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Ren Y, Zheng Y, Wang X, Qu S, Sun L, Song C, Ding J, Ji Y, Wang G, Zhu P, Cheng L. Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes. Front Microbiol 2024; 15:1361180. [PMID: 38650881 PMCID: PMC11033474 DOI: 10.3389/fmicb.2024.1361180] [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: 12/25/2023] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food.
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Affiliation(s)
- Yan Ren
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
- Inner Mongolia Key Laboratory for Biomass-Energy Conversion, Baotou, China
| | - Yang Zheng
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaojing Wang
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
| | - Shuang Qu
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
| | - Lijun Sun
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
| | - Chenyong Song
- Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao, Shandong, China
| | - Jia Ding
- Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao, Shandong, China
| | - Yuetong Ji
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
- Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao, Shandong, China
| | - Guoze Wang
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
- Inner Mongolia Key Laboratory for Biomass-Energy Conversion, Baotou, China
| | - Pengfei Zhu
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
- Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao, Shandong, China
| | - Likun Cheng
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, China
- Inner Mongolia Key Laboratory for Biomass-Energy Conversion, Baotou, China
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de Almeida MP, Rodrigues C, Novais Â, Grosso F, Leopold N, Peixe L, Franco R, Pereira E. Silver Nanostar-Based SERS for the Discrimination of Clinically Relevant Acinetobacter baumannii and Klebsiella pneumoniae Species and Clones. BIOSENSORS 2023; 13:149. [PMID: 36831915 PMCID: PMC9953856 DOI: 10.3390/bios13020149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/12/2023] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
The development of rapid, reliable, and low-cost methods that enable discrimination among clinically relevant bacteria is crucial, with emphasis on those listed as WHO Global Priority 1 Critical Pathogens, such as carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant or ESBL-producing Klebsiella pneumoniae. To address this problem, we developed and validated a protocol of surface-enhanced Raman spectroscopy (SERS) with silver nanostars for the discrimination of A. baumannii and K. pneumoniae species, and their globally disseminated and clinically relevant antibiotic resistant clones. Isolates were characterized by mixing bacterial colonies with silver nanostars, followed by deposition on filter paper for SERS spectrum acquisition. Spectral data were processed with unsupervised and supervised multivariate data analysis methods, including principal component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), respectively. Our proposed SERS procedure using silver nanostars adsorbed to the bacteria, followed by multivariate data analysis, enabled differentiation between and within species. This pilot study demonstrates the potential of SERS for the rapid discrimination of clinically relevant A. baumannii and K. pneumoniae species and clones, displaying several advantages such as the ease of silver nanostars synthesis and the possible use of a handheld spectrometer, which makes this approach ideal for point-of-care applications.
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Affiliation(s)
- Miguel Peixoto de Almeida
- LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
| | - Carla Rodrigues
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Ângela Novais
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- 4TOXRUN, Toxicology Research Unit, University Institute of Health Sciences, CESPU (IUCS-CESPU), 4585-116 Gandra, Portugal
| | - Filipa Grosso
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Nicolae Leopold
- Faculty of Physics, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Luísa Peixe
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Ricardo Franco
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
- UCIBIO—Applied Molecular Biosciences Unit, Departamento de Química, School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
| | - Eulália Pereira
- LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
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Lima C, Muhamadali H, Goodacre R. Simultaneous Raman and Infrared Spectroscopy of Stable Isotope Labelled Escherichia coli. SENSORS (BASEL, SWITZERLAND) 2022; 22:3928. [PMID: 35632337 PMCID: PMC9145054 DOI: 10.3390/s22103928] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
We report the use of a novel technology based on optical photothermal infrared (O-PTIR) spectroscopy for obtaining simultaneous infrared and Raman spectra from the same location of the sample allowing us to study bacterial metabolism by monitoring the incorporation of 13C- and 15N-labeled compounds. Infrared data obtained from bulk populations and single cells via O-PTIR spectroscopy were compared to conventional Fourier transform infrared (FTIR) spectroscopy in order to evaluate the reproducibility of the results achieved by all three approaches. Raman spectra acquired were concomitant with infrared data from bulk populations as well as infrared spectra collected from single cells, and were subjected to principal component analysis in order to evaluate any specific separation resulting from the isotopic incorporation. Similar clustering patterns were observed in infrared data acquired from single cells via O-PTIR spectroscopy as well as from bulk populations via FTIR and O-PTIR spectroscopies, indicating full incorporation of heavy isotopes by the bacteria. Satisfactory discrimination between unlabeled (viz. 12C14N), 13C14N- and 13C15N-labeled bacteria was also obtained using Raman spectra from bulk populations. In this report, we also discuss the limitations of O-PTIR technology to acquire Raman data from single bacterial cells (with typical dimensions of 1 × 2 µm) as well as spectral artifacts induced by thermal damage when analyzing very small amounts of biomass (a bacterium tipically weighs ~ 1 pg).
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Affiliation(s)
| | | | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK; (C.L.); (H.M.)
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2020.11.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Kahle EM, Zarnkow M, Jacob F. Beer Turbidity Part 2: A Review of Raman Spectroscopy and Possible Future Use for Beer Turbidity Analysis. JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS 2020. [DOI: 10.1080/03610470.2020.1800345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Eva-Maria Kahle
- Forschungszentrum Weihenstephan für Brau- und Lebensmittelqualität, Technische Universität München, Alte Akademie 3, 85354 Freising-Weihenstephan, Germany
| | - Martin Zarnkow
- Forschungszentrum Weihenstephan für Brau- und Lebensmittelqualität, Technische Universität München, Alte Akademie 3, 85354 Freising-Weihenstephan, Germany
| | - Fritz Jacob
- Forschungszentrum Weihenstephan für Brau- und Lebensmittelqualität, Technische Universität München, Alte Akademie 3, 85354 Freising-Weihenstephan, Germany
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Jaafreh S, Valler O, Kreyenschmidt J, Günther K, Kaul P. In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis. Talanta 2019; 202:411-425. [PMID: 31171202 DOI: 10.1016/j.talanta.2019.04.082] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/27/2019] [Accepted: 04/30/2019] [Indexed: 01/08/2023]
Abstract
Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.
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Affiliation(s)
- Sawsan Jaafreh
- Institute of Safety and Security Research, Bonn-Rhein-Sieg University of Applied Sciences, Von Liebig-Straße 20, 53359 Rheinbach, Germany.
| | - Ole Valler
- Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533 Kleve, Germany
| | | | - Klaus Günther
- Institute of Nutritional and Food Sciences, Food Chemistry, University of Bonn, Endenicher Allee 11-13, 53115 Bonn, Germany; Institute of Bio- and Geosciences (IBG-2), Research Centre Jülich, 52425 Jülich, Germany
| | - Peter Kaul
- Institute of Safety and Security Research, Bonn-Rhein-Sieg University of Applied Sciences, Von Liebig-Straße 20, 53359 Rheinbach, Germany
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Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches. SENSORS 2018; 18:s18092789. [PMID: 30149555 PMCID: PMC6163207 DOI: 10.3390/s18092789] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 08/17/2018] [Accepted: 08/23/2018] [Indexed: 11/17/2022]
Abstract
Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.
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