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

Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed ( Reynoutria japonica ) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed using four discriminant methods (LDA, QDA, kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM). Of particular interest, is the impact of discrimination when presented with spectral data from a section of the bio-fingerprint region (1430 cm -1 to 900 cm -1 ) in contrast to the more extended bio-fingerprint region used here (1800 cm -1 to 900 cm -1 ). Quality metrics used were the misclassification rate, sensitivity, specificity, and Matthew's correlation coefficient (MCC). For plasma (with spectral data ranging from 1430 cm -1 to 900 cm -1 ), the best performing classifier was kNN, which achieved a sensitivity, specificity and MCC of 0.865 ± 0.043, 0.865 ± 0.023 and 0.762 ± 0.034, respectively. For serum (in the same wavenumber range), the best performing classifier was LDA, achieving a sensitivity, specificity and MCC of 0.899 ± 0.023, 0.763 ± 0.048 and 0.664 ± 0.067, respectively. For plasma (with spectral data ranging from 1800 cm -1 to 900 cm -1 ), the best performing classifier was SVM, with a sensitivity, specificity and MCC of 0.993 ± 0.010, 0.815 ± 0.000 and 0.815 ± 0.010, respectively. For serum (in the same wavenumber range), QDA performed best achieving a sensitivity, specificity and MCC of 0.852 ± 0.023, 0.700 ± 0.162 and 0.557 ± 0.012, respectively. Our findings demonstrate that even when a section of the bio-fingerprint region has been removed, good classification of endometrial cancer versus non-cancerous controls is still maintained. These findings suggest the potential of a MIR screening tool for endometrial cancer screening.

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques.

Patient survival remains poor even after diagnosis in lung cancer cases, and the molecular events resulting from lung cancer progression remain unclear. Raman spectroscopy could be used to noninvasively and accurately reveal the biochemical properties of biological tissues on the basis of their pathological status. This study aimed at probing biomolecular changes in lung cancer, using Raman spectroscopy as a potential diagnostic tool. Herein, biochemical alterations were evident in the Raman spectra (region of 600-1800 cm -1 ) in normal and cancerous lung tissues. The levels of saturated and unsaturated lipids and the protein-to-lipid, nucleic acid-to-lipid, and protein-to-nucleic acid ratios were significantly altered among malignant tissues compared to normal lung tissues. These biochemical alterations in tissues during neoplastic transformation have profound implications in not only the biochemical landscape of lung cancer progression but also cytopathological classification. Based on this spectroscopic approach, classification methods including k-nearest neighbour (kNN) and support vector machine (SVM) were successfully applied to cytopathologically diagnose lung cancer with an accuracy approaching 99%. The present results indicate that Raman spectroscopy is an excellent tool to biochemically interrogate and diagnose lung cancer.

Diagnostic tools for the detection of early-stage oesophageal adenocarcinoma (OAC) are urgently needed. Our aim was to develop an accurate and inexpensive method using biofluids (plasma, serum, saliva or urine) for detecting oesophageal stages through to OAC (squamous; inflammatory; Barrett's; low-grade dysplasia; high-grade dysplasia; OAC) using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. ATR-FTIR spectroscopy coupled with variable selection methods, with successive projections or genetic algorithms (GA) combined with quadratic discriminant analysis (QDA) were employed to identify spectral biomarkers in biofluids for accurate diagnosis in a hospital setting of different stages through to OAC. Quality metrics (Accuracy, Sensitivity, Specificity and F-score) and biomarkers of disease were computed for each model. For plasma, GA-QDA models using 15 wavenumbers achieved 100% classification for four classes. For saliva, PCA-QDA models achieved 100% for the inflammatory stage and high-quality metrics for other classes. For serum, GA-QDA models achieved 100% performance for the OAC stage using 13 wavenumbers. For urine, PCA-QDA models achieved 100% performance for all classes. Selected wavenumbers using a Student's t-test (95% confidence interval) identified a differentiation of the stages on each biofluid: plasma (929 cm-1 to 1431 cm-1, associated with DNA/RNA and proteins); saliva (1000 cm-1 to 1150 cm-1, associated with DNA/RNA region); serum (1435 cm-1 to 1573 cm-1, associated with methyl groups of proteins and Amide II absorption); and, urine (1681 cm-1 to 1777 cm-1, associated with a high frequency vibration of an antiparallel β-sheet of Amide I and stretching vibration of lipids). Our methods have demonstrated excellent efficacy for a rapid, cost-effective method of diagnosis for specific stages to OAC. These findings suggest a potential diagnostic tool for oesophageal cancer and could be translated into clinical practice.

Raman spectroscopy is a powerful technique used to analyse biological materials, where spectral markers such as proteins (1500-1700 cm-1), carbohydrates (470-1200 cm-1) and phosphate groups of DNA (980, 1080-1240 cm-1) can be detected in a complex biological medium. Herein, Raman microspectroscopy imaging was used to investigate 90 brain tissue samples in order to differentiate meningioma Grade I and Grade II samples, which are the commonest types of brain tumour. Several classification algorithms using feature extraction and selection methods were tested, in which the best classification performances were achieved by principal component analysis-quadratic discriminant analysis (PCA-QDA) and successive projections algorithm-quadratic discriminant analysis (SPA-QDA), resulting in accuracies of 96.2%, sensitivities of 85.7% and specificities of 100% using both methods. A biochemical profiling in terms of spectral markers was investigated using the difference-between-mean (DBM) spectrum, PCA loadings, SPA-QDA selected wavenumbers, and the recovered imaging profiles after multivariate curve resolution alternating least squares (MCR-ALS), where the following wavenumbers were found to be associated with class differentiation: 850 cm-1 (amino acids or polysaccharides), 1130 cm-1 (phospholipid structural changes), the region between 1230-1360 cm-1 (Amide III and CH2 deformation), 1450 cm-1 (CH2 bending), and 1858 cm-1 (C[double bond, length as m-dash]O stretching). These findings highlight the potential of Raman microspectroscopy imaging for determination of meningioma tumour grades.

Hyperspectral imaging is a powerful tool to obtain both chemical and spatial information of biological systems. However, few algorithms are capable of working with full three-dimensional images, in which reshaping or averaging procedures are often performed to reduce the data complexity. Herein, we propose a new algorithm of three-dimensional principal component analysis (3D-PCA) for exploratory analysis of complete 3D spectrochemical images obtained through Raman microspectroscopy. Blood plasma samples of ten patients (5 healthy controls, 5 diagnosed with ovarian cancer) were analysed by acquiring hyperspectral imaging in the fingerprint region (∼780-1858 cm-1). Results show that 3D-PCA can clearly differentiate both groups based on its scores plot, where higher loadings coefficients were observed in amino acids, lipids and DNA regions. 3D-PCA is a new methodology for exploratory analysis of hyperspectral imaging, providing fast information for class differentiation.

Alzheimer's disease (AD) is currently under-diagnosed and is predicted to affect a great number of people in the future, due to the unrestrained aging of the population. An accurate diagnosis of AD at an early stage, prior to (severe) symptomatology, is of crucial importance as it would allow the subscription of effective palliative care and/or enrolment into specific clinical trials. Today, new analytical methods and research initiatives are being developed for the on-time diagnosis of this devastating disorder. During the last decade, spectroscopic techniques have shown great promise in the robust diagnosis of various pathologies, including neurodegenerative diseases and dementia. In the current study, blood plasma samples were analysed with near-infrared (NIR) spectroscopy as a minimally-invasive method to distinguish patients with AD (n = 111) from non-demented volunteers (n = 173). After applying multivariate classification models (principal component analysis with quadratic discriminant analysis - PCA-QDA), AD individuals were correctly identified with 92.8% accuracy, 87.5% sensitivity and 96.1% specificity. Our results show the potential of NIR spectroscopy as a simple and cost-effective diagnostic tool for AD. Robust and early diagnosis may be a first step towards tackling this disease by allowing timely intervention.

The current lack of an accurate, cost-effective and non-invasive test that would allow for screening and diagnosis of gynaecological carcinomas, such as endometrial and ovarian cancer, signals the necessity for alternative approaches. The potential of spectroscopic techniques in disease investigation and diagnosis has been previously demonstrated. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse urine samples from women with endometrial (n = 10) and ovarian cancer (n = 10), as well as from healthy individuals (n = 10). After applying multivariate analysis and classification algorithms, biomarkers of disease were pointed out and high levels of accuracy were achieved for both endometrial (95% sensitivity, 100% specificity; accuracy: 95%) and ovarian cancer (100% sensitivity, 96.3% specificity; accuracy 100%). The efficacy of this approach, in combination with the non-invasive method for urine collection, suggest a potential diagnostic tool for endometrial and ovarian cancers.

Vibrational spectroscopies, based on infrared absorption and/or Raman scattering provide a detailed fingerprint of a material, based on the chemical content. Diagnostic and prognostic tools based on these technologies have the potential to revolutionise our clinical systems leading to improved patient outcome, more efficient public services and significant economic savings. However, despite these strong drivers, there are many fundamental scientific and technological challenges which have limited the implementation of this technology in the clinical arena, although recent years have seen significant progress in addressing these challenges. This review examines (i) the state of the art of clinical applications of infrared absorption and Raman spectroscopy, and (ii) the outstanding challenges, and progress towards translation, highlighting specific examples in the areas of in vivo, ex vivo and in vitro applications. In addition, the requirements of instrumentation suitable for use in the clinic, strategies for pre-processing and statistical analysis in clinical spectroscopy and data sharing protocols, will be discussed. Emerging consensus recommendations are presented, and the future perspectives of the field are assessed, particularly in the context of national and international collaborative research initiatives, such as the UK EPSRC Clinical Infrared and Raman Spectroscopy Network, the EU COST Action Raman4Clinics, and the International Society for Clinical Spectroscopy.

Exposure to environmental insults generally occurs at low levels, making it challenging to measure bacterial responses to such interactions. Additionally, microbial behaviour and phenotype varies in differing bacterial types or growth phases, likely giving rise to growth- or species-specific responses to environmental stimuli. The present study applied a spectrochemical tool, infrared (IR) spectral interrogation coupled with multivariate analysis, to investigate the growth- and species-specific responses of two bacterial strains, Gram-negative Pseudomonas fluorescens and Gram-positive Mycobacterium vanbaalenii, to low concentrations of tetracycline, nanoparticulate silver (AgNP) or mixtures thereof. Results indicate the tendency for tetracycline-induced biospectral alterations to occur in outer-cellular components, e.g., phospholipids or proteins, while AgNPs-induced changes are mainly associated with proteins (∼964 cm -1 , ∼1485 cm -1 , ∼1550 cm -1 , ∼1650 cm -1 ). The primary altered targets are correlated with bacterial membranes or outer-cellular components. Furthermore, significant lipid changes at 1705-1750 cm -1 were only present in P. fluorescens cells compared to M. vanbaalenii, owing to differences in cell wall structure between Gram-positive and -negative bacteria. This study also found distinct biospectral alterations in non-log phase compared to log phase, confirming bacterial growth-dependent responses to environmental exposures. It implies that previous studies on log phase only may underestimate the impacts from exposures of interest in situ, where bacteria stay in different growth stages. Our work proves the feasibility of biospectroscopy in determining bacterial responses to low-level environmental exposures in a fast and efficient manner, revealing sufficient biochemical information continuously through growth phases. As a nondestructive approach, biospectroscopy may provide deeper insights into the actual and in situ interactions between microbes and environmental stimuli, regardless of the exposure level, growth phase, or bacterial types.

Terrestrial plants are ideal sentinels of environmental pollution, due to their sedentary nature, abundance and sensitivity to atmospheric changes. However, reliable and sensitive biomarkers of exposure have hitherto been difficult to characterise. Biospectroscopy offers a novel approach to the derivation of biomarkers in the form of discrete molecular alterations detectable within a biochemical fingerprint. We investigated the application of this approach for the identification of biomarkers for pollution exposure using the common sycamore (Acer pseudoplatanus) as a sentinel species. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to interrogate leaf tissue collected from three sites exposed to different levels of vehicle exhaust emissions. Following multivariate analysis of acquired spectra, significant biochemical alterations were detected between comparable leaves from different sites that may constitute putative biomarkers for pollution-induced stress. These included differences in carbohydrate and nucleic acid conformations, which may be indicative of sub-lethal exposure effects. We also observed several corresponding spectral alterations in both the leaves of A. pseudoplatanus exposed to ozone pollution under controlled environmental conditions and in leaves infected with the fungal pathogen Rhytisma acerinum, indicating that some stress-induced changes are conserved between different stress signatures. These similarities may be indicative of stress-induced reactive oxygen species (ROS) generation, although further work is needed to verify the precise identity of infrared biomarkers and to identify those that are specific to pollution exposure. Taken together, our data clearly demonstrate that biospectroscopy presents an effective toolkit for the utilisation of higher plants, such as A. pseudoplatanus, as sentinels of environmental pollution.

Surgical management of ovarian tumours largely depends on their histo-pathological diagnosis. Currently, screening for ovarian malignancy with tumour markers in conjunction with radiological investigations has a low specificity for discriminating benign from malignant tumours. Also, pre-operative biopsy of ovarian masses increases the risk of intra-peritoneal dissemination of malignancy. Intra-operative frozen section, although sufficiently accurate in differentiating tumours according to their histological type, increases operation times. This results in increased surgery-related risks to the patient and additional burden to resource allocation. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, combined with chemometric analysis can be applied to discriminate between normal, borderline and malignant ovarian tumours and classify ovarian carcinoma subtypes according to the unique spectral signatures of their molecular composition. Formalin-fixed, paraffin-embedded ovarian tissue blocks were de-waxed, mounted on Low-E slides and desiccated before being analysed using ATR-FTIR spectroscopy. Chemometric analysis in the form of principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA), followed by linear discriminant analysis (LDA) of the obtained spectra revealed clear segregation between benign versus borderline versus malignant tumours as well as segregation between different histological tumour subtypes, when these approaches are used in combination. ATR-FTIR spectroscopy coupled with chemometric analysis has the potential to provide a novel diagnostic approach in the accurate diagnosis of ovarian tumours assisting surgical decision making to avoid under-treatment or over-treatment, with minimal impact to the patient.

As biospectroscopy techniques continue to be developed for screening or diagnosis within a point-of-care setting, an important development for this field will be high-throughput optimization. For many of these techniques, it is therefore necessary to adapt and develop parameters to generate a robust yet simple approach delivering high-quality spectra from biological samples. Specifically, this is important for surface-enhanced Raman spectroscopy (SERS) wherein there are multiple variables that can be optimised to achieve an enhancement of the Raman signal from a sample. One hypothesis is that "large" diameter (>100 nm) gold nanoparticles provide a greater enhancement at near-infrared (NIR) and infrared (IR) wavelengths than those <100 nm in diameter. Herein, we examine this notion using examples in which SERS spectra were acquired from MCF-7 breast cancer cells incubated with 150 nm gold nanoparticles. It was found that 150 nm gold nanoparticles are an excellent material for NIR/IR SERS. Larger gold nanoparticles may better satisfy the theoretical restraints for SERS enhancement at NIR/IR wavelengths compared to smaller nanoparticles. Also, larger nanoparticles or their aggregates are more readily observed via optical microscopy (and especially electron microscopy) compared to smaller ones. This allows rapid and straightforward identification of target areas containing a high concentration of nanoparticles and facilitating SERS spectral acquisition. To some extent, these observations appear to extend to biofluids such as blood plasma or (especially) serum; SERS spectra of such biological samples often exhibit a low signal-to-noise ratio in the absence of nanoparticles. With protein-rich biofluids such as serum, a dramatic SERS effect can be observed; although this might facilitate improved spectral biomarker identification in the future, it may not always improve classification between control vs. cancer. Thus, use of "large" gold nanoparticles are a good starting point in order to derive informative NIR/IR SERS analysis of biological samples.

Carbon-based nanoparticles (CNPs) have emerged as novel man-made materials with diverse applications, which may present significant risks to organisms. To bridge the gap in our knowledge of nanotoxicology, a number of in vitro or in vivo studies have been carried out. However, toxicity data remains limited. Herein, we employed a biospectroscopy approach to assess CNP-induced effects in zebrafish (Danio rerio). Zebrafish were exposed to Fullerene (C60), long or short multi-walled carbon nanotubes (MWCNTs), or single-walled carbon nanotubes (SWCNTs) for 21 days at two concentrations: 0.1 mg L(-1) or 0.001 mg L(-1). Following exposure, the brain, gills, gonads and liver from zebrafish were interrogated by attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy. Computational analysis was then applied to the acquired infrared (IR) spectra, and distinct biochemical segregations between the exposed tissues vs. control were observed with spectral biomarkers of alterations identified. In addition, lipid-to-protein ratios in all four tissues were calculated by the IR spectra; unsaturated lipid levels in brain and gonad were assessed by Raman spectroscopy. Marked lipid alterations were observed. These findings show that biospectroscopy approaches have the potential to detect CNP-induced biochemical alterations in zebrafish.

Nanoparticles appear to induce toxic effects through a variety of mechanisms including generation of reactive oxygen species (ROS), physical contact with the cell membrane and indirect catalysis due to remnants from manufacture. The development and subsequent increasing usage of nanomaterials has highlighted a growing need to characterize and assess the toxicity of nanoparticles, particularly those that may have detrimental health effects such as carbon-based nanomaterials (CBNs). Due to interactions of nanoparticles with some reagents, many traditional toxicity tests are unsuitable for use with CBNs. Infrared (IR) spectroscopy is a non-destructive, high throughput technique, which is unhindered by such problems. We explored the application of IR spectroscopy to investigate the effects of CBNs on Gram-negative (Pseudomonas fluorescens) and Gram-positive (Mycobacterium vanbaalenii PYR-1) bacteria. Two types of IR spectroscopy were compared: attenuated total reflection Fourier-transform infrared (ATR-FTIR) and synchrotron radiation-based FTIR (SR-FTIR) spectroscopy. This showed that Gram-positive and Gram-negative bacteria exhibit differing alterations when exposed to CBNs. Gram-positive bacteria appear more resistant to these agents and this may be due to the protection afforded by their more sturdy cell wall. Markers of exposure also vary according to Gram status; Amide II was consistently altered in Gram-negative bacteria and carbohydrate altered in Gram-positive bacteria. ATR-FTIR and SR-FTIR spectroscopy could both be applied to extract biochemical alterations induced by each CBN that were consistent across the two bacterial species; these may represent potential biomarkers of nanoparticle-induced alterations. Vibrational spectroscopy approaches may provide a novel means of fingerprinting the effects of CBNs in target cells.

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.

Cervical cancer screening programmes have greatly reduced the burden associated with this disease. However, conventional cervical cytology screening still lacks sensitivity and specificity. There is an urgent need for the development of a low-cost robust screening technique. By generating a spectral "biochemical-cell fingerprint", Fourier-transform infrared (FTIR) spectroscopy has been touted as a tool capable of segregating grades of dysplasia. A total of 529 specimens were collected over a period of one year at two colposcopy centres in Dublin, Ireland. Of these, n = 128 were conventionally classed as high-grade, n = 186 as low-grade and n = 215 as normal. Following FTIR spectroscopy, derived spectra were examined for segregation between classes in scores plots generated with subsequent multivariate analysis. A degree of crossover between classes was noted and this could be associated with imperfect conventional screening resulting in an inaccurate diagnosis or an incomplete transition between classes. Maximal crossover associated with n = 102 of 390 specimens analyzed was found between normal and low-grade specimens. However, robust spectral differences (P≤ 0.0001) were still observed at 1512 cm(-1), 1331 cm(-1) and 937 cm(-1). For high-grade vs. low-grade specimens, spectral differences (P≤ 0.0001) were observed at Amide I (1624 cm(-1)), Amide II (1551 cm(-1)) and asymmetric phosphate stretching vibrations (νasPO2(-); 1215 cm(-1)). Least crossover (n = 50 of 343 specimens analyzed) was seen when comparing high-grade vs. normal specimens; significant inter-class spectral differences (P≤ 0.0001) were noted at Amide II (1547 cm(-1)), 1400 cm(-1) and 995 cm(-1). Deeper understanding of the underlying changes in the transition between cervical cytology classes (normal vs. low-grade vs. high-grade) is required in order to develop biospectroscopy tools as a screening approach. This will then allow for the development of blind classification algorithms.

This study evaluated the potential of deuteration to enhance the mechanistic information obtainable by biospectroscopy techniques in biological-cell models. These techniques were previously demonstrated to identify low-dose effects (≤nM) induced by test agents; this is of critical interest in terms of developing novel approaches to monitor environmentally-induced cell alterations. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was coupled with multivariate analysis to characterize a low-dose (10(-10) M) compared to a high-dose (10(-6) M) exposure of benzo[a]pyrene (B[a]P) in oestrogen-responsive MCF-7 cells; these results were used as a positive control for spectroscopic detection of B[a]P-induced effects. Deuterium oxide (D2O) was then applied as part of a fixative solution and/or at low levels incorporated into growth medium prior to ATR-FTIR spectrochemical analysis. The application of D2O as an alternative solvent in spectroscopy is widespread, but D2O has never before been applied to biospectroscopic analysis of in vitro toxicology assays. This allowed comparison between deuterated- and typically-derived IR spectra, facilitating significant insights into the effects of deuteration, and suggested that the addition of D2O to biospectroscopy assays could improve understanding of low-dose effects.

Understanding stem cell (SC) biology remains challenging and one of the few human tissues within which their in situ location is well characterized is the cornea. Individual human corneal epithelial cells were isolated from biopsies of live tissues using fluorescence-activated cell sorting (FACS); these were divided into putative SCs, transit-amplifying (TA) cells and terminally-differentiated (TD) cells. Employing synchrotron radiation-based Fourier-transform infrared (SR-FTIR) microspectroscopy with a focal plane array (FPA), sub-cellular spatial resolution analysis of unstained isolated cells was achieved as a consequence of the brilliance of a 12 collimated beams arrangement allowing rapid spectral acquisition. Infrared (IR) spectra were extracted and pre-processed. Subsequent categorization with multivariate analysis of IR spectra derived from FPA images was used to investigate biomolecular changes between classes. A progressive segregation in cell-specific spectral categories with differentiation from SC to TA cell to TD cell was noted. Multiple different absorption peaks that discriminated putative SCs, TA cells and TD cells across DNA, protein and lipid spectral regions were identified. DNA regions (1080 and 1225 cm(-1)) and some protein regions (1443 cm(-1)) primarily segregated SCs from TA cells and TD cells, whilst amide regions and lipids (1,550, 1650 and 1740 cm(-1)) segregated TA cells and TD cells. Scanning electron microscopy images verified the external phenotypic characteristics of the different isolated cell types. These findings highlight the applicability of SR-FTIR microspectroscopy towards distinguishing SCs, TA cells and TD cells, and suggest that cellular classification via traditional methods of immunolabelling can be greatly aided by the use of spectral biomarkers.

Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively novel. Potential fields of application include cytological, histological and microbial studies. This potentially provides a rapid and non-destructive approach to clinical diagnosis. Its increase in application is primarily a consequence of developing instrumentation along with computational techniques. In the coming decades, biospectroscopy is likely to become a common tool in the screening or diagnostic laboratory, or even in the general practitioner's clinic. Despite many advances in the biological application of FTIR spectroscopy, there remain challenges in sample preparation, instrumentation and data handling. We focus on the latter, where we identify in the reviewed literature, the existence of four main study goals: Pattern Finding; Biomarker Identification; Imaging; and, Diagnosis. These can be grouped into two frameworks: Exploratory; and, Diagnostic. Existing techniques in Quality Control, Pre-processing, Feature Extraction, Clustering, and Classification are critically reviewed. An aspect that is often visited is that of method choice. Based on the state-of-art, we claim that in the near future research should be focused on the challenges of dataset standardization; building information systems; development and validation of data analysis tools; and, technology transfer. A diagnostic case study using a real-world dataset is presented as an illustration. Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.

Approaches that allow one to rapidly understand tissue structure and functionality in situ remain to be developed. Such techniques are required in many instances, including where there is a need to remove with a high degree of confidence positive tumour margins during surgical excision. As biological tissue has little contrast, gold standard confirmation of surgical margins is conventionally undertaken by histopathological diagnosis of tissue architecture via optical microscopy. Vibrational spectroscopy techniques, when coupled to sophisticated computational analyses, are capable of constructing bio-molecular contrast images of unstained tissue. To assess the relative applicability of a range of candidate algorithms to distinguish the in situ bio-molecular structures of a complex tissue, the empty modelling approach of multivariate curve resolution-alternating least squares (MCR-ALS) was compared to hierarchical cluster analysis (HCA) or principal component analysis (PCA). Such chemometric analyses were applied to Raman images of benign (tumour-adjacent) endometrium, stage I and stage II endometrioid cancer. Re-constructed images from the in situ bio-molecular tissue architectures highlighted features associated with glandular epithelium, stroma, glandular lumen and myometrium. Of the tested chemometric analyses, MCR-ALS provided the best bio-molecular contrast images, superior to those derived following HCA or PCA, with clear and defined margins of histological features. Iteratively-resolved spectra identified wavenumbers responsible for the contrast image. Wavenumbers 1234 cm(-1) (Amide III), 1390 cm(-1) (CH(3) bend), 1675 cm(-1) (Amide I/lipid), 1275 cm(-1) (Amide III), 918 cm(-1) (proline) and 936 cm(-1) (proline, valine and proteins) were responsible for generating the majority of the contrast within MCR-ALS-generated images. Applications of sophisticated computational analyses coupled with vibrational spectroscopy techniques have the potential to lend novel functionality insights into bio-molecular structures in vivo.

Endometriosis is the growth of endometrial tissue outside of the uterine cavity. Its aetiology remains obscure, and it is difficult to diagnose ranging from asymptomatic to debilitating disease. Mid-infrared (IR) spectroscopy has become recognised as a potential clinical diagnostic tool. Biomolecules absorb mid-IR (4000 cm(-1) to 400 cm(-1)) and from this, a biochemical-cell fingerprint in the form of an absorbance spectrum can be derived. We set out to determine if IR spectroscopy could be used to identify underlying biochemical differences between endometrial tissues growing outside of the uterus (ectopic) from endometrial tissue of the uterus (eutopic). For comparative purposes, endometrial tissues from endometriosis-free women were also obtained (benign eutopic). Attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy or transmission FTIR microspectroscopy was employed for spectral acquisition. Principal component analysis (PCA)-linear discriminant analysis (LDA) was used for chemometric analysis. A clear segregation was exhibited between the three categories independent of inter-individual confounding differences. Importantly, there was a marked difference between eutopic endometrial tissue from patients with or without endometriosis. This indicates that IR spectroscopy coupled with multivariate analysis (e.g., PCA-LDA) may provide a non-invasive diagnostic tool for endometriosis. By analysing the underlying biochemistry of these endometrial tissues, this approach may facilitate a better understanding of this pathology.

The cornea is one of the few human tissues where the in situ locations of stem cells (SCs), transient-amplifying (TA) cells and terminally-differentiated (TD) cells have been relatively well localised and characterised. Mid-infrared (IR) (4000-400 cm(-1)) is absorbed by biological molecules and facilitates the acquisition in the biochemical-cell fingerprint region (1800-900 cm(-1)) of spectra representative of structure and function. Human cornea derived from normal or squamous cell carcinoma (SCC) samples were acquired, cryosectioned (10 µm), floated onto BaF(2) windows and interrogated using synchrotron-based radiation (SRS) Fourier-transform IR (FTIR) microspectroscopy. Spectra were analysed using principal component analysis (PCA) with or without linear discriminant analysis (LDA) to allow cluster analysis of the cell categories. A clear cell lineage emanating from SCs to TA cells to TD cells was noted in normal samples. Within the SCC samples, a small sub-population of the cell-derived spectra pointed to a SC-like phenotype with the vast majority pointing to a TA cell-like character; these cells would tend to be the most proliferative within a tissue. Our findings suggest that SRS FTIR microspectroscopy has the potential to identify and characterise cancer SCs.

The prostate gland is the most common site of pathology in human males. Using the urethra as an anatomical reference point, it can be divided into three distinct zones known as the transition zone (TZ), peripheral zone (PZ) and central zone (CZ). The pathological conditions of benign prostatic hypertrophy and/or prostate adenocarcinoma are highly prevalent in this gland. This preliminary study set out to determine whether biochemical intra-individual differences between normal prostate zones could be identified using Raman spectroscopy with subsequent exploratory analyses. A normal (benign) prostate transverse tissue section perpendicular to the rectal surface and above the verumontanum was obtained in a paraffin-embedded block. A 10-µm-thick slice was floated onto a gold substrate, de-waxed and analysed using Raman spectroscopy (200 epithelial-cell and 140 stromal spectra/zone). Raman spectra were subsequently processed in the 1800-367 cm(-1) spectral region employing principal component analysis (PCA) to determine whether wavenumber-intensity relationships expressed as single points in hyperspace might reveal biochemical differences associated with inter-zone pathological susceptibility. Visualisation of PCA scores plots and their corresponding loadings plots highlighted 781 cm(-1) (cytosine/uracil) and 787 cm(-1) (DNA) as the key discriminating factors segregating PZ from less susceptible TZ and CZ epithelia (P < 0.001). Conversely, 1459 cm(-1) (lipids and proteins) and 1003 cm(-1) (phenylalanine) were identified as the key biochemical factor distinguishing TZ from CZ epithelia (P < 0.05). All stromal zones were discriminated by the protein/lipid region (1459 cm(-1) and 1100 cm(-1)) with DNA/RNA region (781 cm(-1) and 787 cm(-1)) only highlighted between PZ and CZ (P < 0.05). This novel approach identifies biochemical markers that may have aetiological functional roles towards susceptibility of human prostate zones to specific pathological conditions.

The Syrian hamster embryo (SHE) assay (pH 6.7) is an in vitro candidate to replace in vivo carcinogenicity tests. However, the conventional method of visual scoring of foci (non-transformed vs. transformed colonies) can be time-consuming and is open to subjectivity. Infrared (IR) spectroscopy has the potential to provide objective assessment of such SHE colonies with the added advantage of potentially providing mechanistic information. In this study, SHE cells were treated with one of eight different chemical regimens, allowed in culture to attach and form foci on IR-reflective glass slides; these were subsequently interrogated by attenuated total reflection (ATR) Fourier-transform IR (FTIR) spectroscopy. Derived mid-IR spectra (n = 13,406) were subjected to chemometric analysis focusing primarily on the extraction of biochemical information related to test agent treatment and/or morphological transformation. The use of ATR-FTIR spectroscopy with chemometrics to analyze the SHE assay is a novel approach to toxicological assessment.