After the training step, the cosine similarity of the SENN model can achieve 0.999, 0.997, and 0.994 for DDM, MVD, and TMTD, respectively, which means that the spectra extracted from the mixture are highly consistent with those collected by the SERS experiment of the corresponding pure samples. Furthermore, a convolutional neural network model for quantitative analysis is combined with the SENN, which can simultaneously and rapidly realize the qualitative and quantitative SERS analysis of mixture solutions with lower than 8.8% relative standard deviation. The result demonstrates that the proposed strategy has great potential in improving SERS analysis in environmental monitoring, food safety, and so on.
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Affiliation(s)
- Jie Zhang
- Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Pei-Lin Xin
- Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Xiao-Yuan Wang
- Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Hua-Ying Chen
- Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Da-Wei Li
- Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
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Barbosa JA, Freitas VMS, Vidotto LHB, Schleder GR, de Oliveira RAG, da Rocha JF, Kubota LT, Vieira LCS, Tolentino HCN, Neckel IT, Gobbi AL, Santhiago M, Lima RS. Biocompatible Wearable Electrodes on Leaves toward the On-Site Monitoring of Water Loss from Plants. ACS APPLIED MATERIALS & INTERFACES 2022; 14:22989-23001. [PMID: 35311272 DOI: 10.1021/acsami.2c02943] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %-1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s-1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating "sense-act" technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.
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Affiliation(s)
- Júlia A Barbosa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
| | - Vitoria M S Freitas
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Faculty of Chemical Engineering, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Lourenço H B Vidotto
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R Schleder
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Ricardo A G de Oliveira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Jaqueline F da Rocha
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Lauro T Kubota
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Luis C S Vieira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Hélio C N Tolentino
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Itamar T Neckel
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Angelo L Gobbi
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Renato S Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
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Alvarado JIM, Meinhardt JM, Lin S. Working at the interfaces of data science and synthetic electrochemistry. TETRAHEDRON CHEM 2022; 1. [PMID: 35441154 PMCID: PMC9014485 DOI: 10.1016/j.tchem.2022.100012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Electrochemistry is quickly entering the mainstream of synthetic organic chemistry. The diversity of new transformations enabled by electrochemistry is to a large extent a consequence of the unique features and reaction parameters in electrochemical systems including redox mediators, applied potential, electrode material, and cell construction. While offering chemists new means to control reactivity and selectivity, these additional features also increase the dimensionalities of a reaction system and complicate its optimization. This challenge, however, has spawned increasing adoption of data science tools to aid reaction discovery as well as development of high-throughput screening platforms that facilitate the generation of high quality datasets. In this Perspective, we provide an overview of recent advances in data-science driven electrochemistry with an emphasis on the opportunities and challenges facing this growing subdiscipline.
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Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton. Polymers (Basel) 2022; 14:polym14050937. [PMID: 35267760 PMCID: PMC8912627 DOI: 10.3390/polym14050937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/14/2022] [Accepted: 02/25/2022] [Indexed: 12/10/2022] Open
Abstract
In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.
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41
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Tan X, Liang Y, Ye Y, Liu Z, Meng J, Li F. Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification. Anal Chem 2022; 94:829-836. [DOI: 10.1021/acs.analchem.1c03508] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xiaoqing Tan
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Yongpeng Liang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Yingying Ye
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Zhihao Liu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Jianxin Meng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Guangdong Engineering & Technology Research Centre of Graphene-like Materials and Products, Jinan University, Guangzhou 510632, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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43
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Lee SM, Balakrishnan HK, Yuan D, Nai YH, Guijt RM. Perspective - what constitutes a quality analytical paper: Microfluidics and Flow analysis. TALANTA OPEN 2021. [DOI: 10.1016/j.talo.2021.100055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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44
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Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. Artificial intelligence: A powerful paradigm for scientific research. Innovation (N Y) 2021; 2:100179. [PMID: 34877560 PMCID: PMC8633405 DOI: 10.1016/j.xinn.2021.100179] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 10/26/2021] [Indexed: 12/18/2022] Open
Abstract
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
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Affiliation(s)
- Yongjun Xu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Liu
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Cao
- Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China
| | - Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Enke Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Sen Qian
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Xingchen Liu
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - Yanjun Wu
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fengliang Dong
- National Center for Nanoscience and Technology, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cheng-Wei Qiu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Junjun Qiu
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
| | - Keqin Hua
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
| | - Wentao Su
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Huiyu Xu
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
| | - Yong Han
- Zhejiang Provincial People’s Hospital, Hangzhou 310014, China
| | - Chenguang Fu
- School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhigang Yin
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
| | - Miao Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Ronald Roepman
- Medical Center, Radboud University, 6500 Nijmegen, the Netherlands
| | - Sabine Dietmann
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marko Virta
- Department of Microbiology, University of Helsinki, 00014 Helsinki, Finland
| | - Fredrick Kengara
- School of Pure and Applied Sciences, Bomet University College, Bomet 20400, Kenya
| | - Ze Zhang
- Agriculture College of Shihezi University, Xinjiang 832000, China
| | - Lifu Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- Agriculture College of Shihezi University, Xinjiang 832000, China
| | - Taolan Zhao
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ji Dai
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | | | - Liang Lan
- Department of Communication Studies, Hong Kong Baptist University, Hong Kong, China
| | - Ming Luo
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Zhaofeng Liu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao An
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
| | - Bin Zhang
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - Xiao He
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Shan Cong
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Xiaohong Liu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Wei Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - James P. Lewis
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Qi Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Zhejiang Lab, Hangzhou 311121, China
| | - Zhulin An
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Libo Zhang
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuan Lu
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3FL, UK
| | - Zhipeng Cai
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Fang Wang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiabao Zhang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Puthongkham P, Wirojsaengthong S, Suea-Ngam A. Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry. Analyst 2021; 146:6351-6364. [PMID: 34585185 DOI: 10.1039/d1an01148k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.
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Affiliation(s)
- Pumidech Puthongkham
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. .,Electrochemistry and Optical Spectroscopy Center of Excellence (EOSCE), Chulalongkorn University, Bangkok 10330, Thailand.,Center of Excellence in Responsive Wearable Materials, Chulalongkorn University, Bangkok 10330, Thailand
| | - Supacha Wirojsaengthong
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
| | - Akkapol Suea-Ngam
- Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK
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Morlock GE. High-performance thin-layer chromatography combined with effect-directed assays and high-resolution mass spectrometry as an emerging hyphenated technology: A tutorial review. Anal Chim Acta 2021; 1180:338644. [DOI: 10.1016/j.aca.2021.338644] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 12/11/2022]
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Lee SY, Lee ST, Suh S, Ko BJ, Oh HB. Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC-MS/MS Machine Learning Models and the Hybrid Similarity Search Algorithm. J Anal Toxicol 2021; 46:732-742. [PMID: 34498039 DOI: 10.1093/jat/bkab098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/11/2021] [Accepted: 09/08/2021] [Indexed: 11/12/2022] Open
Abstract
High-resolution LC-MS/MS tandem mass spectra-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPS's). Using a training set comprised of 770 LC-MS/MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPS's were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine, and others). Using 193 LC-MS/MS barcode spectra as an external test set, accuracy of the ANN, SVM, and k-NN models were evaluated as 72.5%, 90.0%, and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPS's whose data are unavailable in the database. When only 24 representative LC-MS/MS spectra of controlled substances and NPS's were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded AI-SNPS (artificial intelligence screener for narcotic drugs and psychotropic substances) standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPS's to be identified in a convenient manner.
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Affiliation(s)
- So Yeon Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Sang Tak Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Sungill Suh
- Forensic genetics & chemistry division, Supreme prosecutors' office, Seoul 06590, Republic of Korea
| | - Bum Jun Ko
- Forensic genetics & chemistry division, Supreme prosecutors' office, Seoul 06590, Republic of Korea
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
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48
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Wang X, Chen S, Wu M, Zheng R, Liu Z, Zhao Z, Duan Y. Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition. Chem Commun (Camb) 2021; 57:7156-7159. [PMID: 34184021 DOI: 10.1039/d1cc01844b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection was achieved by coupling a linear array of fibres for light collection. Thus, besides the atomic information, the spectral images containing the propagation and spatial distribution characters of a laser induced plasma plume could be recorded. With these additional features, accurate rock type prediction was achieved by processing the raw data directly through a deep learning model.
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Affiliation(s)
- Xu Wang
- Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China.
| | - Sha Chen
- Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China.
| | - Mengfan Wu
- College of Life Sciences, Key Laboratory of Bio-resource and Eco-environment, Ministry of Education, Sichuan University, Chengdu, China 610065, P. R. China
| | - Ruiqin Zheng
- Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China.
| | - Zhuo Liu
- School of Aeronautics and Astronautics, Sichuan University, Chengdu, China 610065, P. R. China
| | - Zhongjun Zhao
- College of Chemical Engineering, Sichuan University, Chengdu, China 610065, P. R. China.
| | - Yixiang Duan
- Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China.
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Giordano GF, Freitas VMS, Schleder GR, Santhiago M, Gobbi AL, Lima RS. Bifunctional Metal Meshes Acting as a Semipermeable Membrane and Electrode for Sensitive Electrochemical Determination of Volatile Compounds. ACS APPLIED MATERIALS & INTERFACES 2021; 13:35914-35923. [PMID: 34309352 DOI: 10.1021/acsami.1c07874] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 μm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni(OH)2 nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions.
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Affiliation(s)
- Gabriela F Giordano
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
| | - Vitoria M S Freitas
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Faculty of Chemical Engineering, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R Schleder
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Murilo Santhiago
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Angelo L Gobbi
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
| | - Renato S Lima
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
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Schade F, Schwack W, Demirbas Y, Morlock GE. Open-source all-in-one LabToGo Office Chromatography. Anal Chim Acta 2021; 1174:338702. [PMID: 34247737 DOI: 10.1016/j.aca.2021.338702] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/16/2021] [Accepted: 05/25/2021] [Indexed: 12/14/2022]
Abstract
Print and media technologies were used uncommonly in the field of chromatography and explored in application to create a miniaturized all-in-one LabToGo system. This novel research field termed Office Chromatography (OC) uses additive manufacturing in terms of 3D printing of operational parts as well as open-source hard- and software. The OCLab2 presented here has been considerably extended in its functionalities. For inkjet printing of solutions, a newly designed printhead was manufactured controlled by a self-constructed ink-jet board, allowing to check the nozzles' resistance heating circuit. Plate heating was newly integrated, especially favorable for the demonstrated application of higher volumes of aqueous samples. The UV/Vis/FLD plate images were captured by a Raspberry Pi V2 camera module under illumination by novel light emitting diodes (LEDs) for highly selective RGBW color (Vis), UVC 278-nm (UV) and UVA 366-nm (FLD) detection, installed in a newly created miniature cabinet to protect from extraneous light. The spectral separation of differently colored food dyes was achieved by the fully addressable driver controlled RGBW LEDs. The software was newly written in R to speed-up the processes, supported by the new Raspberry Pi 4B computer with 4 GB RAM. The analysis of Stevia leaves for steviol glycosides yielded results comparable to the status quo. Different water samples were analyzed for bioactive compounds. Thereby, compounds of general cytotoxicity were effect-directed detected by bioluminescent A. fischeri bacteria. It allowed the bioanalytical screening for potential risks in tap water, surface waters, rain water, landfill leachates and biogas slurries.
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Affiliation(s)
- Fred Schade
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Wolfgang Schwack
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Yetkin Demirbas
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Gertrud E Morlock
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany.
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