Non-lethal molecular biomonitoring of pyrethroid exposure in freshwater fish using FTIR spectroscopy and machine learning.

This work aims to investigate and differentiate skin cancers as Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) using a combination of Fourier Transform Infrared (FTIR) spectroscopy, Mueller Matrix Polarimetry (MMP), and Support Vector Machine (SVM) classification. Ex vivo characterization was performed on SCC (malignant) and BCC (malignant and more aggressive) tissue samples collected from 100 different patients. Each sample was analyzed using microscopy for morphological insights, FTIR spectroscopy for biochemical composition across the wavenumber range of 400–4000 cm⁻1, and MMP in the visible range (400–800 nm) for polarimetric behavior. A total of 13 polarimetric parameters were extracted and statistically analyzed. Furthermore, a Machine learning SVM model was trained using 1150 observations and 34 predictors to differentiate between the tissue types. FTIR analysis revealed significant spectral differences between SCC and BCC. Notably, SCC exhibited peaks related to Aldehyde (lipids), Amide I (proteins), Z-DNA (nucleic acids), and Glycosylation, whereas BCC showed prominent signatures of Amide B (proteins), CH₂ stretching (lipids), Aldehyde (lipids), Amide I, B-DNA (nucleic acids), and Glycosylation. Polarimetric analysis through MMP demonstrated statistically significant (p < 0.05) differences in all parameters except latitude, orientation, and ellipticity, which were higher in BCC samples. The SVM classifier achieved 90% accuracy, sensitivity, and specificity, demonstrating strong potential for automated cancer classification. The integrated approach of FTIR spectroscopy and polarimetric analysis, coupled with machine learning via SVM, provides a powerful diagnostic tool for distinguishing between SCC and BCC. This novel methodology supports the potential for developing automated, non-invasive, and accurate systems for early cancer diagnosis.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis

Fourier transform infrared (FTIR) spectroscopy is widely used in structural characterization of proteins or peptides. While the method does not have the capability of providing the precise, atomic-resolution molecular structure, it is exquisitely sensitive to conformational changes occurring in proteins upon functional transitions or upon intermolecular interactions. Sensitivity of vibrational frequencies to atomic masses has led to development of "isotope-edited" FTIR spectroscopy, where structural effects in two proteins, one unlabeled and the other labeled with a heavier stable isotope, such as (13)C, are resolved simultaneously based on spectral downshift (separation) of the amide I band of the labeled protein. The same isotope effect is used to identify site-specific conformational changes in proteins by site-directed or segmental isotope labeling. Negligible light scattering in the infrared region provides an opportunity to study intermolecular interactions between large protein complexes, interactions of proteins and peptides with lipid vesicles, or protein-nucleic acid interactions without light scattering problems often encountered in ultraviolet spectroscopy. Attenuated total reflection FTIR (ATR-FTIR) is a surface-sensitive version of infrared spectroscopy that has proved useful in studying membrane proteins and lipids, protein-membrane interactions, mechanisms of interfacial enzymes, and molecular architecture of membrane pore or channel forming proteins and peptides. The purpose of this article was to provide a practical guide to analyze protein structure and protein-membrane interactions by FTIR and ATR-FTIR techniques, including procedures of sample preparation, measurements, and data analysis. Basic background information on FTIR spectroscopy, as well as some relatively new developments in structural and functional characterization of proteins and peptides in lipid membranes, are also presented.

Phospholipid-protein balance in affective disorders: Analysis of human blood serum using Raman and FTIR spectroscopy. A pilot study

Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm−1) and in the high-wavenumber range (2700–3700 cm−1). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

Abstract. Retrieving high-precision concentrations of atmospheric trace gases from FTIR (Fourier transform infrared) spectrometry requires a precise knowledge of the instrumental performance. In this context, this paper examines the impact on the ozone (O3) retrievals of several approaches used to characterize the instrumental line shape (ILS) function of ground-based FTIR spectrometers within NDACC (Network for the Detection of Atmospheric Composition Change). The analysis has been carried out at the subtropical Izaña Observatory (IZO, Spain) by using the 20-year time series of the high-resolution FTIR solar absorption spectra acquired between 1999 and 2018. The theoretical quality assessment and the comparison to independent O3 observations available at IZO (Brewer O3 total columns and electrochemical concentration cell, ECC, sondes) reveal consistent findings. The inclusion of a simultaneous retrieval of the ILS parameters in the O3 retrieval strategy allows, on the one hand, a rough instrumental characterization to be obtained and, on the other hand, the precision of the FTIR O3 products to be slightly improved. The improvement is of special relevance above the lower stratosphere, where the cross-interference between the O3 vertical distribution and the instrumental performance is more significant. However, it has been found that the simultaneous ILS retrieval leads to a misinterpretation of the O3 variations on daily and seasonal scales. Therefore, in order to ensure the independence of the O3 retrievals and the instrumental response, the optimal approach to deal with the FTIR instrumental characterization is found to be the continuous monitoring of the ILS function by means of independent observations, such as gas cell measurements.

Some protein and peptide aggregates, such as those of amyloid-β protein (Aβ), are neurotoxic and have been implicated in several neurodegenerative diseases. Aβ accumulates at nanoclusters enriched in neuronal lipids called gangliosides in the presynaptic neuronal membrane, and the resulting oligomeric and/or fibrous forms accelerate the development of Alzheimer's disease. Although the presence of Aβ deposits at such nanoclusters is known, the mechanism of their assembly and the relationship between Aβ secondary structure and topography are still unclear. Here, we first confirmed by atomic force microscopy that Aβ40 fibrils can be obtained by incubating seed-free Aβ40 monomers with a membrane composed of sphingomyelin, cholesterol, and the ganglioside GM1. Using Fourier transform infrared (FTIR) reflection-absorption spectroscopy, we then found that these lipid-associated fibrils contained parallel β-sheets, whereas self-assembled Aβ40 molecules formed antiparallel β-sheets. We also found that the fibrils obtained at GM1-rich nanoclusters were generated from turn Aβ40 Our findings indicate that Aβ generally self-assembles into antiparallel β-structures but can also form protofibrils with parallel β-sheets by interacting with ganglioside-bound Aβ. We concluded that by promoting the formation of parallel β-sheets, highly ganglioside-enriched nanoclusters help accelerate the elongation of Aβ fibrils. These results advance our understanding of ganglioside-induced Aβ fibril formation in neuronal membranes and may help inform the development of additional therapies for Alzheimer's disease.

Ultraviolet (UV)-visible and Fourier-transformed infrared (FTIR) spectroscopy are two of the most popular and readily available laboratory instruments. Fingerprinting analysis of the UV-visible and FTIR spectra has been applied for food classification and authentication studies. In this study, the UV-visible and FTIR spectra of brewed tea, and their data fusion data sets, were used to build models for the classification of tea based on tea types and origins. The study included black and green tea samples from several provinces in Sumatra and Java Islands (Indonesia). Chemometric models of principal component analysis (PCA), k-nearest neighbor (kNN), and logistic regression were developed for classification purposes. All PCA models were able to well-separate the tea groups. kNN and logistic regression models based on UV-visible spectra successfully classified green and black tea with &gt;0.8 classification accuracy. The kNN model of FTIR spectra had good accuracy (0.903) for classifying tea based on its origin. ReliefF algorithm was employed to select the best features among the data fusion data sets of UV-visible and FTIR spectra. The data fusion data sets of UV-visible and FTIR spectra demonstrated good separation of tea types and origins with a high area under the ROC curve (&gt;0.8) and moderate accuracy (0.548). Therefore, UV-visible and FTIR spectroscopy may provide complementary information for tea classification based on tea types and origins.

Rapid characterization of microbial biodegradation pathways by FT-IR spectroscopy

Analytical applications of Fourier transform-infrared (FT-IR) spectroscopy in microbiology and prion research

The genetic relationship among three Streptocarpus parents and twelve F1 hybrids was assessed using sequence-related amplified polymorphism (SRAP) molecular markers and Fourier-transform infrared (FT-IR) spectroscopy. Both methods were able to discriminate F1 hybrids and parents as revealed by cluster analysis. For hybrid identification, the type III SRAP marker was the most effective due to the presence of male-specific bands in the hybrids. Different behaviors in the biochemical variability of DNA samples have been observed by FT-IR spectral analysis, which might be attributed to the inherent nature of the genomic DNA from parents and their F1 progenies. Mantel test was also carried out to compare morphological, SRAP, and FT-IR results based on genetic distances. The highest correlation coefficient was found between morphological and SRAP marker distances (R = 0.607; p ≤ 0.022). A lower correlation was observed between the morphological and FT-IR distance matrix (R = 0.231; p ≤0.008). Moreover, a positive correlation was found between the distances generated with SRAP and FT-IR analyses (R = 0.026) but was not statistically significant. These findings show that both SRAP and FT-IR techniques combined with morphological descriptions can be used effectively for nonconventional breeding programs for Streptocarpus to obtain new and valuable varieties.

Fourier transform infrared (FT-IR) spectroscopy is an effective tool for investigation of chemical changes at the molecular level. We previously demonstrated that FT-IR spectroscopy can reliably distinguish multiple types of carcinoma from healthy tissue. Because various stomach diseases are common, it is important to explore a noninvasive and rapid method to detect malignancy and gastritis in endoscopic biopsies. Our aim was to classify endoscopic biopsies into healthy, gastritis, and malignancy through the use of FT-IR spectroscopy. A total of 103 endoscopic samples, including 19 cases of cancer, 35 cases of chronic atrophic gastritis, 29 cases of chronic superficial gastritis, and 20 healthy tissue samples, were obtained at the First Hospital of Xi'an Jiaotong University, China. A modified attenuated total reflectance accessory was linked to a WQD-500 FT-IR spectrometer for biopsy measurement. The spectral characteristics for different types of tissues were correlated with the corresponding pathology results. The gastric biopsies were classified by FT-IR spectroscopy and a discriminant analysis method. There were significant differences in the FT-IR spectra of four types of gastric biopsies. The discriminant analysis results demonstrated that the sensitivity of FT-IR detection for healthy, superficial gastritis, atrophic gastritis, and gastric cancer was 90%, 90%, 66%, 74%, respectively, which could help satisfy clinical diagnostic requirements. FT-IR spectroscopy can distinguish disease processes in gastric endoscopic biopsies.

Objective To explore the feasibility of superficially differentiating breast cancer from the benign fourier transform infrared ( FTIR) spectroscopy. Methods Twenty-six cases of breast cancer and 34 cases of the benign were examined superficially by FTIR spectroscopy. For each FTIR spectrum,the peak positions (P), relative intensity (I) and full width at half maximum ( F) of the band were recorded and the relative intensity ratios were calculated. Results In the group of breast cancer, both the peaks of 1546 cm-1 and 1303 cm-1 shifted toward lower wave number (P ＜0. 05), as compared to those of the benign group. The relative intensity ratio of I1256/I1460) was increased significantly (P ＜ 0. 05). Conclusion There existed many significant differences in FTIR spectra between malignant and benign breast masses. Key words: Breast carcinoma; Spectroscopy

Palm oil is one of the important commodities of Malaysia and is superior for frying due to its high oxidative stability and high content of unsaturated fatty acids. The quality of frying oil will decrease with time and repeated cycles of frying, and may subsequently affect the safety of the food. Several parameters such as peroxide value (PV) and conjugated diene value (CDV) have been used conventionally to monitor the quality of edible oils. In this study, Fourier Transform Infrared (FTIR) spectroscopy was used to evaluate the quality ofpalm oils after repeated cycles of frying. FTIR spectra were recorded from 4,000 to 400 cm-1, and analysedusing multivariate data analysis (MVA) to select important spectral regions and identify clustering pattern among samples. FTIR frequencies at 1,744 and 2,922 cm-1 were found to be the most discriminating variablesinfluencing the separation between fresh and used frying oils. The correlations between peak areas at 1,744 and 2,922 cm-1 with PV and CDV values were also determined, yielding linear regression models with high correlation coefficient, R2. The results demonstrate that FTIR can be used as a rapid, reliable and accurateanalytical method for the quality assessment of frying oils.

The high-affinity receptor for IgE is a tetrameric complex of subunits of the type alpha beta gamma 2. We report here conformational studies of the intact gamma subunit in trifluoroethanol and water/liposomes by circular dichroism and Fourier-transform infrared (FTIR) spectroscopy. In trifluoroethanol, the FTIR amide I' frequencies were consistent with two predominant conformational components, the beta-turn and alpha-helix, whilst in liposomes consisting of D2O and dimyristoylglycerophosphocholine (Myr2GroPCho), three components were observed. The third component present may contain some left-handed extended helix. Spectral simulation was carried out to demonstrate that the CD spectra were consistent with the component conformations identified from FTIR spectroscopy. The stimulated CD spectra were in excellent agreement with the experimental spectra. The intact gamma subunit conformation in trifluoroethanol was shown to possess 72% alpha-helical and 28% beta-turn conformations. In water/Myr2GroPCho liposomes the percentage of each conformational component present is 37%, 38% and 25% for the alpha-helix, beta-turn and extended structures, respectively. Assuming that the transmembrane fragment was alpha-helical, an excellent correlation was found between this derived alpha-helical content in water/liposomes (37%) and from hydrophobicity plots where the percentage of amino acids in the transmembrane domain is predicted by others to be 34%. It is suggested that the beta-turn detected by CD and FTIR was attributable to a 3(10) helix rather than a type I or type III reverse turn.