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Article: Attenuated Total Reflection Fourier-Transform Infrared Spectral Discrimination in Human Tissue of Oesophageal Transformation to Adenocarcinoma.

Maitra, Ishaan / Morais, Camilo L M / Lima, Kássio M G / Ashton, Katherine M / Bury, Danielle / Date, Ravindra S / Martin, Francis L

Journal of personalized medicine

2023 Volume 13, Issue 8

Abstract: This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous ... ...

Abstract	This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous human tissue studies identifying the potential for ATR-FTIR spectroscopy in differentiating among all classes of oesophageal transformation to OAC.
Language	English
Publishing date	2023-08-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm13081277
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Article ; Online: Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM).

Dos Santos, Ricardo Fernandes / Paraskevaidi, Maria / Mann, David M A / Allsop, David / Santos, Marfran C D / Morais, Camilo L M / Lima, Kássio M G

Scientific reports

2022 Volume 12, Issue 1, Page(s) 16199

Abstract: Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and ... ...

Abstract	Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F
MeSH term(s)	Alzheimer Disease/diagnosis ; Biomarkers ; Discriminant Analysis ; Humans ; Plasma ; Spectrometry, Fluorescence
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-09-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-20611-y
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Article ; Online: Multivariate assessment for predicting antioxidant activity from clove and pomegranate extracts by MCR-ALS and PLS models combined to IR spectroscopy

Câmara, Anne B.F. / de Oliveira, Keverson G. / Santos, Marfran C.D. / de Lima, Ramoni R.S. / de Lima, Kássio M.G. / S. de Carvalho, Luciene

Food Chemistry. 2022 Aug., v. 384 p.132321-

2022

Abstract: This study evaluated the feasibility of infrared (MIR/NIR) spectroscopy coupled to chemometrics as an alternative method for determining the antioxidant activity (AA%) of pomegranate (Punica granatum) and clove (Syzygium aromaticum) alcoholic extracts ... ...

Abstract	This study evaluated the feasibility of infrared (MIR/NIR) spectroscopy coupled to chemometrics as an alternative method for determining the antioxidant activity (AA%) of pomegranate (Punica granatum) and clove (Syzygium aromaticum) alcoholic extracts versus the conventional DPPH method. Multivariate curve resolution with alternating least squares (MCR-ALS) and Partial least squares (PLS) regression were efficient to predict the AA%, thus providing good accuracy and low residuals compared to the standard method. The MCR-ALS combined with NIR data stood out among the other models with R² ≥ 0.962 and RMSEP ≤ 3.38 %; furthermore, this technique presents the great feature of recovering the pure spectral profile of the analytes and identifying interferents in the sample. The application of chemometrics tools to predict the antioxidant activity of natural extracts resulted in a greener, low-cost and efficient process for the food industry.
Keywords	Punica granatum ; Syzygium aromaticum ; antioxidant activity ; chemical species ; chemometrics ; cloves ; food chemistry ; food industry ; infrared spectroscopy ; pomegranates ; Clove ; Pomegranate ; MIR/NIR spectroscopy ; MCR-ALS, PLS
Language	English
Dates of publication	2022-08
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2022.132321
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Article ; Online: Tutorial: multivariate classification for vibrational spectroscopy in biological samples.

Morais, Camilo L M / Lima, Kássio M G / Singh, Maneesh / Martin, Francis L

Nature protocols

2020 Volume 15, Issue 7, Page(s) 2143–2162

Abstract: Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. ... ...

Abstract	Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.
MeSH term(s)	Animals ; Humans ; Multivariate Analysis ; Spectroscopy, Fourier Transform Infrared ; Spectrum Analysis/methods ; Spectrum Analysis, Raman ; Statistics as Topic/methods ; Vibration
Language	English
Publishing date	2020-06-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2244966-8
ISSN	1750-2799 ; 1754-2189
ISSN (online)	1750-2799
ISSN	1754-2189
DOI	10.1038/s41596-020-0322-8
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Article ; Online: Molecular fluorescence spectroscopy with multi-way analysis techniques detects spectral variations distinguishing uninfected serum versus dengue or chikungunya viral infected samples.

Santos, Marfran C D / Monteiro, Joelma D / Araújo, Josélio M G / Lima, Kássio M G

Scientific reports

2020 Volume 10, Issue 1, Page(s) 13758

Abstract: Significant attempts are being made worldwide in an attempt to develop a tool that, with a simple analysis, is capable of distinguishing between different arboviruses. Herein, we employ molecular fluorescence spectroscopy as a sensitive and specific ... ...

Abstract	Significant attempts are being made worldwide in an attempt to develop a tool that, with a simple analysis, is capable of distinguishing between different arboviruses. Herein, we employ molecular fluorescence spectroscopy as a sensitive and specific rapid tool, with simple methodology response, capable of identifying spectral variations between serum samples with or without the dengue or chikungunya viruses. Towards this, excitation emission matrices (EEM) of clinical samples from patients with dengue or chikungunya, in addition to uninfected controls, were separated into a training or test set and analysed using multi-way classification models such as n-PLSDA, PARAFAC-LDA and PARAFAC-QDA. Results were evaluated based on calculations of accuracy, sensitivity, specificity and F score; the most efficient model was identified to be PARAFAC-QDA, whereby 100% was obtained for all figures of merit. QDA was able to predict all samples in the test set based on the scores present in the factors selected by PARAFAC. The loadings obtained by PARAFAC can be used in future studies to prove the direct or indirect relationship of spectral changes caused by the presence of these viruses. This study demonstrates that molecular fluorescence spectroscopy has a greater capacity to detect spectral variations related to the presence of such viruses when compared to more conventional techniques.
MeSH term(s)	Algorithms ; Chikungunya Fever/diagnosis ; Chikungunya virus/isolation & purification ; Computational Biology/methods ; Dengue/diagnosis ; Dengue Virus/isolation & purification ; Humans ; Least-Squares Analysis ; Molecular Diagnostic Techniques/methods ; Principal Component Analysis/methods ; Sensitivity and Specificity ; Serum/virology ; Spectrometry, Fluorescence/methods ; Viremia/diagnosis
Language	English
Publishing date	2020-08-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-70811-7
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Article ; Online: Spectrochemical analysis of blood combined with chemometric techniques for detecting osteosarcopenia.

da Silva, Tales Gomes / Morais, Camilo L M / Santos, Marfran C D / de Lima, Leomir A S / de Medeiros Freitas, Raysa Vanessa / Guerra, Ricardo Oliveira / Lima, Kássio M G

Scientific reports

2023 Volume 13, Issue 1, Page(s) 9686

Abstract: Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Currently, ... ...

Abstract	Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Currently, the test to identify this health condition is complex. In this study, we use mid-infrared spectroscopy combined with chemometric techniques to identify osteosarcopenia based on blood serum samples. The purpose of this study was to evaluate the mid-infrared spectroscopy power to detect osteosarcopenia in community-dwelling older women (n = 62, 30 from patients with osteosarcopenia and 32 healthy controls). Feature reduction and selection techniques were employed in conjunction with discriminant analysis, where a principal component analysis with support vector machines (PCA-SVM) model achieved 89% accuracy to distinguish the samples from patients with osteosarcopenia. This study shows the potential of using infrared spectroscopy of blood samples to identify osteosarcopenia in a simple, fast and objective way.
MeSH term(s)	Humans ; Female ; Aged ; Chemometrics ; Spectrophotometry, Infrared ; Principal Component Analysis ; Discriminant Analysis ; Support Vector Machine
Language	English
Publishing date	2023-06-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-36834-6
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Article ; Online: Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI-MS) and multivariate classification.

Alves, Marcelo V S / Maciel, Lanaia I L / Passos, João O S / Morais, Camilo L M / Dos Santos, Marfran C D / Lima, Leomir A S / Vaz, Boniek G / Pegado, Rodrigo / Lima, Kássio M G

Scientific reports

2023 Volume 13, Issue 1, Page(s) 4658

Abstract: This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual ... ...

Abstract	This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fibromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire-physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classification using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fibromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classification sensitivity, and associated with the VAS symptom achieved 100% classification specificity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially significant for classification according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fibromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classification ability of different datasets. The good classification results obtained confirm this technique is as a good analytical tool for the detection of fibromyalgia, and provides theoretical support for other studies about fibromyalgia diagnosis.
MeSH term(s)	Humans ; Fibromyalgia/diagnosis ; Quality of Life ; Mass Spectrometry ; Discriminant Analysis ; Principal Component Analysis
Language	English
Publishing date	2023-03-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-31565-0
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Article ; Online: Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination.

Câmara, Anne B F / da Silva, Wellington J O / Neves, Ana C de O / Moura, Heloise O M A / de Lima, Kassio M G / de Carvalho, Luciene S

Talanta

2023 Volume 266, Issue Pt 2, Page(s) 125126

Abstract: The contamination of jet fuel has gained attention in the past years as a notable factor in aircraft accidents. Identifying the contamination sources is still a challenge, especially when they have a similar composition to the fuel, such as kerosene ... ...

Abstract	The contamination of jet fuel has gained attention in the past years as a notable factor in aircraft accidents. Identifying the contamination sources is still a challenge, especially when they have a similar composition to the fuel, such as kerosene solvent (KS). A novel analytical methodology was developed by combining a set of excitation-emission matrix (EEM) fluorescence to area constrained multivariate curve resolution with alternating least-squares (MCR-ALS) and PARAllel FACtor (PARAFAC) analysis, in order to identify KS in blends with JET-A1. For this purpose, a dataset with 50 samples (KS and JET-A1 blends, 2.0-100% v/v) was used to build the multivariate models. Both PARAFAC and MCR-ALS allowed fuel quantification with 4.64% and 3.46% RMSEP, respectively; both models (PARAFAC and MCR-ALS) could quantify KS with high accuracy (RMSEP <5.36%). In addition, MCR-ALS model was able to recover the pure spectral profiles of KS, JET-A1 and interferers. GC-MS data of the samples proved the composition similarities between both petroleum distillates, thus being inefficient for identifying the contamination. These results indicate that the development of multivariate models using EEM was the key for contributing with a new low-cost and accurate method for on-line screening of jet fuel contamination.
Language	English
Publishing date	2023-08-25
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1500969-5
ISSN	1873-3573 ; 0039-9140
ISSN (online)	1873-3573
ISSN	0039-9140
DOI	10.1016/j.talanta.2023.125126
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Article ; Online: Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines.

Morais, Camilo L M / Lima, Kássio M G / Martin, Francis L

Analytica chimica acta

2018 Volume 1063, Page(s) 40–46

Abstract: Uncertainty estimation provides a quantitative value of the predictive performance of a classification model based on its misclassification probability. Low misclassification probabilities are associated with a low degree of uncertainty, indicating high ... ...

Abstract	Uncertainty estimation provides a quantitative value of the predictive performance of a classification model based on its misclassification probability. Low misclassification probabilities are associated with a low degree of uncertainty, indicating high trustworthiness; while high misclassification probabilities are associated with a high degree of uncertainty, indicating a high susceptibility to generate incorrect classification. Herein, misclassification probability estimations based on uncertainty estimation by bootstrap were developed for classification models using discriminant analysis [linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)] and support vector machines (SVM). Principal component analysis (PCA) was used as variable reduction technique prior classification. Four spectral datasets were tested (1 simulated and 3 real applications) for binary and ternary classifications. Models with lower misclassification probabilities were more stable when the spectra were perturbed with white Gaussian noise, indicating better robustness. Thus, misclassification probability can be used as an additional figure of merit to assess model robustness, providing a reliable metric to evaluate the predictive performance of a classifier.
Language	English
Publishing date	2018-09-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1483436-4
ISSN	1873-4324 ; 0003-2670
ISSN (online)	1873-4324
ISSN	0003-2670
DOI	10.1016/j.aca.2018.09.022
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Article ; Online: Infrared spectroscopy and forensic entomology: Can this union work? A literature review.

Jales, Jessica T / Barbosa, Taciano M / de Medeiros, Jucélia R / de Lima, Leomir A S / de Lima, Kássio M G / Gama, Renata A

Journal of forensic sciences

2021 Volume 66, Issue 6, Page(s) 2080–2091

Abstract: For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in ... ...

Abstract	For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in Forensic entomology (FE) field, with vibrational techniques such as Near-infrared spectroscopy (NIRS) and Medium-infrared spectroscopy (MIRS) underutilized in forensic sciences. Thus, this work describes the potential of NIRS, MIRS, and other spectroscopic methodologies, for entomological analysis in FE, as well as discusses its future uses for criminal or civil investigations. After a thorough research on scientific journals database, a total of 33 publications were found in scientific journals, with direct or indirect application to FE, including experimental applications of NIRS and MIRS in taxonomic discrimination of species, larval age prediction, detection of toxic substances in insects from environments or crime scenes, and detection of internal or external infestations by live or dead insects in stored products. Besides, NIRS and MIRS combined with multivariate analysis were efficient, inexpensive, fast, and non-destructive analytical tools. However, more than 51% of the spectroscopic publications are concentrated in the stored products field, and so we discuss the need for expansion and more direct application in other FE areas. We hope the number of articles continues to increase, and as NIRS and MIRS technology progress, they advance in forensic research and routine use.
MeSH term(s)	Agriculture ; Algorithms ; Animals ; Conservation of Natural Resources ; Crime ; Forensic Entomology ; Humans ; Multivariate Analysis ; Postmortem Changes ; Spectroscopy, Near-Infrared
Language	English
Publishing date	2021-07-22
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	219216-0
ISSN	1556-4029 ; 0022-1198
ISSN (online)	1556-4029
ISSN	0022-1198
DOI	10.1111/1556-4029.14800
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