Investigating antibacterial effects of garlic (Allium sativum) concentrate and garlic-derived organosulfur compounds on Campylobacter jejuni by using Fourier transform infrared spectroscopy, Raman spectroscopy, and electron microscopy

Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy were used to study the cell injury and inactivation of Campylobacter jejuni from exposure to antioxidants from garlic. C. jejuni was treated with various concentrations of garlic concentrate and garlic-derived organosulfur compounds in growth media and saline at 4, 22, and 35°C. The antimicrobial activities of the diallyl sulfides increased with the number of sulfur atoms (diallyl sulfide < diallyl disulfide < diallyl trisulfide). FT-IR spectroscopy confirmed that organosulfur compounds are responsible for the substantial antimicrobial activity of garlic, much greater than those of garlic phenolic compounds, as indicated by changes in the spectral features of proteins, lipids, and polysaccharides in the bacterial cell membranes. Confocal Raman microscopy (532-nm-gold-particle substrate) and Raman mapping of a single bacterium confirmed the intracellular uptake of sulfur and phenolic components. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to verify cell damage. Principal-component analysis (PCA), discriminant function analysis (DFA), and soft independent modeling of class analogs (SIMCA) were performed, and results were cross validated to differentiate bacteria based upon the degree of cell injury. Partial least-squares regression (PLSR) was employed to quantify and predict actual numbers of healthy and injured bacterial cells remaining following treatment. PLSR-based loading plots were investigated to further verify the changes in the cell membrane of C. jejuni treated with organosulfur compounds. We demonstrated that bacterial injury and inactivation could be accurately investigated by complementary infrared and Raman spectroscopies using a chemical-based, "whole-organism fingerprint" with the aid of chemometrics and electron microscopy.     

Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra

The development of new methods for the early diagnosis of cartilage disease could offer significant improvement in patient care. Raman spectroscopy is an emerging biomedical technology with unique potential to recognize disease tissues, though difficulty in obtaining the samples needed to train a diagnostic and excessive signal noise could slow its development into a clinical tool. In the current report we detail the use of principal component analysis – linear discriminant analysis (PCA‐LDA) on spectra from pairs of materials modeling cartilage disease to create multiple spectral scoring metrics, which could limit the reliance on primary training data for identifying disease in low signal‐to‐noise‐ratio (SNR) Raman spectra. Our proof‐of‐concept experiments show that combinations of these model‐metrics has the potential to improve the classification of low‐SNR Raman spectra from human normal and osteoarthritic (OA) cartilage over a single metric trained with spectra from the same healthy and OA tissues.

Scatter plot showing the PCA‐LDA derived human‐disease‐metric scores versus rat‐model‐metric scores for 7656 low signal‐to‐noise spectra from healthy (blue) and osteoarthritic (red) cartilage. Light vertical and horizontal lines represent the optimized single metric classification boundary. Dark diagonal line represents the classification of boundary resulting from the optimized combination of the two metrics. Abbreviations: er (error rate), PCA‐LDA (principal component analysis – linear discriminant analysis), HOA (human osteoarthritis), HAC (human articular cartilage), RIF (rat injury fibrocartilage), RAC (rat articular cartilage).     

Near-infrared autofluorescence spectroscopy for in vivo identification of hyperplastic and adenomatous polyps in the colon

This study reports the implementation of an endoscope-based near-infrared (NIR) autofluorescence (AF) spectroscopy technique for in vivo differentiation of normal, hyperplastic and adenomatous colonic polyps during clinical colonoscopic examination. A total of 198 in vivo NIR AF spectra in the range of 810–1050 nm were acquired from colonic tissues (normal (n = 116); hyperplastic (n = 48); and adenomatous polyps (n = 34)) of 96 patients undergoing colonoscopic screening. Significant differences (p < 0.001, one-way analysis of variance (ANOVA)) in in vivo NIR AF intensity among normal, hyperplastic, and adenomatous polyps are observed. Multivariate statistical techniques, including principal components analysis (PCA) and linear discriminate analysis (LDA) together with the leave-one tissue site-out, cross-validation, were used to develop diagnostic algorithms for distinguishing adenomatous polyps from normal and hyperplastic colonic polyps based on NIR AF spectral features. The PCA–LDA modeling on in vivo colonic NIR AF dataset yields diagnostic sensitivities of 83.6%, 77.1%, and 88.2%; and specificities of 96.3%, 88.0%, and 92.1%, respectively, for classification of normal, hyperplastic and adenomatous colonic polyps. This work suggests that NIR AF spectroscopy associated with PCA–LDA algorithms has potential for in vivo diagnosis and detection of colonic precancer at colonoscopy.     

CoronaVac (Sinovac) COVID-19 vaccine-induced molecular changes in healthy human serum by infrared spectroscopy coupled with chemometrics

From the beginning of the COVID-19 coronavirus pandemic in December of 2019, the disease has infected millions of people worldwide and caused hundreds of thousands of deaths. Since then, several vaccines have been developed. One of those vaccines is inactivated CoronaVac-Sinovac COVID-19 vaccine. In this proof of concept study, we first aimed to determine CoronaVac-induced biomolecular changes in healthy human serum using infrared spectroscopy. Our second aim was to see whether the vaccinated group can be separated or not from the non-vaccinated group by applying chemometric techniques to spectral data. The results revealed that the vaccine administration induced significant changes in some functional groups belonging to lipids, proteins and nucleic acids. In addition, the non-vaccinated and vaccinated groups were successfully separated from each other by principal component analysis (PCA) and linear discriminant analysis (LDA). This proof-of-concept study will encourage future studies on CoronaVac as well as other vaccines and will lead to make a comparison between different vaccines to establish a better understanding of the vaccination outcomes on serum biomolecules.     

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.     

Near‐infrared spectroscopic evaluation of lyophilized viral vaccine formulations

This article examines the applicability of near‐infrared spectroscopy (NIRS) to evaluate the virus state in a freeze‐dried live, attenuated vaccine formulation. Therefore, this formulation was freeze‐dried using different virus volumes and after applying different pre‐freeze‐drying virus treatments (resulting in different virus states): (i) as used in the commercial formulation; (ii) without antigen (placebo); (iii) concentrated via a centrifugal filter device; and (iv) stressed by 96 h exposure to room temperature. Each freeze‐dried product was measured directly after freeze‐drying with NIR spectroscopy and the spectra were analyzed using principal component analysis (PCA). Herewith, two NIR spectral regions were evaluated: (i) the 7300–4000 cm^?1 region containing the amide A/II band which might reflect information on the coated proteins of freeze‐dried live, attenuated viruses; and (ii) the C–H vibration overtone regions (10,000–7500 and 6340–5500 cm^?1) which might supply information on the lipid layer surrounding the freeze‐dried live, attenuated viruses. The different pre‐freeze‐drying treated live, attenuated virus formulations (different virus states and virus volumes) resulted in different clusters in the scores plots resulting from the PCA of the collected NIR spectra. Secondly, partial least squares discriminant analysis models (PLS‐DA) were developed and evaluated, allowing classification of the freeze‐dried formulations according to virus pretreatment. The results of this study suggest the applicability of NIR spectroscopy for evaluating live, attenuated vaccine formulations with respect to their virus pretreatment and virus volume. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:1573–1586, 2013     

Differentiation of outer membrane proteins from Salmonellaenterica serotypes using Fourier transform infrared spectroscopy and chemometrics

AIMS: To differentiate between outer membrane proteins (OMPs) from six Salmonellaenterica serotypes using a Fourier transform infrared (FTIR) spectroscopy method and chemometrics. METHODS AND RESULTS: The OMPs from Salmonella serotypes (Typhimurium, Enteritidis, Thomasville, Hadar, Seftenberg and Brandenburg) were isolated using a sarcosyl extraction method. OMP profiles on SDS-PAGE exhibited two or three bands between 48 and 54 kDa. Spectra of 10 microl of OMP preparations (5 mg ml(-1)) dried on a gold reflective slide were collected using 128 scans at 4 cm(-1) resolution and units of log (1/R) and analyzed using canonical variate analysis (CVA) and linear discriminant analysis (LDA). The CVA of Salmonella OMP spectra in the 1800-1500 cm(-1) region separated the serotypes and LDA provided a 100% correct classification. CONCLUSIONS: The use of a FTIR method combined with chemometrics provided better differentiation of Salmonella OMPs than the OMP pattern analysis by SDS-PAGE. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to demonstrate that spectra of OMP extracts from Salmonella serotypes can be used for 100% correct classification of the serotypes studied.     

Timing of malolactic fermentation inoculation in Shiraz grape must and wine: influence on chemical composition

Malolactic fermentation (MLF) is an integral step in red winemaking, which in addition to deacidifying wine can also influence the composition of volatile fermentation-derived compounds with concomitant affects on wine sensory properties. Long-established winemaking protocols for MLF induction generally involve inoculation of bacteria starter cultures post alcoholic fermentation, however, more recently there has been a trend to introduce bacteria earlier in the fermentation process. For the first time, this study shows the impact of bacterial inoculation on wine quality parameters that define red wine, including wine colour and phenolics, and volatile fermentation-derived compounds. This study investigates the effects of inoculating Shiraz grape must with malolactic bacteria at various stages of alcoholic fermentation [beginning of alcoholic fermentation (co-inoculation, with yeast), mid-alcoholic fermentation, at pressing and post alcoholic fermentation] on the kinetics of MLF and wine chemical composition. Co-inoculation greatly reduced the overall fermentation time by up to 6 weeks, the rate of alcoholic fermentation was not affected by the presence of bacteria and the fermentation-derived wine volatiles profile was distinct from wines produced where bacteria were inoculated late or post alcoholic fermentation. An overall slight decrease in wine colour density observed following MLF was not influenced by the MLF inoculation regime. However, there were differences in anthocyanin and pigmented polymer composition, with co-inoculation exhibiting the most distinct profile. Differences in yeast and bacteria metabolism at various stages in fermentation are proposed as the drivers for differences in volatile chemical composition. This study demonstrates, with an in-depth analysis, that co-inoculation of yeast and bacteria in wine fermentation results in shorter total vinification time and produces sound wines, thus providing the opportunity to stabilise wines more rapidly than traditional inoculation regimes permit and thereby reducing potential for microbial spoilage.     

ATR microspectroscopy with multivariate analysis segregates grades of exfoliative cervical cytology

Although cervical cancer screening in the UK has led to reductions in the incidence of invasive disease, this programme remains flawed. We set out to examine the potential of infrared (IR) microspectroscopy to allow the profiling of cellular biochemical constituents associated with disease progression. Attenuated total reflection-Fourier Transform IR (ATR) microspectroscopy was employed to interrogate spectral differences between samples of exfoliative cervical cytology collected into liquid based cytology (LBC). These were histologically characterised as normal (n = 5), low-grade (n = 5), high-grade (n = 5) or severe dyskaryosis (? carcinoma) (n = 5). Examination of resultant spectra was coupled with principal component analysis (PCA) and subsequent linear discriminant analysis (LDA). The interrogation of LBC samples using ATR microspectroscopy with PCA-LDA facilitated the discrimination of different categories of exfoliative cytology and allowed the identification of potential biomarkers of abnormality; these occurred prominently in the IR spectral region 1200 cm(-1) - 950 cm(-1) consisting of carbohydrates, phosphate, and glycogen. Shifts in the centroids of amide I (approximately 1650 cm(-1)) and II (approximately 1530 cm(-1)) absorbance bands, indicating conformational changes to the secondary structure of intracellular proteins and associated with increasing disease progression, were also noted. This work demonstrates the potential of ATR microspectroscopy coupled with multivariate analysis to be an objective alternative to routine cytology.     

High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm⁻¹) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.     

On-line process monitoring and chemometric modeling with 2D fluorescence spectra obtained in recombinant E. coli fermentations

2D spectrofluorometry produces a large volume of spectral data during fermentation processes with recombinant E. coli, which can be analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). An analysis of the spectral data by PCA results in scores and loadings that are not only visualized in the score-loading plots but are also used to monitor the fermentation processes on-line. The score plots provided useful qualitative information on four fermentation processes for the production of extracellular 5-aminolevulinic acid (ALA). Two chemometric models (PCR and PLS) were used to examine the correlation between the 2D fluorescence spectra and a few parameters of the fermentation processes. The results showed that PLS had slightly better calibration and prediction performance than PCR.     

High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm(-1)) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.     

Effectiveness of near-infrared spectroscopy as a non-invasive tool to discriminate spectral profiles of in vitro cultured oocytes from goats

Here, we aimed to discriminate between the spectral profiles of spent culture media after oocyte in vitro maturation (IVM) and culture (IVC) from goats of different ages subjected to repeated hormonal treatments. The profiles were discriminated using near infrared (NIR) spectroscopy combined with multivariate methods. A total of 19 goats (young = 10; old = 9) were subjected to serial hormonal stimulation (HS) with gonadotropins. Cumulus oophorus complexes (COCs) were collected using laparoscopic ovum pick-up (LOPU) and subjected to IVM and parthenogenetic activation. The initial embryos were subjected to IVC. Spent culture media were collected after oocyte IVM and on day 2 of IVC and analyzed using NIR spectroscopy. NIR spectral data were interpreted through chemometric methods, such as principle component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The results of PCA analysis clearly showed a separation in the spectral profiles between the experimental groups (HS sessions; young and old animals) both after IVM and IVC. Overall, the main absorption bands were attributed to the C-H group second overtone, first overtone of O-H and N-H, and C-H combinations and may serve as molecular markers. On the other hand, the spectral data obtained using PLS-DA models provided a better classification of the groups. The results showed the possibility of discriminating young and old groups as well as the three HS sessions with high specificity, sensitivity, and accuracy using NIR spectra. Thus, the culture medium analysis using NIR spectroscopy combined with multivariate methods indicated the dissimilarities between the groups and provided an insight into the in vitro development of goat oocytes. This technique serves as an efficient, objective, rapid, and non-invasive method to discriminate spectral profiles.     

In situ non-invasive spectral discrimination between bone cell phenotypes used in tissue engineering

Raman micro-spectroscopy was used to discriminate between different types of bone cells commonly used in tissue engineering of bone, with the aim of developing a method of phenotypic identification and classification. Three types of bone cells were analysed: human primary osteoblasts (HOB), retroviral transfected human alveolar bone cells with SV40 large T antigen (SV40 AB), and osteoblast-like human osteosarcoma derived MG63 cell line. Unsupervised principal component analysis (PCA) and linear discriminant analysis (LDA) of the Raman spectra succeeded in discriminating the osteosarcoma derived MG63 cells from the non-tumour cells (HOB and SV40 AB). No significant differences were observed between the Raman spectra of the HOB and SV40 AB cells, confirming the biochemical similarities between the two cell types. Difference spectra between tumour and non-tumour cells suggested that the spectral discrimination is based on the fact that MG63 osteosarcoma derived cells are characterised by lower concentrations of nucleic acids and higher relative concentrations of proteins compared to the non-tumour bone cells. A supervised classification model (LDA) was built and showed high cross-validation sensitivity (100%) and specificity (95%) for discriminating the MG63 cells and the non-tumour cells, with 96% of the cells being correctly classified either as tumour or non-tumour derived cells. This study proves the feasibility of using Raman spectroscopy to identify in situ phenotypic differences in living cells.     

Application of FT-NIR Analysis for In-line and Real-Time Monitoring of Pharmaceutical Hot Melt Extrusion: a Technical Note

Continuous manufacturing, a gaining interest paradigm in the pharmaceutical industry, requires in-process monitoring of critical process parameters to ensure product consistency. This study demonstrated the application of Fourier transform near-infrared (FT-NIR) spectroscopy in conjunction with chemometrics modeling for in-line hot melt extrusion process monitoring. The obtained results suggested that inline FT-NIR analysis, along with a tailored NIR reflector, is a viable process analytical tool to monitor active pharmaceutical ingredient concentration as well as processing parameters.     

Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n = 149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 μm. The results show the NIR technique to have promise as a semi-quantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows.     

Combining near-infrared-excited autofluorescence and Raman spectroscopy improves in vivo diagnosis of gastric cancer

This study aims to evaluate the diagnostic utility of the combined near-infrared (NIR) autofluorescence (AF) and Raman spectroscopy for improving in vivo detection of gastric cancer at clinical gastroscopy. A rapid Raman endoscopic technique was employed for in vivo spectroscopic measurements of normal (n=1098) and cancer (n=140) gastric tissues from 81 gastric patients. The composite NIR AF and Raman spectra in the range of 800-1800 cm(-1) were analyzed using principal component analysis (PCA) and linear discriminant (LDA) to extract diagnostic information associated with distinctive spectroscopic processes of gastric malignancies. High quality in vivo composite NIR AF and Raman spectra can routinely be acquired from the gastric within 0.5s. The integrated intensity over the range of 800-1800 cm(-1) established the diagnostic implications (p=1.6E-14) of the change of NIR AF intensity associated with neoplastic transformation. PCA-LDA diagnostic modeling on the in vivo tissue NIR AF and Raman spectra acquired yielded a diagnostic accuracy of 92.2% (sensitivity of 97.9% and specificity of 91.5%) for identifying gastric cancer from normal tissue. The integration area under the receiver operating characteristic (ROC) curve using the combined NIR AF and Raman spectroscopy was 0.985, which is superior to either the Raman spectroscopy or NIR AF spectroscopy alone. This work demonstrates that the complementary Raman and NIR AF spectroscopy techniques can be integrated together for improving the in vivo diagnosis and detection of gastric cancer at endoscopy.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.     

Detection of Vero Cells Infected with Herpes Simplex Types 1 and 2 and Varicella Zoster Viruses Using Raman Spectroscopy and Advanced Statistical Methods

Of the eight members of the herpes family of viruses, HSV1, HSV2, and varicella zoster are the most common and are mainly involved in cutaneous disorders. These viruses usually are not life-threatening, but in some cases they might cause serious infections to the eyes and the brain that can lead to blindness and possibly death. An effective drug (acyclovir and its derivatives) is available against these viruses. Therefore, early detection and identification of these viral infections is highly important for an effective treatment. Raman spectroscopy, which has been widely used in the past years in medicine and biology, was used as a powerful spectroscopic tool for the detection and identification of these viral infections in cell culture, due to its sensitivity, rapidity and reliability. Our results showed that it was possible to differentiate, with a 97% identification success rate, the uninfected Vero cells that served as a control, from the Vero cells that were infected with HSV-1, HSV-2, and VZV. For that, linear discriminant analysis (LDA) was performed on the Raman spectra after principal component analysis (PCA) with a leave one out (LOO) approach. Raman spectroscopy in tandem with PCA and LDA enable to differentiate among the different herpes viral infections of Vero cells in time span of few minutes with high accuracy rate. Understanding cell molecular changes due to herpes viral infections using Raman spectroscopy may help in early detection and effective treatment.