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Xiong C, Zhong Q, Yan D, Zhang B, Yao Y, Qian W, Zheng C, Mei X, Zhu S. Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders. BIOMEDICAL OPTICS EXPRESS 2024; 15:3523-3540. [PMID: 38867772 PMCID: PMC11166416 DOI: 10.1364/boe.514196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.
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Affiliation(s)
- Changchun Xiong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Qingshan Zhong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Denghui Yan
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Baihua Zhang
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Wei Qian
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Chengying Zheng
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Xi Mei
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Shanshan Zhu
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology , Fujian Normal University, Fuzhou 350117, China
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2
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Georgiev D, Pedersen SV, Xie R, Fernández-Galiana Á, Stevens MM, Barahona M. RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis. Anal Chem 2024; 96:8492-8500. [PMID: 38747470 PMCID: PMC11140669 DOI: 10.1021/acs.analchem.4c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024]
Abstract
Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.
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Affiliation(s)
- Dimitar Georgiev
- Department
of Computing & UKRI Centre
for Doctoral Training in AI for Healthcare, Imperial College London, London SW7 2AZ, United
Kingdom
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Simon Vilms Pedersen
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Ruoxiao Xie
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Álvaro Fernández-Galiana
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Molly M. Stevens
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mauricio Barahona
- Department
of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
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3
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Chen Z, Tian X, Chen C, Chen C. Research on disease diagnosis based on teacher-student network and Raman spectroscopy. Lasers Med Sci 2024; 39:129. [PMID: 38735976 DOI: 10.1007/s10103-024-04078-z] [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: 11/20/2023] [Accepted: 05/06/2024] [Indexed: 05/14/2024]
Abstract
Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.
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Affiliation(s)
- Zishuo Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Xuecong Tian
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, 830046, China.
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4
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Wang J, Chen J, Studts J, Wang G. Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models. Biotechnol Bioeng 2024; 121:1729-1738. [PMID: 38419489 DOI: 10.1002/bit.28679] [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: 11/14/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 03/02/2024]
Abstract
Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.
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Affiliation(s)
- Jiarui Wang
- Late Stage Downstream Process Development, Boehringer Ingelheim Pharma GmbH/Co. KG, Biberach an der Riss, Germany
| | - Jingyi Chen
- Late Stage Downstream Process Development, Boehringer Ingelheim Pharma GmbH/Co. KG, Biberach an der Riss, Germany
- Bioprocess development and modelling, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Joey Studts
- Late Stage Downstream Process Development, Boehringer Ingelheim Pharma GmbH/Co. KG, Biberach an der Riss, Germany
| | - Gang Wang
- Late Stage Downstream Process Development, Boehringer Ingelheim Pharma GmbH/Co. KG, Biberach an der Riss, Germany
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Yuan Q, Gu B, Liu W, Wen X, Wang J, Tang J, Usman M, Liu S, Tang Y, Wang L. Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers. J Cell Mol Med 2024; 28:e18292. [PMID: 38652116 PMCID: PMC11037414 DOI: 10.1111/jcmm.18292] [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: 01/12/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.
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Affiliation(s)
- Quan Yuan
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Bin Gu
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Wei Liu
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Xin‐Ru Wen
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Ji‐Liang Wang
- Department of Laboratory MedicineShengli Oilfield Central HospitalDongyingChina
| | - Jia‐Wei Tang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Muhammad Usman
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Su‐Ling Liu
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Yu‐Rong Tang
- Department of Laboratory MedicineShengli Oilfield Central HospitalDongyingChina
| | - Liang Wang
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Division of Microbiology and Immunology, School of Biomedical SciencesThe University of Western AustraliaCrawleyWestern AustraliaAustralia
- School of Agriculture and Food SustainabilityUniversity of QueenslandBrisbaneQueenslandAustralia
- Centre for Precision Health, School of Medical and Health SciencesEdith Cowan UniversityPerthWestern AustraliaAustralia
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6
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Ma H, Guo J, Liu G, Xie D, Zhang B, Li X, Zhang Q, Cao Q, Li X, Ma F, Li Y, Wan G, Li Y, Wu D, Ma P, Guo M, Yin J. Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review. Crit Rev Food Sci Nutr 2024:1-23. [PMID: 38523442 DOI: 10.1080/10408398.2024.2329956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.
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Affiliation(s)
- Haiyang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Jiajun Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guishan Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Delang Xie
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Bingbing Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaojun Li
- School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
| | - Qian Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Qingqing Cao
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaoxue Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Fang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Yang Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guoling Wan
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Yan Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Di Wu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Ping Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Mei Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Junjie Yin
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
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7
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Coca-Lopez N. An intuitive approach for spike removal in Raman spectra based on peaks' prominence and width. Anal Chim Acta 2024; 1295:342312. [PMID: 38355231 DOI: 10.1016/j.aca.2024.342312] [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: 09/20/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Raman spectroscopists are familiar with the challenge of dealing with spikes caused by cosmic rays. These artifacts may lead to errors in subsequent data processing steps, such as for example calibration, normalization or spectral search. Spike removal is therefore a fundamental step in Raman spectral data pre-treatment, but access to publicly accessible code for spike removal tools is limited, and their performance for spectra correction often unknown. Therefore, there is a need for development and testing open-source and easy-to-implement algorithms that improve the Raman data processing workflow. RESULTS In this work, we present and validate two approaches for spike detection and correction in Raman spectral data from graphene: i) An algorithm based on the peaks' widths and prominences and ii) an algorithm based on the ratio of these two peak features. The first algorithm provides an efficient and reliable approach for spike detection in real and synthetic Raman spectra by imposing thresholds on the peaks' width and prominence. The second approach leverages the prominence/width ratio for outlier detection. It relies on the calculation of a limit of detection based on either one or several spectra, enabling the automatic identification of cosmic ray and low-intensity noise-originated spikes alike. Both algorithms detect low-intensity spikes down to at least ≈10% of the highest Raman peak of spectra with different noise levels. To address their limitations and prove their versatility, the algorithms were further tested in Raman spectra from calcite and polystyrene. SIGNIFICANCE Our intuitive, open-source algorithms have been validated and allow automatic correction for a given set of samples. They do not require any pre-processing steps such as calibration or baseline subtraction, and their implementation with Python libraries is computationally efficient, allowing for immediate utilization within existing open-source packages for Raman spectra processing.
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Affiliation(s)
- Nicolas Coca-Lopez
- Instituto de Catálisis y Petroleoquímica (ICP), CSIC, Marie Curie, 2, Madrid, 28049, Spain.
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8
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Barton B, Thomson J, Lozano Diz E, Portela R. Chemometrics for Raman Spectroscopy Harmonization. APPLIED SPECTROSCOPY 2022; 76:1021-1041. [PMID: 35622984 DOI: 10.1177/00037028221094070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Raman spectroscopy is used in a wide variety of fields, and in a plethora of different configurations. Raman spectra of simple analytes can often be analyzed using univariate approaches and interpreted in a straightforward manner. For more complex spetral data such as time series or line profiles (1D), Raman maps (2D), or even volumes (3D), multivariate data analysis (MVDA) becomes a requirement. Even though there are some existing standards for creation, implementation, and validation of methods and models employed in industry and academics, further research and development in the field must contribute to their improvement. This review will cover, in broad terms, existing techniques as well as new developments for MVDA for Raman spectroscopic data, and in particular the use associated with instrumentation and data calibration. Chemometric models are often generated via fusion of analytical data from different sources, which enhances model discrimination and prediction abilities as compared to models derived from a single data source. For Raman spectroscopy, raw or unprocessed data is rarely ever used. Instead, spectra are usually corrected and manipulated,1 often by case-specific rather than universal methods. Calibration models can be used to characterize qualitatively and/or quantitatively samples measured with the same instrumentation that was used to create the model. However, regular validation is required to ensure that aging or incorrect maintenance of the instrument does not alter the model's predictions, particularly when applied in regulated fields such as pharmaceuticals. Furthermore, a model transfer may be required for different reasons, such as replacement or significant repair of the instrumentation. Modeling can also be used to consistently harmonize Raman spectroscopic data across several instrumental designs, accounting for variations in the resulting spectrum induced by different components. Data for Raman harmonization models should be processed in a protocolled manner, and the original data accessible to allow for model reconstruction or transfer when new data is added. Important processing steps will be the calibration of the spectral axes and instrument dependent effects, such as spectral resolution. In addition, data fusion and model transfer are essential for allowing new instrumentation to build on existing models to harmonize their own data. Ideally, an open access database would be created and maintained, for the purpose of allowing for continued harmonization of new Raman instruments using an outlined and accepted protocol.
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Affiliation(s)
| | | | | | - Raquel Portela
- Institute of Catalysis and Petrochemistry, 16379CSIC-ICP, Madrid, Spain
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Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy. Foods 2022; 11:foods11101506. [PMID: 35627076 PMCID: PMC9141442 DOI: 10.3390/foods11101506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 01/27/2023] Open
Abstract
As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.
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10
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Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116623] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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11
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Guo S, Popp J, Bocklitz T. Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling. Nat Protoc 2021; 16:5426-5459. [PMID: 34741152 DOI: 10.1038/s41596-021-00620-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/19/2021] [Indexed: 02/01/2023]
Abstract
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.
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Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China.,Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany.,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany.,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany. .,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany.
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