Identification of species’ blood by attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy

Blood is one of the most common and informative forms of biological evidence found at a crime scene. A very crucial step in forensic investigations is identifying a blood stain’s origin. The standard methods currently employed for analyzing blood are destructive to the sample and time-consuming. In this study, attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy is used as a confirmatory, nondestructive, and rapid method for distinction between human and animal (nonhuman) blood. Partial least squares-discriminant analysis (PLS-DA) models were built and demonstrated complete separation between human and animal donors, as well as distinction between three separate species: human, cat, and dog. Classification predictions of unknown blood donors were performed by the model, resulting in 100 % accuracy. This study demonstrates ATR FT-IR spectroscopy’s great potential for blood stain analysis and species discrimination, both in the lab and at a crime scene since portable ATR FT-IR instrumentation is commercially available.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood–tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.     

Raman spectroscopic signature of blood and its potential application to forensic body fluid identification

Near-infrared (NIR) Raman spectroscopy was used to measure spectra of dried human blood samples from multiple donors. Two major questions addressed in this paper involve the influence of sample heterogeneity and potential Raman spectral variations that could arise between different donors of blood. Advanced statistical analysis of spectra obtained from multiple spots on dry samples showed that dry blood is chemically heterogeneous, and its Raman spectra could be presented very well as a linear combination of a fluorescent background and two Raman spectroscopic components that are dominated by hemoglobin and fibrin, respectively. Each sample Raman spectrum contains the same major peaks, but the relative contribution of the hemoglobin and fibrin components varies with the donor. Therefore, no single spectrum could adequately represent an experimental Raman spectrum of dry blood in a quantitative way, but rather the combination of hemoglobin and fibrin spectral components could be considered to be a spectroscopic signature for blood. This proof-of-concept approach shows the potential for Raman spectroscopy to be used in forensic analysis to identify an unknown substance such as blood.     

Biomedical tissue phantoms with controlled geometric and optical properties for Raman spectroscopy and tomography

To support the translation of Raman spectroscopy into clinical applications, synthetic models are needed to accurately test, optimize and validate prototype fiber optic instrumentation. Synthetic models (also called tissue phantoms) are widely used for developing and testing optical instrumentation for diffuse reflectance, fluorescence, and Raman spectroscopies. While existing tissue phantoms accurately model tissue optical scattering and absorption, they do not typically model the anatomic shapes and chemical composition of tissue. Because Raman spectroscopy is sensitive to molecular composition, Raman tissue phantoms should also approximate the bulk tissue composition. We describe the fabrication and characterization of tissue phantoms for Raman tomography and spectroscopy. These phantoms have controlled chemical and optical properties, and also multilayer morphologies which approximate the appropriate anatomic shapes. Tissue phantoms were fabricated to support on-going Raman studies by simulating the human wrist and rat leg. Surface meshes (triangle patch models) were generated from computed tomography (CT) images of a human arm and rat leg. Rapid prototyping was used to print mold templates with complex geometric patterns. Plastic casting techniques used for movie special effects were adapted to fabricate molds from the rapid prototypes, and finally to cast multilayer gelatin tissue phantoms. The gelatin base was enriched with additives to model the approximate chemistry and optical properties of individual tissue layers. Additional studies were performed to determine optimal casting conditions, phantom stability, layer delamination and chemical diffusion between layers. Recovery of diffuse reflectance and Raman spectra in tissue phantoms varied with probe placement. These phantoms enable optimization of probe placement for human or rat studies. These multilayer tissue phantoms with complex geometries are shown to be stable, with minimal layer delamination and chemical diffusion.     

Tumour cell identification by means of Raman spectroscopy in combination with optical traps and microfluidic environments

Raman spectroscopy has been recognized to be a powerful tool for label-free discrimination of cells. Sampling methods are under development to utilize the unique capabilities to identify cells in body fluids such as saliva, urine or blood. The current study applied optical traps in combination with Raman spectroscopy to acquire spectra of single cells in microfluidic glass channels. Optical traps were realized by two 1070 nm single mode fibre lasers. Microflows were controlled by a syringe pump system. A novel microfluidic glass chip was designed to inject single cells, modify the flow speed, accommodate the laser fibres and sort cells after Raman based identification. Whereas the integrated microchip setup used 514 nm for excitation of Raman spectra, a quartz capillary setup excited spectra with 785 nm laser wavelength. Classification models were trained using linear discriminant analysis to differentiate erythrocytes, leukocytes, acute myeloid leukaemia cells (OCI-AML3), and breast tumour cells BT-20 and MCF-7 with accuracies that are comparable with previous Raman experiments of dried cells and fixed cells in a Petri dish. Implementation into microfluidic environments enables a high degree of automation that is required to improve the throughput of the approach for Raman activated cell sorting.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.     

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

The identification of normal and cancer breast tissue of rats was investigated using high-frequency (HF) FT-Raman spectroscopy with a near-infrared excitation source on in vivo and ex vivo measurements. Significant differences in the Raman intensities of prominent Raman bands of lipids and proteins structures (2,800?C3,100?cm^?1) as well as in the broad band of water (3,100?C3,550?cm^?1) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high-frequency Raman spectra of normal and cancer tissues. LDA results with the leave-one-out cross-validation option yielded a discrimination accuracy of 77.2, 83.3, and 100% for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy HF Raman spectra. Despite the lower discrimination value for the in vivo transcutaneous measurements, which could be explained by the breathing movement and skin influences, our results showed good accuracy in discriminating between normal and cancer breast tissue samples. To support this, the calculated integration areas from the receiver-operating characteristic (ROC) curve yielded 0.86, 0.94, and 1.0 for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy measurements, respectively. The feasibility of using HF Raman spectroscopy as a clinical diagnostic tool for breast cancer detection and monitoring is due to no interfering contribution from the optical fiber in the HF Raman region, the shorter acquisition time due to a more intense signal in the HF Raman region, and the ability to distinguish between normal and cancerous tissues.     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

Nondestructive discrimination of ivories and prediction of their specific gravity by Fourier-transform Raman spectroscopy and chemometrics

Fourier-transform (FF) Raman spectroscopy and chemometrics were used for nondestructive analysis of ivories. The discrimination of five kinds of ivories, two subspecies of African elephant, mammoth, hippopotamus, and sperm whale, was investigated, and a calibration model for predicting their specific gravity was developed. FT-Raman spectra were measured in situ for them and chemometrics analyses were carried out for the 3050-350 cm(-1) region. The five kinds of ivories were clearly discriminated from each other on the scores plots of two or three principal components (PCs) obtained by principal component analysis (PCA). The loadings plot for PC 1 shows that the discrimination relies on the content ratio of organic collagenous protein and inorganic hydroxyapatite of ivories. The loadings plot for PC 2 shows that bands due to the CH3 and CH2 stretching modes of the protein also play a role in the discrimination. Using partial least squares regression (PLSR), we developed a calibration model that predicts the specific gravity of the ivories from the FT-Raman spectra. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.980 and 0.024, respectively.     

Fiber-optic Raman spectroscopy of joint tissues

In this study, we report adaptation of Raman spectroscopy for arthroscopy of joint tissues using a custom-built fiber-optic probe. Differentiation of healthy and damaged tissue or examination of subsurface tissue, such as subchondral bone, is a challenge in arthroscopy because visual inspection may not provide sufficient contrast. Discrimination of healthy versus damaged tissue may be improved by incorporating point spectroscopy or hyperspectral imaging into arthroscopy where the contrast is based on the molecular structure or chemical composition. Articular joint surfaces of knee cadaveric human tissue and tissue phantoms were examined using a custom-designed Raman fiber-optic probe. Fiber-optic Raman spectra were compared against reference spectra of cartilage, subchondral bone and cancellous bone collected using Raman microspectroscopy. In fiber-optic Raman spectra of the articular surface, there was an effect of cartilage thickness on recovery of signal from subchondral bone. At sites with intact cartilage, the bone mineralization ratio decreased but there was a minimal effect in the bone mineral chemistry ratios. Tissue phantoms were prepared as experimental models of the osteochondral interface. Raman spectra of tissue phantoms suggested that optical scattering of cartilage has a large effect on the relative cartilage and bone signal. Finite element analysis modeling of light fluence in the osteochondral interface confirmed experimental findings in human cadaveric tissue and tissue phantoms. These first studies demonstrate the proof of principle for Raman arthroscopic measurement of joint tissues and provide a basis for future clinical or animal model studies.     

Raman spectroscopic identification of single bacterial cells at different stages of their lifecycle

Early, rapid, and reliable bacterial identification is of great importance in natural environments and in medical situations. Numerous studies have shown that Raman spectroscopy can be used to differentiate between different bacteria under controlled laboratory conditions. However, individual bacteria within a population exhibit macromolecular and metabolic heterogeneity over their lifetime. Therefore it is important to be able to identify and classify specific bacteria at different time points of the growth cycle. In this study, four species of bacteria were used to explore the capability of confocal Raman spectroscopy as a tool for the identification of (and discrimination between) diverse bacterial species at various growth time points. The results show that bacterial cells from different growth time points (as well as from a random growth phase) can be discriminated among the four species using principal component analysis (PCA). The results also show that bacteria selected from different growth phases can be classified with the help of a prediction model based on principal component and linear discriminant analysis (PC-LDA). These findings demonstrate that Raman spectroscopy with the application of a PC-LDA model rooted in chemotaxonomic analysis has potential for rapid sensing of microbial cells in environmental and clinical studies.     

Micro-Raman spectroscopy of natural and synthetic indigo samples

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.     

Prenucleation Self‐Assembly and Chiral Discrimination Mechanisms during Solution Crystallisation of Racemic Diprophylline

The crystallisation behaviour of (RS)‐diprophylline (DPL) in two different solvents is investigated to assess the incidence of solvated pre‐associations on nucleation, crystal growth and chiral discrimination. In the solvated state, Raman spectroscopy shows that dimeric associations similar to those depicted in the crystalline solid solution (ssRII) predominate in isopropanol (IPA), which may account for the systematic spontaneous nucleation of this crystal form from this solvent. By contrast, spontaneous nucleation in DMF yields the stable racemic compound RI, consistently with the distinct features of the Raman spectrum collected in this solvent. A crystal growth study of ssRII in IPA reveals that the crystal habitus is impacted by the solution enantiomeric excess; this is explained by increased competition between homo‐ and heterochiral pre‐associations. This is supported by a molecular modelling study on the enantiomeric selectivity of the DPL crystal lattices. The combination of assessment methods on solution chemistry, nucleation and chiral discrimination provides methodological tools from which the occurrence of solid solutions can be rationalised.     

Determination of glucose and ethanol after enzymatic hydrolysis and fermentation of biomass using Raman spectroscopy

Raman spectroscopy has been used for the quantitative determination of the conversion efficiency at each step in the production of ethanol from biomass. The method requires little sample preparation; therefore, it is suitable for screening large numbers of biomass samples and reaction conditions in a complex sample matrix. Dilute acid or ammonia-pretreated corn stover was used as a model biomass for these studies. Ammonia pretreatment was suitable for subsequent measurements with Raman spectroscopy, but dilute acid-pretreated corn stover generated a large background signal that surpassed the Raman signal. The background signal is attributed to lignin, which remains in the plant tissue after dilute acid pretreatment. A commercial enzyme mixture was used for the enzymatic hydrolysis of corn stover, and glucose levels were measured with a dispersive 785 nm Raman spectrometer. The glucose detection limit in hydrolysis liquor by Raman spectroscopy was 8 g L⁻¹. The mean hydrolysis efficiency for three replicate measurements obtained with Raman spectroscopy (86 ± 4%) was compared to the result obtained using an enzymatic reaction with UV-vis spectrophotometry detection (78 ± 8%). The results indicate good accuracy, as determined using a Student's t-test, and better precision for the Raman spectroscopy measurement relative to the enzymatic detection assay. The detection of glucose in hydrolysis broth by Raman spectroscopy showed no spectral interference, provided the sample was filtered to remove insoluble cellulose prior to analysis. The hydrolysate was further subjected to fermentation to yield ethanol. The detection limit for ethanol in fermentation broth by Raman spectroscopy was found to be 6 g L⁻¹. Comparison of the fermentation efficiencies measured by Raman spectroscopy (80 ± 10%) and gas chromatrography-mass spectrometry (87 ± 9%) were statistically the same. The work demonstrates the utility of Raman spectroscopy for screening the entire conversion process to generate lignocellulosic ethanol.     

Micro-Raman study of copper hydroxychlorides and other corrosion products of bronze samples mimicking archaeological coins

Three bronze samples created by CNR-ISMN (National Research Council—Institute of Nanostructured Materials) to be similar to Punic and Roman coins found in Tharros (OR, Sardinia, Italy) were studied to identify the corrosion products on their surfaces and to evaluate the reliability of the reproduction process. Micro-Raman spectroscopy was chosen to investigate the corroded surfaces because it is a non-destructive technique, it has high spatial resolution, and it gives the opportunity to discriminate between polymorphs and to correlate colour and chemical composition. A significant amount of green copper hydroxychlorides (Cu₂(OH)₃Cl) was detected on all the coins. Their discrimination by Raman spectroscopy was challenging because the literature on the topic is currently confusing. Thus, it was necessary to determine the characteristic peaks of atacamite, clinoatacamite, and the recently discovered anatacamite by acquiring Raman spectra of comparable natural mineral samples. Clinoatacamite, with different degrees of order in its structure, was the major component identified on the three coins. The most widespread corrosion product, besides hydroxychlorides, was the red copper oxide cuprite (Cu₂O). Other corrosion products of the elements of the alloy (laurionite, plumbonacrite, zinc carbonate) and those resulting from burial in the soil (anatase, calcite, hematite) were also found. This study shows that identification of corrosion products, including discrimination of copper hydroxychlorides, could be accomplished by micro-Raman on valuable objects, for example archaeological findings or works of art, avoiding any damage because of extraction of samples or the use of a destructive analytical technique.     

Quantitative reagent-free detection of fibrinogen levels in human blood plasma using Raman spectroscopy

Fibrinogen assays are commonly used as part of clinical screening tests to investigate haemorrhagic states, for detection of disseminated intravascular coagulation and as a predictor of a variety of cardiovascular events. The Clauss assay, which measures thrombin clotting time, is the most commonly used method for measuring fibrinogen levels. Nevertheless, inconsistencies are present in inter-manufacturer reagent sources, calibration standards and methodologies. Automated coagulation analysers, which measure changes in optical density during the prothrombin time (PT-Fg), have found use in many hospitals. However, the PT-Fg method is found to give falsely elevated values due to varying choices of calibrants, reagents and analysers. As an alternative, Raman spectroscopy has previously been applied to the analysis of blood and its various constituents to determine various analyte concentrations such as glucose, urea, triglycerides and cholesterol. In this study, Raman spectroscopy was investigated for its ability to accurately quantify fibrinogen concentration in blood plasma. Samples collected from 34 patients were analysed by Raman spectroscopy and the resultant spectra were fitted with a Partial Least Squares Regression model using target values obtained through a pre-calibrated Clauss fibrinogen assay. Various spectral pre-processing methods were utilised to prepare data to be entered into a calibration model. A root mean square error of prediction of 0.72 ± 0.05 g/L was achieved with as few as 25 spectra. In this pilot study, Raman spectroscopy has been demonstrated to be a robust technique providing rapid and reagent-free quantification of fibrinogen levels in blood plasma and a potential alternative to the Clauss assay.     

Electron enhanced Raman scattering and its applications in solution chemistry.

The present review describes a new enhancement technique for Raman scattering in aqueous solutions. Raman scattering spectroscopy has an inherent ability to distinguish between molecules with great similarity and provides useful information on local physical and chemical environments at their functional groups' level. Since the Raman scattering signals from water molecules are quite weak, Raman spectroscopy has great advantage for detection or discrimination of a trace amount of analytes in aqueous environments. However, Raman scattering cross-sections are inherently small and it generally requires high power excitation and long acquisition times to obtain high-quality Raman spectra. These conditions create disadvantages for the analyses for living cells and real-time monitoring for environmental analyses. Here, I describe a new Raman enhancement technique, namely "electron enhanced Raman scattering (EERS)", where artificially generated electrons additionally affect the polarizability of target molecular systems and enhance their inherent Raman cross-section. Principles of the EERS and its applications to aqueous solutions are presented.     

Spatially resolved Raman spectroelectrochemistry of solid-state polythiophene/viologen memory devices

A three terminal molecular memory device was monitored with in situ Raman spectroscopy during bias-induced switching between two metastable states having different conductivity. The device structure is similar to that of a polythiophene field effect transistor, but ethylviologen perchlorate was added to provide a redox counter-reaction to accompany polythiophene redox reactions. The conductivity of the polythiophene layer was reversibly switched between high and low conductance states with a "write/erase" (W/E) bias, while a separate readout circuit monitored the polymer conductance. Raman spectroscopy revealed reversible polythiophene oxidation to its polaron form accompanied by a one-electron viologen reduction. "Write", "read", and "erase" operations were repeatable, with only minor degradation of response after 200 W/E cycles. The devices exhibited switching immediately after fabrication and did not require an "electroforming" step required in many types of memory devices. Spatially resolved Raman spectroscopy revealed polaron formation throughout the polymer layer, even away from the electrodes in the channel and drain regions, indicating that thiophene oxidation "propagates" by growth of the conducting polaron form away from the source electrode. The results definitively demonstrate concurrent redox reactions of both polythiophene and viologen in solid-state devices and correlate such reactions with device conductivity. The mechanism deduced from spectroscopic and electronic monitoring should guide significant improvements in memory performance.     

In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques

This study aimed to evaluate the clinical utility of applying near-infrared (NIR) Raman spectroscopy and genetic algorithm-partial least squares-discriminant analysis (GA-PLS-DA) to identify biomolecular changes of cervical tissues associated with dysplastic transformation during colposcopic examination. A total of 105 in vivo Raman spectra were measured from 57 cervical sites (35 normal and 22 precancer sites) of 29 patients recruited, in which 65 spectra were from normal sites, while 40 spectra were from cervical precancerous lesions (i.e., 7 low-grade CIN and 33 high-grade CIN). The GA feature selection technique incorporated with PLS was utilized to study the significant biochemical Raman bands for differentiation between normal and precancer cervical tissues. The GA-PLS-DA algorithm with double cross-validation (dCV) identified seven diagnostically significant Raman bands in the ranges of 925-935, 979-999, 1080-1090, 1240-1260, 1320-1340, 1400-1420, and 1625-1645 cm(-1) related to proteins, nucleic acids and lipids in tissue, and yielded a diagnostic accuracy of 82.9% (sensitivity of 72.5% (29/40) and specificity of 89.2% (58/65)) for precancer detection. The results of this exploratory study suggest that Raman spectroscopy in conjunction with GA-PLS-DA and dCV methods has the potential to provide clinically significant discrimination between normal and precancer cervical tissues at the molecular level.