Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother's milk.

This study presents differentiation in milk samples of mother's feeding male and female infants using Raman spectroscopy combined with a support vector machine (SVM). Major differences have been observed in the Raman spectra of both types of milk based on their chemical compositions. Overall, it has been found that milk samples of mother's having a female infant are richer in fatty acids, phospholipids, and tryptophan. In contrast, milk samples of mother's having a male infant contain more carotenoids and saccharides. Principal component analysis and SVM further highlighted the differences between the two groups on the basis of differentiating features obtained from their Raman spectra. The SVM model with two different kernels, i.e. polynomial kernel function (order-2) and Gaussian radial basis function (RBF sigma-2), are used for gender based milk differentiations. The performance of the proposed model in terms of accuracy, precision, sensitivity, and specificity using the polynomial kernel function of order-2 have been found to be 86%, 88%, 85% and 88%, respectively.

Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain.

This work presents a diagnostic system for the hepatitis C infection using Raman spectroscopy and proximity based classification. The proposed method exploits transformed Raman spectra using the proximity based machine learning technique and is denoted as RS-PCA-Prox. First, Raman spectral data is baseline corrected by subtracting noise and low intensity background. After this, a feature transformation of Raman spectra is adopted, not only to reduce the feature's dimensionality but also to learn different deviations in Raman shifts. The proposed RS-PCA-Prox shows significant diagnostic power in terms of accuracy, sensitivity, and specificity as 95%, 0.97 and 0.94 in PCA based transformed domain. The comparison of the RS-PCA-Prox with linear and ensemble based classifiers shows that proximity based classification performs better for the discrimination of HCV infected individuals and is able to differentiate the infected individuals from normal ones on the basis of molecular spectral information. Furthermore, it is observed that characteristic spectral changes are due to variation in the intensity of lectin, chitin, lipids, ammonia and viral protein as a consequence of the HCV infection.

Raman spectroscopy of bone composition during healing of subcritical calvarial defects.

Subcritical calvarial defects heal spontaneously and optical methods can study the healing without mechanically perturbing the bone. In this study, 1mm defects were created on the skulls (in vivo) of Sprague-Dawley rats (n = 14). After 7 (n = 7) and 14 days (n = 7) of healing, the subjects were sacrificed and additional defects were similarly created (control). Raman spectroscopy (785nm) was performed at the two time points and defect types. Spectra were quantified by the mineral/matrix ratio, carbonate/phosphate ratio and crystallinity. Mineral/matrix of in vivo defects is lower than that of controls by ~34% after 7 days and ~21% after 14 days. Carbonate/phosphate is 8% and 5% higher while crystallinity is 7% and 3% lower, respectively. Optical profiling shows that the surface roughness increases 1.2% from controls to in vivo after 7 days, then decreases 13% after 14 days. Overall, the results show maturation of mineral crystals during healing and agree with microscopic assessment.

Prenatal detection of thalassemia by cell-free fetal DNA (cffDNA) in maternal plasma using surface enhanced Raman spectroscopy combined with PCR.

Thalassemias are widely occurring genetic hemoglobin disorders; patients with severe thalassemia often require regular blood transfusions for survival. Prenatal detection of thalassemia is currently invasive and carries the risk of miscarriage and infection. A polymerase chain reaction (PCR)-based surface enhanced Raman spectroscopy (SERS) technique was investigated in this paper for the purpose of detecting prenatal α-thalassemia Southeast Asian (SEA) type deletion using maternal plasma. Couples with the same SEA thalassemia (-SEA/αα) were selected, and the quantification of SEA and wild type (WT) alleles in the maternal plasma sample predicted the fetal genotype. PCR was performed using two pairs of fluorescence tag-labeled primers to produce tag-labeled PCR products for both the SEA (labeled with R6G) and WT (labeled with Cy3) alleles. Then, the labeled PCR products containing the two fluorescence tags were measured by SERS. The ratios between the R6G and Cy3 tags were obtained using multiple linear regressions (MLR), and these ratios corresponded with the physical ratio of WT and SEA concentrations in maternal plasma. After verifying this technique on DNA mixtures with known SEA and WT ratios, the plasma from 24 pregnant women was screened. An accuracy of 91.7% was achieved for detecting the fetal genotypes of Hb Bart's, alpha-trait, and normal trait. The results indicated that the simple PCR-SERS method may be sensitive enough for use on cell free fetal DNA (cffDNA) in maternal plasma for non-invasive prenatal detection (NIPD).

Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy.

The discrimination accuracy for human and nonhuman blood is important for customs inspection and forensic applications. Recently, Raman spectroscopy has shown effectiveness in analyzing blood droplets and stains with an excitation wavelength of 785 nm. However, the discrimination of liquid whole blood in a vacuum blood tube using Raman spectroscopy, which is a form of noncontact and nondestructive detection, has not been achieved. An excitation wavelength of 532 nm was chosen to avoid the fluorescent background of the blood tube, at the cost of reduced spectroscopic information and discrimination accuracy. To improve the accuracy and true positive rate (TPR) for human blood, a dual-model analysis method is proposed. First, model 1 was used to discriminate human-unlike nonhuman blood. Model 2 was then used to discriminate human-like nonhuman blood from the "human blood" obtained by model 1. A total of 332 Raman spectra from 10 species were used to build and validate the model. A blind test and external validation demonstrated the effectiveness of the model. Compared with the results obtained by the single partial least-squares model, the discrimination performance was improved. The total accuracy and TPR, which are highly important for practical applications, increased to 99.1% and 97.4% from 87.2% and 90.6%, respectively.

Lactate based optical screening of dengue virus infection in human sera using Raman spectroscopy.

This study presents the screening of dengue virus (DENV) infection in human blood sera based on lactate concentration using Raman spectroscopy. A total of 70 samples, 50 from confirmed DENV infected patients and 20 from healthy volunteers have been used in this study. Raman spectra of all these samples have been acquired in the spectral range from 600 cm-1 to 1800 cm-1 using a 532 nm laser as an excitation source. Spectra of all these samples have been analyzed for assessing the biochemical changes resulting from infection. In DENV infected samples three prominent Raman peaks have been found at 750, 830 and 1450 cm-1. These peaks are most probably attributed to an elevated level of lactate due to an impaired function of different body organs in dengue infected patients. This has been proven by an addition of lactic acid solution to the healthy serum in a controlled manner. By the addition of lactic acid solution, the intense Raman bands at 1003, 1156 and 1516 cm-1 found in the spectrum of healthy serum got suppressed when the new peaks appeared around 750, 830, 925, 950, 1123, 1333, 1450, 1580 and 1730 cm-1. The current study predicts that lactate may possibly be a potential biomarker for the diagnosis of DENV infection.

In situ cellular level Raman spectroscopy of the thyroid.

We report a novel Raman spectroscopy method for in situ cellular level analysis of the thyroid. Thyroids are harvested from control and lithium treated mice. Lithium is used to treat bipolar disorder, but affects thyroid function. Raman spectra are acquired with a confocal setup (514 nm laser, 20 µm spot) focused on a follicular lumen. Raman peaks are observed at 1440, 1656, and 1746 cm-1, corresponding to tyrosine, an important amino acid for protein synthesis. Peaks are also observed at 563, 1087, 1265 and 1301 cm-1. With lithium, the tyrosine peaks increase, indicating tyrosine buildup. Raman spectroscopy can study the impact of many exogenous treatments on thyroid biochemistry.

Core/shell nanofiber characterization by Raman scanning microscopy.

Core/shell nanofibers are becoming increasingly popular for applications in tissue engineering. Nanofibers alone provide surface topography and increased surface area that promote cellular attachment; however, core/shell nanofibers provide the versatility of incorporating two materials with different properties into one. Such synthetic materials can provide the mechanical and degradation properties required to make a construct that mimics in vivo tissue. Many variations of these fibers can be produced. The challenge lies in the ability to characterize and quantify these nanofibers post fabrication. We developed a non-invasive method for the composition characterization and quantification at the nanoscale level of fibers using Confocal Raman microscopy. The biodegradable/biocompatible nanofibers, Poly (glycerol-sebacate)/Poly (lactic-co-glycolic) (PGS/PLGA), were characterized as a part of a fiber scaffold to quickly and efficiently analyze the quality of the substrate used for tissue engineering.

Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery.

Multimodal spectral histopathology (MSH), an optical technique combining tissue auto-fluorescence (AF) imaging and Raman micro-spectroscopy (RMS), was previously proposed for detection of residual basal cell carcinoma (BCC) at the surface of surgically-resected skin tissue. Here we report the development of a fully-automated prototype instrument based on MSH designed to be used in the clinic and operated by a non-specialist spectroscopy user. The algorithms for the AF image processing and Raman spectroscopy classification had been first optimised on a manually-operated laboratory instrument and then validated on the automated prototype using skin samples from independent patients. We present results on a range of skin samples excised during Mohs micrographic surgery, and demonstrate consistent diagnosis obtained in repeat test measurement, in agreement with the reference histopathology diagnosis. We also show that the prototype instrument can be operated by clinical users (a skin surgeon and a core medical trainee, after only 1-8 hours of training) to obtain consistent results in agreement with histopathology. The development of the new automated prototype and demonstration of inter-instrument transferability of the diagnosis models are important steps on the clinical translation path: it allows the testing of the MSH technology in a relevant clinical environment in order to evaluate its performance on a sufficiently large number of patients.

Raman active components of skin cancer.

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

Characterisation of signal enhancements achieved when utilizing a photon diode in deep Raman spectroscopy of tissue.

We characterise the performance of a beam enhancing element ('photon diode') for use in deep Raman spectroscopy (DRS) of biological tissues. The optical component enhances the number of laser photons coupled into a tissue sample by returning escaping photons back into it at the illumination zone. The method is compatible with transmission Raman spectroscopy, a deep Raman spectroscopy concept, and its implementation leads to considerable enhancement of detected Raman photon rates. In the past, the enhancement concept was demonstrated with a variety of samples (pharmaceutical tablets, tissue, etc) but it was not systematically characterized with biological tissues. In this study, we investigate the enhancing properties of the photon diode in the transmission Raman geometry as a function of: a) the depth and b) the optical properties of tissue samples. Liquid tissue phantoms were employed to facilitate systematic variation of optical properties. These were chosen to mimic optical properties of human tissues, including breast and prostate. The obtained results evidence that a photon diode can enhance Raman signals of tissues by a maximum of × 2.4, although it can also decrease the signals created towards the back of samples that exhibit high scattering or absorption properties.

Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

SERS-active Au/SiO2 clouds in powder for rapid ex vivo breast adenocarcinoma diagnosis.

In the present work, we report a dry-based application technique of Au/SiO2 clouds in powder for rapid ex vivo adenocarcinoma diagnosis through surface-enhanced Raman scattering (SERS); using low laser power and an integration time of one second. Several characteristic Raman peaks frequently used for the diagnosis of breast adenocarcinoma in the range of the amide III are successfully enhanced by breading the tissue with Au/SiO2 powder. The SERS activity of these Au/SiO2 powders is attributed to their rapid rehydration upon contact with the wet tissues, which promotes the formation of gold nanoparticle aggregates. The propensity of the Au/SiO2 cloud structures to adsorb biomolecules in the vicinity of the gold nanoparticle clusters promotes the necessary conditions for SERS detection. In addition, electron microscopy, together with elemental analysis, have been used to confirm the structure of the new Au/SiO2 cloud material and to investigate its distribution in breast tissues.

Tissue diagnosis using power-sharing multifocal Raman micro-spectroscopy and auto-fluorescence imaging.

We describe a multifocal Raman micro-spectroscopy detection method based on a digital micromirror device, which allows for simultaneous "power-sharing" acquisition of Raman spectra from ad hoc sampling points. As the locations of the points can be rapidly updated in real-time via software control of a liquid-crystal spatial light modulator (LC-SLM), this technique is compatible with automated adaptive- and selective-sampling Raman spectroscopy techniques, the latter of which has previously been demonstrated for fast diagnosis of skin cancer tissue resections. We describe the performance of this instrument and show examples of multiplexed measurements on a range of test samples. Following this, we show the feasibility of reducing measurement time for power-shared multifocal Raman measurements combined with confocal auto-fluorescence imaging to provide guided diagnosis of tumours in human skin samples.

Rapid spontaneous Raman light sheet microscopy using cw-lasers and tunable filters.

We perform rapid spontaneous Raman 2D imaging in light-sheet microscopy using continuous wave lasers and interferometric tunable filters. By angularly tuning the filter, the cut-on/off edge transitions are scanned along the excited Stokes wavelengths. This allows obtaining cumulative intensity profiles of the scanned vibrational bands, which are recorded on image stacks; resembling a spectral version of the knife-edge technique to measure intensity profiles. A further differentiation of the stack retrieves the Raman spectra at each pixel of the image which inherits the 3D resolution of the host light sheet system. We demonstrate this technique using solvent solutions and composites of polystyrene beads and lipid droplets immersed in agar and by imaging the C-H (2800-3100cm(-1)) region in a C. elegans worm. The image acquisition time results in 4 orders of magnitude faster than confocal point scanning Raman systems, allowing the possibility of performing fast spontaneous Raman·3D-imaging on biological samples.

Spontaneous and coherent anti-Stokes Raman spectroscopy of human gastrocnemius muscle biopsies in CH-stretching region for discrimination of peripheral artery disease.

Peripheral Artery Disease (PAD) is a common manifestation of atherosclerosis, characterized by lower leg ischemia and myopathy in association with leg dysfunction. In the present study, Spontaneous and coherent anti-Stokes Raman scattering (CARS) spectroscopic techniques in CH-stretching spectral region were evaluated for discriminating healthy and diseased tissues of human gastrocnemius biopsies of control and PAD patients. Since Raman signatures of the tissues in the fingerprint region are highly complex and CH containing moieties are dense, CH-stretching limited spectral range was used to classify the diseased tissues. A total of 181 Raman spectra from 9 patients and 122 CARS spectra from 12 patients were acquired. Due to the high dimensionality of the data in Raman and CARS measurements, principal component analysis (PCA) was first performed to reduce the dimensionality of the data (6 and 9 principal scores for Raman and CARS, respectively) in the CH-stretching region, followed by a discriminant function analysis (DFA) to classify the samples into different categories based on disease severity. The CH2 and CH3 vibrational signatures were observed in the Raman and CARS spectroscopy. Raman and CARS data in conjunction with PCA-DFA analysis were capable of differentiating healthy and PAD gastrocnemius with an accuracy of 85.6% and 78.7%, respectively.

Clinical superficial Raman probe aimed for epithelial tumor detection: Phantom model results.

A novel clinical Raman probe for sampling superficial tissue to improve in vivo detection of epithelial malignancies is compared to a non-superficial probe regarding depth response function and signal-to-noise ratio. Depth response measurements were performed in a phantom tissue model consisting of a polyethylene terephthalate disc in an 20%-Intralipid(®) solution. Sampling ranges of 0-200 and 0-300 µm were obtained for the superficial and non-superficial probe, respectively. The mean signal-to-noise ratio of the superficial probe increased by a factor of 2 compared with the non-superficial probe. This newly developed superficial Raman probe is expected to improve epithelial cancer detection in vivo.

Classification for breast cancer diagnosis with Raman spectroscopy.

In order to promote the development of the portable, low-cost and in vivo cancer diagnosis instrument, a miniature laser Raman spectrometer was employed to acquire the conventional Raman spectra for breast cancer detection in this paper. But it is difficult to achieve high discrimination accuracy. Then a novel method of adaptive weight k-local hyperplane (AWKH) is proposed to increase the classification accuracy. AWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN). It considers the features weights of the training data in the nearest neighbor selection and local hyperplane construction stage, which resolve the basic shortcoming of HKNN works well only for small values of the nearest-neighbor. Experimental results on Raman spectra of breast tissues in vitro show the proposed method can realize high classification accuracy.

Evaluation of antibiotic effects on Pseudomonas aeruginosa biofilm using Raman spectroscopy and multivariate analysis.

We investigate the mode of action and classification of antibiotic agents (ceftazidime, patulin, and epigallocatechin gallate; EGCG) on Pseudomonas aeruginosa (P. aeruginosa) biofilm using Raman spectroscopy with multivariate analysis, including support vector machine (SVM) and principal component analysis (PCA). This method allows for quantitative, label-free, non-invasive and rapid monitoring of biochemical changes in complex biofilm matrices with high sensitivity and specificity. In this study, the biofilms were grown and treated with various agents in the microfluidic device, and then transferred onto gold-coated substrates for Raman measurement. Here, we show changes in biochemical properties, and this technology can be used to distinguish between changes induced in P. aeruginosa biofilms using three antibiotic agents. The Raman band intensities associated with DNA and proteins were decreased, compared to control biofilms, when the biofilms were treated with antibiotics. Unlike with exposure to ceftazidime and patulin, the Raman spectrum of biofilms exposed to EGCG showed a shift in the spectral position of the CH deformation stretch band from 1313 cm(-1) to 1333 cm(-1), and there was no difference in the band intensity at 1530 cm(-1) (C = C stretching, carotenoids). The PCA-SVM analysis results show that antibiotic-treated biofilms can be detected with high sensitivity of 93.33%, a specificity of 100% and an accuracy of 98.33%. This method also discriminated the three antibiotic agents based on the cellular biochemical and structural changes induced by antibiotics with high sensitivity and specificity of 100%. This study suggests that Raman spectroscopy with PCA-SVM is potentially useful for the rapid identification and classification of clinically-relevant antibiotics of bacteria biofilm. Furthermore, this method could be a powerful approach for the development and screening of new antibiotics.

Confocal Raman micro-spectroscopy for rapid and label-free detection of maleic acid-induced variations in human sperm.

Confocal Raman microspectroscopy is a valuable analytical tool in biological and medical research, allowing the detection of sample variations without external labels or extensive preparation. To determine whether this method can assess the effect of maleic acid on sperm, we prepared human sperm samples incubated in different concentrations of maleic acid, after which Raman spectra from the various regions of sperm cells were recorded. Following the maleic acid treatment, Raman spectra indicated significant changes. Combined with other means, we found that the structures and chemical compositions of sperm membranes were damaged, and even the sperm DNA was damaged by the incorporation of maleic acid. Thus, this technique can be used for detection and identification of maleic acid-induced changes in human sperm at a molecular level. Although this particular application of Raman microspectroscopy still requires further validation, it has potentially promise as a diagnostic tool for reproductive medicine.