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Thümmel L, Tintner-Olifiers J, Amendt J. A methodological approach to age estimation of the intra-puparial period of the forensically relevant blow fly Calliphora vicina via Fourier transform infrared spectroscopy. MEDICAL AND VETERINARY ENTOMOLOGY 2024. [PMID: 39093723 DOI: 10.1111/mve.12748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
Estimating the age of immature blow flies is of great importance for forensic entomology. However, no gold-standard technique for an accurate determination of the intra-puparial age has yet been established. Fourier transform infrared (FTIR) spectroscopy is a method to (bio-)chemically characterise material based on the absorbance of electromagnetic energy by functional groups of molecules. In recent years, it also has become a powerful tool in forensic and life sciences, as it is a fast and cost-effective way to characterise all kinds of material and biological traces. This study is the first to collect developmental reference data on the changes in absorption spectra during the intra-puparial period of the forensically important blow fly Calliphora vicina Robineau-Desvoidy (Diptera: Calliphoridae). Calliphora vicina was reared at constant 20°C and 25°C and specimens were killed every other day throughout their intra-puparial development. In order to investigate which part yields the highest detectable differences in absorption spectra throughout the intra-puparial development, each specimen was divided into two different subsamples: the pupal body and the former cuticle of the third instar, that is, the puparium. Absorption spectra were collected with a FTIR spectrometer coupled to an attenuated total reflection (ATR) unit. Classification accuracies of different wavenumber regions with two machine learning models, i.e., random forests (RF) and support vector machines (SVMs), were tested. The best age predictions for both temperature settings and machine learning models were obtained by using the full spectral range from 3700 to 600 cm-1. While SVMs resulted in better accuracies for C. vicina reared at 20°C, RFs performed almost as good as SVMs for data obtained from 25°C. In terms of sample type, the pupal body gave smoother spectra and usually better classification accuracies than the puparia. This study shows that FTIR spectroscopy is a promising technique in forensic entomology to support the estimation of the minimum post-mortem interval (PMImin), by estimating the age of a given insect specimen.
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Affiliation(s)
- Luise Thümmel
- Goethe-University Frankfurt, University Hospital, Institute of Legal Medicine, Frankfurt am Main, Germany
- Department of Aquatic Ecotoxicology, Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany
| | | | - Jens Amendt
- Goethe-University Frankfurt, University Hospital, Institute of Legal Medicine, Frankfurt am Main, Germany
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2
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Nascimento ALF, de Medeiros AGJ, Neves ACO, de Macedo ABN, Rossato L, Assis Santos D, dos Santos ALS, Lima KMG, Bastos RW. Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii. Front Chem 2024; 12:1412288. [PMID: 39050373 PMCID: PMC11266292 DOI: 10.3389/fchem.2024.1412288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/10/2024] [Indexed: 07/27/2024] Open
Abstract
Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.
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Affiliation(s)
- Ayrton L. F. Nascimento
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Anthony G. J. de Medeiros
- Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Ana C. O. Neves
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Ana B. N. de Macedo
- Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Luana Rossato
- Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados, Dourados, Brazil
| | - Daniel Assis Santos
- Laboratório de Micologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- National Institute of Science and Technology in Human Pathogenic Fungi, Ribeirão Preto, Brazil
| | - André L. S. dos Santos
- Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ, Brazil
| | - Kássio M. G. Lima
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Rafael W. Bastos
- Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- National Institute of Science and Technology in Human Pathogenic Fungi, Ribeirão Preto, Brazil
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Wang H, Du Z, Li Y, Zeng F, Qiu X, Li G, Li C. Non-destructive prediction of TVB-N using color-texture features of UV-induced fluorescence image for freeze-thaw treated frozen-whole-round tilapia. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:2574-2586. [PMID: 37851503 DOI: 10.1002/jsfa.13055] [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: 04/26/2023] [Revised: 08/26/2023] [Accepted: 10/18/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction. RESULTS Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 × 10-2 g N kg-1 and RPD value of 6.721 for TVB-N content predictiin. CONCLUSION The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Huihui Wang
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Zhonglin Du
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Yule Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Fanyi Zeng
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Xinjing Qiu
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Gaobin Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Chunpeng Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
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4
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Farias LR, Panero JDS, Riss JSP, Correa APF, Vital MJS, Panero FDS. Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:7336. [PMID: 37687792 PMCID: PMC10490430 DOI: 10.3390/s23177336] [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: 07/14/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 09/10/2023]
Abstract
Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.
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Affiliation(s)
- Leovergildo R. Farias
- Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil; (L.R.F.); (J.d.S.P.)
| | - João dos S. Panero
- Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil; (L.R.F.); (J.d.S.P.)
| | - Jordana S. P. Riss
- Instituto Federal de Roraima, Campus Novo Paraíso, BR-174, Km-512—Vila Novo Paraíso, Caracaraí 69365-000, Brazil;
| | - Ana P. F. Correa
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
| | - Marcos J. S. Vital
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
| | - Francisco dos S. Panero
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
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5
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Nascimento MC, Marcarini WD, Folli GS, da Silva Filho WG, Barbosa LL, Paulo EH, Vassallo PF, Mill JG, Barauna V, Martin FL, de Castro ER, Romão W, Filgueiras PR. Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis. Anal Chem 2022; 94:2425-2433. [PMID: 35076208 PMCID: PMC8805707 DOI: 10.1021/acs.analchem.1c04162] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/13/2022] [Indexed: 01/22/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.
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Affiliation(s)
- Márcia
H. C. Nascimento
- Chemometrics
Laboratory of the Center of Competence in Petroleum Chemistry −
NCQP, Universidade Federal do Espírito
Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil
| | - Wena D. Marcarini
- Department
of Physiological Sciences, Universidade
Federal do Espírito Santo (UFES), Vitória, Espírito Santo 29040-090, Brazil
| | - Gabriely S. Folli
- Chemometrics
Laboratory of the Center of Competence in Petroleum Chemistry −
NCQP, Universidade Federal do Espírito
Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil
| | - Walter G. da Silva Filho
- Department
of Physiological Sciences, Universidade
Federal do Espírito Santo (UFES), Vitória, Espírito Santo 29040-090, Brazil
| | - Leonardo L. Barbosa
- Department
of Physiological Sciences, Universidade
Federal do Espírito Santo (UFES), Vitória, Espírito Santo 29040-090, Brazil
| | - Ellisson Henrique
de Paulo
- Chemometrics
Laboratory of the Center of Competence in Petroleum Chemistry −
NCQP, Universidade Federal do Espírito
Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil
| | - Paula F. Vassallo
- Clinical
Hospital, Universidade Federal de Minas
Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - José G. Mill
- Department
of Physiological Sciences, Universidade
Federal do Espírito Santo (UFES), Vitória, Espírito Santo 29040-090, Brazil
| | - Valério
G. Barauna
- Department
of Physiological Sciences, Universidade
Federal do Espírito Santo (UFES), Vitória, Espírito Santo 29040-090, Brazil
| | | | - Eustáquio
V. R. de Castro
- Chemometrics
Laboratory of the Center of Competence in Petroleum Chemistry −
NCQP, Universidade Federal do Espírito
Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil
| | - Wanderson Romão
- Instituto
Federal de Educação, Ciência
e Tecnologia do Espírito Santo, Vila Velha 29106-010, Brazil
| | - Paulo R. Filgueiras
- Chemometrics
Laboratory of the Center of Competence in Petroleum Chemistry −
NCQP, Universidade Federal do Espírito
Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil
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6
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Attenuated total reflection: Fourier transform infrared spectroscopy for detection of heterogeneous vancomycin—intermediate Staphylococcus aureus. World J Microbiol Biotechnol 2020; 36:22. [DOI: 10.1007/s11274-019-2788-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 12/17/2019] [Indexed: 10/25/2022]
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7
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Barbosa TM, de Lima LAS, Dos Santos MCD, Vasconcelos SD, Gama RA, Lima KMG. A novel use of infra-red spectroscopy (NIRS and ATR-FTIR) coupled with variable selection algorithms for the identification of insect species (Diptera: Sarcophagidae) of medico-legal relevance. Acta Trop 2018; 185:1-12. [PMID: 29698658 DOI: 10.1016/j.actatropica.2018.04.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/14/2018] [Accepted: 04/22/2018] [Indexed: 11/16/2022]
Abstract
Unequivocal identification of fly specimens is an essential requirement in forensic entomology. Herein, a simple, non-destructive and rapid method based on two vibrational spectroscopy techniques [Near-Infrared Spectroscopy (NIRS) and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy] coupled with variable selection techniques such as genetic algorithm-linear discriminant analysis (GA-LDA) and successive projection algorithm-linear discriminant analysis (SPA-LDA) were applied for identifying and discriminating six species of flesh flies (Diptera: Sarcophagidae) native to Neotropical regions. This novel approach is based on the unique spectral "fingerprints" of their biochemical composition. One hundred sixty (160) NIRS and FT-IR specimens (120 male, 40 female) were acquired; different pre-processing methods such as baseline correction, derivative and Savitzky-Golay smoothing were also performed. In addition, the multivariate classification accuracy results were tested based on sensitivity, specificity, positive (or precision) and negative predictive values, Youden index, positive and negative likelihood ratios. Principal components analysis (PCA) was employed for male vs. female category using NIRS, strongly showing the separation between the classes with only three principal components and 99% explained variance. Differentiation between the genera Oxysarcodexia, Peckia and Ravinia was efficiently confirmed by both techniques. In comparison with other biological methods, this approach represents an effective choice for fast and non-destructive identification in forensic entomology.
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Affiliation(s)
- Taciano M Barbosa
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil
| | - Leomir A S de Lima
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Marfran C D Dos Santos
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil; Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Simão D Vasconcelos
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil
| | - Renata A Gama
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Kássio M G Lima
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil.
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Abstract
This review presents a retrospective of the studies carried out in the last 10 years (2006–2016) using spectroscopic methods as a research tool in the field of virology. Spectroscopic analyses are sensitive to variations in the biochemical composition of the sample, are non-destructive, fast and require the least sample preparation, making spectroscopic techniques tools of great interest in biological studies. Herein important chemometric algorithms that have been used in virological studies are also evidenced as a good alternative for analyzing the spectra, discrimination and classification of samples. Techniques that have not yet been used in the field of virology are also suggested. This methodology emerges as a new and promising field of research, and may be used in the near future as diagnosis tools for detecting diseases caused by viruses. A retrospective study of 2006–2016 using spectroscopic methods as a research tool in the field of virology. Chemometric algorithms used in virological studies were evidenced. This review emerges as a new and promising field of research in virology.
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de Lima LAS, Lima KMG, de Oliveira LSS, Araújo AA, Fernandes de Araújo Junior R. Evaluation of the bony repair in rat cranial defect using near infrared reflectance spectroscopy and discriminant analysis. Biotechnol Prog 2017; 33:1160-1168. [DOI: 10.1002/btpr.2476] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 12/30/2016] [Indexed: 11/10/2022]
Affiliation(s)
- Leomir A. S. de Lima
- Div. of Analytical Chemistry, Inst. of Chemistry, Biological Chemistry and Chemometrics; Federal University of Rio Grande do Norte; Natal RN 59072-970 Brazil
| | - Kássio M. G. Lima
- Div. of Analytical Chemistry, Inst. of Chemistry, Biological Chemistry and Chemometrics; Federal University of Rio Grande do Norte; Natal RN 59072-970 Brazil
| | - Lana S. S. de Oliveira
- Dept. of Biophysics and Pharmacology; Post-graduation programme in Public Health/Post graduation programme in Pharmaceutical Science, Federal University of Rio Grande do Norte; Natal RN 59072-970 Brazil
| | - Aurigena A. Araújo
- Dept. of Biophysics and Pharmacology; Post-graduation programme in Public Health/Post graduation programme in Pharmaceutical Science, Federal University of Rio Grande do Norte; Natal RN 59072-970 Brazil
| | - Raimundo Fernandes de Araújo Junior
- Dept. of Morphology, Post-graduation programme in Health Science/Post graduation programme in Structural and Functional Biology; Federal University of Rio Grande do Norte; Natal RN 59072-970 Brazil
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10
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Santos MCD, Nascimento YM, Araújo JMG, Lima KMG. ATR-FTIR spectroscopy coupled with multivariate analysis techniques for the identification of DENV-3 in different concentrations in blood and serum: a new approach. RSC Adv 2017. [DOI: 10.1039/c7ra03361c] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
In most cases of virus infections the viral load is directly related to the intensity of the disease.
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Affiliation(s)
- Marfran C. D. Santos
- Biological Chemistry and Chemometrics
- Institute of Chemistry
- Federal University of Rio Grande do Norte
- Natal 59072-970
- Brazil
| | - Yasmin M. Nascimento
- Laboratory of Molecular Biology for Infectious Diseases and Cancer
- Department of Microbiology and Parasitology
- Federal University of Rio Grande do Norte
- Natal 59072-970
- Brazil
| | - Josélio M. G. Araújo
- Laboratory of Molecular Biology for Infectious Diseases and Cancer
- Department of Microbiology and Parasitology
- Federal University of Rio Grande do Norte
- Natal 59072-970
- Brazil
| | - Kássio M. G. Lima
- Biological Chemistry and Chemometrics
- Institute of Chemistry
- Federal University of Rio Grande do Norte
- Natal 59072-970
- Brazil
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11
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Butler HJ, Ashton L, Bird B, Cinque G, Curtis K, Dorney J, Esmonde-White K, Fullwood NJ, Gardner B, Martin-Hirsch PL, Walsh MJ, McAinsh MR, Stone N, Martin FL. Using Raman spectroscopy to characterize biological materials. Nat Protoc 2016; 11:664-87. [PMID: 26963630 DOI: 10.1038/nprot.2016.036] [Citation(s) in RCA: 619] [Impact Index Per Article: 77.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.
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Affiliation(s)
- Holly J Butler
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,Centre for Global Eco-Innovation, Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Lorna Ashton
- Department of Chemistry, Lancaster University, Lancaster, UK
| | | | - Gianfelice Cinque
- Diamond Light Source, Harwell Science and Innovation Campus, Chilton, Oxfordshire, UK
| | - Kelly Curtis
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Jennifer Dorney
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Karen Esmonde-White
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nigel J Fullwood
- Department of Biomedical and Life Sciences, School of Health and Medicine, Lancaster University, Lancaster, UK
| | - Benjamin Gardner
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Pierre L Martin-Hirsch
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK
| | - Michael J Walsh
- Department of Pathology, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Martin R McAinsh
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Nicholas Stone
- Biophotonics Research Unit, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Francis L Martin
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
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12
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Nan L, Yang K, Ren G. Anti-biofilm formation of a novel stainless steel against Staphylococcus aureus. MATERIALS SCIENCE & ENGINEERING. C, MATERIALS FOR BIOLOGICAL APPLICATIONS 2015; 51:356-61. [DOI: 10.1016/j.msec.2015.03.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 01/08/2015] [Accepted: 03/09/2015] [Indexed: 01/22/2023]
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13
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Lima KMG, Gajjar KB, Martin-Hirsch PL, Martin FL. Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR-FTIR spectroscopy coupled with variable selection methods. Biotechnol Prog 2015; 31:832-9. [PMID: 25832726 DOI: 10.1002/btpr.2084] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 03/19/2015] [Indexed: 02/03/2023]
Abstract
Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II-IV; serous vs. non-serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real-world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II-IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA-LDA model with 33 wavenumbers. For serous vs. non-serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA-LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA-LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II-IV; up to 93.0% serous vs. non-serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population-based assessment of biomarkers signatures using ATR-FTIR spectroscopy as a screening tool for stage of ovarian cancer.
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Affiliation(s)
- Kássio M G Lima
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK.,Inst. of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, 59072-970, RN-Brazil
| | - Ketan B Gajjar
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK.,Dept. of Obstetrics and Gynaecology, Central Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | | | - Francis L Martin
- Centre for Biophotonics, LEC, Lancaster University, Lancaster, LA14YQ, UK
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14
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Marques AS, Moraes EP, Júnior MA, Moura AD, Neto VF, Neto RM, Lima KM. Rapid discrimination of klebsiella pneumoniae carbapenemase 2 – producing and non-producing klebsiella pneumoniae strains using near-infrared spectroscopy (NIRS) and multivariate analysis. Talanta 2015; 134:126-131. [DOI: 10.1016/j.talanta.2014.11.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 11/01/2014] [Accepted: 11/03/2014] [Indexed: 10/24/2022]
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15
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de Lima LAS, Baia TC, Gama RA, da Silva Gasparotto LH, Lima KM. Near Infrared Spectroscopy as an Emerging Tool for Forensic Entomotoxicology. ACTA ACUST UNITED AC 2014. [DOI: 10.1255/nirn.1489] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Leomir Aires Silva de Lima
- Universidade Federal do Rio Grande do Norte, Instituto de Química, Programa de Pós-Graduação em Quíamica, Grupo de Pesquisa em Química Biológica e Quimiometria, CEP 59072-970 - Natal, RN, Brazil
| | - Tainá C. Baia
- Universidade Federal do Rio Grande do Norte, Departamento de Microbiologia e Parasitologia, CEP 59072-970 - Natal, RN, Brazil
| | - Renata A. Gama
- Universidade Federal do Rio Grande do Norte, Departamento de Microbiologia e Parasitologia, CEP 59072-970 - Natal, RN, Brazil
| | - Luiz Henrique da Silva Gasparotto
- Universidade Federal do Rio Grande do Norte, Instituto de Química, Programa de Pós-Graduação em Quíamica, Grupo de Pesquisa em Química Biológica e Quimiometria, CEP 59072-970 - Natal, RN, Brazil
| | - Kássio M.G. Lima
- Universidade Federal do Rio Grande do Norte, Instituto de Química, Programa de Pós-Graduação em Quíamica, Grupo de Pesquisa em Química Biológica e Quimiometria, CEP 59072-970 - Natal, RN, Brazil
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16
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de Almeida VE, da Costa GB, de Sousa Fernandes DD, Gonçalves Dias Diniz PH, Brandão D, de Medeiros ACD, Véras G. Using color histograms and SPA-LDA to classify bacteria. Anal Bioanal Chem 2014; 406:5989-95. [DOI: 10.1007/s00216-014-8015-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 06/28/2014] [Accepted: 06/30/2014] [Indexed: 12/19/2022]
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