Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples.

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Gas chromatographic techniques and spectroscopic approaches for a deep characterization of Piper gaudichaudianum Kunth essential oil from Brazil.

Piper gaudichaudianum Kunth essential oil (EO) is a natural source of bioactive components, having multiple therapeutic applications. Its chemical composition is highly variable, and strictly depends on abiotic factors, resulting in various biological activities. The present study details the utilization of multiple gas chromatographic techniques alongside nuclear magnetic resonance (NMR) spectroscopy to characterize the essential oil of Piper gaudichaudianum Kunth from Brazil. Seventy-six components were identified using GC-MS analysis, while enantio­selective multidimensional gas chromatography elucidated the enantiomeric distribution of eight chiral components, for the first time in the literature. Following GC-MS analysis, an unidentified component, constituting approximately 27 % of the total oil, prompted an isolation step through preparative gas chromatography. Through the combined use of nuclear magnetic resonance, GC-Fourier transform infrared spectroscopy (FTIR), and mass spectrometry (MS), the unknown molecule was structurally identified as 4-[(3E)­dec-3-en-1-yl]phenol. Remarkably, it was identified as a known molecule, gibbilimbol B, and not previously listed in any MS database. Subsequently, the spectrum was included in a commercial library, specifically the FFNSC 4.0 MS database, for the first time.

Oral Mucosa and Saliva Alterations Related to Vape.

OBJECTIVES: Electronic nicotine delivery systems (e-cigarette, pod, and vape) are currently among the tobacco consumption of adolescents and young adults. The aim is to show oral mucosa and saliva alterations related to vape. MATERIAL AND METHODS: A vape-user patient, presenting a white plaque in the posterior region of the hard palate, underwent clinical examination, sialometry, pH evaluation, and excisional biopsy of the white lesion. Molecular changes in saliva and vape liquid were analyzed by vibrational spectroscopy. RESULTS: The histopathological analyses showed hyperparakeratosis without dysplasia. Formaldehyde, ketones, and aromatic hydrocarbon species were identified in e-cig liquid by the FTIR. CONCLUSIONS: The use of vape may be related to the development of hyperkeratotic lesions in the oral mucosa as well as significantly modify the patient's salivary patterns as the vape liquid presents carcinogenic and cytotoxic components in its composition.

Analysis of the immune response using FTIR spectroscopy in mothers and their newborns with different vaccination schemes for COVID-19.

The SARS-CoV-2 outbreak has provoked more than 6 million deaths worldwide. The scarcity of effective treatments and its virulence converted the vaccines into an essential tool to face it. The most used vaccines were the mRNA, adenovirus vector, and inactivated whole-virus. However, nowadays, infants aged < 6 months are not eligible for any vaccines against COVID-19, and their immunization relies on passive immunity. In this research, we investigated the humoral and cellular immune response generated on newborns of SARS-CoV-2 vaccinated mothers with mRNA or viral vector (VV) vaccine employing Fourier transformed infrared (FTIR) spectroscopy in saliva samples. For this purpose, saliva samples of newborns and their mothers were collected; the population was divided into two groups, VV and mRNA, which were subdivided into three subgroups: before pregnancy (BP), at the first (FTP) and second (STP) trimesters of pregnancy. The samples were analyzed using FTIR spectroscopy, and the bands associated with the humoral and cellular immune responses, such as IgG, IgA, and IFN-γ were analyzed. The integrated areas were calculated and compared to elucidate the quantity of those immunoglobins and the cytokine. Likewise, the correlation of the humoral and cellular immune response between the newborns and their mothers and the correlation between cellular and humoral immune response was also evaluated. The VV vaccine produced a significant humoral and cellular immune response in newborns and their mothers when they received it at the STP compared with the mRNA vaccine, evidencing statistical significance. However, no correlation was observed between newborns and their mothers when the vaccine was applied in this trimester of pregnancy. When administered BP, the mRNA vaccine generated more humoral immunity in newborns and their mothers. Nevertheless, compared with the VV vaccine, it only showed statistical significance in the mothers, highlighting that IgG showed a moderate positive correlation between the newborns and their mothers.

Chemical and Energetic Characterization of the Wood of Prosopis laevigata: Chemical and Thermogravimetric Methods.

Diverse methodologies exist to determine the chemical composition, proximate analysis, and calorific value of biomass. Researchers select and apply a specific methodology according to the lignocellulosic material they study and the budgetary resources available. In this project, we determined the primary chemical constitution and proximate analysis of Prosopis laevigata (Humb. & Bonpl.) Jonhst wood using a traditional chemical method and a novel procedure based on the deconvolution of the DTG signal produced by TGA. The highest calorific value was verified using a calorimetric pump based on mathematical models. We also conducted elemental analysis and a microanalysis of ash, and applied Fourier transform infrared spectroscopic analysis (FT-IR). The means of the results obtained by the chemical method and TGA-DTG, respectively, were: hemicelluloses 7.36%-(8.72%), cellulose 48.28%-(46.08%), lignin 30.57%-(32.44%), extractables 13.53%-(12.72%), moisture 2.03%-(4.96%), ash 1.77%-(1.90%), volatile matter 75.16%-(74.14%), and fixed carbon 23.05%-(18.93%). The procedure with the calorimetric pump generated a calorific value above 20.16 MJ/kg. The range generated by the various models was 18.23-21.07 MJ/kg. The results of the elemental analysis were: carbon 46.4%, hydrogen 6.79%, oxygen 46.43%, nitrogen 0.3%, and sulfur 0.5%. The microanalysis of ash identified 18 elements. The most abundant ones were potassium ˃ calcium ˃ sodium. Based on the infrared spectrum (FT-IR) of Prosopis laevigata wood, we detected the following functional groups: OH, C-H, C=O, CH2, CH3, C-O-C, C-OH, and C4-OH. Our conclusion is that the TGA-DTG method made it possible to obtain results in less time with no need for the numerous reagents that chemical procedures require. The calorific value of P. laevigata wood is higher than the standards. Finally, according to our results, proximate analysis provides the best model for calculating calorific value.

Preliminary Screening on Antibacterial Crude Secondary Metabolites Extracted from Bacterial Symbionts and Identification of Functional Bioactive Compounds by FTIR, HPLC and Gas Chromatography-Mass Spectrometry.

Secondary metabolites, bioactive compounds produced by living organisms, can unveil symbiotic relationships in nature. In this study, soilborne entomopathogenic nematodes associated with symbiotic bacteria (Xenorhabdus stockiae and Photorhabdus luminescens) were extracted from solvent supernatant containing secondary metabolites, demonstrating significant inhibitory effects against E. coli, S. aureus, B. subtilus, P. mirabilis, E. faecalis, and P. stutzeri. The characterization of these secondary metabolites by Fourier transforms infrared spectroscopy revealed amine groups of proteins, hydroxyl and carboxyl groups of polyphenols, hydroxyl groups of polysaccharides, and carboxyl groups of organic acids. Furthermore, the obtained crude extracts were analyzed by high-performance liquid chromatography for the basic identification of potential bioactive peptides. Gas chromatography-mass spectrometry analysis of ethyl acetate extracts from Xenorhabdus stockiae identified major compounds including nonanoic acid derivatives, proline, paromycin, octodecanal derivatives, trioxa-5-aza-1-silabicyclo, 4-octadecenal, methyl ester, oleic acid, and 1,2-benzenedicarboxylicacid. Additional extraction from Photorhabdus luminescens yielded functional compounds such as indole-3-acetic acid, phthalic acid, 1-tetradecanol, nemorosonol, 1-eicosanol, and unsaturated fatty acids. These findings support the potential development of novel natural antimicrobial agents for future pathogen suppression.

Quantification of natural rubber blends by reflection/reflectance infrared and confocal Raman spectroscopy: a comparison of statistical methods.

The blend of butadiene and acrylonitrile copolymer (NBR) with natural poly-cis-isoprene (NR) shows increased resistance to swelling in solvents in comparison to the individual components. In aerospace, NBR rubber is used as thermal protection for rockets and shall not contain other polymers, even in low contents, otherwise, it can affect the protection performance and rocket safety by causing detachment of the elastomer/propellant interface; therefore, this investigation presents methodologies to determine the NR/NBR contents. This study explores different analytical techniques, such as Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy, in the mid-infrared (MIR) by reflection and in the near-infrared by reflectance (NIRA) modes, Furthermore, quantification strategies by univariate, bivariate and multivariate (chemometric) models are evaluated and compared. A proposed methodology, based on multivariate Raman microscopy with partial least squares regression (PLS), showed high linearity (R2 > 0.99) and low error (< 0.82 %). The validation of FT-MIR data for the CH3, which presented lower error (1.3%) than vinylidene band (6%), showed that both methodologies (reflection and NIRA reflectance) can be used for the quantification of NR in NR/NBR. These results constitute a contribution to the state of the art in researching industrial and aerospace elastomeric applications.

Highly Soluble Dacarbazine Multicomponent Crystals Less Prone to Photodegradation.

Dacarbazine (DTIC) is a widely prescribed oncolytic agent to treat advanced malignant melanomas. Nevertheless, the drug is known for exhibiting low and pH-dependent solubility, in addition to being photosensitive. These features imply the formation of the inactive photodegradation product 2-azahypoxanthine (2-AZA) during pharmaceutical manufacturing and even drug administration. We have focused on developing novel DTIC salt/cocrystal forms with enhanced solubility and dissolution behaviors to overcome or minimize this undesirable biopharmaceutical profile. By cocrystallization techniques, two salts, two cocrystals, and one salt-cocrystal have been successfully prepared through reactions with aliphatic carboxylic acids. A detailed structural study of these new multicomponent crystals was conducted using X-ray diffraction (SCXRD, PXRD), spectroscopic (FT-IR and 1H NMR), and thermal (TG and DSC) analyses. Most DTIC crystal forms reported display substantial enhancements in solubility (up to 19-fold), with faster intrinsic dissolution rates (from 1.3 to 22-fold), contributing positively to reducing the photodegradation of DTIC in solution. These findings reinforce the potential of these new solid forms to enhance the limited DTIC biopharmaceutical profile.

Salivary spectral signature using ATR-FTIR spectroscopy in different exercise protocols.

The Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) has been applied to determine salivary biomarkers with high sensitivity and cost-effectiveness. Our study aimed to test the hypothesis that the spectral profile of saliva demonstrates distinct vibrational modes corresponding to different exercise protocols, thereby facilitating exercise monitoring. Saliva samples were collected from trained male subjects at three intervals: pre-exercise, post-exercise, and 3 h post-exercise. The protocols included acute sessions of continuous exercise (CE), high-intensity interval exercise (HIIE), and resistance exercise (RE). ATR-FTIR analysis revealed that salivary biochemical components changed uniquely with each exercise protocol. Specific spectral vibrational modes were identified as potential biomarkers for each exercise type. Notably, the salivary spectrum pattern of CE closely resembled that of HIIE, whereas RE showed minor alterations. Furthermore, we attempted to apply an algorithm capable of distinguishing the spectral range that differentiates the exercise modalities. This pioneering study is the first to compare changes in saliva spectra following different exercise protocols and to suggest spectrum peaks of vibrational modes as markers for specific types of exercises. We emphasize that the spectral wavenumbers identified by FTIR could serve as practical markers in distinguishing between different exercise modalities, with sensitivity, specificity, and accuracy correlating with the metabolic changes induced by exercise. Therefore, this study contributes a panel of ATR-FTIR spectral wavenumbers that can be referenced as a spectral signature capable of distinguishing between resistance and endurance exercises.

Infrared spectroscopy as a predictive tool for the severity of COVID-19 using patient's saliva: A strategy to avoid hyperinflammation.

Discriminate the severity level of COVID-19 disease is still a challenge. Here we investigate the capability of micro-infrared absorption spectroscopy (micro-FTIR) to probe COVID-19 severity level and predict hyperinflammation, correlating the assigned vibrational data to relevant biomolecules related to the immune system. Saliva of 184 patients was analysed by ELISA assay (Hepcidin) and micro-FTIR. Vibrational bands related to IgM and IgA can discriminate healthy from Severe individuals (sensitivity ≥ 0.749, specificity ≥ 0.945) and are less effective in discriminating Mild or Moderate individuals from the Severe group (sensitivity ≥ 0.628, specificity ≥ 0.867). Analysis of the second derivative of spectra probed increased levels of IL-6 in the saliva a key additional information for the degree of severity prediction. Because the model discriminates all the groups regarding the Severe group, it predicts an intense state of inflammation based on FTIR analysis. It is a powerful tool for predicting hyperinflammation conditions related to SARS-CoV-2 infection and may be an ally in implementing drugs or therapeutic approaches to manage COVID-19 in the Severe stage in healthcare facilities.

ESI(-)FT-ICR MS for the determination of best conditions for producing extract abundant in phenolic compounds from leaves of E. uniflora and FTIR-PCA as a sample screening method.

E. uniflora leaves are a rich source of phenolic compounds with biological activities, including myricitrin. In this study, the chemical profile of nine extracts prepared with leaves collected in three regions (mountain, beach, and mangrove) and at three different times of the day (8 am, 1 pm, and 6 pm) was evaluated from spectra originating from ultra-high resolution mass spectrometry (Fourier transform ion cyclotron resonance, FT-ICR) coupled to electrospray ionisation (ESI). The best time of the day and location for collecting the leaves of E. uniflora used as raw materials for producing extracts and the best ethanol concentration for obtaining an extract more abundant in compounds of interest were verified. Several flavonoids and phenolic acids were detected in their deprotonated form in the regions from m/z 200 to 1200. Myricitrin ([C21H20O12-H]-, m/ztheo 463.08820), its chloride adduct ([C21H20O12+Cl]-, m/ztheo 499.06488), other myricitrin derivatives, and some tannins were the main compounds detected. Considering obtaining an extract rich in phenolic compounds, including myricitrin, the best place and time of the day to collect E. uniflora leaves is in the beach region at 1 pm. In contrast, the best ethanol concentration for extract production is 70 wt%. Therefore, extraction at 96 wt% ethanol is better for obtaining an extract more abundant in phenolic acids, although 70 wt% ethanol also extracted these compounds. FTIR-PCA models were used to check for possible similarities in the data according to collection time of the day and location. These models demonstrated an excellent solution for sample screening.

Infrared spectroscopy for fast screening of diabetes and periodontitis.

SIGNIFICANCE: FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool. AIM: Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP). APPROACH: Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm-1, followed by data preprocessing and analysis using machine learning tools. RESULTS: Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased. CONCLUSION: The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

Effect of Organic Solvents on the Activity, Stability and Secondary Structure of asclepain cI, Using FTIR and Molecular Dynamics Simulations.

The present study aims at understanding the effect of organic solvents on the specific proteolytic activity and operational stability of asclepain cI in aqueous-organic media, using correlations between geometrical and structural parameters of asclepain cI. These correlations were determined by molecular dynamics (MD) simulations and the secondary structure of the enzyme validated by Fourier-transform Infrared (FTIR) spectroscopy. Asclepain cI exhibited significantly higher catalytic potential in 29 of the 42 aqueous-organic media tested, composed by 0.1 mM TRIS hydrochloride buffer pH 8 (TCB) and an organic solvent, than in buffer alone. Asclepain cI in water-organic miscible systems showed high FTIR spectral similarity with that obtained in TCB, while in immiscible systems the enzyme acquired different secondary structures than in buffer. Among the conditions studied, asclepain cI showed the highest catalytic potential in 50% v/v ethyl acetate in TCB. According to MD simulations, that medium elicited solvation and flexibility changes around the active center of asclepain cI and conducted to a new secondary structure with the active center preserved. These results provide valuable insights into the elucidation of the molecular mechanism of asclepain cI tolerance to organic solvents and pave the way for its future application for the synthesis of peptides in aqueous-organic media.

Recognition of breast cancer subtypes using FTIR hyperspectral data.

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics.

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Bone samples' behavior in sunlight, IR light, and temperature increase with FEM simulation.

Biological and environmental factors produce biochemical processes that modify the bone structure. A few studies have attempted to show the adverse biological effects of sun radiation. The bone tissue exposures to infrared and sunlight radiation are analyzed by using focused sound, characterization spectroscopy techniques, and image processing. The study is complemented with a finite element method simulation on temperature behaviors. The crystal morphology on the bone hydroxyapatite and functional groups was characterized by X-ray diffraction and infrared spectroscopy. The infrared spectra confirmed the hydroxyl group of bovine hydroxyapatite, amines, and lipids are also correlated with modifications of the hydroxyapatite. The diffractograms showed the characteristic peaks of hydroxyapatite, with the main intensity at 2θ = 32.02°. Bone samples exposed to sun radiation presented a peak at 2θ = 27.5°, evidencing the possible formation of ß-TCP y α-TCP. The analysis with the spectroscopy techniques about the structural changes in the samples suggests interpreting an increase of sound obtained by expanding the exposure time. It is possible to verify that there are some structural changes in the bone samples due to exposure to non-ionizing radiation. These results show an increase in the registered intensity sound correlated with the interpretation of the structural changes of bone. Thanks to the different novel analysis techniques established in the present study, it could establish the changes that experienced the bone structure under different sources of radiation, which will help to better detect scenarios of bone deficiency.

Development and evaluation of the in vitro schistosomicidal activity of solid dispersions based on 2-(-5-bromo-1-H-indole-3-yl-methylene)-N-(naphthalene-1-ylhydrazine-carbothiamide.

Among all the neglected diseases, schistosomiasis is considered the second most important parasitic infection after malaria. Praziquantel is the most widely used drug for this disease, but its exclusive use may result in the development of drug-resistant schistosomiasis. To increase the control of the disease, new drugs have been developed as alternative treatments, among them 2-(-5-bromo-1-h-indole-3-yl-methylene)-N-(naphthalene-1-ylhydrazine-carbothiamide (LQIT/LT-50), which showed promising schistosomicidal activity in nonclinical studies. However, LQIT/LT-50 presents low solubility in water, resulting in reduced bioavailability. To overcome this solubility problem, the present study aimed to develop LQIT/LT-50 solid dispersions for the treatment of schistosomiasis. Solid dispersions were prepared through the solvent method using Soluplus©, polyethylene glycol (PEG) or polyvinylpyrrolidone (PVP K-30) as hydrophilic carriers. The formulations with the best results in the compatibility tests, aqueous solubility and preliminary stability studies have undergone solubility tests and physicochemical characterizations by Fourier-transform infrared spectroscopy (FTIR), x-ray diffractometry (XRD), exploratory differential calorimetry (DSC), thermogravimetry (TG) and Raman spectroscopy. Finally, the schistosomicidal activity was evaluated in vitro. The phycochemical analyzes showed that when using PVP K-30, there was an interaction between the PVP K-30 and LQIT/LT-50, proving the successful development of the solid dispersion. Furthermore, an increase in the solubility of the new system was observed (LQIT/LT-50:PVP K-30) in addition to the improvement in the in vitro shistosomidal activity at 1:4 (w/w) molar ratio (i.e., 20% drug loading) when compared to LQIT/LT-50 alone. The development of the LQIT/LT-50:PVP K-30 1:4 solid dispersion is encouraging for the future development of new pharmaceutical solid formulations, aiming the schistosomicidal treatment.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value.

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 µg/mL and >0.5 µg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Lignin Analysis by HPLC and FTIR: Spectra Deconvolution and S/G Ratio Determination.

Fourier transform infrared spectroscopy (FTIR) is a simple nondestructive technique that allows the user to obtain quick and accurate information about the structure of the constituents of wood. Spectra deconvolution is a computational technique, complementary to FTIR analysis, which improves the resolution of overlapped or unobserved bands in the raw spectra. High performance liquid chromatography (HPLC) is an analytical technique useful to determine the ratio of the lignin monomers obtained by the alkaline nitrobenzene oxidation method. Furthermore, lignin content has been commonly determined by wet chemical methods; Klason lignin determination is a quick and accessible method. Here, we detail the procedures for chemical analysis of the wood lignin using these techniques. Additionally, the deconvolution process of FTIR spectra for the determination of the S/G ratio, in lignin isolated by this or other methods, is explained in detail.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil.

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.