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Hamad R, Chakraborty SK. A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging. Sci Rep 2024; 14:15643. [PMID: 38977722 PMCID: PMC11231289 DOI: 10.1038/s41598-024-63073-0] [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: 01/10/2024] [Accepted: 05/24/2024] [Indexed: 07/10/2024] Open
Abstract
The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.
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Affiliation(s)
- Rajendra Hamad
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Beraisa Road, Nabibagh, Bhopal, 462038, India
| | - Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Beraisa Road, Nabibagh, Bhopal, 462038, India.
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2
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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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3
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Chen H, Shin T, Park B, Ro K, Jeong C, Jeon HJ, Tan PL. Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134346. [PMID: 38653139 DOI: 10.1016/j.jhazmat.2024.134346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024]
Abstract
Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400-1000 nm), InGaAs (800-1600 nm), and MCT (1000-2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil-normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil-MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st-3rd overtones and combination bands of C-H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil-MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92-100%) and MCT (97-100%) compared to VNIR (44-87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SYNOPSIS: One of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.
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Affiliation(s)
- Huan Chen
- Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29634, USA; Biogeochemistry & Environmental Quality Research Group, Clemson University, Georgetown, SC 29442, USA
| | - Taesung Shin
- USDA Agricultural Research Service, US National Poultry Research Center, Athens, GA 30605, USA
| | - Bosoon Park
- USDA Agricultural Research Service, US National Poultry Research Center, Athens, GA 30605, USA.
| | - Kyoung Ro
- USDA Agricultural Research Service, Coastal Plains Soil, Water & Plant Research Center, Florence, SC 29501, USA
| | - Changyoon Jeong
- Red River Research Station, Louisiana State University Agricultural Center, Bossier City, LA 71112, USA
| | - Hwang-Ju Jeon
- Red River Research Station, Louisiana State University Agricultural Center, Bossier City, LA 71112, USA
| | - Pei-Lin Tan
- Biogeochemistry & Environmental Quality Research Group, Clemson University, Georgetown, SC 29442, USA
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4
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Stolte Bezerra Lisboa Oliveira L, Ristroph KD. Critical Review: Uptake and Translocation of Organic Nanodelivery Vehicles in Plants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5646-5669. [PMID: 38517744 DOI: 10.1021/acs.est.3c09757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Nanodelivery vehicles (NDVs) are engineered nanomaterials (ENMs) that, within the agricultural sector, have been investigated for their ability to improve uptake and translocation of agrochemicals, control release, or target specific tissues or subcellular compartments. Both inorganic and organic NDVs have been studied for agrochemical delivery in the literature, but research on the latter has been slower to develop than the literature on the former. Since the two classes of nanomaterials exhibit significant differences in surface chemistry, physical deformability, and even colloidal stability, trends that apply to inorganic NDVs may not hold for organic NDVs, and vice versa. We here review the current literature on the uptake, translocation, biotransformation, and cellular and subcellular internalization of organic NDVs in plants following foliar or root administration. A background on nanomaterials and plant physiology is provided as a leveling ground for researchers in the field. Trends in uptake and translocation are examined as a function of NDV properties and compared to those reported for inorganic nanomaterials. Methods for assessing fate and transport of organic NDVs in plants (a major bottleneck in the field) are discussed. We end by identifying knowledge gaps in the literature that must be understood in order to rationally design organic NDVs for precision agrochemical nanodelivery.
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Affiliation(s)
- Luiza Stolte Bezerra Lisboa Oliveira
- Agricultural and Biological Engineering Department, Purdue University, 225 South University Street, West Lafayette, Indiana 47907, United States
| | - Kurt D Ristroph
- Agricultural and Biological Engineering Department, Purdue University, 225 South University Street, West Lafayette, Indiana 47907, United States
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5
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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6
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Gonçalves WB, Teixeira WSR, Sampaio ANDCE, Martins OA, Cervantes EP, Mioni MDSR, Gruber J, Pereira JG. Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella. J Microbiol Methods 2023; 212:106805. [PMID: 37558057 DOI: 10.1016/j.mimet.2023.106805] [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: 05/24/2023] [Revised: 06/26/2023] [Accepted: 08/05/2023] [Indexed: 08/11/2023]
Abstract
Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.
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Affiliation(s)
- Wellington Belarmino Gonçalves
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Wanderson Sirley Reis Teixeira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Aryele Nunes da Cruz Encide Sampaio
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Otávio Augusto Martins
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Evelyn Perez Cervantes
- Instituto de Matemática e Estatística, Universidade de São Paulo, 05508-090, São Paulo, SP, Brazil.
| | - Mateus de Souza Ribeiro Mioni
- Departamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 14884-900, Jaboticabal, SP, Brazil.
| | - Jonas Gruber
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Juliano Gonçalves Pereira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
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7
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Du J, Tao C, Xue S, Zhang Z. Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features. Diagnostics (Basel) 2023; 13:2002. [PMID: 37370897 DOI: 10.3390/diagnostics13122002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/23/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410-900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.
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Affiliation(s)
- Jian Du
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Chenglong Tao
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Shuang Xue
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Zhoufeng Zhang
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
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Kabiraz MP, Majumdar PR, Mahmud MC, Bhowmik S, Ali A. Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review. Heliyon 2023; 9:e15482. [PMID: 37151686 PMCID: PMC10161726 DOI: 10.1016/j.heliyon.2023.e15482] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/13/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023] Open
Abstract
Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.
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Affiliation(s)
- Meera Probha Kabiraz
- Department of Biotechnology, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
| | - Priyanka Rani Majumdar
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, 2052, Australia
- Department of Fisheries and Marine Science, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
| | - M.M. Chayan Mahmud
- CASS Food Research Centre, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, VIC, 3125, Australia
| | - Shuva Bhowmik
- Department of Fisheries and Marine Science, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
- Centre for Bioengineering and Nanomedicine, Faculty of Dentistry, Division of Health Sciences, University of Otago, Dunedin, 9054, New Zealand
- Department of Food Science, University of Otago, Dunedin, 9054, New Zealand
- Corresponding author. Centre for Bioengineering and Nanomedicine, Faculty of Dentistry, Division of Health Sciences, University of Otago, Dunedin, 9054, New Zealand.
| | - Azam Ali
- Centre for Bioengineering and Nanomedicine, Faculty of Dentistry, Division of Health Sciences, University of Otago, Dunedin, 9054, New Zealand
- Corresponding author.
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9
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Lifante J, de la Fuente-Fernández M, Román-Carmena M, Fernandez N, Jaque García D, Granado M, Ximendes E. In vivo grading of lipids in fatty liver by near-infrared autofluorescence and reflectance. JOURNAL OF BIOPHOTONICS 2023; 16:e202200208. [PMID: 36377726 DOI: 10.1002/jbio.202200208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/16/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
The prevalence of nonalcoholic fatty liver (NAFLD) is rapidly increasing worldwide. When untreated, it may lead to complications such as liver cirrhosis or hepatocarcinoma. The diagnosis of NAFLD is usually obtained by ultrasonography, a technique that can underestimate its prevalence. For this reason, physicians aspire for an accurate, cost-effective, and noninvasive method to determine both the presence and the specific stage of the NAFLD. In this paper, we report an integrated approach for the quantitative estimation of the density of triglycerides in the liver based on the use of autofluorescence and reflectance signals generated by the abdomen of obese C57BL6/J mice. Singular value decomposition is applied to the generated spectra and its corresponding regression model provided a determination coefficient of 0.99 and a root mean square error of 240 mg/dl. This, in turn, enabled the quantitative imaging of triglycerides density in the livers of mice under in vivo conditions.
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Affiliation(s)
- José Lifante
- Nanomaterials for Bioimaging Group (nanoBIG), Universidad Autónoma de Madrid, Madrid, Spain
- IRYCIS, Madrid, Spain
| | | | | | - Nuria Fernandez
- Nanomaterials for Bioimaging Group (nanoBIG), Universidad Autónoma de Madrid, Madrid, Spain
| | - Daniel Jaque García
- Nanomaterials for Bioimaging Group (nanoBIG), Universidad Autónoma de Madrid, Madrid, Spain
- IRYCIS, Madrid, Spain
| | - Miriam Granado
- Nanomaterials for Bioimaging Group (nanoBIG), Universidad Autónoma de Madrid, Madrid, Spain
| | - Erving Ximendes
- Nanomaterials for Bioimaging Group (nanoBIG), Universidad Autónoma de Madrid, Madrid, Spain
- IRYCIS, Madrid, Spain
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10
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Doh IJ, Zuniga DVS, Shin S, Pruitt RE, Rajwa B, Robinson JP, Bae E. Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns. SENSORS (BASEL, SWITZERLAND) 2023; 23:3485. [PMID: 37050545 PMCID: PMC10098818 DOI: 10.3390/s23073485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.
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Affiliation(s)
- Iyll-Joon Doh
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | | | - Sungho Shin
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
| | - Robert E. Pruitt
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - J. Paul Robinson
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
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11
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Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level. Talanta 2023; 254:124112. [PMID: 36463804 DOI: 10.1016/j.talanta.2022.124112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/07/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022]
Abstract
Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.
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12
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Detection of Oxacillin/Cefoxitin Resistance in Staphylococcus aureus Present in Recurrent Tonsillitis. Microorganisms 2023; 11:microorganisms11030615. [PMID: 36985189 PMCID: PMC10055619 DOI: 10.3390/microorganisms11030615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Background: Recurrent tonsillitis is one of the most common diseases in childhood, caused many times by ß-lactam-resistant S. aureus. The objective of this study was to investigate an alternative method to identify resistance to oxacillin/cefoxitin in S. aureus from hospitalized children with recurrent tonsillitis. Methods: The samples of S. aureus came from patients with recurrent tonsillitis and were used in 16S rRNA sequencing and an antibiogram test for identification and verifying resistance, after which HSI methodology were applied for separation of S. aureus resistances. Results: The S. aureus isolated showed sensitivity to oxacillin/cefoxitin and the diagnostic images show a visual description of the resistance different groups formed, that may be related to sensitivity and resistance to oxacillin/cefoxitin, characterizing the MRSA S. aureus. Conclusions: Samples that showed phenotypic resistance to oxacillin/cefoxitin were clearly separated from samples that did not show this resistance. A PLS-DA model predicted the presence of resistance to oxacillin/cefoxitin in S. aureus samples and it was possible to observe the pixels classified as MRSA. The HSI was able to successfully discriminate samples in replicas that were sensitive and resistant, based on the calibration model it received.
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Bhunia AK, Singh AK, Parker K, Applegate BM. Petri-plate, bacteria, and laser optical scattering sensor. Front Cell Infect Microbiol 2022; 12:1087074. [PMID: 36619754 PMCID: PMC9813400 DOI: 10.3389/fcimb.2022.1087074] [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: 11/01/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.
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Affiliation(s)
- Arun K. Bhunia
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Purdue University, Purdue University Interdisciplinary Life Science Program (PULSe), West Lafayette, IN, United States,Purdue Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, IN, United States,Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, United States,*Correspondence: Arun K. Bhunia,
| | - Atul K. Singh
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Clear Labs, San Carlos, CA, United States
| | - Kyle Parker
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Bruce M. Applegate
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Purdue University, Purdue University Interdisciplinary Life Science Program (PULSe), West Lafayette, IN, United States,Purdue Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, IN, United States,Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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Jiang H, Yuan W, Ru Y, Chen Q, Wang J, Zhou H. Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 282:121689. [PMID: 35914356 DOI: 10.1016/j.saa.2022.121689] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 07/14/2022] [Accepted: 07/26/2022] [Indexed: 05/10/2023]
Abstract
Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.
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Affiliation(s)
- Hongzhe Jiang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
| | - Weidong Yuan
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Yu Ru
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Qing Chen
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jinpeng Wang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hongping Zhou
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
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Hashemi-Nasab FS, Talebian S, Parastar H. Multiple adulterants detection in turmeric powder using VIS-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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16
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Kamruzzaman M, Kalita D, Ahmed MT, ElMasry G, Makino Y. Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data. Anal Chim Acta 2022; 1202:339390. [DOI: 10.1016/j.aca.2021.339390] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/23/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
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17
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Lin S, Ke Z, Liu K, Zhu S, Li Z, Yin H, Chen Z. Identification of DAPI-stained normal, inflammatory, and carcinoma hepatic cells based on hyperspectral microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:2082-2090. [PMID: 35519237 PMCID: PMC9045905 DOI: 10.1364/boe.451006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/19/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Gross chromatin imbalance and high DNA content are distinct features of various types of cancer cells. However, severe inflammation can also produce similar symptoms in cells. In this study, normal, inflammatory, and carcinoma hepatic cells were stained with 4',6-diamidino-2-phenylindole (DAPI) and investigated by hyperspectral microscopy. DAPI is a DNA-sensitive fluorochrome. Therefore, the differences in the cellular DNA of the samples can be revealed by the corresponding fluorescence. Our experimental results demonstrate that although chromosomal disorder and high DNA content both occur in severely inflammatory and carcinoma hepatic cells, there is still a slight difference in their DNA, making their fluorescent intensity and even their spectral shapes distinguishable. Based on these spectral features, we developed a method for the precise identification of normal, inflammatory, and carcinoma hepatic cells in the field of view. The identification accuracy for these three types of cells was 99.8%. We believe that examination that combines DAPI staining with hyperspectral microscopy is a potential method for the identification and investigation of various types of cancer tissues.
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Affiliation(s)
- Sifan Lin
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou, 510632, China
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Ze Ke
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Kunxing Liu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou, 510632, China
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Siqi Zhu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou, 510632, China
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Zhen Li
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Hao Yin
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Zhenqiang Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou, 510632, China
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou, 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
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18
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Chemical Imaging of the Polylactic Acid − Wood Adhesion Interface of Bonded Veneer Products. FIBERS 2022. [DOI: 10.3390/fib10020017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The increasing use and potential of polylactic acid (PLA) in wood-based composite materials due to its greater performance over common polyolefins provides the justification for a closer examination of the PLA−wood adhesion performance. In PLA-bonded laminates and composites, the optical differentiation between PLA polymer and wood is not possible and necessitates complex techniques such as fluorescence microscopy to characterize the PLA adhesion interface. In this study, spatial chemical imaging via FTIR analysis has been successfully applied to directly identify PLA bondlines within PLA-bonded veneer laminates and to determine the migration of semi-crystalline and amorphous PLAs from the bondline into the wood structure. This method uses involved point contouring line spectra over the bondline area to distinguish the PLA polymer from the wood. From this quantitative analysis, it is revealed that bondline thickness and PLA penetration values depend on pressing temperature, and this has implications for the reinforcement of the adhesion interface and the bondline performance. Furthermore, in developing a methodology for this assessment, this spatial chemical imaging approach can equally be applied to other polyester, amide, and urethane systems used to bond wood laminates.
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Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2021.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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20
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Advancement in Salmonella Detection Methods: From Conventional to Electrochemical-Based Sensing Detection. BIOSENSORS-BASEL 2021; 11:bios11090346. [PMID: 34562936 PMCID: PMC8468554 DOI: 10.3390/bios11090346] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 02/07/2023]
Abstract
Large-scale food-borne outbreaks caused by Salmonella are rarely seen nowadays, thanks to the advanced nature of the medical system. However, small, localised outbreaks in certain regions still exist and could possess a huge threat to the public health if eradication measure is not initiated. This review discusses the progress of Salmonella detection approaches covering their basic principles, characteristics, applications, and performances. Conventional Salmonella detection is usually performed using a culture-based method, which is time-consuming, labour intensive, and unsuitable for on-site testing and high-throughput analysis. To date, there are many detection methods with a unique detection system available for Salmonella detection utilising immunological-based techniques, molecular-based techniques, mass spectrometry, spectroscopy, optical phenotyping, and biosensor methods. The electrochemical biosensor has growing interest in Salmonella detection mainly due to its excellent sensitivity, rapidity, and portability. The use of a highly specific bioreceptor, such as aptamers, and the application of nanomaterials are contributing factors to these excellent characteristics. Furthermore, insight on the types of biorecognition elements, the principles of electrochemical transduction elements, and the miniaturisation potential of electrochemical biosensors are discussed.
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21
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Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model. Food Res Int 2021; 147:110577. [PMID: 34399549 DOI: 10.1016/j.foodres.2021.110577] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 06/24/2021] [Accepted: 06/27/2021] [Indexed: 11/23/2022]
Abstract
Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547-1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (102 to 106 CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4% and 8% in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90-94% when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (<102 CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays.
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22
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Fakhrullin R, Nigamatzyanova L, Fakhrullina G. Dark-field/hyperspectral microscopy for detecting nanoscale particles in environmental nanotoxicology research. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145478. [PMID: 33571774 DOI: 10.1016/j.scitotenv.2021.145478] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/22/2021] [Accepted: 01/24/2021] [Indexed: 06/12/2023]
Abstract
Nanoscale contaminants (including engineered nanoparticles and nanoplastics) pose a significant threat to organisms and environment. Rapid and non-destructive detection and identification of nanosized materials in cells, tissues and organisms is still challenging, although a number of conventional methods exist. These approaches for nanoparticles imaging and characterisation both inside the cytoplasm and on the cell or tissue outer surfaces, such as electron or scanning probe microscopies, are unquestionably potent tools, having excellent resolution and supplemented with chemical analysis capabilities. However, imaging and detection of nanomaterials in situ, in wet unfixed and even live samples, such as living isolated cells, microorganisms, protozoans and miniature invertebrates using electron microscopy is practically impossible, because of the elaborate sample preparation requiring chemical fixation, contrast staining, matrix embedding and exposure into vacuum. Atomic force microscopy, in several cases, can be used for imaging and mechanical analysis of live cells and organisms under ambient conditions, however this technique allows for investigation of surfaces. Therefore, a different approach allowing for imaging and differentiation of nanoscale particles in wet samples is required. Dark-field microscopy as an optical microscopy technique has been popular among researchers, mostly for imaging relatively large specimens. In recent years, the so-called "enhanced dark field" microscopy based on using higher numerical aperture light condensers and variable numerical aperture objectives has emegred, which allows for imaging of nanoscale particles (starting from 5 nm nanospheres) using almost conventional optical microscopy methodology. Hyperspectral imaging can turn a dark-field optical microscope into a powerful chemical characterisation tool. As a result, this technique is becoming popular in environmental nanotoxicology studies. In this Review Article we introduce the reader into the methodology of enhanced dark-field and dark-field-based hyperspectral microscopy, covering the most important advances in this rapidly-expanding area of environmental nanotoxicology.
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Affiliation(s)
- Rawil Fakhrullin
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kreml uramı 18, Kazan 420008, Republic of Tatarstan, Russian Federation.
| | - Läysän Nigamatzyanova
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kreml uramı 18, Kazan 420008, Republic of Tatarstan, Russian Federation
| | - Gölnur Fakhrullina
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kreml uramı 18, Kazan 420008, Republic of Tatarstan, Russian Federation
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Krauz L, Páta P, Bednář J, Klíma M. Quasi-collinear IR AOTF based on mercurous halide single crystals for spatio-spectral hyperspectral imaging. OPTICS EXPRESS 2021; 29:12813-12832. [PMID: 33985030 DOI: 10.1364/oe.420571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/24/2021] [Indexed: 06/12/2023]
Abstract
The paper aims to show the advantages of the infrared-optimised quasi-collinear AOTF (acousto-optic tunable filter) for the spatio-spectral hyperspectral imaging system. The optimisation process is presented based on the selected tetragonal anisotropic materials with exceptional optical and acousto-optical properties in IR (infrared) spectral region. These materials are further compared in terms of their features and suitability for AOTF design. The spectral resolution is considered as the main optimising parameter. Resulting from the analysis, the mercurous chloride (Hg2Cl2) single crystal is selected as a representative of the mercurous halide family for the presentation of the quasi-collinear AOTF model operating in LWIR (long-wave infrared) spectral band. The overall parameters of the AOTF model such as spectral resolution, chromatic field of view, acoustic frequency, and operational power requirements are estimated and discussed in results.
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Tong Y, Ach T, Curcio CA, Smith RT. Hyperspectral autofluorescence characterization of drusen and sub-RPE deposits in age-related macular degeneration. ACTA ACUST UNITED AC 2021; 6. [PMID: 33791592 PMCID: PMC8009528 DOI: 10.21037/aes-20-12] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Background: Soft drusen and basal linear deposit (BLinD) are two forms of the same extracellular lipid rich material that together make up an Oil Spill on Bruch’s membrane (BrM). Drusen are focal and can be recognized clinically. In contrast BLinD is thin and diffusely distributed, and invisible clinically, even on highest resolution OCT, but has been detected on en face hyperspectral autofluorescence (AF) imaging ex vivo. We sought to optimize histologic hyperspectral AF imaging and image analysis for recognition of drusen and sub-RPE deposits (including BLinD and basal laminar deposit), for potential clinical application. Methods: Twenty locations specifically with drusen and 12 additional locations specifically from fovea, perifovea and mid-periphery from RPE/BrM flatmounts from 4 AMD donors underwent hyperspectral AF imaging with 4 excitation wavelengths (λex 436, 450, 480 and 505 nm), and the resulting image cubes were simultaneously decomposed with our published non-negative matrix factorization (NMF). Rank 4 recovery of 4 emission spectra was chosen for each excitation wavelength. Results: A composite emission spectrum, sensitive and specific for drusen and presumed sub-RPE deposits (the SDr spectrum) was recovered with peak at 510–520 nm in all tissues with drusen, with greatest amplitudes at excitations λex 436, 450 and 480 nm. The RPE spectra of combined sources Lipofuscin (LF)/Melanolipofuscin (MLF) were of comparable amplitude and consistently recapitulated the spectra S1, S2 and S3 previously reported from all tissues: tissues with drusen, foveal and extra-foveal locations. Conclusions: A clinical hyperspectral AF camera, with properly chosen excitation wavelengths in the blue range and a hyperspectral AF detector, should be capable of detecting and quantifying drusen and sub-RPE deposits, the earliest known lesions of AMD, before any other currently available imaging modality.
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Affiliation(s)
- Yuehong Tong
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Ach
- Department of Ophthalmology, University Hospital Bonn, Germany
| | - Christine A Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, AL, USA
| | - R Theodore Smith
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Nardecchia A, Vitale R, Duponchel L. Fusing spectral and spatial information with 2-D stationary wavelet transform (SWT 2-D) for a deeper exploration of spectroscopic images. Talanta 2021; 224:121835. [PMID: 33379053 DOI: 10.1016/j.talanta.2020.121835] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/25/2020] [Accepted: 10/27/2020] [Indexed: 11/25/2022]
Abstract
Nowadays, it is clear that there is an increasing importance in spectroscopic imaging in all fields of science. Obviously, one bulk analysis can no longer be satisfactory, as the interest focuses more on the chemical nature and the location of the compounds present within a given complex matrix. This is, evidently, due to the fact that for a more comprehensive exploration of complex samples, one single acquired hyperspectral data cube can provide both spectral and spatial information simultaneously. Although many techniques were proposed by the chemometric community in explorations of these specific datasets, unfortunately, they are almost always focusing on spectral information, even if chemical images were ultimately observed. In other words, spatial information is not well exploited, and therefore lost during the actual chemometric calculation phase. The goal of this short communication is to present a very simple and fast spectral/spatial fusion approach based on 2-D stationary wavelet transform (SWT 2-D) which is able to improve the obtainable information, compared with a classical data analysis, in which the spatial domain would not be considered nor used.
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Affiliation(s)
- Alessandro Nardecchia
- Univ. Lille, CNRS, UMR 8516 - LASIRe - Laboratoire de Spectroscopie pour Les Interactions, La Réactivité et L'Environnement, F-59000, Lille, France
| | - Raffaele Vitale
- Univ. Lille, CNRS, UMR 8516 - LASIRe - Laboratoire de Spectroscopie pour Les Interactions, La Réactivité et L'Environnement, F-59000, Lille, France
| | - Ludovic Duponchel
- Univ. Lille, CNRS, UMR 8516 - LASIRe - Laboratoire de Spectroscopie pour Les Interactions, La Réactivité et L'Environnement, F-59000, Lille, France.
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Elbasuney S, Baraka A, Gobara M, El-Sharkawy YH. 3D spectral fluorescence signature of cerium(III)-melamine coordination polymer: A novel sensing material for explosive detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118941. [PMID: 32980756 DOI: 10.1016/j.saa.2020.118941] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/25/2020] [Accepted: 09/03/2020] [Indexed: 06/11/2023]
Abstract
Hidden or buried explosives are the most common scenario by terrorist attacks; therefore explosive vapour detection is a vital demand. Explosives are electron deficient materials; the vicinity of explosives to fluorescent material can encounter electron migration. This study reports on facile synthesis of cerium (III)-melamine coordination polymer (CeM-CP) with exclusive optical properties. CeM-CP demonstrated novel spectral fluorescence properties over visible and infrared bands when stimulated with UVA LED source at 385 nm of 100 mW power. Stimulated CeM-CP demonstrated unique spectral fluorescence signal at 400, 700, and 785 nm. These fluorescent signals were correlated to cerium coordination with four nitrogen atoms; vacant orbital will be available for electron excitation migration. Spectral fluorescent signals were quenched as CeM-CP was subjected to TNT vapours. Hyperspectral imaging offered 3D plot of fluorescence signature. The main outcome is that complete fluorescence signal attenuation was achieved at 785 nm. CeM-CP could act as as a novel sensing element for explosive vapour detection.
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Affiliation(s)
- Sherif Elbasuney
- Nanotechnology Research Center, Military Technical College, Cairo, Egypt.
| | - Ahmad Baraka
- School of Chemical Engineering, Military Technical College, Cairo, Egypt
| | - Mohamed Gobara
- Department of Chemical Engineering, School of Chemical Engineering, Military Technical College, Cairo, Egypt
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Zhu H, Gowen A, Feng H, Yu K, Xu JL. Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products. SENSORS 2020; 20:s20185322. [PMID: 32957597 PMCID: PMC7570506 DOI: 10.3390/s20185322] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/05/2020] [Accepted: 09/14/2020] [Indexed: 11/25/2022]
Abstract
Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.
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Affiliation(s)
- Hongyan Zhu
- College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China;
| | - Aoife Gowen
- UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland;
| | - Hailin Feng
- School of Information Engineering, Zhejiang Agricultural and Forestry University, Hangzhou 310000, China;
| | - Keping Yu
- Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan;
| | - Jun-Li Xu
- UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland;
- Correspondence:
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Ferone M, Gowen A, Fanning S, Scannell AGM. Microbial detection and identification methods: Bench top assays to omics approaches. Compr Rev Food Sci Food Saf 2020; 19:3106-3129. [PMID: 33337061 DOI: 10.1111/1541-4337.12618] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 12/26/2022]
Abstract
Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.
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Affiliation(s)
- Mariateresa Ferone
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Aoife Gowen
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Séamus Fanning
- UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland.,UCD School of Public Health, Physiotherapy and Sport Science University College Dublin, Dublin, Ireland
| | - Amalia G M Scannell
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland
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29
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Mehta N, Sahu SP, Shaik S, Devireddy R, Gartia MR. Dark-field hyperspectral imaging for label free detection of nano-bio-materials. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 13:e1661. [PMID: 32755036 DOI: 10.1002/wnan.1661] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/21/2020] [Accepted: 06/19/2020] [Indexed: 12/12/2022]
Abstract
Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.
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Affiliation(s)
- Nishir Mehta
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Sushant P Sahu
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Shahensha Shaik
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Ram Devireddy
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Manas Ranjan Gartia
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
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30
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Shen Y, Lifante J, Fernández N, Jaque D, Ximendes E. In Vivo Spectral Distortions of Infrared Luminescent Nanothermometers Compromise Their Reliability. ACS NANO 2020; 14:4122-4133. [PMID: 32227917 DOI: 10.1021/acsnano.9b08824] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Luminescence nanothermometry has emerged over the past decade as an exciting field of research due to its potential applications where conventional methods have demonstrated to be ineffective. Preclinical research has been one of the areas that have benefited the most from the innovations proposed in the field. Nevertheless, certain questions concerning the reliability of the technique under in vivo conditions have been continuously overlooked by most of the scientific community. In this proof-of-concept, hyperspectral in vivo imaging is used to explain how unverified assumptions about the thermal dependence of the optical transmittance of biological tissues in the so-called biological windows can lead to erroneous measurements of temperature. Furthermore, the natural steps that should be taken in the future for a reliable in vivo luminescence nanothermometry are discussed together with a perspective view of the field after the findings here reported.
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Affiliation(s)
- Yingli Shen
- Fluorescence Imaging Group, Departamento de Fı́sica de Materiales, Facultad de Ciencias, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente 7, Madrid 28049, Spain
| | - José Lifante
- Fluorescence Imaging Group, Departamento de Fisiologı́a, Facultad de Medicina, Universidad Autónoma de Madrid, Avda. Arzobispo Morcillo 2, Madrid 28029, Spain
- Nanobiology Group, Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS, Ctra. Colmenar km. 9.100, Madrid 28034, Spain
| | - Nuria Fernández
- Fluorescence Imaging Group, Departamento de Fisiologı́a, Facultad de Medicina, Universidad Autónoma de Madrid, Avda. Arzobispo Morcillo 2, Madrid 28029, Spain
- Nanobiology Group, Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS, Ctra. Colmenar km. 9.100, Madrid 28034, Spain
| | - Daniel Jaque
- Fluorescence Imaging Group, Departamento de Fı́sica de Materiales, Facultad de Ciencias, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente 7, Madrid 28049, Spain
- Nanobiology Group, Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS, Ctra. Colmenar km. 9.100, Madrid 28034, Spain
| | - Erving Ximendes
- Fluorescence Imaging Group, Departamento de Fı́sica de Materiales, Facultad de Ciencias, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente 7, Madrid 28049, Spain
- Nanobiology Group, Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS, Ctra. Colmenar km. 9.100, Madrid 28034, Spain
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Mallmann M, Wendl S, Strobel P, Schmidt PJ, Schnick W. Sr 3 P 3 N 7 : Complementary Approach by Ammonothermal and High-Pressure Syntheses. Chemistry 2020; 26:6257-6263. [PMID: 32030819 PMCID: PMC7318702 DOI: 10.1002/chem.202000297] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Indexed: 12/27/2022]
Abstract
Nitridophosphates exhibit an intriguing structural diversity with different structural motifs, for example, chains, layers or frameworks. In this contribution the novel nitridophosphate Sr3P3N7 with unprecedented dreier double chains is presented. Crystalline powders were synthesized using the ammonothermal method, while single crystals were obtained by a high‐pressure multianvil technique. The crystal structure of Sr3P3N7 was solved and refined from single‐crystal X‐ray diffraction and confirmed by powder X‐ray methods. Sr3P3N7 crystallizes in monoclinic space group P2/c. Energy‐dispersive X‐ray and Fourier‐transformed infrared spectroscopy were conducted to confirm the chemical composition, as well as the absence of NHx functionality. The optical band gap was estimated to be 4.4 eV using diffuse reflectance UV/Vis spectroscopy. Upon doping with Eu2+, Sr3P3N7 shows a broad deep‐red to infrared emission (λem=681 nm, fwhm≈3402 cm−1) with an internal quantum efficiency of 42 %.
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Affiliation(s)
- Mathias Mallmann
- Department of Chemistry, University of Munich (LMU), Butenandtstraße 5-13 (D), 81377, Munich, Germany
| | - Sebastian Wendl
- Department of Chemistry, University of Munich (LMU), Butenandtstraße 5-13 (D), 81377, Munich, Germany
| | - Philipp Strobel
- Lumileds Phosphor Center Aachen, Lumileds (Germany) GmbH, Philipsstraße 8, 52068, Aachen, Germany
| | - Peter J Schmidt
- Lumileds Phosphor Center Aachen, Lumileds (Germany) GmbH, Philipsstraße 8, 52068, Aachen, Germany
| | - Wolfgang Schnick
- Department of Chemistry, University of Munich (LMU), Butenandtstraße 5-13 (D), 81377, Munich, Germany
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Falkovskaya A, Gowen A. Literature review: spectral imaging applied to poultry products. Poult Sci 2020; 99:3709-3722. [PMID: 32616267 PMCID: PMC7597839 DOI: 10.1016/j.psj.2020.04.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 04/03/2020] [Indexed: 12/30/2022] Open
Abstract
Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging (HSI), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: “poultry” and “hyperspectral imaging”. A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.
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Affiliation(s)
- Anastasia Falkovskaya
- UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Aoife Gowen
- UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
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Gu P, Feng YZ, Zhu L, Kong LQ, Zhang XL, Zhang S, Li SW, Jia GF. Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning. Molecules 2020; 25:molecules25081797. [PMID: 32295273 PMCID: PMC7221630 DOI: 10.3390/molecules25081797] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/14/2020] [Accepted: 03/17/2020] [Indexed: 11/16/2022] Open
Abstract
A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.
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Affiliation(s)
- Peng Gu
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
| | - Yao-Ze Feng
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
- Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
- Correspondence:
| | - Le Zhu
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
| | - Li-Qin Kong
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
| | - Xiu-ling Zhang
- Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China; (X.-l.Z.); (S.-W.L.)
| | - Sheng Zhang
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
| | - Shao-Wen Li
- Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China; (X.-l.Z.); (S.-W.L.)
| | - Gui-Feng Jia
- Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (P.G.); (L.Z.); (L.-Q.K.); (S.Z.); (G.-F.J.)
- Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
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Beć KB, Grabska J, Huck CW. Biomolecular and bioanalytical applications of infrared spectroscopy - A review. Anal Chim Acta 2020; 1133:150-177. [PMID: 32993867 DOI: 10.1016/j.aca.2020.04.015] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 12/11/2022]
Abstract
Infrared (IR; or mid-infrared, MIR; 4000-400 cm-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.
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Affiliation(s)
- Krzysztof B Beć
- Institute of Analytical Chemistry and Radiochemistry, Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80/82, A-6020, Innsbruck, Austria.
| | - Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80/82, A-6020, Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80/82, A-6020, Innsbruck, Austria.
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Vítek P, Ascaso C, Artieda O, Casero MC, Wierzchos J. Raman imaging of microbial colonization in rock-some analytical aspects. Anal Bioanal Chem 2020; 412:3717-3726. [PMID: 32249342 DOI: 10.1007/s00216-020-02622-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/20/2020] [Accepted: 03/26/2020] [Indexed: 01/29/2023]
Abstract
Raman imaging allows one to obtain spatially resolved chemical information in a nondestructive manner. Herein, we present analytical aspects of effective in situ and in vivo Raman imaging of algae and cyanobacteria from within their native rock habitats. Specifically, gypsum and halite inhabited by endolithic communities from the hyperarid Atacama Desert were analyzed. Raman imaging of these phototrophic colonization reveals a pigment composition within the aggregates that helps in understanding some of their adaptation strategies to survive in this harsh polyextreme environment. The study is focused on methodical aspects of Raman imaging acquisition and subsequent data processing. Point imaging is compared with line imaging in terms of their image quality, spatial resolution, spectral signal-to-noise ratio, time requirements, and risk of laser-induced sample alteration. The roles of excitation wavelength, exposure time, and step size of the imaging grid on successful Raman imaging results are also discussed. Graphical abstract.
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Affiliation(s)
- Petr Vítek
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00, Brno, Czech Republic.
| | - Carmen Ascaso
- Museo Nacional de Ciencias Naturales, CSIC, c/ Serrano 115 dpdo., 28006, Madrid, Spain
| | - Octavio Artieda
- Departamento Biología Vegetal, Ecología y Ciencias de la Tierra, and IACYS, Universidad de Extremadura, 10600, Plasencia, Spain
| | - M Cristina Casero
- Museo Nacional de Ciencias Naturales, CSIC, c/ Serrano 115 dpdo., 28006, Madrid, Spain
| | - Jacek Wierzchos
- Museo Nacional de Ciencias Naturales, CSIC, c/ Serrano 115 dpdo., 28006, Madrid, Spain
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36
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Beć KB, Grabska J, Bonn GK, Popp M, Huck CW. Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review. FRONTIERS IN PLANT SCIENCE 2020; 11:1226. [PMID: 32849759 PMCID: PMC7427587 DOI: 10.3389/fpls.2020.01226] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/27/2020] [Indexed: 05/08/2023]
Abstract
Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.
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Affiliation(s)
- Krzysztof B. Beć
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
| | - Justyna Grabska
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
| | - Günther K. Bonn
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- ADSI, Austrian Drug Screening Institute, Innsbruck, Austria
| | - Michael Popp
- Michael Popp Research Institute for New Phyto Entities, University of Innsbruck, Innsbruck, Austria
| | - Christian W. Huck
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
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Bonah E, Huang X, Aheto JH, Osae R. Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization. Foodborne Pathog Dis 2019; 16:712-722. [PMID: 31305129 PMCID: PMC6785170 DOI: 10.1089/fpd.2018.2617] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Microbial food safety is a persistent and exacting global issue due to the multiplicity and complexity of foods and food production systems. Foodborne illnesses caused by foodborne bacterial pathogens frequently occur, thus endangering the safety and health of human beings. Factors such as pretreatments, that is, culturing, enrichment, amplification make the traditional routine identification and enumeration of large numbers of bacteria in a complex microbial consortium complex, expensive, and time-consuming. Therefore, the need for rapid point-of-use detection systems for foodborne bacterial pathogens with high sensitivity and specificity is crucial in food safety control. Hyperspectral imaging (HSI) as a powerful testing technology provides a rapid, nondestructive approach for pathogen detection. This article reviews some fundamental information about HSI, including instrumentation, data acquisition, image processing, and data analysis-the current application of HSI for the detection, classification, and discrimination of various foodborne pathogens. The merits and demerits of HSI for pathogen detection as well as current and future trends are discussed. Therefore, the purpose of this review is to provide a brief overview of HSI, and further lay emphasis on the emerging trend and importance of this technique for foodborne pathogen detection.
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Affiliation(s)
- Ernest Bonah
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
- Laboratory Services Department, Food and Drugs Authority, Cantonments, Ghana
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Joshua Harrington Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Richard Osae
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
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38
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Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties. Molecules 2019; 24:molecules24183268. [PMID: 31500333 PMCID: PMC6766998 DOI: 10.3390/molecules24183268] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/02/2019] [Accepted: 09/06/2019] [Indexed: 01/17/2023] Open
Abstract
Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.
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Sharifi M, Attar F, Saboury AA, Akhtari K, Hooshmand N, Hasan A, El-Sayed MA, Falahati M. Plasmonic gold nanoparticles: Optical manipulation, imaging, drug delivery and therapy. J Control Release 2019; 311-312:170-189. [PMID: 31472191 DOI: 10.1016/j.jconrel.2019.08.032] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/25/2019] [Accepted: 08/26/2019] [Indexed: 12/20/2022]
Abstract
Over the past two decades, the development of plasmonic nanoparticle (NPs), especially gold (Au) NPs, is being pursued more seriously in the medical fields such as imaging, drug delivery, and theranostic systems. However, there is no comprehensive review on the effect of the physical and chemical parameters of AuNPs on their plasmonic properties as well as the use of these unique characteristic in medical activities such as imaging and therapeutics. Therefore, in this literature the surface plasmon resonance (SPR) modeling of AuNPs was accurately captured toward precision medicine. Indeed, we investigated the importance of plasmonic properties of AuNPs in optical manipulation, imaging, drug delivery, and photothermal therapy (PTT) of cancerous cells based on their physicochemical properties. Finally, some challenges regarding the commercialization of AuNPs in future medicine such as, cytotoxicity, lack of standards for medical applications, high cost, and time-consuming process were discussed.
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Affiliation(s)
- Majid Sharifi
- Department of Nanotechnology, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Farnoosh Attar
- Department of Biology, Faculty of Food Industry & Agriculture, Standard Research Institute, Karaj, Iran
| | - Ali Akbar Saboury
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Keivan Akhtari
- Department of Physics, University of Kurdistan, Sanandaj, Iran
| | - Nasrin Hooshmand
- Laser Dynamics Laboratory, School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, United States
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center, Qatar University, Doha 2713, Qatar.
| | - Mostafa A El-Sayed
- Laser Dynamics Laboratory, School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, United States.
| | - Mojtaba Falahati
- Department of Nanotechnology, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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Gasser C, González‐Cabrera M, Ayora‐Cañada MJ, Domínguez‐Vidal A, Lendl B. Comparing mapping and direct hyperspectral imaging in stand-off Raman spectroscopy for remote material identification. JOURNAL OF RAMAN SPECTROSCOPY : JRS 2019; 50:1034-1043. [PMID: 31598032 PMCID: PMC6774338 DOI: 10.1002/jrs.5607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 03/07/2019] [Accepted: 03/08/2019] [Indexed: 06/10/2023]
Abstract
Stand-off Raman spectroscopy offers a highly selective technique to probe unknown substances from a safe distance. Often, it is necessary to scan large areas of interest. This can be done by pointwise imaging (PI), that is, spectra are sequentially acquired from an array of points over the region of interest (point-by-point mapping). Alternatively, in this paper a direct hyperspectral Raman imager is presented, where a defocused laser beam illuminates a wide area of the sample and the Raman scattered light is collected from the whole field of view (FOV) at once as a spectral snapshot filtered by a liquid crystal tunable filter to select a specific Raman shift. Both techniques are compared in terms of achievable FOV, spectral resolution, signal-to-noise performance, and time consumption during a measurement at stand-off distance of 15 m. The HSRI showed superior spectral resolution and signal-to-noise ratio, while more than doubling the FOV of the PI at laser power densities reduced by a factor of 277 at the target. Further, the output hyperspectral image data cube can be processed with state of the art chemometric algorithms like vertex component analysis in order to get a simple deterministic false color image showing the chemical composition of the target. This is shown for an artificial polymer sample, measured at a distance of 15 m.
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Affiliation(s)
- Christoph Gasser
- Institute of Chemical Technologies and AnalyticsTU WienViennaAustria
| | | | | | | | - Bernhard Lendl
- Institute of Chemical Technologies and AnalyticsTU WienViennaAustria
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Seviour T, Derlon N, Dueholm MS, Flemming HC, Girbal-Neuhauser E, Horn H, Kjelleberg S, van Loosdrecht MCM, Lotti T, Malpei MF, Nerenberg R, Neu TR, Paul E, Yu H, Lin Y. Extracellular polymeric substances of biofilms: Suffering from an identity crisis. WATER RESEARCH 2019; 151:1-7. [PMID: 30557778 DOI: 10.1016/j.watres.2018.11.020] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 11/02/2018] [Accepted: 11/10/2018] [Indexed: 06/09/2023]
Abstract
Microbial biofilms can be both cause and cure to a range of emerging societal problems including antimicrobial tolerance, water sanitation, water scarcity and pollution. The identities of extracellular polymeric substances (EPS) responsible for the establishment and function of biofilms are poorly understood. The lack of information on the chemical and physical identities of EPS limits the potential to rationally engineer biofilm processes, and impedes progress within the water and wastewater sector towards a circular economy and resource recovery. Here, a multidisciplinary roadmap for addressing this EPS identity crisis is proposed. This involves improved EPS extraction and characterization methodologies, cross-referencing between model biofilms and full-scale biofilm systems, and functional description of isolated EPS with in situ techniques (e.g. microscopy) coupled with genomics, proteomics and glycomics. The current extraction and spectrophotometric characterization methods, often based on the principle not to compromise the integrity of the microbial cells, should be critically assessed, and more comprehensive methods for recovery and characterization of EPS need to be developed.
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Affiliation(s)
- Thomas Seviour
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore.
| | - Nicolas Derlon
- EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Department of Process Engineering, CH-8600, Dübendorf, Switzerland
| | - Morten Simonsen Dueholm
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Hans-Curt Flemming
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore; University of Duisburg-Essen, Faculty of Chemistry, Biofilm Centre, Essen, Germany
| | - Elisabeth Girbal-Neuhauser
- Laboratoire de Biotechnologies Agroalimentaire et Environmentale (LBAE), Universite Paul Sabatier, Toulouse, France
| | - Harald Horn
- Karlsruhe Institute of Technology (KIT), Engler-Bunte-Institut, Water Chemistry and Water Technology and DVGW Research Laboratories, Karlsruhe, Germany
| | - Staffan Kjelleberg
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | | | - Tommaso Lotti
- Department of Civil and Environmental Engineering - DICEA, University of Florence, Florence, Italy
| | - M Francesca Malpei
- Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milan, Italy
| | - Robert Nerenberg
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, USA
| | - Thomas R Neu
- Department of River Ecology, Helmholtz Centre for Environmental Research - UFZ, Magdeburg, Germany
| | - Etienne Paul
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, Université de Toulouse, Toulouse, France
| | - Hanqing Yu
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Yuemei Lin
- Department of Biotechcnology, Delft University of Technology, Delft, the Netherlands.
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Derruau S, Gobinet C, Mateu A, Untereiner V, Lorimier S, Piot O. Shedding light on confounding factors likely to affect salivary infrared biosignatures. Anal Bioanal Chem 2019; 411:2283-2290. [DOI: 10.1007/s00216-019-01669-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 01/22/2019] [Accepted: 02/05/2019] [Indexed: 01/21/2023]
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Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
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Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
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Hameed S, Xie L, Ying Y. Conventional and emerging detection techniques for pathogenic bacteria in food science: A review. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.05.020] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Wehbe K, Vezzalini M, Cinque G. Detection of mycoplasma in contaminated mammalian cell culture using FTIR microspectroscopy. Anal Bioanal Chem 2018; 410:3003-3016. [PMID: 29549508 PMCID: PMC5889780 DOI: 10.1007/s00216-018-0987-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 02/15/2018] [Accepted: 02/26/2018] [Indexed: 01/06/2023]
Abstract
Mycoplasma contamination represents a significant problem to the culture of mammalian cells used for research as it can cause disastrous effects on eukaryotic cells by altering cellular parameters leading to unreliable experimental results. Mycoplasma cells are very small bacteria therefore they cannot be detected by visual inspection using a visible light microscope and, thus, can remain unnoticed in the cell cultures for long periods. The detection techniques used nowadays to reveal mycoplasma contamination are time consuming and expensive with each having significant drawbacks. The ideal detection should be simple to perform with minimal preparation time, rapid, inexpensive, and sensitive. To our knowledge, for the first time, we employed Fourier transform infrared (FTIR) microspectroscopy to investigate whether we can differentiate between control cells and the same cells which have been infected with mycoplasmas during the culturing process. Chemometric methods such as HCA and PCA were used for the data analysis in order to detect spectral differences between control and intentionally infected cells, and spectral markers were revealed even at low contamination level. The preliminary results showed that FTIR has the potential to be used in the future as a reliable complementary detection technique for mycoplasma-infected cells. Graphical abstract FTIR microspectroscopy is able to differentiate between mycoplasma infected cells (LC for low contamination and HC for high contamination) and control non-infected cells (CN).
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Affiliation(s)
- Katia Wehbe
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, UK.
| | - Marzia Vezzalini
- Department of Medicine, General Pathology Section, University of Verona, Strada Le Grazie, 8, 37134, Verona, Italy
| | - Gianfelice Cinque
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, UK
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Zamora-Perez P, Tsoutsi D, Xu R, Rivera Gil P. Hyperspectral-Enhanced Dark Field Microscopy for Single and Collective Nanoparticle Characterization in Biological Environments. MATERIALS (BASEL, SWITZERLAND) 2018; 11:E243. [PMID: 29415420 PMCID: PMC5848940 DOI: 10.3390/ma11020243] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 01/18/2018] [Accepted: 01/31/2018] [Indexed: 11/16/2022]
Abstract
We review how the hyperspectral dark field analysis gives us quantitative insights into the manner that different nanoscale materials interact with their environment and how this relationship is directly expressed in an optical readout. We engage classification tools to identify dominant spectral signatures within a scene or to qualitatively characterize nanoparticles individually or in populations based on their composition and morphology. Moreover, we follow up the morphological evolution of nanoparticles over time and in different biological environments to better understand and establish a link between the observed nanoparticles' changes and cellular behaviors.
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Affiliation(s)
- Paula Zamora-Perez
- Integrative Biomedical Materials and Nanomedicine Lab, Department of Experimental and Health Sciences (DCEXS), Pompeu Fabra University (UPF), PRBB, Barcelona 08003, Spain.
| | - Dionysia Tsoutsi
- Integrative Biomedical Materials and Nanomedicine Lab, Department of Experimental and Health Sciences (DCEXS), Pompeu Fabra University (UPF), PRBB, Barcelona 08003, Spain.
| | - Ruixue Xu
- Integrative Biomedical Materials and Nanomedicine Lab, Department of Experimental and Health Sciences (DCEXS), Pompeu Fabra University (UPF), PRBB, Barcelona 08003, Spain.
| | - Pilar Rivera Gil
- Integrative Biomedical Materials and Nanomedicine Lab, Department of Experimental and Health Sciences (DCEXS), Pompeu Fabra University (UPF), PRBB, Barcelona 08003, Spain.
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Wang K, Pu H, Sun DW. Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview. Compr Rev Food Sci Food Saf 2018; 17:256-273. [DOI: 10.1111/1541-4337.12323] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/01/2017] [Accepted: 11/02/2017] [Indexed: 02/04/2023]
Affiliation(s)
- Kaiqiang Wang
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Hongbin Pu
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Da-Wen Sun
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
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Zhang R, Chouket R, Plamont MA, Kelemen Z, Espagne A, Tebo AG, Gautier A, Gissot L, Faure JD, Jullien L, Croquette V, Le Saux T. Macroscale fluorescence imaging against autofluorescence under ambient light. LIGHT, SCIENCE & APPLICATIONS 2018; 7:97. [PMID: 30510693 PMCID: PMC6261969 DOI: 10.1038/s41377-018-0098-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 10/27/2018] [Accepted: 10/27/2018] [Indexed: 05/07/2023]
Abstract
Macroscale fluorescence imaging is increasingly used to observe biological samples. However, it may suffer from spectral interferences that originate from ambient light or autofluorescence of the sample or its support. In this manuscript, we built a simple and inexpensive fluorescence macroscope, which has been used to evaluate the performance of Speed OPIOM (Out of Phase Imaging after Optical Modulation), which is a reference-free dynamic contrast protocol, to selectively image reversibly photoswitchable fluorophores as labels against detrimental autofluorescence and ambient light. By tuning the intensity and radial frequency of the modulated illumination to the Speed OPIOM resonance and adopting a phase-sensitive detection scheme that ensures noise rejection, we enhanced the sensitivity and the signal-to-noise ratio for fluorescence detection in blot assays by factors of 50 and 10, respectively, over direct fluorescence observation under constant illumination. Then, we overcame the strong autofluorescence of growth media that are currently used in microbiology and realized multiplexed fluorescence observation of colonies of spectrally similar fluorescent bacteria with a unique configuration of excitation and emission wavelengths. Finally, we easily discriminated fluorescent labels from the autofluorescent and reflective background in labeled leaves, even under the interference of incident light at intensities that are comparable to sunlight. The proposed approach is expected to find multiple applications, from biological assays to outdoor observations, in fluorescence macroimaging.
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Affiliation(s)
- Ruikang Zhang
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Raja Chouket
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Marie-Aude Plamont
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Zsolt Kelemen
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Saclay Plant Science (SPS), Université Paris-Saclay, Versailles, France
| | - Agathe Espagne
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Alison G. Tebo
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Arnaud Gautier
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Lionel Gissot
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Saclay Plant Science (SPS), Université Paris-Saclay, Versailles, France
| | - Jean-Denis Faure
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Saclay Plant Science (SPS), Université Paris-Saclay, Versailles, France
| | - Ludovic Jullien
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Vincent Croquette
- Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University, Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Université, CNRS, 75005 Paris, France
- Institut de biologie de l’École normale supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, 75005 Paris, France
| | - Thomas Le Saux
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
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Su WH, Sun DW. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr Rev Food Sci Food Saf 2017; 17:104-122. [DOI: 10.1111/1541-4337.12314] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/12/2017] [Accepted: 09/14/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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50
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Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.08.013] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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