Potential Of Raman Spectroscopy Research Articles

Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy.

Rice (Oryza sativa) is the primary crop for nearly half of the world's population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS's potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.

Identification of three medically important mosquito species using Raman spectroscopy

AbstractAccurate identification of disease vectors is crucial when collecting epidemiological data. In mosquitoes, which transmit diseases like malaria, yellow fever, chikungunya, and dengue fever, identification mainly relies on the observation of external morphological features at different life cycle stages. This process is tedious and labor‐intensive. In this paper, the utility of Raman spectroscopy to discriminate and classify three mosquito species, namely,Aedes aegypti,Anopheles gambiae,andCulex quinquefasciatus,is presented. The three species were chosen to represent two subfamilies of medically important mosquitoes, that is, the Anophelinae and the Culicinae. The study is primarily a proof of concept on the potential of Raman spectroscopy in mosquito taxonomy. A dispersive Raman microscope was used to record spectra from the legs (femur and tibia) of fresh anesthetized laboratory‐bred mosquitoes. Broad peaks centered around 1400, 1590, and 2060 cm−1dominated the spectra. These peaks, attributed to cuticular melanin, were important in mosquito discrimination. Variance threshold (VT) and principal component analysis (PCA) were used for feature selection and feature extraction, respectively. The extracted features were then used to train and test linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. VT/PCA/QDA achieved an overall accuracy of 94%, a sensitivity of 87%, and a specificity of 96%, whereas VT/PCA/LDA attained an accuracy of 85%, a sensitivity of 69%, and a specificity of 90%. The success of these relatively simple classification models on Raman spectroscopy data lays the ground for future development of machine learning models that may include discrimination of cryptic species.

Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women's Health Issue-A Review.

This review focuses on recent advances and future perspectives in the use of Raman spectroscopy for cervical cancer, a global women's health issue. Cervical cancer is the fourth most common women's cancer in the world, and unfortunately mainly affects younger women. However, when detected at the early precancer stage, it is highly treatable. High-quality cervical screening programmes and the introduction of the human papillomavirus (HPV) vaccine are reducing the incidence of cervical cancer in many countries, but screening is still essential for all women. Current gold standard methods include HPV testing and cytology for screening, followed by colposcopy and histopathology for diagnosis. However, these methods are limited in terms of sensitivity/specificity, cost, and time. New methods are required to aid clinicians in the early detection of cervical precancer. Over the past 20 years, the potential of Raman spectroscopy together with multivariate statistical analysis has been shown for the detection of cervical cancer. This review discusses the research to date on Raman spectroscopic approaches for cervical cancer using exfoliated cells, biofluid samples, and tissue ex vivo and in vivo.

Study on the Raman spectral characteristics of dynamic and static blood and its application in species identification.

This paper proposes a method to identify the blood of 4 poultry species (chicken, duck, goose and pigeon) based on Raman spectroscopy and its baseline. Samples were prepared by pretreatment methods of freezing, thawing, and dilution. The Raman spectra of dynamic blood and static blood were measured, respectively, and the spectral differences between the two research schemes were analyzed. The four species of poultry blood were identified based on the Raman spectroscopy and its baseline. The results show that the method can realize the identification of four species of poultry blood. In addition, the potential of Raman spectroscopy as a technique for determining carotenoids in blood has been clearly confirmed, which opens up the possibility to quickly determine whether poultry eats feed containing carotenoids without sample preparation.

Quantification of clinical mAb solutions using Raman spectroscopy: Macroscopic vs microscopic analysis

Raman Spectroscopy is well emerged in the field of Analytical Quality Control (AQC) as a rapid and cost-effective technique useful in many applications. The advantage of Raman spectroscopy is the non-invasiveness of measurements that enablesto analyse samples directly in its container. In this study, the potential of Raman spectroscopy was investigated for analysis of clinical preparations of mAbs. Three commercial formulations of monoclonal antibodies (mAbs) Avastin®, Ontruzant® and Tecentriq® corresponding to Bevacizumab (BVC), Trastuzumab (TRS) and Atezolizumab (ATZ) respectively, were analysed in quartz cuvette in macroscopic analysis and through the wall of perfusion bags in microscopic analysis. The spectra have been compared to those of excipients (trehalose and sucrose) and of γ-Globulin, in order to investigate the origin of Raman bands. As expected, Raman spectra were a combination of bands from monoclonal antibodies and correspoding excipients found in formulas. For quantitative analysis of the solutions, models have been constructed using Partial Least Square Regression (PLSR) with Leave K-Out Cross Validation (LKOCV). The quantification performance was comparable for both macroscopic and microscopic analysis, in terms of error and linearity. The results are thus promising for future AQC in situ, in perfusion bags.

Detection and identification of drug traces in latent fingermarks using Raman spectroscopy

Recent advancements in analytical techniques have greatly contributed to the analysis of latent fingermarks’ (LFMs) “touch chemistry” and identification of materials that a suspect might have come into contact with. This type of information about the FM donor is valuable for criminal investigations because it narrows the pool of suspects. It is estimated that at least 30 million people around the world take over-the-counter and prescription nonsteroidal anti-inflammatory drugs (NSAIDs) for pain relief, headaches and arthritis every day. The daily use of such drugs can lead to an increased risk of their abuse. In the present study, Raman spectroscopy combined with multivariate statistical analysis was used for the detection and identification of drug traces in LFMs when NSAID tablets of aspirin, ibuprofen, diclofenac, ketoprofen and naproxen have been touched. Partial least squares discriminant analysis of Raman spectra showed an excellent separation between natural FMs and all NSAID-contaminated FMs. The developed classification model was externally validated using FMs deposited by a new donor and showed 100% accuracy on a FM level. This proof-of-concept study demonstrated the great potential of Raman spectroscopy in the chemical analysis of LFMs and the detection and identification of drug traces in particular.

Quantitative analysis of solid dosage forms of Losartan potassium by Raman spectroscopy

Raman spectroscopy is an outstanding analytical tool increasingly utilized in the pharmaceutical field for the solid-state pharmaceutical drug analysis. In current study, the potential of Raman spectroscopy has been investigated for qualitative and quantitative analysis of solid dosage form of Losartan potassium. For this purpose, different solid dosage forms/concentrations of losartan potassium were prepared to compensate the commercially available pharmaceutical drug formulations and their Raman spectral data showed a gradual change in the specific Raman spectral features associated with the active pharmaceutical ingredient (API) of Losartan potassium as a function of change in the concentration. The Raman spectral data was analyzed by using Principal Component Analysis (PCA) for the classification of different spectral data sets of different concentrations of drug. Moreover, partial least square regression (PLSR) analysis was performed for monitoring the quantitative relation among different concentrations of Losartan potassium API and spectral data by constructing a predictive model. From the model, the value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were observed to be 0.38 and 2.98 respectively and the value of goodness of fit was found to be 0.99. Furthermore, the quantity of unknown/blind sample of Losartan potassium formulation was also estimated by using PLSR model. From these results, it is demonstrated that Raman spectroscopy can be considered to be used for quick and reliable quantitative analysis of pharmaceutical solids.

Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.

Raman spectroscopic detection of interleukin-10 and angiotensin converting enzyme

In this report we present a confocal Raman system to identify the unique spectral features of two proteins, Interleukin-10 and Angiotensin Converting Enzyme. Characteristic Raman spectra were successfully acquired and identified for the first time to our knowledge, showing the potential of Raman spectroscopy as a non-invasive investigation tool for biomedical applications.

Risk prediction by Raman spectroscopy for disease-free survival in oral cancers

In the present study, the potential of Raman spectroscopy (RS) in predicting disease-free survival (DFS) in oral cancer patients has been explored. Raman spectra were obtained from the tumor and contralateral regions of 94 oral squamous cell carcinoma patients. These patients were managed surgically and recommended for adjuvant therapy. The Cox proportional survival analysis was carried out to identify the spectral regions that can be correlated to DFS. The survival analysis was performed with 95% confidence intervals, hazard ratio, and p-values in the 1200-1800 cm-1 spectral region. Out of a total of 182 spectral points, 76 were found to be correlating with DFS, suggesting their utility to predict the patient outcome. The cut-off points of each correlating RS-point values were defined and tested towards predicting the DFS. The performance of predicting the power of spectral points was validated through Brier value, and it was found to be closer to the actual progression. The 76 spectral points identified from the tumors have the potential to accurately predict DFS in oral squamous cell carcinoma through a relatively simplistic prediction model in the absence of confounding factors.

Exploring a possibility of using Raman spectroscopy for detection of Lyme disease.

Lyme disease (LD), one of the most prevalent tick-borne diseases in the United States (US), is caused by Borreliella burgdorferi sensu stricto (Bb). To date, in the US, LD diagnostics is primarily based on validated two-tiered serological testing, which overall exhibits low sensitivity among other drawbacks. In the present study, a potential of Raman spectroscopy (RS) to detect Bb infection in mice has been explored. For that, C3H mice were infected with wild-type Bb strains, 297, B31, or B31-derived mutant, ∆vlsE. Blood samples taken prior to and post Bb infection were subjected to RS. The data demonstrated that RS did not directly detect Bb spirochetes in blood, but rather sensed biochemical changes associated with Bb infection. Despite Bb infection-associated blood changes detectable by RS were very limited, the partial least square discriminant analysis showed that the average true positive rates were 86% for 297 and 89% for B31 and ∆vlsE.

The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions.

Simple SummaryRaman spectroscopy, a light scattering technique that provides the biochemical fingerprint of a sample, was used on samples taken from patients with cancer and precancerous lesions. This information was then used to build a classifier to identify cancer and the precancerous phases. The ability to distinguish cancerous tissue from normal and precancerous tissue is diagnostically crucial as it can alter the patients’ prognosis and management. Moreover, as cellular changes are often present at the tumour margin, the ability to distinguish these changes from cancer can help in preserving more of the tissue and maintaining aesthetics and functionality for the patient.Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter- and intra-observer errors and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%.

Microbial phenomics linking the phenotype to fonction: The potential of Raman spectroscopy.

Raman spectroscopy is a promising tool for identifying microbial phenotypes based on single cell Raman spectra reflecting cellular biochemical biomolecules. Recent studies using Raman spectroscopy have mainly analyzed phenotypic changes caused by microbial interactions or stress responses (e.g., antibiotics) and evaluated the microbial activity or substrate specificity under a given experimental condition using stable isotopes. Lack of labelling and the nondestructive pretreatment and measurement process of Raman spectroscopy have also aided in the sorting of microbial cells with interesting phenotypes for subsequently conducting physiology experiments through cultivation or genome analysis. In this review, we provide an overview of the principles, advantages, and status of utilization of Raman spectroscopy for studies linking microbial phenotypes and functions. We expect Raman spectroscopy to become a next-generation phenotyping tool that will greatly contribute in enhancing our understanding of microbial functions in natural and engineered systems.

A Raman spectroscopy study of the oxidation processes in synthetic chromite FeCr2O4

A crystal of synthetic chromite FeCr2O4 has been annealed in air at 700 °C for 50 days at room pressure in order to study physical-chemical changes. After the annealing treatment, Scanning Electron Microscopy (SEM) images of the polished surface of the sample showed areas of different composition. Detailed Raman mapping revealed that the annealed chromite undergoes an oxidation process, proceeding inwards from the outer surface and leading to the transformation of chromite to magnetite, and ultimately to hematite. The oxidation process also leads to the formation of trellis-like lines, arguably via stress-related mechanisms associated with the phase transformation and consequent volume changes. The present study confirms the possibility of non-stoichiometry or structural defects causing slight structural asymmetries and the formation of new modes, resulting in a valuable support in characterizing both natural and artificial material. The potential of Raman spectroscopy in the identification and spatial resolution of mineralogical phases, is also highlighted.

Raman spectroscopy as a tool for provenancing black limestones (bigi morati) used in antiquity

AbstractAmong precious stones used in antiquity, bigi morati stand out for their importance. They consist in black limestones mainly employed in sculptures and architectural heritages and quarried from several sites through the Mediterranean area. Their provenance is difficult to assess due to their similar appearance to the naked‐eye observation and the possible confusion with black marbles (i.e., bigi antichi). Classical methods used for studying ancient marbles, such as minero‐petrographic and stable isotopes analyses (δ13C and δ 16O), resulted to be powerful in provenancing these black lithotypes. However, these methods are microdestructive and cannot be always applied in the case of precious artefacts for which sampling is not allowed. Bearing carbonaceous matter conferring them the typical black‐dark grey colour, Raman spectroscopy might support studies on these limestones, bringing important information about the structural state of organic/carbonaceous matter and their geological evolution. In this study, the potential of Raman spectroscopy in determining geological and geographic provenance of bigi morati was tested on quarry samples from six among the most famous ancient exploitation sources. The obtained results demonstrated that the use of Raman parameters typical of low‐ordered carbon matter is an effective tool in discriminating provenance. Moreover, the laboratory‐based approach lays the groundwork for future development and validation of the method for in situ measurements on artefacts by using portable equipment.

Characterization of the Degradation of Sheepskin by Monitoring Cytochrome c of Bacteria by Raman Spectroscopy

To probe the bacterial growth on animal skin, there is a need for a rapid, reliable, and sensitive method that is able shed light on the mechanism by which bacteria grow under different environmental conditions. Raman spectroscopy provides information about the changes in the molecular structure of skin through rapid and continuous monitoring of the growth of bacteria. In this study, sheepskin inoculated with and without Aeromonas hydrophilia bacteria followed by storage at 4 °C for 10 days was investigated using Raman spectroscopy. Using the resonance Raman of cytochrome c found in Aeromonas hydrophilia, the alterations in the biochemistry of sheepskin upon bacterial growth was studied. A detailed analysis of spectra reveals that bacterial cytochrome c band at 747 cm−1 increased by 72.3% and another prominent cytochrome c marker band at 1582 cm−1 increased by 70.6% from day 3 to day 7. The changes in the Raman band area of biomolecules such as proteins, lipids, and cytochrome c were measured and reported using ratiometric analysis of four novel ratiometric markers (I747/1646, I1445/1646, I1581/1646, I1618/I1646). Curve-fitting by the sum-of-Gaussians method was carried out on the amide I band at 1646 cm−1 to investigate the secondary structures responsible for alterations in the collagen network for a period of 10 days. Multivariate analysis methods such as principal component analysis also supported the ratiometric markers of bacterial growth. Altogether the findings obtained highlight the potential of Raman spectroscopy, in combination with multivariate analysis, for the identification of universal biochemical signatures indicative of bacterial growth on sheepskin. This study opens the possibility for the development of preservation methods for the prevention of bacterial degradation in skins stored in a refrigerator at an optimum temperature of 4 °C.

Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil

This study presents a method to determine adulteration of olive oil (obtained from Olea europea, i.e. olives) with rapeseed oil (obtained from Brassica napus) or with corn oil (also named maize oil, obtained from Zea mays, i.e. maize) using Raman spectroscopy and a mathematical method based on exponential equation fit. The samples were prepared by mixing olive oil with volume fractions (0–100%) of rapeseed or corn oil. The oils were differentiated spectroscopically using intensity ratio for specific Raman peaks; Raman spectroscopy is able to detect changes within a liquid molecular environment without the need for sample treatment. It was possible to determine rapeseed or corn oil volume fractions added into the olive oil using the method proposed. Thus, the potential of Raman spectroscopy as a technique for determining adulteration of olive oil was corroborated clearly, opening the potential to investigate adulteration of other liquid foods, without any need for sample preparation.

Optical diagnosis of hepatitis B virus infection in blood plasma using Raman spectroscopy and chemometric techniques

AbstractThe potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy.

Estimating species concentration in CO2-loaded monoethanolamine using Raman spectroscopy

Operation of amine plant for CO2 removal had long been plagued with inefficiencies due to suboptimal operating parameter leading to losses in operational expenditure. Improving the system requires understanding into thermodynamics and kinetics of the process which can be made possible through having information on the qualitative and quantitative speciation in the alkanolamine system. In this work, potential of Raman spectroscopy as a monitoring tool for species concentration in CO2-loaded aqueous monoethanolamine (MEA) system was investigated. CO2 loading data from experimental work were used with Kent Eisenberg model to estimate species concentrations (dissolved CO2, Protonated amine, Bicarbonate, Carbonate, Carbamate and unreacted MEA). Each species concentration were fitted to experimentally-acquired Raman spectrum using Partial Least Square Regression (PLSR) technique to develop calibration model. Evaluation of regression plots, R2 and Root Mean Square Error (RMSE) shows good predictive accuracy compared to the thermodynamically-predicted species concentration.

Characterization of desi ghee obtained from different extraction methods using Raman spectroscopy.

In this study, the potential of Raman spectroscopy has been utilized to characterize the methods direct cream (DC), milk butter (MB) and milk skin (MS) used for the extraction of desi ghee from buffalo and cow milk. Raman spectra from six types of ghee samples extracted by above methods were acquired using two laser wavelengths of 532 and 785 nm. The Raman spectra of cow ghee revealed that it contains three bands of beta-carotene at 1005, 1156 and 1520 cm-1 which differentiated it from buffalo ghee. To highlight small spectral differences, statistical analysis through principal component analysis (PCA) has been performed on the Raman spectra of ghee samples to reach subsequent conclusion. Based on the variations in molecular composition of cow ghee samples, it has been found that DC method retain relatively higher concentration of beta-carotene and MB method contain higher concentration of conjugated linoleic acid (CLA) and fatty acids than MS method. Similarly, DC & MS methods were found best for retaining relatively higher concentration of CLA and fatty acids in buffalo ghee as compared to MB method which retains relatively higher concentration of fatty acids.