Spectrochemical analysis of blood combined with chemometric techniques for detecting osteosarcopenia.

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.

Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI-MS) and multivariate classification.

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.

Detection of terbufos in cases of intoxication by means of entomotoxicological analysis using ATR-FTIR spectroscopy combined with chemometrics.

The detection of toxic substances in larvae from carcasses in an advanced stage of decomposition may help criminal expertise in elucidating the cause of death in suspected cases of poisoning. Terbufos (Counter®) or O,O-diethyl-S-[(tert-butylsulfanyl)methyl] phosphorodithioate is an insecticide and systemic nematicide, which has very high toxicity from an acute point of view (oral LD50 in rodents ranging from 1.4 to 9.2 mg/kg) that has been marketed irregularly and indiscriminately in Brazil as a rodenticide, often being used to practice homicides. The present study aims to evaluate the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to detect traces of terbufos pesticide in fly larvae (Sarcophagidae). ATR-FTIR spectra of scavenger fly larvae from control (n = 31) and intoxicated (n = 80) groups were collected and submitted to chemometric analysis by means of multivariate classification using principal component analysis with quadratic discriminant analysis (PCA-QDA), successive projections algorithm with quadratic discriminant analysis (SPA-QDA) and genetic algorithm with quadratic discriminant analysis (GA-QDA) in order to distinguish between control and intoxicated groups. All discriminant models showed sensitivity and specificity above 90%, with the GA-QDA model showing the best performance with 98.9% sensitivity and specificity. The proposed methodology proved to be sensitive and promising for the detection of terbufos in scavenger fly larvae from intoxicated rat carcasses. In addition, the non-destructive nature of the ATR-FTIR technique may be useful in preserving the forensic evidence, meeting the precepts of the chain of custody and allowing for counter-proof.

Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM).

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 F2-score (F2), Matthews correlation coefficient (MCC) and test effectiveness ([Formula: see text]). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F2; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F2. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.

Near infrared spectroscopy (NIRS) coupled with chemometric methods to identify and estimate taxonomic relationships of flies with forensic potential (Diptera: Calliphoridae and Sarcophagidae).

Infrared spectroscopy has been gaining prominence in entomology, such as for solving taxonomic problems, sexing adult specimens, determining the age of immature specimens, detecting drugs of abuse in fly larvae, and can be an important technique in Forensic Entomology. In order to help identify the species of Calliphoridae and Sarcophagidae families, the present study aimed to evaluate the use of near infrared spectroscopy (NIRS) coupled with chemometric methods for separating fly specimens into taxonomic categories and understanding the taxonomic relationship between them. Spectra collected from nine species of flies were subjected to unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA), in which we sought to visualize the relationship between the samples (segregation of genera and families) with subsequent identification. In PCA, the best model was achieved using five principal components (PCs), which explained 99.16% of total variance of the original data set. The first principal component (PC1) and the fourth principal component (PC4) provided the best segregation, the latter being more important in the segregation of the species Chrysomya albiceps, Lucilia eximia, and Ravinia belforti from the others. In the HCA dendrogram, there was a clear separation between the specimens by family (Calliphoridae and Sarcophagidae) and genera (Chrysomya, Lucilia, Oxysarcodexia, Peckia and Ravinia). This study shows that NIRS is efficient to identify flies' taxonomic properties, such as family and genera, providing quick evidence for the tested species identity.

Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females.

One of the most important steps in preventing arboviruses is entomological surveillance. The main entomological surveillance action is to detect vector foci in the shortest possible stages. In this work, near and medium infrared spectra collected from female Aedes aegypti mosquitoes recently infected and not infected with dengue were used in order to build chemometric models capable of differentiating the spectra of each class. For this, computational algorithms such as Successive Projection Algorithm (SPA) and Genetic Algorithm (GA) were used together with Linear Discriminant Analysis (LDA). The constructed models were evaluated with sensitivity and specificity calculations. It was observed that models based on near infrared (NIR) spectra have better classification results when compared to mid infrared (MIR) spectra, as well as models based on GA present better results when compared to those based on SPA. Thus, NIR-GA-LDA obtained the best results, reaching 100.00 % for sensitivity and specificity. NIR spectroscopy is 18 times faster and 116 times cheaper than RT-qPCR. The findings reported in this study may have important applications in the field of entomological surveillance, prevention and control of dengue vectors. In the future, mosquito traps equipped with portable NIR instruments capable of detecting infected mosquitoes may be used, in order to enable an action plan to prevent future outbreaks of the disease.

Multivariate assessment for predicting antioxidant activity from clove and pomegranate extracts by MCR-ALS and PLS models combined to IR spectroscopy.

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.

Molecular fluorescence spectroscopy with multi-way analysis techniques detects spectral variations distinguishing uninfected serum versus dengue or chikungunya viral infected samples.

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.

Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach.

MOTIVATION: Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test sets; herein, the training set is used for model construction and the test set for model validation. Some of the most-used methodologies for data splitting are the random selection (RS) and the Kennard-Stone (KS) algorithms; here, the former works based on a random splitting process and the latter is based on the calculation of the Euclidian distance between the samples. We propose an algorithm called the Morais-Lima-Martin (MLM) algorithm, as an alternative method to improve data splitting in classification models. MLM is a modification of KS algorithm by adding a random-mutation factor. RESULTS: RS, KS and MLM performance are compared in simulated and six real-world biospectroscopic applications using principal component analysis linear discriminant analysis (PCA-LDA). MLM generated a better predictive performance in comparison with RS and KS algorithms, in particular regarding sensitivity and specificity values. Classification is found to be more well-equilibrated using MLM. RS showed the poorest predictive response, followed by KS which showed good accuracy towards prediction, but relatively unbalanced sensitivities and specificities. These findings demonstrate the potential of this new MLM algorithm as a sample selection method for classification applications in comparison with other regular methods often applied in this type of data. AVAILABILITY AND IMPLEMENTATION: MLM algorithm is freely available for MATLAB at https://doi.org/10.6084/m9.figshare.7393517.v1.

Advances in chemometric control of commercial diesel adulteration by kerosene using IR spectroscopy.

Adulteration is a recurrent issue found in fuel screening. Commercial diesel contamination by kerosene is highly difficult to be detected via physicochemical methods applied in market. Although the contamination may affect diesel quality and storage stability, there is a lack of efficient methodologies for this evaluation. This paper assessed the use of IR spectroscopies (MIR and NIR) coupled with partial least squares (PLS) regression, support vector machine regression (SVR), and multivariate curve resolution with alternating least squares (MCR-ALS) calibration models for quantifying and identifying the presence of kerosene adulterant in commercial diesel. Moreover, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) tools coupled to linear discriminant analysis were used to observe the degradation behavior of 60 samples of pure and kerosene-added diesel fuel in different concentrations over 60 days of storage. Physicochemical properties of commercial diesel with 15% kerosene remained within conformity with Brazilian screening specifications; in addition, specified tests were not able to identify changes in the blends' performance over time. By using multivariate classification, the samples of pure and contaminated fuel were accurately classified by aging level into two well-defined groups, and some spectral features related to fuel degradation products were detected. PLS and SVR were accurate to quantify kerosene in the 2.5-40% (v/v) range, reaching RMSEC < 2.59% and RMSEP < 5.56%, with high correlation between real and predicted concentrations. MCR-ALS with correlation constraint was able to identify and recover the spectral profile of commercial diesel and kerosene adulterant from the IR spectra of contaminated blends.

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.

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.

Spectroscopy with computational analysis in virological studies: A decade (2006-2016).

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.