Fourier transform infrared (FTIR) spectroscopy, a technology traditionally used in chemistry to determine the molecular composition of a wide range of sample types, has gained growing interest in microbial typing. It is based on the different vibrational modes of the covalent bonds between atoms of a given sample, as bacterial cells, induced by the absorption of infrared radiation. This technique has been largely used for the study of pathogenic species, especially in the clinical field, and has been proposed also for the typing at different subspecies levels. The high throughput, speed, low cost, and simplicity make FTIR spectroscopy an attractive technique also for industrial applications, in particular, for probiotics. The aim of this study was to compare FTIR spectroscopy with established genotyping methods, pulsed-field gel electrophoresis (PFGE), whole-genome sequencing (WGS), and multilocus sequence typing (MLST), in order to highlight the FTIR spectroscopy potential discriminatory power at strain level. Our study focused on bifidobacteria, an important group of intestinal commensals generally recognized as probiotics. For their properties in promoting and maintaining health, bifidobacteria are largely marketed by the pharmaceutical, food, and dairy industries. Strains belonging to Bifidobacterium longum subsp. longum and Bifidobacterium animalis subsp. lactis were taken into consideration together with some additional type strains. For B. longum subsp. longum, it was possible to discriminate the strains with all the methods used. Although two isolates were shown to be strictly phylogenetically related, constituting a unique cluster, based on PFGE, WGS, and MLST, no clustering was observed with FTIR. For B. animalis subsp. lactis group, PFGE, WGS, and MLST were non-discriminatory, and only one strain was easily distinguished. On the other hand, FTIR discriminated all the isolates one by one, and no clustering was observed. According to these results, FTIR analysis is not only equivalent to PFGE, WGS, and MLST, but also for some strains, in particular, for B. animalis subsp. lactis group, more informative, being able to differentiate strains not discernible with the other two methods based on phenotypic variations likely deriving from certain genetic changes. Fourier transform infrared spectroscopy has highlighted the possibility of using the cell surface as a kind of barcode making tracing strains possible, representing an important aspect in probiotic applications. Furthermore, this work constitutes the first investigation on bifidobacterial strain typing using FTIR spectroscopy.

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

The IR Biotyper and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) using ClinProTools software (MALDI-TOF MS-ClinProTools) are two novel typing methods that rely on the analysis of carbohydrate and peptide residues in intact bacterial cells. These two methods have shown promising results in the rapid and accurate typing of bacteria. In this study, we evaluated these novel typing methods in comparison with genotypic typing for cluster analysis of Burkholderia cenocepacia epidemic strain ET12, isolated from adult cystic fibrosis patients. Sixty-six isolates of B. cenocepacia were used in this study, 35 of which were identified as the ET12 strain and 31 as non-ET12 strains by repetitive-element PCR (rep-PCR). Twelve isolates were used for the creation of typing models using IR Biotyper and MALDI-TOF MS-ClinProTools, and 54 isolates were used for external validation of the typing models. The IR Biotyper linear discriminant analysis (LDA) model had a diagnostic sensitivity of 84.6% for typing the epidemic strain, ET12. At a cutoff of 70%, MALDI-TOF MS-ClinProTools had 87.5% diagnostic sensitivity in detecting the ET12 strain (P = 1.00). Both methods had a diagnostic specificity of ≥80% for detecting the ET12 strain. In conclusion, IR Biotyper and MALDI-TOF MS-ClinProTools offer rapid typing using proteomics and analysis of small cellular molecules with a low running cost. Our pilot study showed suboptimal accuracy of both methods for typing outbreak strains of B. cenocepacia. Extending the spectral region analyzed by the IR Biotyper can improve the accuracy and has the potential of improving the generalizability of this technique for typing other organisms. IMPORTANCE Respiratory infections due to Burkholderia cenocepacia, particularly the ET12 epidemic strain, are considered sentinel events for persons with cystic fibrosis, as they are often associated with person-to-person transmission and accelerated decline in lung function and early mortality. Current typing methods are generally only available at reference centers, with long turn-around-times, which can affect the identification of outbreaks and critical patient triage. This pilot study aims to add to the growing literature illustrating the potential utility of Fourier transform infrared spectroscopy (FTIR), a novel rapid method, for the successful typing of clinically significant bacteria. In this study, we evaluated its utility to discriminate between the ET12 clone and non-ET12 isolates of B. cenocepacia and compared it to proteomics cluster analysis using MALDI-TOF MS and ClinProTools software. Both methods had encouraging but suboptimal accuracy (≥85% sensitivity and ≥83% specificity), which will likely be improved by extending the spectral region analyzed by the IR Biotyper with updated software.

Fourier transform infrared (FT-IR) spectroscopy (IR Biotyper; Bruker) allows highly discriminatory fingerprinting of closely related bacterial strains. In this study, FT-IR spectroscopy-based capsular typing of Streptococcus pneumoniae was validated as a rapid, cost-effective, and medium-throughput alternative to the classical phenotypic techniques. A training set of 233 strains was defined, comprising 34 different serotypes and including all 24 vaccine types (VTs) and 10 non-vaccine types (NVTs). The acquired spectra were used to (i) create a dendrogram where strains clustered together according to their serotypes and (ii) train an artificial neural network (ANN) model to predict unknown pneumococcal serotypes. During validation using 153 additional strains, we reached 98.0% accuracy for determining serotypes represented in the training set. Next, the performance of the IR Biotyper was assessed using 124 strains representing 59 non-training set serotypes. In this setting, 42 of 59 serotypes (71.1%) could be accurately categorized as being non-training set serotypes. Furthermore, it was observed that comparability of spectra was affected by the source of the Columbia medium used to grow the pneumococci and that this complicated the robustness and standardization potential of FT-IR spectroscopy. A rigorous laboratory workflow in combination with specific ANN models that account for environmental noise parameters can be applied to overcome this issue in the near future. The IR Biotyper has the potential to be used as a fast, cost-effective, and accurate phenotypic serotyping tool for S. pneumoniae.

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

A rapid method for determining the carbonyl value of frying oils has been developed using Fourier-transform infrared (FTIR) spectroscopy and chemometrics. One hundred and fifty-six frying oils with different carbonyl values were collected from an actual potato frying process. FTIR spectra in the range of 4000-650 cm(-1) were scanned with a FTIR spectroscopy apparatus using the attenuated total reflectance (ATR) method. A good calibration model was obtained using the partial least-squares (PLS) regression method with full cross validation for predicting the carbonyl value of frying oils. For the model, the coefficients of determination (R(2)), standard errors of cross validation (SECV) and standard errors of prediction (SEP) were 0.99, 1.87 μmol g(-1) and 1.93 μmol g(-1), respectively. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3. This study shows that the carbonyl value of frying oils can be successfully determined to a high accuracy using FTIR spectroscopy combined with PLS regression.

The characterization and quantification of functional groups in technical lignins are among the chief obstacles of the utilization of this highly abundant biopolymer. Although several techniques were developed for this purpose, there is still a need for quick, cost-efficient, and reliable quantification methods for lignin. In this paper, three sampling techniques for fourier transform infrared (FTIR) spectroscopy were assessed both qualitatively and quantitatively, delineating how these affected the resultant spectra. The attenuated total reflectance (ATR) of neat powders and DMSO-d6 solutions, as well as transmission FTIR using the KBr pelleting method (0.5 wt%), were investigated and compared for eight lignin samples. The ATR of neat lignins provided a quick and easy method, but the signal-to-noise ratios in the afforded spectra were limited. The ATR of the DMSO-d6 solutions was highly concentration dependent, but at a 30 wt%, acceptable signal-to-noise ratios were obtained, allowing for the lignins to be studied in the dissolved state. The KBr pelleting method gave a significant improvement in the smoothness and resolution of the resultant spectra compared to the ATR techniques. Subsequently, the content of phenolic OH groups was calculated from each FTIR mode, and the best correlation was seen between the transmission mode using KBr pellets and the ATR of the neat samples (R2 = 0.9995). Using the titration measurements, the total OH and the phenolic OH group content of the lignin samples were determined as well. These results were then compared to the FTIR results, which revealed an under-estimation of the phenolic OH groups from the non-aqueous potentiometric titration, which was likely due to the differences in the pKa between the lignin and the calibration standard 4-hydroxybenzoic acid. Further, a clear correlation was found between the lower Mn and the increased phenolic OH group content via SEC analyses. The work outlined in this paper give complementary views on the characterization and quantification of technical lignin samples via FTIR.

Ovarian cancer is the most lethal of gynecological malignancies. Fourier Transform Infrared (FTIR) spectroscopy has gradually developed as a convenient, inexpensive and non-destructive technique for the study of many diseases. In this study, FTIR spectra of normal and several heterogeneous ovarian cancer cell lines as well as ovarian cancer tissue samples were compared in the spectral region of 4000 cm- 1 - 600 cm- 1.

Despite the increasing popularity of liquid chromatography−mass spectrometry (LC-MS)-based lipidomics, there is a lack of accepted and validated methods for lipid extract quality and quantity assessment prior to LC-MS. Fourier-Transform Infrared Spectroscopy (FTIR) has been reported for quantification of pure lipids. However, the impact of complex lipid sample complexity and purity on total lipid quantification accuracy has not been investigated. Here, we report comprehensive assessment of the sample matrix on the accuracy of lipid quantification using Attenuated Total Reflectance (ATR)-FTIR and establish a simple workflow for lipidomics sample quantification. We show that both pure and complex lipids show characteristic FTIR vibrations of CH- and C=O-stretching vibrations, with a quantitative range of 40−3000 ng and a limit of detection of 12 ng, but sample extraction method and local baseline subtraction during FTIR spectral processing significantly impact lipid quantification via CH stretching. To facilitate sample quality screening, we developed the Lipid Quality (LiQ) score from a spectral library of common contaminants, using a ratio of peak heights between CH stretching vibrations maxima and the collective vibrations from amide/amine, CH-stretching minima and sugar moieties. Taking all tested parameters together, we propose a rapid FTIR workflow for routine lipidomics sample quality and quantity assessment and tested this workflow by comparing to the total LC-MS intensity of targeted lipidomics of 107 human plasma lipid extracts. Exclusion of poor-quality samples based on LiQ score improved the correlation between FTIR and LC-MS quantification. The uncertainty of absolute quantification by FTIR was estimated using a 795 ng SPLASH LipidoMix standard to be <10%. With low sample requirement, we anticipate this simple and rapid method will enhance lipidomics workflow by enabling accurate total lipid quantification and normalization of lipid quantity for MS analysis.

Members of the Enterobacter (E.) cloacae complex have emerged as important pathogens frequently encountered in nosocomial infections. Several outbreaks with E. cloacae complex have been reported in recent years, especially in neonatal units. Fast and reliable strain typing methods are crucial for real-time surveillance and outbreak analysis to detect pathogen reservoirs and transmission routes. The aim of this study was to evaluate the performance of Fourier-transform infrared (FTIR) spectroscopy as a fast method for typing of clinical E. cloacae complex isolates, when whole genome sequencing (WGS) analysis was used as reference. First, the technique was used retrospectively on 24 first isolates of E. cloacae complex strains from neonatal patients and showed good concordance with SNP-based clustering [adjusted rand index (ARI) = 0.818] and with the sequence type (ST) (ARI = 0.801). 29 consecutive isolates from the same patients were shown by WGS analysis to almost always belong to the same SNP cluster as the first isolates, which was only inconsistently recognized by FTIR spectroscopy. Training of an artificial neural network (ANN) with all FTIR spectra from sequenced strains markedly improved the recognition of related and unrelated isolate spectra. In a second step, FTIR spectroscopy was applied on 14 strains during an outbreak with E. cloacae complex and provided fast typing results that were confirmed by WGS analysis. In conclusion, FTIR spectroscopy is a promising tool for strain typing of clinical E. cloacae complex strains. Discriminatory power can be improved by implementing an ANN for spectrum analysis. Due to its low costs and fast turnaround times, the method presents a valuable tool for real-time surveillance as well as outbreak analysis.

More reliable biomarkers using near-patient technologies are needed to improve early diagnosis and intervention for patients with renal disease. Infrared (IR) vibrational spectroscopy/microspectroscopy is an established analytical method that was first used in biomedical research over 20 years ago. With the advances in instrumentation, computational and mathematical techniques, this technology has now been applied to a variety of diseases; however, applications in nephrology are just beginning to emerge. In the present study, we used attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to analyze urine samples collected from rodent models of inflammatory glomerulonephritis (GN) as well as from patients with crescentic GN, with the aim of identifying potential renal biomarkers; several characteristic mid-IR spectral markers were identified in urine samples. Specifically, a 1545 cm-1 band increased in intensity with the progression and severity of GN in rats, mice and humans. Furthermore, its intensity declined significantly in response to corticosteroid treatment in nephritic rats. In conclusion, our results suggest that specific urinary FTIR biomarkers may provide a rapid, sensitive and novel non-invasive means of diagnosing inflammatory forms of GN, and for real-time monitoring of progress, and response to treatment.

Inducible nitric oxide synthase (iNOS) is a homodimeric heme enzyme that catalyzes the formation of nitric oxide (NO) from dioxygen and L-arginine (L-Arg) in a two-step process. The produced NO can either diffuse out of the heme pocket into the surroundings or it can rebind to the heme iron and inhibit enzyme action. Here we have employed Fourier transform infrared (FTIR) photolysis difference spectroscopy at cryogenic temperatures, using the carbon monoxide (CO) and NO stretching bands as local probes of the active site of iNOS. Characteristic changes were observed in the spectra of the heme-bound ligands upon binding of the cofactors. Unlike photolyzed CO, which becomes trapped in well-defined orientations, as indicated by sharp photoproduct bands, photoproduct bands of NO photodissociated from the ferric heme iron were not visible, indicating that NO does not reside in the protein interior in a well-defined location or orientation. This may be favorable for NO release from the enzyme during catalysis because it reduces self-inhibition. Moreover, we used temperature derivative spectroscopy (TDS) with FTIR monitoring to explore the dynamics of NO and carbon monoxide (CO) inside iNOS after photodissociation at cryogenic temperatures. Only a single kinetic photoproduct state was revealed, but no secondary docking sites as in hemoglobins. Interestingly, we observed that intense illumination of six-coordinate ferrous iNOS oxy-NO ruptures the bond between the heme iron and the proximal thiolate to yield five-coordinate ferric iNOS oxy-NO, demonstrating the strong trans effect of the heme-bound NO.

We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers' signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum's 10-30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on.

Diatoms are feedstock for the production of sustainable biocommodities, including biofuel. The biochemical characterization of newly isolated or genetically modified strains is seminal to identify the strains that display interesting features for both research and industrial applications. Biochemical quantification of organic macromolecules cellular quotas are time-consuming methodologies which often require large amount of biological sample. Vibrational spectroscopy is an essential tool applied in several fields of research. A Fourier transform infrared (FTIR) microscopy-based imaging protocol was developed for the simultaneous cellular quota quantification of lipids, carbohydrates, and proteins of the diatom Phaeodactylum tricornutum. The low amount of sample required for the quantification allows the high throughput quantification on small volume cultures. A proof of concept was performed (1) on nitrogen-starved experimental cultures and (2) on three different P. tricornutum wild-type strains. The results are supported by the observation in situ of lipid droplets by confocal and brightfield microscopy. The results show that major differences exist in the regulation of lipid metabolism between ecotypes of P. tricornutum.

In this study we explore the potential of using Fourier-transform infrared (FTIR) spectra of trifluoroacetate-protein and peptide complexes for monitoring proteolytic reactions. The idea of treating dry-films of protein hydrolysates with trifluoroacetic acid (TFA) prior to FTIR analysis is based on the unique properties of TFA. By adding a large excess of TFA to protein hydrolysate samples, the possible protonation sites of the proteins and peptides will be saturated. In addition, TFA has a low boiling point when protonated as well as complex-forming abilities. When forming TFA-treated dry-films of protein hydrolysates, the excess TFA will evaporate and the deprotonated acid (CF3COO-) will interact as a counter ion with the positive charges on the sample materials. In the study, spectral changes in TFA-treated dry-films of protein hydrolysates from a pure protein and poultry by-products, were compared to the FTIR fingerprints of untreated dry-films. The results show that time-dependent information related to proteolytic reactions and, consequently, on the characteristics of the protein hydrolysates can be obtained. With additional developments, FTIR on dry-films treated with TFA may be regarded as a potential future tool for the analysis of all types of proteolytic reactions in the laboratory as well as in industry.

Detection of pesticide residues is important for ensuring food safety, and it has assumed increased significance. Traditional analytical methods are known for being destructive and cost- and time-intensive. In this study, depth-profiling Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) was successfully used as an in situ, nondestructive, and rapid method for detecting tricyclazole residues on three metal surfaces (copper, aluminum, and iron) and subsequently, on the surfaces of fresh rice leaves and ripe husks. Four moving mirror velocities, that is, 0.32, 0.63, 0.95, and 1.90 cm s-1 were used for recording the spectra. The results indicated that the moving mirror velocity of 0.95 cm s-1 was optimal for depth profiling, and the obtained spectra showed a strong absorption band at around 1200 cm-1, corresponding to the C-N bond in tricyclazole. This band could be used for monitoring tricyclazole residues on plant surfaces. Principal component analysis confirmed the detection of tricyclazole on the basis of its spectral information. Considering the scanning depth and the thickness of the plant cuticle, FTIR-PAS can be an effective means for detecting and monitoring similar organonitrogen pesticide residues.

Persistent colonic inflammation increases risk for cancer, but mucosal appearance on conventional endoscopy correlates poorly with histology. Here we demonstrate the use of a flexible silver halide fiber to collect mid-infrared absorption spectra and an interval model to distinguish colitis from normal mucosa in dextran sulfate sodium treated mice. The spectral regime between 950 and 1800 cm(-1) was collected from excised colonic specimens and compared with histology. Our model identified 3 sub-ranges that optimize the classification results, and the performance for detecting inflammation resulted in a sensitivity, specificity, accuracy, and positive predictive value of 92%, 88%, 90%, and 88%, respectively.

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm-1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

Chicken feathers are major byproducts of the livestock processing industry with high potential in the feed sector. In this study, we present a new approach using Fourier transform infrared (FTIR) spectroscopy to detect the structural changes of feather keratin and its availability for enzymatic hydrolysis (AEH) induced by the thermal pressure hydrolysis (TPH) process. Compared to time-consuming in vitro measurement techniques, the proposed method provides rapid information about the structural changes during TPH which enables quick adaptation of TPH conditions as the quality of the incoming feather changes. By analyzing the FTIR spectra of raw and processed feathers, it was found that AEH negatively relates to the β-sheet content (represented by two IR peaks centered at 1635 and 1689 cm-1), while it positively relates to a new series of peaks centered around 1700 cm-1 appearing after the TPH process. The proposed FTIR technique provides a reliable and rapid approach to determine the digestibility indicated by AEH of the processed feather and may be used in process control and optimization.

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm-1 with a spectral resolution of 8 cm-1. In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm-1, Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm-1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.