The effect of naringenin-phospholipid complex on thermal oxidative stability of soybean oil under heating condition

Naringenin (NGE), a typical flavanone abundant in citrus fruits, exhibits remarkable antioxidant activities. However, its low solubility in oil restricts its widespread use in inhibiting lipid oxidation. In this study, we present a novel and effective approach to address this limitation by developing a naringenin-phospholipid complex (NGE-PC COM). Comprehensive analytical techniques including Fourier transform infrared (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) were employed to confirm the formation of the NGE-PC COM and elucidate the interaction mechanism between NGE and phospholipids molecules. Notably, the oil-solubility of NGE was significantly enhanced by approximately 2700-fold when formulated as a phospholipid complex in soybean oil. The improved oil-solubility of NGE-PC COM enabled effective inhibition of oil thermal oxidation under high temperature conditions. Generally, this investigation proposed a novel and promising strategy for employing flavanones with strong antioxidant activities to enhance the thermal oxidative stability of edible oil during heating processes.

Application of UV-Vis-NIR and FTIR spectroscopy coupled with chemometrics for quality prediction of katsuobushi based on the number of smoking treatments

Katsuobushi, a smoked, dried skipjack tuna, is a traditional Japanese food additive with a unique flavor and taste. Gas chromatography mass spectrometry (GC-MS), fourier transform infrared (FTIR), and ultraviolet-visible-near infrared spectroscopy (UV-Vis-NIR) combined with chemometric methods were evaluated the quality of katsuobushi according to the number of smoking treatments. Using GC-MS, 46 metabolites were identified and five metabolites were selected as key compounds. All samples were classified according to their smoking number via principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and hierarchical cluster analysis (HCA) of the FTIR and NIR spectra. Partial least squares regression (PLSR) analysis revealed that the FTIR and NIR spectra were highly correlated with the metabolites by GC-MS. These results demonstrated the potential of using the FTIR and NIR spectroscopy combined with chemometrics to assess the quality of katsuobushi based on the smoking treatments, with NIR spectroscopy showed particularly promising.

Study of the molecular structure of proteins in fermented Maize-Soybean meal-based rations based on FTIR spectroscopy

This research investigates the dynamic alterations that occur in protein molecular structure during the fermentation process of feed. Fourier transform infrared spectroscopy (FTIR), coupled with deconvolution, second derivative and curve-fitting methodologies, was employed to comparatively analyse the protein molecular structures in fermented feed. At the 48-h fermentation mark, the α-helix and β-sheet contents reached their peaks, while the random coil and β-turn contents were at their lowest. Simultaneously, the β-sheet/α-helix ratio was minimized. FTIR spectroscopy emerged as a comprehensive tool, revealing the nuanced changes in molecular structure throughout the fermentation process of corn-soybean meal feed. When integrated with spectral quantitative analysis, it provides a novel perspective for evaluating the nutritional value of fermented feed.

Human erythrocytes under stress. Spectroscopic fingerprints of known oxidative mechanisms and beyond

In this work, we investigated the oxidative stress-related biochemical alterations in red blood cells (RBCs) and their membranes with the use of spectroscopic techniques. We aimed to show their great advantage for the in situ detection of lipid classes and secondary structures of proteins without the need for their extraction in the cellular environment. The exposition of the cells to peroxides, t-butyl hydroperoxide (tBOOH) or hydrogen peroxide (H2O2) led to different degradation processes encompassing the changes in the composition of membranes and structural modifications of hemoglobin (Hb). Our results indicated that tBOOH is generally a stronger oxidizing agent than H2O2 and this observation was congruent with the activity of superoxide and glutathione peroxidase. ATR-FTIR and Raman spectroscopies of membranes revealed that tBOOH caused primarily the partial loss and peroxidation of the lipids resulting in loss of the integrity of membranes. In turn, both peroxides induced several kinds of damage in the protein layer, including the partial decrease of their content and irreversible aggregation of spectrin, ankyrin, and membrane-bound globin. These changes were especially pronounced on the membrane surface where stress conditions induced the formation of β-sheets and intramolecular aggregates, particularly for tBOOH. Interestingly, nano-FTIR spectroscopy revealed the lipid peroxidative damage on the membrane surface in both cases. As far as hemoglobin was concerned, tBOOH and H2O2 caused the increase of the oxyhemoglobin species and conformational alterations of its polypeptide chain into β-sheets. Our findings confirm that applied spectroscopies effectively track the oxidative changes occurring in the structural components of red blood cells and the simplicity of conducting measurements and sample preparation can be readily applied to pharmacological and clinical studies.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

Distinction and quantification of Panax polysaccharide extracts via attenuated total reflectance-Fourier transform infrared spectroscopy with first-order derivative processing

Derivative spectroscopy is used to separate the small absorption peaks superimposed on the main absorption band, which is widely adopted in modern spectral analysis to increase both the valid spectral information and the identification accuracy. In this study, a method based on attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with first-order derivative (FD) processing combined with chemometrics is proposed for rapid qualitative and quantitative analysis of Panax ginseng polysaccharides (PGP), Panax notoginseng polysaccharides (PNP), and Panax quinquefolius polysaccharides (PQP). First, ATR-FTIR with FD processing was used to establish the discriminant model combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA). After that, two-dimensional ATR-FTIR based on single-characteristic temperature as external interference (2D-sATR-FTIR) was established using ATR-FTIR with FD processing. Then, ATR-FTIR with FD processing was combined with PLS to establish and optimize the quantitative regression model. Finally, the established discriminant model and 2D-sATR-FTIR successfully distinguished PGP, PNP and PQP, and the optimal PLS regression model had a good prediction ability for the Panax polysaccharide extracts content. This strategy provides an efficient, economical and nondestructive method for the distinction and quantification of PGP, PNP and PQP in a short detection time.

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

Evaluation and optimization of FTIR spectroscopy to quantify PHA production by municipal wastewater sludge

Polyhydroxyalkanoate (PHA) is a family of naturally-occurring biopolymers synthesized by more than 300 microorganisms in the environment. These biopolymers have been investigated as a source material to substitute fossil fuel-based polymers; hence the synthesis of biopolymers and their characterization is a critical step in optimizing the process. Because of this, the biological production of PHA using PHA-producing microorganisms is currently the dominating process; however, the use of microbial mixed culture (MMC), such as wastewater sludge, is gaining attention. Different than pure cultures, MMC has higher culturing condition tolerance since the complex species composition and is easily obtained from wastewater treatment plants, which shortens the culturing time, lowers the cost, and promotes the application. The main constraint in MMC-based PHA is the extraction and quantification of PHA from the more complex matrix. In this paper, Fourier-transform infrared (FTIR) spectroscopy is evaluated to be used as a quantification method of PHA in MMC systems. Firstly, commercially available analytical standards, which consist of PHA/PHB, and two different solvents (chloroform and dichloromethane), were used and tested by this method, with KBr card and liquid cell methods, and the results are validated by gas chromatography mass spectrometry (GC/MS). The method was then tested using 12 samples from wastewater treatment plants. The PHA content in biomass varied from 3.42 w/w% to 1.22 w/w% following extraction with chloroform as solvent as determined by this method. In the four different combination standards, the best one is consisted of PHB and chloroform, and FTIR-liquid cell showed higher promise for PHA quantification in complex matrices.

Sex and blood group determination from hair using ATR-FTIR spectroscopy and chemometrics

Examination of hair with its intact root is commonly used for DNA profiling of the donor. However, its use for gathering other types of information is less explored. Using attenuated total reflectance-Fourier transform infrared spectroscopy, the present study aims to explore other relevant aspects in a non-destructive manner for forensics. Determining the sex and blood group of human hair samples were the major goals of the study. Sex determination was accomplished by analyzing the differential vibrational intensities and stretching of various chemical groups associated with hair and its proteins. Statistical inference of spectral data was performed using chemometric algorithms such as PCA and PLS-DA. The PLS-DA model determined sex with 100% accuracy and blood grouping with an average accuracy of 95%. The present study is the first of its kind to determine sex and blood grouping from human scalp hair shafts, as far as the author knows. By acting as a preliminary screening test, this study could have significant implications for forensic analysis of crime scene samples. Human and synthetic hair were used in validation studies, resulting in 100% accuracy, specificity, and sensitivity, with 0% false positives and false negatives. The technique ATR FTIR spectroscopy could complement the currently used methods of hair analysis such as physical examination and mitochondrial or genomic DNA analysis.

Identifying hypothermia death in a mouse model by ATR-FTIR

The identification of hypothermia death (HD) is difficult for cadavers, especially the distinction from death due to alternative causes. A large number of studies have shown that brown adipose tissue (BAT) plays critical roles in thermoregulation of mammals. In this study, BAT of mice was used for the discrimination of HD using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). A modified mouse HD model conducted by Feeney DM was used in this study to obtain infrared spectra of BAT. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to establish discrimination models. The PLS-DA and OPLS-DA models exhibit prominent discriminative efficiency, and the accuracy of HD identification using fingerprint regions and ratios of absorption intensity is near 100% in both the calibration and validation sets. Our preliminary study suggests that BAT may be an extremely effective target tissue for identification of cadavers of HD, and ATR-FTIR spectra combined with chemometrics have also shown potential for cadaver identification in forensic practice in a fast and accurate manner.

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning

OBJECTIVE

The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

METHODS

ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.

RESULTS

A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.

CONCLUSION

Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Quantifying flare combustion efficiency using an imaging Fourier transform spectrometer

Mid-wavelength infrared (MWIR) imaging Fourier transform spectrometers (IFTSs) are a promising technology for measuring flare combustion efficiency (CE) and destruction removal efficiency (DRE). These devices generate spectrally resolved intensity images of the flare plume, which may then be used to infer column densities of relevant species along each pixel line-of-sight. In parallel, a 2D projected velocity field may be inferred from the apparent motion of flow features between successive images. Finally, the column densities and velocity field are combined to estimate the mass flow rates for the species needed to calculate the CE or DRE. Since the MWIR IFTS can measure key carbon-containing species in the flare plume, it is possible to measure CE without knowing the fuel flow rate, which is important for fenceline measurements. This work demonstrates this approach on a laboratory heated vent, and then deploys the technique on two working flares: a combustor burning natural gas at a known rate, and a steam-assisted flare at a petrochemical refinery. Analysis of the IFTS data highlights the potential of this approach, but also areas for future development to transform this approach into a reliable technique for quantifying flare emissions.Implications: Our research is motivated by the need to assess hydrocarbon emissions from flaring, which is a critical problem of global significance. For example, recent studies have shown that methane destruction efficiency of flaring from upstream oil may be significantly lower than the commonly assumed figure of 98%; work by Plant et al. , in particular, suggest that this discrepancy amounts to CO2 emissions from 2 to 8 million automobiles annually, considering the US alone. Similarly, the international energy agency (IEA) estimates a global flare efficiency of 92%, which translates in 8 million tons of CH4 emitted by flares in 2020. Highlighted by these studies and supported by the World Bank initiatives toward zero routine flaring emissions, there is an urgent need for oil and gas industry to assess their flare methane emission, and overall hydrocarbon emissions. At the very least, it is critical to identify problematic flare operating conditions and means to mitigate flare emissions. Focusing on remote quantification of plume species, the measurement technique and quantification method presented in this paper is a considerable step forward in that direction by computing combustion efficiency and key components for destruction efficiency.

Fabrication and In-Vivo Evaluation of Polyvinyl pyrrolidone/Poloxamer 188 Hybrid Nanofibers of Deflazacort

The superior flexibility, efficient drug loading, high surface-to-volume ratio, ease of formulation, and cost-controlled production are considered exceptional advantages of nanofibers (NFs) as a smart delivery system. Deflazacort (DEF) is an anti-inflammatory and immunosuppressant agent. It is categorized as a poorly soluble class II drug. In this study, DEF-loaded polymeric nanofibrous using the electrospinning technique mats, Polyvinyl pyrrolidone (PVP) with or without Poloxamer 188 (PX) were used as mat-forming polymers. Microscopical imaging, drug content (%), and in vitro dissolution studies were conducted for all NFs formulae (F1-F7). All NFs improved the DEF dissolution compared to the unprocessed form, with the superiority of the PVP/PX hybrid. The optimized formula (F7) exhibited an average diameter of 655.46 ± 90.4 nm and % drug content of 84.33 ± 5.58. The dissolution parameters of DEF loaded in PVP/PX NFs (F7) reflected a release of 95.3 % ± 3.1 and 102.6 % ± 1.7 after 5 and 60 min, respectively. NFs (F7) was investigated for drug-polymer compatibility using Fourier-Transform Infrared Spectroscopy (FTIR), Powder X-ray diffraction analysis (PXRD), and Differential Scanning Calorimetry (DSC). In vivo anti-inflammatory study employing male Sprague-Dawley rats showed a significant reduction of rat paw edema for F7 (p < 0.05) compared with unprocessed DEF with a normal epidermal and dermal skin structure comparable to the healthy negative control. Immunohistochemical and morphometric data displayed similarities between the immune reaction of F7 and the negative healthy control. The finding of this work emphasized that DEF loaded in PVP/PX NFs could be considered a useful strategy for enhancing the therapeutic performance of DEF.

Characterization of skin barrier defects using infrared spectroscopy in patients with atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) is characterized by skin barrier defects that are often measured by biophysical tools that observe the functional properties of the stratum corneum (SC).

OBJECTIVES

To employ in vivo infrared spectroscopy alongside biophysical measurements to analyse changes in the chemical composition of the SC in relation to AD severity.

METHODS

We conducted an observational cross-sectional cohort study where attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy measurements were collected on the forearm alongside surface pH, capacitance, erythema and transepidermal water loss (TEWL), combined with tape stripping, in a cohort of 75 participants (55 patients with AD stratified by phenotypic severity and 20 healthy controls). Common FLG variant alleles were genotyped.

RESULTS

Reduced hydration, elevated TEWL and redness were all associated with greater AD severity. Spectral analysis showed a reduction in 1465 cm-1 (full width half maximum) and 1340 cm-1 peak areas, indicative of less orthorhombic lipid ordering and reduced carboxylate functional groups, which correlated with clinical severity (lipid structure r = -0.59, carboxylate peak area r = -0.50).

CONCLUSIONS

ATR-FTIR spectroscopy is a suitable tool for the characterization of structural skin barrier defects in AD and has potential as a clinical tool for directing individual treatment based on chemical structural deficiencies.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

FTIR microspectroscopic study of gastric cancer AGS cells apoptosis induced by As2O3

As2O3 has shown significant anti-gastric cancer effects, but the mechanism is still unclear. Thus, biomacromolecular changes induced by As2O3 were investigated by using human gastric cancer AGS cells as the model. Flow cytometry results confirmed that As2O3 induced AGS cells apoptosis. Fourier transform infrared (FTIR) microspectroscopy detected biomacromolecular changes during As2O3-induced AGS cells apoptosis sensitively: IR spectra showed significant changes in the lipids content and the proteins and DNA structure. Peak-area ratios indicated obvious changes in the lipids and DNA content and the proteins structure, while also showing a relatively good linear relationship between A1733/A969 and the apoptosis rate. PCA exhibited significant alteration in nucleic acids while curve fitting further revealed the changes in nucleic acids and proteins. On the whole, our study explored As2O3-induced gastric cancer cells apoptosis in depth on the basis of analyzing biomacromolecular changes, in addition, it also suggested FTIR microspectroscopy to be possibly useful in the research of apoptosis.

Quantitative determination of ademetionine in tablets utilizing ATR-FTIR and partial least squares methods approaches

In this study there were utilized a combination of Fourier transform infrared spectroscopy in attenuated total reflection mode (ATR-FTIR) and partial least squares (PLS) regression method to develop quantitative models for determining the concentration of ademetionine in commercial tablets. The established and validated models were specifically designed for a commercial product containing ademetionine 1,4-butandiesulfonate. The coefficient of determination for the developed model was 0.999. Relative standard deviation (RSD) does not exceed 1.6% for repeatability and intermediate accuracy, which meets the international ICH and AOAC requirements for the method performance. The validation results effectively confirmed that this method is suitable and meets the current requirements for analytical methods in drug quality control. Consequently, this approach can be used for routine ademetionine analysis in pharmaceutical products and has the potential to be applied to other active pharmaceutical ingredients (APIs) in drug quality control.

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.

Identification and determination of different processed products and their extracts of Crataegi Fructus by infrared spectroscopy combined with two-dimensional correlation analysis

The fruit of Crataegus sp. is known as "Shanzha (SZ)" in China and is widely used in the food, beverage, and traditional Chinese medicine (TCM) industries. SZ usually requires thermal processing to reduce the irritation of its acidity to the gastric mucosa. Different processed products of SZ resulting from thermal processing have different or even opposite functions in clinical applications. In addition, 5-hydroxymethylfurfural (5-HMF) intermediates produced during thermal processing are carcinogenic to humans. Therefore, the aim of this study was to explore a rapid and accurate method by Fourier transform infrared spectroscopy (FT-IR) for the identification of different processed products and the determination of 5-HMF in extracts. In qualitative identification, a three-stage infrared spectroscopy identification method (raw spectra, the second derivative spectra, and two-dimensional correlation (2DCOS) spectra) was developed to distinguish different processed products of SZ step by step. In quantitative determination, partial least squares regression combined with different variable selection methods, especially the 2DCOS method, was applied to determine the 5-HMF content. The results show that temperature-induced 2DCOS synchronous spectra can effectively identify different processed products of SZ by shape, intensity, and position of auto-peaks or cross-peaks, and the variables selected by power spectra from concentration-induced 2DCOS synchronous spectra have better prediction ability for 5-HMF compared to full variables. The above results demonstrate that 2D-COS analysis is a potential tool in qualitative and quantitative analysis, which can improve sample identification accuracy and determination capabilities. This study not only establishes a rapid and accurate method for the identification of different processed products but also provides a practical reference for food safety and the efficient use of TCM.

Structural characterization of DNA amplicons by ATR-FTIR spectroscopy as a guide for screening metainflammatory disorders in blood plasma

Attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy is a promising rapid, reagent-free, and low-cost technique considered for clinical translation. It allows to characterize biofluids proteome, lipidome, and metabolome at once. Metainflammatory disorders share a constellation of chronic systemic inflammation, oxidative stress, aberrant adipogenesis, and hypoxia, that significantly increased cardiovascular and cancer risk. As a result, these patients have elevated concentration of cfDNA in the bloodstream. Considering this, DNA amplicons were analyzed by ATR-FTIR at 3 concentrations with 1:100 dilution: (IU/mL): 718, 7.18, and 0.0718. The generated IR spectrum was used as a guide for variable selection. The main peaks in the biofingerprint (1800-900 cm-1) give important information about the base, base-sugar, phosphate, and sugar-phosphate transitions of DNA. To validate our method of selecting variables in blood plasma, 38 control subjects and 12 with metabolic syndrome were used. Using the wavenumbers of the peaks in the biofingerprint of the DNA amplicons, was generated a discriminant analysis model with Mahalanobis distance in blood plasma, and 100 % discrimination accuracy was obtained. In addition, the interval 1475-1188 cm-1 showed the greatest sensitivity to variation in the concentration of DNA amplicons, so curve fitting with Gaussian funcion was performed, obtaining adjusted-R2 of 0.993. PCA with Mahalanobis distance in the interval 1475-1188 cm-1 obtained an accuracy of 96 % and PLS-DA modeling in the interval 1475-1088 cm-1 obtained AUC = 0.991 with sensitivity of 95 % and specificity of 100 %. Therefore, ATR-FTIR spectroscopy with variable selection guided by DNA IR peaks is a promising and efficient method to be applied in metainflammatory disorders.

ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy

Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples. In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model's performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R2 of 0.933, an RMSE of 0.02, and an RPD of 3.856. These results support the accuracy and applicability of this method for rapidly predicting antioxidant activity in gentian root samples.

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.

Stone mining work and dust pollution in Birbhum district, West Bengal, India

Dust pollution is common in Indian roads and several industrial settings (including mines) that affects human health. Identification and characterization of the dust particles in the mining area is essential for knowing the properties of the dust that effectively causes ailments to humans, particularly among workers those who are working in unorganized industrial settings. The present study aimed to determine the level of dust pollution and to know the size and characterize the dust particles in the Pachami-Hatgacha stone mine areas of Birbhum district, West Bengal, India. Dust samples were collected and analysed for Dynamic Light Scattering (DLS) to determine the size and shape of the particles, Fourier Transform Infrared Spectroscopy (FT-IR) to determine the free silica content, and X-ray Florence (XRF) analysis for quantitative estimation of components in the sample. All the analyses were done following standard instrumentation and techniques. The size of the dust particles was much less (ranges 101-298 nm) than the size of respirable particles (2500 nm). Those were mostly generated as well as precipitated during peak working hours of the day. Presence of considerable amounts of silica was confirmed by the FT-IR (strong and broad band at 1000 cm-1) and XRF analysis (76.85% SiO2). Exposure to these dust particles may cause severe health impairments. Therefore, interventions like wet drilling and blasting, sprinkling of water during peak working hours, and awareness of use of personal protective devices among workers are required to reduce the risk and hazards associated with dust pollution to the health of miners and inhabitants around the mines.

Co-Processed Crystalline Solids of Ivermectin with Span® 60 as Solubility Enhancers of Ivermectin in Natural Oils

Despite being discovered over five decades ago, little is still known about ivermectin. Ivermectin has several physico-chemical properties that can result in it having poor bioavailability. In this study, polymorphic and co-crystal screening was used to see if such solid-state modifications can improve the oil solubility of ivermectin. Span® 60, a lipophilic non-ionic surfactant, was chosen as co-former. The rationale behind attempting to improve oil solubility was to use ivermectin in future topical and transdermal preparations to treat a range of skin conditions like scabies and head lice. Physical mixtures were also prepared in the same molar ratios as the co-crystal candidates, to serve as controls. Solid-state characterization was performed using X-ray powder diffraction (XRPD), Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The FTIR spectra of the co-crystal candidates showed the presence of Span® 60's alkyl chain peaks, which were absent in the spectra of the physical mixtures. Due to the absence of single-crystal X-ray data, co-crystal formation could not be confirmed, and therefore these co-crystal candidates were referred to as co-processed crystalline solids. Following characterization, the solid-state forms, physical mixtures and ivermectin raw material were dissolved in natural penetration enhancers, i.e., avocado oil (AVO) and evening primrose oil (EPO). The co-processed solids showed increased oil solubility by up to 169% compared to ivermectin raw material. The results suggest that co-processing of ivermectin with Span® 60 can be used to increase its oil solubility and can be useful in the development of oil-based drug formulations.

Study of Monoclonal Antibody Aggregation at the Air-Liquid Interface under Flow by ATR-FTIR Spectroscopic Imaging

Throughout bioprocessing, transportation, and storage, therapeutic monoclonal antibodies (mAbs) experience stress conditions that may cause protein unfolding and/or chemical modifications. Such structural changes may lead to the formation of aggregates, which reduce mAb potency and may cause harmful immunogenic responses in patients. Therefore, aggregates need to be detected and removed or ideally prevented from forming. Air-liquid interfaces, which arise during various stages of bioprocessing, are one of the stress factors causing mAb aggregation. In this study, the behavior of an immunoglobulin G (IgG) at the air-liquid interface was investigated under flow using macro attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic imaging. This chemically specific imaging technique allows observation of adsorption of IgG to the air-liquid interface and detection of associated secondary structural changes. Chemical images revealed that IgG rapidly accumulated around an injected air bubble under flow at 45 °C; however, no such increase was observed at 25 °C. Analysis of the second derivative spectra of IgG at the air-liquid interface revealed changes in the protein secondary structure associated with increased intermolecular β-sheet content, indicative of aggregated IgG. The addition of 0.01% w/v polysorbate 80 (PS80) reduced the amount of IgG at the air-liquid interface in a static setup at 30 °C; however, this protective effect was lost at 45 °C. These results suggest that the presence of air-liquid interfaces under flow may be detrimental to mAb stability at elevated temperatures and demonstrate the power of ATR-FTIR spectroscopic imaging for studying the structural integrity of mAbs under bioprocessing conditions.

To what extent does freezing impact the mid-infrared signature of urine? Case of patients attending urology department

Urine is a very interesting and attractive biofluid for biomarker discovery and medical diagnosis research due to its non-invasiveness collection and richness of potential biomarkers. Fourier-Transform Infrared (FTIR) spectroscopy applied on urine samples is a promising tool that could be used as a screening method for various diseases. However, during method development, frozen urine is more accessible, especially for inter-laboratory studies, whereas in routine application fresh urine is more convenient. Here, the objective of our work is to evaluate the freezing impact on mid-infrared signature of urine samples. Therefore, both fresh and frozen urine samples from twenty patients were analysed in a dried form. These samples were collected from patients consulting for cystoscopy examination. Simultaneously, centrifugation was also conducted on 10 of all included patients. Principal component analysis (PCA) revealed that patient inter-variabilities are higher than variability due to the freezing step. Then, Euclidean distance between fresh and frozen urine of each patient highlighted that the impact of freezing is different from one patient to another. Adding the centrifugation step slightly minimized intra-patient variability compared to not centrifugated samples. This study contributes to define experimental conditions for urine analysis development for translational application in biomedical field.

Comparison of ATR-FTIR and NIR spectroscopy for identification of microplastics in biosolids

Biosolids are considered a potentially major input of microplastics (MPs) to agricultural soils. Our study aims to identify the polymeric origin of MPs extracted from biosolid samples by comparing their Attenuated Total Reflection (ATR) - Fourier-transform infrared (FTIR) spectra with the corresponding near-infrared (NIR) spectra. The reflectance spectra were preprocessed by Savitzky-Golay (SG), first derivative (FD) and compared with analogous spectra acquired on a set of fifty-two selected commercial plastic (SCP) materials collected from readily available products. According to the results portrayed in radar chart and built from both ATR-FTIR and NIR spectral datasets, the MPs showed high correlations with polymers such as polyethylene (LDPE, HDPE), polyethylene terephthalate (PET), polystyrene (PS), polypropylene (PP) and polyamide (PA), determined in SCP samples. Each unknown MP sample had on average three or more links to several types of SCP, according to the correlation coefficients for each polymer ranging from 0.7 up to 1. The comparison analysis classified the majority of MPs as composed mainly by LDPE/HDPE, according to the top correlation coefficients (r > 0.90). PP and PET were better identified with NIR than ATR-FTIR. In contrast to ATR-FTIR analysis, NIR was unable to identify PS. Based on these results, the primary sources of MPs in the biosolids could be identified as discarded consumer packaging (containers, bags, bottles) and fibers from laundry, disposable glove, and cleaning cloth. SYNOPSIS: Microplastics (MPs) are considered contaminants of emerging concern. This study compares two simple and fast spectroscopy techniques to identify microplastics in the biosolid matrix.

The impact of photoreceptor layer loss on different ocular tissues: Insights from FTIR spectroscopy

Photoreceptor loss has significant consequences for visual function, and its management is a critical component for treating not only retinal diseases such as age-related macular degeneration and retinitis pigmentosa but also its ocular consequences. On the other hand, Fourier transform infrared spectroscopy is an excellent tool to investigate molecular structure and dynamics of biological samples, and as a non-destructive and label free measurement, it does not perturb the samples. In this study, detailed analyses of the recorded FTIR spectra from cornea, lens and sclera were performed to monitor the distribution of ocular abnormalities due to photoreceptor layer loss after 1, 3 and 6 days. FTIR data were statistically evaluated by multivariate analysis and Bonferroni means comparison. The obtained results revealed that ocular abnormalities associated with photoreceptor layer loss are varied among the investigated tissues, and comprise changes in both hydrogen bond network around proteins and lipid disorder. Structural modifications of protein secondary structure were reported in all investigated tissues. Clinically, the concluded information from FTIR data and its statistical evaluation can contribute to the development of therapeutic strategies for these heterogeneous changes.

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Detection of fragrances on the skin and study of their interaction using infrared and Raman spectroscopy

Nowadays, fragrances belong to the widely used cosmetics. Their composition is designed in a way that it evolves and changes over time. In this work, the effect of fragrances on the skin was studied - the interactions between pig skin samples and fragrances and the possibility of their detection and mutual differentiation. Non-invasive techniques of vibrational spectroscopy were used to obtain the data, namely FT-IR spectroscopy with attenuated total reflection accessory and Raman microspectroscopy. Vibrational spectra were measured within 8 h with different time intervals and after 22 h from the application of fragrance for FT-IR and Raman measurements, respectively. The obtained spectra were pre‑processed and subsequently evaluated by multivariate statistical methods. The study showed that skin treated by fragrances is well distinguishable from untreated skin, even after 22 h. In addition, it is possible to differentiate individual fragrances from each other; therefore, the use of spectroscopical techniques could be a potential tool for forensic analysis of fragrances.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.

Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method

The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Evaluation of physiochemical and biomedical properties of psyllium-poly(vinyl phosphonic acid-co-acrylamide)-cl-N,N-methylene bis acrylamide based hydrogels

Present investigation deals with the synthesis of psyllium based copolymeric hydrogels and evaluation of their physiochemical and biomedical properties. These copolymers have been prepared by grafting of poly(vinyl phosphonic acid) (poly (VPA)) and poly(acrylamide) (poly(AAm)) onto psyllium in the presence of crosslinker N,N-methylene bis acrylamide (NNMBA). These copolymers [psyllium-poly(VPA-co-AAm)-cl-NNMBA] were characterized by field emission-scanning electron micrographs (FE-SEM), electron dispersion X-ray analysis (EDAX), Atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), 13C-nuclear magnetic resonance (NMR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA)- differential thermal analysis (DTG). FESEM, AFM and XRD demonstrated heterogeneous morphology with a rough surface and an amorphous nature. Diffusion of ornidazole occurred with a non-Fickian diffusion mechanism, and the release profile data was fitted in the Korsemeyer-Peppas kinetic model. Biochemical analysis of hydrogel properties confirmed the blood-compatible nature during blood-polymer interactions and revealed haemolysis value 3.95 ± 0.05 %. The hydrogels exhibited mucoadhesive character during biomembrane-polymer interactions and demonstrated detachment force = 99.0 ± 0.016 mN. During 2,2-diphenyl-1-picrylhydrazyl reagent (DPPH) assay, free radical scavenging was observed 37.83 ± 3.64 % which illustrated antioxidant properties of hydrogels. Physiological and biomedical properties revealed that these hydrogels could be explored for drug delivery uses.

Cookie composition analysis by Fourier transform near infrared spectroscopy coupled to chemometric analysis

The consumption ofcookies is ever growing and during the COVID-19 pandemic reached record consumption values and it is imperative to guarantee the quality and safety of the products.Fourier transform near infrared (FT-NIR) spectroscopy, combined with chemometric techniques, provides a promising solution in that regard, due to its speed and simple sample preparation. The objective of this study was to investigate the possibilities of using FT-NIR to predict lipids, carbohydrates, fibers, proteins, salt and energy contents, as well as to identify cookies type and main cereals present in a batch of 120 commercially acquired samples. The prediction models were performed using ordinary least squares (OLS), partial least squares (PLS), and PLS based classification models including discriminant analysis (PLS-DA), k-nearest neighbors (PLS-kNN) and naïve Bayes (PLS-NB). The best prediction models allowed for good accuracies, with correlation coefficients higher than 0.9 for all studied nutritional parameters. PLS-kNN methodology was able to identify all 5 main cereals (wheat, integral wheat, oat, corn and rice) as well as the 14 types of cookies based on the nutritional contents. The developed methods were able to accurately identify the cookies type and composition, confirming the proposed methodology as a fast, reliable, environmentally friendly and non-destructive alternative to standard analytical methods.

Nanosecond time-resolved infrared spectroscopy for the study of electron transfer in photosystem I

Microsecond time-resolved step-scan FTIR difference spectroscopy was used to study photosystem I (PSI) from Thermosynechococcus vestitus BP-1 (T. vestitus, formerly known as T. elongatus) at 77 K. In addition, photoaccumulated (P700+-P700) FTIR difference spectra were obtained at both 77 and 293 K. The FTIR difference spectra are presented here for the first time. To extend upon these FTIR studies nanosecond time-resolved infrared difference spectroscopy was also used to study PSI from T. vestitus at 296 K. Nanosecond infrared spectroscopy has never been used to study PSI samples at physiological temperatures, and here it is shown that such an approach has great value as it allows a direct probe of electron transfer down both branches in PSI. In PSI at 296 K, the infrared flash-induced absorption changes indicate electron transfer down the B- and A-branches is characterized by time constants of 33 and 364 ns, respectively, in good agreement with visible spectroscopy studies. These time constants are associated with forward electron transfer from A1- to FX on the B- and A-branches, respectively. At several infrared wavelengths flash-induced absorption changes at 296 K recover in tens to hundreds of milliseconds. The dominant decay phase is characterized by a lifetime of 128 ms. These millisecond changes are assigned to radical pair recombination reactions, with the changes being associated primarily with P700+ rereduction. This conclusion follows from the observation that the millisecond infrared spectrum is very similar to the photoaccumulated (P700+-P700) FTIR difference spectrum.

Preparation and formulation of progesterone para-aminobenzoic acid co-crystals with improved dissolution and stability

The crystal structure of a new Progesterone (PROG) co-crystal with para-aminobenzoic acid (PABA) showing enhanced solution properties is reported. PROG-PABA co-crystal was first identified though an in silico coformer screening process using the CSD Co-crystal deign function, then confirmed through a solution evaporation crystallisation experiment. The resulting co-crystal was characterized using single crystal X-ray diffraction, differential scanning calorimetry and Fourier-transform infrared spectroscopy. Liquid assisted grinding was selected as a suitable scale up method compared to spray drying and antisolvent methods due to minimal starting material phases in the final product. Following scale up, aqueous solubility, stability and dissolution measurements were carried out. PROG-PABA showed increased distinct aqueous solubility and dissolution compared to PROG starting material and was shown to be stable at 75 % relative humidity for 3 months. Tablets containing co-crystal were produced then compared to the Utrogestan® soft gel capsule formulation through a dissolution experiment. PROG-PABA tablets showed a substantial increase in dissolution over the course of the experiment with over 30× the amount of PROG dissolved at the 3-hour time point. This co-crystal shows positive implications for developing an improved oral PROG formulation.

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.

Formulation and characterization of glipizide solid dosage form with enhanced solubility

Glipizide, a poor water-soluble drug belongs to BCS class II. The proposed work aimed to enhance the solubility of glipizide by preparing solid dispersions, using polyvinyl pyrrolidone (PVP) and polyethylene glycol (PEG). Solvent evaporation method was used for the preparation of glipizide solid dispersions. Solid dispersions were prepared in four different drug-to-polymer ratios i.e. 1:1, 1:2, 1:3 and 1:4. Mainly effect of three polymers (PVP K30, PVP K90 and PEG 6000) was evaluated on the solubility and dissolution of glipizide. The in-vitro dissolution of all prepared formulations was performed under pH 6.8 at 37°C using USP type II apparatus. In-vitro dissolution results revealed that the formulations having high concentrations of the polymer showed enhanced solubility. Enhancements in the solubility and rate of dissolution of the drug were noted in solid dispersion formulations compared to the physical blends and pure drug. Solid dispersions containing polyvinyl pyrrolidone exhibited a more favorable pattern of drug release compared to the corresponding solid dispersions with PEG. An increase in the maximum solubility of the drug within the solid dispersion systems was observed in all instances. Two solid dispersion formulations were optimized and formulated into immediate-release tablets, which passed all the pharmacopoeial and non-pharmacopoeial tests. Fourier transformed Infrared (FTIR) spectroscopy X-ray diffraction (XRD) and Differential scanning calorimetry (DSC) were used to indicate drug: polymer interactions in solid state. Analysis of the solid dispersion samples through characterization tests indicated the compatibility between the drug and the polymer.

Extrapolation study for determining the time since injury in a rat subcutaneous hematoma model utilizing ATR-FTIR spectroscopy

The determination of the time of an injury has been a major problem in forensic science due to the lack of objective, reliable and portable methods. In this study, a subcutaneous hemorrhage model in rats was established over six days, and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics was used to determine the time since injury. Initial principal component analysis (PCA) showed variance among hematoma sites. Subsequently, spectral data were acquired to establish a dependable partial least square (PLS) regression model with predictive abilities. The root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP) values produced by a genetic algorithm (GA) were 0.64 d (R2 = 0.88) and 0.57 d (R2 = 0.90), respectively. Few variables were involved in the model, and significantly better results were obtained in comparison to the conventional full-spectrum PLS model. In combination with the results of variable importance in projection (VIP) scores, all components, including proteins, nucleic acids and phospholipids, provided inferences regarding the samples at different time points; additionally, amide I and II bands represented the secondary structure of proteins and provided the largest contribution. Based on our preliminary study, the combination of swift and nondamaging ATR-FTIR spectroscopy with chemometrics could prove to be an advantageous approach for gauging the age of an injury in the forensic field.

Detection limit of FT-IR-based bacterial typing based on optimized sample preparation and typing model

Accurate and efficient bacterial typing methods are crucial to microbiology. Fourier transform infrared (FT-IR) spectroscopy enables highly distinguishable fingerprint identification of closely related bacterial strains by producing highly specific fingerprints of bacteria, which is increasingly being considered as an alternative to genotypic methods, such as pulsed field gel electrophoresis (PFGE) and whole genome sequencing (WGS), for bacterial typing. Compared with genotypic methods, FT-IR has significant advantages of convenient operation, fast speed, and low cost. Fundamental research into the detection limit based on optimized analytical conditions for FT-IR bacterial typing, which can avoid excessive bacterial culture time or sampling volume, is particularly important, especially in clinical practice. However, the corresponding parameters have not been fully investigated. In this study, we developed a simplified and reliable procedure for sample preparation, optimized the data analysis procedure, and evaluated the FT-IR detection limit based on the above conditions. In particular, we combined the film mold and calcium fluoride plate for sample preparation. We evaluated the detection limit (about 108 CFU/mL) after parameter optimization using hierarchical cluster analysis (HCA) and artificial neural network (ANN). The optimization and evaluation of these key fundamentals will better promote future application of FT-IR-based bacterial typing.

Membrane-specific and calcium-dependent binding of the Arabidopsis C2 domain protein CaLB revealed by ATR-FTIR spectroscopy

C2 domain-containing proteins bind to cellular membranes and mediate diverse cellular processes. Although many of these membrane-interacting proteins have been identified, the molecular mechanisms of protein-membrane interactions and conformational dynamics are often poorly understood and remain to be investigated with appropriate methods. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and biomimetic membrane systems to analyse CalB, a yet uncharacterized Arabidopsis C2 domain protein. We studied membrane binding, lipid specificity and calcium dependency with solid-supported lipid membranes (SSLB) and small unilamellar lipid vesicles (SUVs). Membranes were composed of pure POPC lipids or of POPC/PI(3)P lipid mixtures. A significantly increased protein binding affinity was observed with membranes containing 1% PI(3)P indicating the high binding specificity of CaLB for PI(3)P. Furthermore, membrane binding occurs in a calcium-dependent manner with a higher calcium concentration increasing the binding of CaLB to the POPC/PI(3)P membrane. Secondary structure analysis of IR-spectra reveals that only minor conformational changes take place upon binding with a slight increase in the helical and disordered regions of CaLB.

Sustained release polymer and surfactant based solid dispersion of andrographolide exhibited improved solubility, dissolution, pharmacokinetics, and pharmacological activity

Andrographolide (AD) is a potent natural product with a wide range of pharmacological activities. However, it has low oral bioavailability due to poor solubility and dissolution rate. Solid dispersion (SD) is a promising technique to improve the solubility and dissolution rate of such molecules. In this study, SD formulation of AD was prepared using Kollidon-SR (KSR) and Poloxamer-407 (P-407) as carriers. SD was prepared using the solvent evaporation method and evaluated for the modulation of solubility of AD. The developed SD formulation was characterized using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), and Fourier transform infrared spectroscopy (FTIR). Further, dissolution rate, yield, drug content, stability, flowability, and pharmacokinetic profile of SD were evaluated. The compatibility of SD with the Caco-2 cells and its impact on the P-glycoprotein (P-gp) mediated efflux was also investigated. Furthermore, carrageenan-induced paw edema, and adjuvant-induced arthritic model were used to evaluate the efficacy of SD. The results showed that SD3 (AD + KSR + P-407, 1:6:8) exhibited the highest solubility and dissolution rate, and significantly improved pharmacokinetic profile compared to native AD. SD3 was found to be stable during storage and displayed excellent yield, drug content, and flowability. This formulation was found to be compatible with the Caco-2 cells and retarded the efflux of P-gp substrate. SD3 also demonstrated substantially better efficacy than native AD in terms of paw edema inhibition (carrageenan-induced paw edema, Wistar rats), and overall improvement of disease condition (in terms of paw edema, arthritic score, AST, ALT, cytokines, radiological changes, and histopathology) in arthritic Wistar rats. In conclusion, SD3 exhibited improved solubility, dissolution rate, pharmacokinetic profile, and pharmacological activity than native AD.

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

A novel approach using ATR-FTIR spectroscopy and chemometric analysis to distinguish male and female human hair samples

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

Molecular interactions and binding dynamics of Alpelisib with serum albumins: insights from multi-spectroscopic techniques and molecular docking

Alpelisib (ALP) is a potent anti-cancer drug showing promising activity against advanced breast cancers. Hence, profound understanding of its binding dynamics within the physiological system is vital. Herein, we have investigated interaction of ALP with human serum albumin (HSA) and bovine serum albumin (BSA) using spectroscopic techniques like absorption, fluorescence, time-resolved, synchronous and 3D-fluorescence, FRET, FT-IR, CD, and molecular docking studies. The intrinsic fluorescence of both BSA and HSA quenched significantly by ALP with an appreciable red shift in its emission maxima. Stern-Volmer analysis showed increase in Ksv with temperature indicating involvement of dynamic quenching process. This was further validated by no significant change in absorption spectrum of BSA and HSA (at 280 nm) upon ALP interaction, and by results of fluorescence time-resolved lifetime studies. ALP exhibited moderately strong binding affinity with BSA (of the order 106 M-1) and HSA (of the order 105 M-1), and the major forces accountable for stabilizing the interactions are hydrophobic forces. Competitive drug binding experiments and molecular docking suggested that ALP binds to site I in subdomain IIA of BSA and HSA. The Förster distance r was found to be less than 8 nm and 0.5 Ro < r < 1.5 Ro which suggests possible energy transfer between donors BSA/HSA and acceptor ALP. Synchronous and 3D-fluoresecnce, FT-IR and CD studies indicated that ALP induces conformational changes of BSA and HSA upon interaction.Communicated by Ramaswamy H. Sarma.

Formulation, characterization and and evaluation of aloe-emodin-loaded solid dispersions for dissolution enhancement

OBJECTIVE

To prepare aloe-emodin solid dispersion (AE-SD) and determine the metabolic process of AE and AE-SD in vivo.

METHODS

AE-SD was prepared viasolvent evaporation or solvent melting using PEG-6000 and PVP-K30 as carriers. Thermogravimetric analysis, X-ray diffraction spectroscopy, differential scanning calorimetry, Fourier transform infrared spectroscopy and scanning electron microscopy were used to identify the physical state of AE-SD. Optimal prescriptions were screened viathe dissolution degree determination method. Using Phoenix software, AE suspension and AE-SD were subjected to a pharmacokinetic comparison study analyzing the alteration of behavior in vivo after AE was prepared as a solid dispersion. Acute toxicity was assessed in mice, and the physiological toxicity was used as the determination criterion for toxicity.

RESULTS

AE-SD showed that AE existed in the carrier in an amorphous state. Compared with polyethylene glycol, polyvinylpyrrolidone (PVP) inhibited AE crystallization, causing the drug to transform from a dense crystalline state to an amorphous form and increasing the degree of drug dispersion. Therefore, it was more suitable as a carrier material for AE-SD. The addition of poloxamer (POL) was more beneficial to the stability of solid dispersions and could reduce the amount of PVP. The dissolution test confirmed that the optimal ratio of AE to the composite vector AE-PVP-POL was 1：2：2, and its dissolution effect was also optimal. Based on the pharmacokinetic comparison, the drug absorption was faster and quickly reached the peak of blood drug concentration in AE-SD compared to AE, the Cmax of AE-SD was greater than that of AE, and t1/2 and mean residence time of AE-SD were less than AE. The results showed that the drug metabolism in AE-SD was better, and the residence time was shorter. The toxicology study showed that both AE and AE-SD had no toxicity.

CONCLUSION

This paper established that the solubility of the drug could be increased after preparing a solid dispersion, as demonstrated by in vitro dissolution experiments. In vivo pharmacokinetics studies confirmed that AE-SD could improve the bioavailability of AE in vivo, providing a new concept for the research and development of AE preparations.

A rapid spectroscopic method for the identification of the filamentous fungi isolated from Turkish traditional mold-ripened cheeses

Fourier transform infrared spectroscopy (FTIR) is an alternative microbial identification technique due to its faster analysis times and lower cost compared to molecular methods. In this study, forty-three fungal strains isolated from different Turkish traditional mold-ripened cheeses representing nine different Penicillium species (P. roqueforti, P. corylophilum, P. before, P. crustosum, P. spinulosum, P. rubens, P. brevicompactum, P. paneum, and P. solitum) were analyzed by using FTIR HTS-XT (High Throughput Screening Extension) method in the 4000-400 cm-1 wavenumber range. The spectra of the isolates were evaluated, and the chemical structures corresponding to the fungus-specific spectral regions were determined as fatty acids (3600-2800 cm-1), amide I and amide II of proteins and peptides (1740-1500 cm-1), polysaccharides (1200-900 cm-1) and carbohydrates (900-600 cm-1). The isolates were grouped according to the hierarchical clustering analysis (HCA) by applying chemometrics combined with FTIR spectroscopy. Results showed that FTIR spectroscopy has a high capability for rapid determination of cheese fungi based on their FTIR spectra.