Raman spectroscopy can discriminate distinct glioma subtypes as defined by RNA expression profiling

Raman spectroscopy is a molecular spectroscopic technique that can measure the molecular composition of tissue samples within seconds without any extraction processes or dyes. In microbiology, Raman spectroscopy is used to identify bacteriae. In glioblastoma tissue, it was reported that necrosis, normal brain and tumor can be discriminated using Raman spectroscopy. Therefore, we hypothesized that Raman spectroscopy could discriminate glioblastoma tissue from different glioma subtypes defined by RNA expression profiling. We analyzed 20 glioma samples from two distinct molecular subtypes. Both subtypes consisted of glioblastoma samples showing a variety in glioma grading and typing. The Raman spectroscopic results could be grouped in two distinct clusters in an unsupervised cluster analysis. Further analysis of these clusters showed that they were fully congruent with the two clusters as defined by RNA expression profiling. Conclusion: our results demonstrate that Raman spectroscopy can discriminate between different molecular subtypes of glioma and, therefore, may prove to be a valuable tool in in vitro cancer research. Copyright © 2013 John Wiley & Sons, Ltd.     

Characterizing variability in in vivo Raman spectroscopic properties of different anatomical sites of normal tissue in the oral cavity

Raman spectroscopy is an inelastic light scattering technique that is capable of probing biochemical and biomolecular structures and conformations of tissue. This study aims to characterize the in vivo Raman spectroscopic properties of different normal oral tissues in the fingerprint region (800–1800 cm⁻¹) and to assess distinctive biochemical variations of different anatomical regions in the oral cavity. A specially designed fiber‐optic Raman probe with a ball lens was utilized for real‐time, in vivo Raman measurements of various oral tissue sites (i.e. inner lip, attached gingiva, floor, dorsal tongue, ventral tongue, hard palate, soft palate, and buccal). The semiquantitative non‐negativity‐constrained least squares minimization fitting of reference biochemicals representing oral tissue constituents (i.e. hydroxyapatite, keratin, collagen, DNA, and oleic acid) and partial least squares‐discriminant analysis (PLS‐DA) were employed to assess the significance of inter‐anatomical variability. A total of 402 high‐quality in vivo oral Raman spectra were acquired from 20 subjects. The histological characteristics of different oral tissues were found to have influence on the in vivo Raman spectra and could be grossly divided into three major clusterings: (1) buccal, inner lip, and soft palate; (2) dorsal, ventral tongue, and floor; (3) gingiva and hard palate. The PLS‐DA multiclass algorithms were able to identify different tissue sites with varying accuracies (inner lip 83.1%, attached gingiva 91.3%, floor 86.1%, dorsal tongue 88.8%, ventral tongue 83.1%, hard palate 87.6%, soft palate 83.3%, and buccal mucosa 85.3%), bringing out the similarities among different oral tissues at the biomolecular level. This study discloses that inter‐anatomical variability is significant and should be considered as an important parameter in the interpretation and rendering of Raman diagnostic algorithms for oral tissue diagnosis and characterization. Copyright © 2011 John Wiley & Sons, Ltd.     

运用PLS算法由小麦冠层反射光谱反演氮素垂直分布

文章提出了利用遥感光谱数据反演小麦冠层氮素垂直分布的化学计量学方法,运用偏最小二乘算法(PLS),穷尽测定的小麦田间冠层可见光和近红外光谱不同波长处的冠层光谱反射率及其组合与小麦不同层次的叶绿素、叶片全氮含量之间的关系。通过2001～2002年的建模和2003～2004年的验证试验,求得了用PLS算法对叶片全氮上层、中层、下层垂直分布估算结果的相关性。表明PLS算法能够用于反演作物冠层生物化学参数的垂直分布。运用PLS的小麦氮素垂直分布的估算方法,较以往单一冠层估算方法精度明显提高,对于生产上迫切需求对作物中、下层叶片氮素状况的监测来指导适时和适量施肥具有指导意义。     

Raman Spectroscopy Can Detect and Monitor Cancer at Cellular Level: Analysis of Resistant and Sensitive Subtypes of Testicular Cancer Cell Lines

Abstract: Raman spectroscopy has been applied to analyze testicular cancer cell lines. Spectral differences between resistant and sensitive subtypes of testicular cancer cell line 833k samples were successfully analysed. The technique allowed reproducible and quantitative analysis of the specimen and illustrated the chemical specifications of the samples precisely. Six pairs of testicular cancer cell line 833k were studied and the findings were backed by statistical methods; that is, partial least squares discriminant analysis (PLS-DA).

It was concluded that Raman spectroscopy can objectively differentiate between resistant and sensitive cell lines. These results suggest that in the future it may be possible to use cell lines and diagnostic Raman spectroscopy for preoperative classification of biological molecules. Further research is underway to determine whether results obtained from spectroscopic analysis of cell lines can be applied to actual human tissue samples.     

Blood analysis by Raman spectroscopy

Concentrations of multiple analytes were simultaneously measured in whole blood with clinical accuracy, without sample processing, using near-infrared Raman spectroscopy. Spectra were acquired with an instrument employing nonimaging optics, designed using Monte Carlo simulations of the influence of light-scattering-absorbing blood cells on the excitation and emission of Raman light in turbid medium. Raman spectra were collected from whole blood drawn from 31 individuals. Quantitative predictions of glucose, urea, total protein, albumin, triglycerides, hematocrit, and hemoglobin were made by means of partial least-squares (PLS) analysis with clinically relevant precision (r(2) values >0.93). The similarity of the features of the PLS calibration spectra to those of the respective analyte spectra illustrates that the predictions are based on molecular information carried by the Raman light. This demonstrates the feasibility of using Raman spectroscopy for quantitative measurements of biomolecular contents in highly light-scattering and absorbing media.     

Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

We have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.     

Raman Spectroscopy Based Techniques in Tissue Engineering—An Overview

Abstract: This article reviews the most recent applications of Raman spectroscopy (RS) in tissue engineering and regenerative medicine (TERM). Raman spectroscopy can potentially alleviate the current lack of capabilities to capture important biological data beyond the standard metabolite and pH indicators in tissue bioreactors. Key process components in bioreactors that are important to evaluate cellular integrity and functionality of tissue-engineered constructs include cells, scaffolds, and proteins. A number of studies are discussed to highlight the capability of RS in noninvasive, nondestructive, and label-free in situ analysis. Protein detection is emphasized due to the urgent need for faster and cheaper techniques. The extensiveness of protein detection studies and the inadequacy of current technology formulate the basis for detailing relevant methods seeking to improve the detection limit, specificity, or multiplexing performance of RS. We argue that RS has great promise to solve the key problem in tissue bioprocessing, namely, inadequate monitoring capabilities that handicap TERM from rising to prevalence in clinical practice.     

Fourier‐transform Raman analysis of milk powder: a potential method for rapid quality screening

In this work, FT‐Raman spectroscopy was explored as a fast and reliable screening method for the assessment of milk powder quality and the identification of samples adulterated with whey (1–40% w/w). Raman measurements can easily differentiate milk powders without the need of sample preparation, whereas the traditional methods of quality control, including high‐performance liquid chromatography, are laborious and slow. The FT‐Raman spectra of whole, low‐fat, and skimmed milk powder samples were obtained and distinguished from commercial milk powder samples. In addition, the exploratory analysis employing data from Raman spectroscopy and principal component analysis (PCA)allowed the separation of milk powder samples according to type,identifying differences between samples in the same group. Multivariate analysis was also developed to classify the adulterated milk powder samples using PCA and partial least squares discriminate analysis (PLS‐DA). The resulting PLS‐DA model correctly classified 100% of the adulterated samples. These results clearly demonstrate the utility of FT‐Raman spectroscopy combined with chemometrics as a rapid method for screening milk powder. Copyright © 2011 John Wiley & Sons, Ltd.     

利用拉曼光谱检测丙烯共聚物的性质

采用拉曼光谱检测了7个丙烯共聚物样品,并详细分析了谱图中所有特征峰来源;最终选择2 700～3 100 cm-1作为特征谱段,采用偏最小二乘(PLS)回归的方法分别建立了样品中二甲苯可溶物、乙烯含量及可溶物中乙烯含量与拉曼特征峰相对强度的关联模型。通过预测模型求得的预测含量与真实含量的相关系数(r)均大于0.94,平均相对误差(ARE)均小于5%。还对2 700～3 100 cm-1区域拉曼光谱的第二项PLS成分进行了研究,发现采用PLS回归能够准确、有效地提取出样品的原有信息。为采用拉曼光谱在线检测共聚聚丙烯中的二甲苯可溶物量、乙烯含量及可溶物中的乙烯含量提供了可能性。     

Comparison of the discriminating power of Raman and surface‐enhanced Raman spectroscopy with established techniques for the examination of liquid and gel inks

The ability to discriminate between inks is important for forensic document analysis. Here, Raman spectroscopy (RS) and surface‐enhanced RS have been compared to the traditional document examination techniques of video spectral comparison and thin layer chromatography on a population of blue and black‐coloured liquid and gel inks. It was found that in most cases, the Raman techniques provided a similar or better discriminating power than the conventional methods. Importantly, this study allowed us to determine whether the same underlying changes in composition were being exploited by the different methods to discriminate between samples. It was found that there was indeed a high degree of commonality in the sample pairs being discriminated by the various techniques. This work can therefore underpin introduction of Raman methods into standard operating procedures for ink analysis since it not only measures the extent of discrimination between samples but can also explain the origin of the spectral changes that are used to distinguish between them. © 2013 John Wiley & Sons Ltd and Crown copyright     

Raman spectral signatures of mouse mammary tissue and associated lymph nodes: normal,tumor and mastitis

Raman spectroscopy involves the interaction of light with the molecular vibrations and therefore can provide information about molecular structure, tissue composition and changes in its environment. We explored whether Raman spectroscopy can reliably distinguish mammary tumors from normal mammary tissues and other pathological states in mice. We analyzed a large number of Raman spectra from the tumor and normal mammary glands of mice injected with 4T1 tumor cells, which were collected using a high‐resolution (less than 4 cm⁻¹) Raman spectrometer at a fixed (785 nm) laser excitation wavelength and with 60 mW of laser power. The spectra of normal and tumor mammary glands showed consistent differences in the intensity of certain Raman bands and loss of some bands in the tumor spectra. Multivariate statistical methods—principal component analysis (PCA) and discriminant functional analysis (DFA)—were used to separate the data into different groups of mammary tumors, mastitis, lymph nodes contralateral and tumor‐cell‐injected sides, and normal contralateral and tumor‐cell‐injected sides. We demonstrate that this spectroscopic technique has the feasibility of discriminating tumor and mastitis from normal tissues and other pathological states in a short period of time and may detect tumor transformation earlier than the standard histological examination stage. Copyright © 2006 John Wiley & Sons, Ltd.     

EADC Values in Diagnosis of Renal Lesions by 3.0 T Diffusion-Weighted Magnetic Resonance Imaging: Compared with the ADC Values

Exponential apparent diffusion coefficient (EADC) is an indicator of diffusion-weighted imaging (DWI) and reflects the pathological changes of tissues quantitatively. However, no study has been investigated in the space-occupying kidney disease using EADC values. This study aims to evaluate the diagnostic role of EADC values at a high magnetic field strength (3.0 T) in kidney neoplastic lesions, compared with that of the ADC values. Ninety patients with suspected renal tumors (including 101 suspected renal lesions) and 20 healthy volunteers were performed MRI scanning. Diffusion-weighted imaging was performed with a single-shot spin-echo echo-planar imaging (SE-EPI) sequence at a diffusion gradient of b = 500 s/mm². We found renal cell carcinoma (RCC) can be distinguished from angiomyolipoma, and clear cell carcinoma can be distinguished from non-clear cell carcinoma by EADC value. There was significant difference in overall EADC values between renal cell carcinoma (0.150 ± 0.059) and angiomyolipoma (0.270 ± 0.108) when b value was 500 s/mm². When receiver operating characteristic (ROC) was higher than 0.192, the sensitivity and specificity of EADC value of renal cell carcinoma were 84.6 and 81.1 %, respectively. In conclusion, EADC map shows the internal structure of the kidney tumor more intuitively than the ADC map dose, and is also in line with the observation habits of the clinicians. EADC can be used as an effective imaging method for tumor diagnosis.     

SERS技术的小白菜中西维因农药残留检测

西维因是一种广谱、高效的氨基甲酸酯杀虫剂。提出一个基于表面增强拉曼光谱进行定量和定性分析小白菜中西维因残留的方法。密度泛函理论B3LYP/6-311G基组被用于计算西维因农药的理论拉曼光谱。硫酸镁、PSA、石墨化炭黑和C18被用来去除叶绿素、矿物质和维生素等物质的影响。采用MSC，SNV和归一化三种方法对原始光谱进行预处理，建立小白菜中西维因残留的偏最小二乘模型。研究表明，小白菜中西维因农药残留检测可以达到0.976 mg·L-1以下。经MSC预处理后所建PLS模型预测性能最好，当主成分数为9时所建模型的性能最好，R_c为0.977，RMSECV为2.09 mg·L-1，R_p为0.986 5，RMSEP为1.71 mg·L-1。五个未知西维因农药浓度小白菜样本用来验证模型的准确度，相对误差为1.98%~7.28%，预测回收率为95.73%~107.28%，T值为0.397, 小于t_{0.05, 4}=2.776，说明模型是准确可靠的。SERS方法是一种有效的方法，可以实现小白菜中西维因农药残留的快速可靠检测。     

Analysis of alizarin by surface‐enhanced and FT‐Raman spectroscopy

This paper presents the application of Raman spectroscopy (RS) for the structural study of alizarin adsorbed on a metallic surface. As a biologically active molecule, alizarin has remarkable antigenotoxic activity like other anthraquinone dyes. Alizarin is highly fluorescent and that limits the application of RS as an investigation method; however, the Fourier transform‐RS (FTRS) can be applied since the near‐infrared excitation line lies far away from the absorption region of alizarin. The surface enhanced‐RS (SERS) technique also makes the fluorescence quenching possible. In this work, monolayers of alizarin were deposited on the surface of an electrode by the immersion of silver substrates in methanolic solution of the analyte. From such prepared samples, by using the excitation of 488, 514.5 and 647.1 nm the Raman spectra were registered. Depending on the excitation line, SERS or surface‐enhanced resonance Raman scattering (SERRS) spectra of alizarin were observed. The interpretation of experimental data was supported by theoretical calculations. Copyright © 2008 John Wiley & Sons, Ltd.     

Combined Raman spectroscopy and optical coherence tomography device for tissue characterization

We report a dual-modal device capable of sequential acquisition of Raman spectroscopy (RS) and optical coherence tomography (OCT) along a common optical axis. The device enhances application of both RS and OCT by precisely guiding RS acquisition with OCT images while also compensating for the lack of molecular specificity in OCT with the biochemical specificity of RS. We characterize the system performance and demonstrate the capability to identify structurally ambiguous features within an OCT image with RS in a scattering phantom, guide acquisition of RS from a localized malignancy in ex vivo breast tissue, and perform in vivo tissue analysis of a scab.     

Quantitative Analysis of Simulated Illicit Street-Drug Samples Using Raman Spectroscopy and Partial Least Squares Regression

Modern drug laws require that a seized sample be characterized for both the illegal substances present and the quantity of each of those substances. The goal of this work was to develop a common approach to model development based on Raman spectroscopic analysis followed by partial least squares (PLS) regression that would allow us to obtain quantitative information from simulated street-drug samples. Each drug sample contained one drug surrogate—either isoxsuprine, norephedrine, benzocaine, or lidocaine—and up to 3 different cutting agents. All spectra were acquired on a homebuilt Raman instrument equipped with a rotating sample holder. The same steps were employed for developing separate models for each drug surrogate, including spectral preprocessing by Savitsky-Golay smoothing, differentiation, mean-centering, and autoscaling. PLS models were developed using 2 latent variables that yielded root mean square errors of calibration (RMSEC) values in the 3% range and root mean square error of prediction (RMSEP) values in the 4% range.     

聚氯乙烯溶液浓度的拉曼光谱检测

借助拉曼光谱,对聚氯乙烯(PVC)-环己酮溶液的浓度进行了检测.通过偏最小二乘法(PLS)分析发现,第一项 PLS 成分(factor 1)的得分与 PVC 溶液浓度成正比关系,factor 1 的载荷分布能有效地反映出溶液浓度与溶液中的 PVC 和环已酮含量的关系.建立了 PVC 溶液浓度的 PLS 回归预测模型,模...     

拉曼光谱结合偏最小二乘的甲醇汽油甲醇含量快速定量分析方法研究

甲醇汽油是一种用以替代传统汽油的新型燃料,其品质受到甲醇含量的严重影响。因此,甲醇汽油中甲醇含量的快速分析对其品质把控具有深远意义。基于拉曼光谱（Raman）结合偏最小二乘（PLS）建立了一种甲醇汽油中甲醇含量快速定量分析方法。采用激光拉曼光谱仪对49组甲醇汽油样品的Raman光谱进行采集,并进行光谱解析。比较了五种光谱预处理方法对甲醇汽油原始Raman光谱的预处理效果,并采用变量重要性投影（VIP）对小波变换（WT）预处理后的甲醇汽油Raman光谱数据进行了特征变量提取。其次,采用五折交叉验证（5-flod cross-validation (CV)）对PLS校正模型的潜变量数目（LVs）及VIP阈值进行优化。在最优输入变量和模型参数下,分别构建了基于不同输入变量的PLS模型。研究表明,相较于原始光谱-偏最小二乘模型（RAW-PLS）和小波变换-偏最小二乘模型（WT-PLS）,变量重要性投影-偏最小二乘模型（VIP-PLS）可以获得更好的分析性能,其预测集决定系数（R2_p）为0.960 4,均方根误差（RMSEP）为0.0341。因此,Raman光谱结合PLS是一种快速准确的甲醇汽油中甲醇含量分析方法。     

基于拉曼光谱和多元统计分析技术的结肠癌早期诊断

为了寻找结肠癌的病理发展规律在拉曼光谱属性上的体现,采用共聚焦显微拉曼光谱仪对60例离体的正常结肠组织、腺瘤性息肉和腺癌组织的近红外拉曼光谱进行对比检测,初步探讨了三类组织的拉曼光谱特征及其改变的规律。结果表明:三类组织拉曼光谱的差异明显存在于830,855,1 032,1 210,1 323,1 335,1 445,1 450,1 655 cm^-1处,腺瘤性息肉的光谱大体位于正常组织与腺癌组织之间。以组织病理诊断为金标准,主成分分析结合线性判别分析技术建立的诊断算法区分3类组织的灵敏度分别为96.9%、85.7%和97.3%,特异性分别为82.8%、90%和92.3%。因此,拉曼光谱有潜力在分子水平上区分正常结肠组织、腺瘤性息肉和腺癌组织,有望成为结肠癌早期诊断的一种有效方法。     

Raman scattering characterization of vertical aligned 1D IrO2 nanocrystals grown on single crystal oxide substrates

Raman scattering (RS) has been used as a technique for characterization of IrO₂ one dimensional (1D) nanocrystals (NCs) deposited on sapphire(100) and LiNbO₃(100) substrates under various conditions. The IrO₂ NCs were grown via metalorganic chemical vapor deposition method using (MeCp)(COD)Ir as the precursor and reactive magnetron sputtering using Ir metal target. The red-shifts and asymmetric broadening of the Raman lineshape for the NCs were analyzed by a modified spatial correlation (MSC) model, which includes the factor of stress induced shift. The proposed MSC model showed that the effects of stress and nanometric size can be separated in analyzing the observed Raman features. The usefulness of the experimental RS together with the MSC model analysis as a residual stress and structural characterization technique for 1D NCs has been demonstrated.