Profiling of liquid crystal displays with Raman spectroscopy: preprocessing of spectra

Raman spectroscopy is applied for characterizing paintable displays. Few other options than Raman spectroscopy exist for doing so because of the liquid nature of functional materials. The challenge is to develop a method that can be used for estimating the composition of a single display cell on the basis of the collected three-dimensional Raman spectra. A classical least squares (CLS) model is used to model the measured spectra. It is shown that spectral preprocessing is a necessary and critical step for obtaining a good CLS model and reliable compositional profiles. Different kinds of preprocessing are explained. For each data set the type and amount of preprocessing may be different. This is shown using two data sets measured on essentially the same type of display cell, but under different experimental conditions. For model validation three criteria are introduced: mean sum of squares of residuals, percentage of unexplained information (PUN), and average residual curve. It is shown that the decision about the best combination of preprocessing techniques cannot be based only on overall error indicators (such as PUN). In addition, local residual analysis must be done and the feasibility of the extracted profiles should be taken into account.     

Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples

Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR spectroscopy with statistical total correlation spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.     

Raman spectra and structure of non-crystalline samples of the As---S---Br system

The Raman spectra of glassy and liquid As_xS_yBr_z samples from the glass-forming region of the As-S-Br system were studied and analyzed in terms of basic structural units. The structural network based on AsS₃ pyramidal units connected S-bridging atoms is supplied with S₂As-AsS₂ and As-S-S-As structural units on the As-rich side and S-rich side of the glass-forming region, respectively. At a higher content of the excess sulfur, S₈ isolated molecules were identified. Bromine atoms are bonded to As atoms forming AsBr₃ molecules dissolved in the structural network like S₈ molecules.     

Automated method for subtraction of fluorescence from biological Raman spectra

One of the challenges of using Raman spectroscopy for biological applications is the inherent fluorescence generated by many biological molecules that underlies the measured spectra. This fluorescence can sometimes be several orders of magnitude more intense than the weak Raman scatter, and its presence must be minimized in order to resolve and analyze the Raman spectrum. Several techniques involving hardware and software have been devised for this purpose; these include the use of wavelength shifting, time gating, frequency-domain filtering, first- and second-order derivatives, and simple curve fitting of the broadband variation with a high-order polynomial. Of these, polynomial fitting has been found to be a simple but effective method. However, this technique typically requires user intervention and thus is time consuming and prone to variability. An automated method for fluorescence subtraction, based on a modification to least-squares polynomial curve fitting, is described. Results indicate that the presented automated method is proficient in fluorescence subtraction, repeatability, and in retention of Raman spectral lineshapes.     

Shift-excitation Raman difference spectroscopy-difference deconvolution method for the luminescence background rejection from Raman spectra of solid samples

The feasibility of the shift-excitation Raman difference spectroscopy-difference deconvolution (SERDS-DDM) method for fluorescence suppression from Raman spectra of solid samples is discussed. For SERDS measurements a tunable diode laser source with an emission band centered at 684 nm is coupled to a conventional micro-Raman apparatus and a monochromator device is used for checking the excitation frequency stability. The shifted Raman spectra are then mathematically treated and a deconvolution procedure is used to reconstruct the Raman spectrum devoid of fluorescence. Two different cases are presented. In the first one, fluorescence is intrinsic to the sample and the Raman spectrum of cinnabar pigment is finally reconstructed. In the second, the presence of an external luminescence background in the spectrum of a pure sulfur crystal is considered. The SERDS-DDM reconstructed spectra are compared with spectra obtained via multi-point baseline subtraction and a significant improvement in the detection of weak bands is demonstrated. Practical insights for the application of this method are presented as well.     

Spike removal and denoising of Raman spectra by wavelet transform methods

Wavelet decompositions of Raman spectra were investigated with respect to their usability for spike removal and denoising of the raw data. It could be shown that those operations should be performed sequentially. Suppression of spikes is not straightforwardly possible by wavelet transformation; however, the wavelet transform may be used to recognize the spikes by their first level detail coefficients. Spike locations could be projected from the details to the approximations and, further, to appropriate locations of the original spectrum. After spike recognition, those regions will be replaced by interpolated values. To complete processing, denoising is performed with the despiked spectrum by repeated application of wavelet transform methods.     

生物体中重金属元素分析方法的研究进展

重金属污染威胁人类健康和物种生存,已成为全社会关注的环境热点,因此,重金属防治和检测显得尤为重要。在分析重金属元素检测现状的基础上,该文对生物体内重金属的主要检测及样品前处理方法进行了综述,讨论了当前生物体中重金属检测的新技术,提出了所存在的问题并展望了这一方面的发展趋势,以期为发展出更灵敏、更准确、更快速的检测方法提供参考。     

A comparison of analytical and data preprocessing methods for spectral fingerprinting

Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS-) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS-), and derivatives (IR, NIR, UV, and VIS).     

A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

掺铕氧化钇发光纳米晶的拉曼光谱研究

采用凝胶燃烧法制备了8～50nm的Y2O3:Eu3+纳米晶.利用XRD确定了纳米晶的结构及晶粒大小,测定了不同晶粒大小的纳米晶Y2O3:Eu3+的拉曼光谱.通过测定不同的激发光所激发的拉曼光谱,以及比对荧光谱,指认了纳米晶Y2O3:Eu3+的Raman振动光谱,并且观察和研究了Raman光谱随晶粒尺寸的变化,发现了低维材料的一些反常拉曼效应.     

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal preprocessing. The SE indicator performs better than the two other indicators, and it is also applicable to other situations where the Beer-Lambert law is valid.     

Effects of preprocessing of ultraviolet-induced fluorescence spectra in plant fingerprinting applications

Preprocessing is an important step in data analysis. Dealing with spectral data, normalization is mandatory in order to compare items collected under various conditions. This paper addresses normalization of frontface fluorescence spectroscopy data where spectra are affected by an unknown multiplicative effect. The usual methods for reducing multiplicative problems are reviewed and a more detailed analysis of the normalization by closure is provided based on data on the fluorescence of plants as a means for plant species fingerprinting. As normalization is essentially the reduction of information, some methods of carrying it out are likely to remove either meaningful or discriminant pieces of information. As a result, it is demonstrated that normalization by closure should be performed using spectral data in a range where the spectra contain no information relevant to the problem at hand. This applies provided that in this range the signal-to-noise ratio is high enough. When the noise level is too high, a compromise should be found between preserving useful information and limiting the amount of noise introduced by the normalization procedure. Even if this study were carried out using fluorescence spectra, the overall process is likely to be applied to other spectral data.     

Variations in predicted values from near-infrared spectra of samples in vials by using a calibration model developed from spectra of samples in vials: causes of the variations and compensation methods

The causes and compensation methods of variations in the predicted values from near-infrared (NIR) spectra of samples in vials by using a calibration model developed from spectra of samples in vials were studied. From the viewpoint of the path length and the peak height of spectra in the NIR region, vials with 1 mL volume (external diameter: 8 mm, inside diameter: about 6 mm, length: 40 mm) were selected as disposable vials for the samples. To investigate the causes of the variations in the predicted values, the optical path lengths and transmittance spectra of empty vials in five lots produced during the last four years were examined. The results showed that there were some differences in the path lengths of the vials and in the intensities of peaks near 7130 and 4515 cm-1, which were attributed to the hydroxyl groups in the spectra of the vials among the five lots. The calibration models for the concentration of isopropyl alcohol (IPA) in toluene contained in vials of the same lot were developed. To search the pretreatment methods for the best calibration model with small variations in the predictive values of the samples in the vials, six kinds of pretreatments (no pretreatment, baseline correction, first derivative, second derivative, multiplicative scattering correction (MSC), and standard normal variate (SNV)) were compared. The results indicated that MSC is a very effective method with small variation in the predicted values from the spectra of samples in vials. It was found that the variations in the predicted values are caused mainly by variations in the path lengths of vials. In real quality control, the NIR-chemometrics method is requested for long-term traceability and good reproducibility of the predictive value. Therefore, it is crucial to watch changes in the path length under a fixed path length as a reference. From this standpoint, modified MSC, which uses a particular spectrum as a reference instead of the average spectrum of a sample set, was proposed in the present study, and it was demonstrated that the variations due to the individual differences in the vials can not only be compensated, but the path lengths of vials could also be estimated by the modified MSC. Moreover, a calibration model for the toluene concentration in silicone oil was developed to confirm the effects of modified MSC by using another sample set. The results showed that the compensation of vial path lengths using the modified MSC is also useful for other samples, similar to the case of the sample of IPA in toluene.     

Fe@洋葱状富勒烯的拉曼特征

采用化学气相沉积方法制备了Fe@洋葱状富勒烯并采用高分辨透射电镜（HRTEM）、拉曼（Raman）分析对其进行了表征。结果表明：产物粒径稳定在50nm左右，拉曼特征峰相对于HOPG发生了软化，这是由于其特殊的弯曲层状结构造成。     

Raman spectra of iron-modified amorphous carbon

Raman spectra of amorphous carbon films containing encapsulated iron (a-C:Fe) have been measured in the frequency range 200–1000 cm⁻¹. The concentration of encapsulated iron atoms (3, 26, 38, and 54 at. %) was controlled by changing the relative areas of iron and graphite targets during the film deposition by RF magnetron sputtering and checked by Rutherford backscattering. The Raman spectra of a-C:Fe films display a series of almost equidistant bands spaced by approximately 110 cm⁻¹. This character of the spectrum is explained in terms of the atomic vibrations in short carbon nanotubes formed during the introduction of iron into an amorphous carbon matrix.     

A model-free, fully automated baseline-removal method for Raman spectra

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ(2)-statistic. The approach provides for multiple passes or iterations, based on a given χ(2)-statistic for convergence. If the baseline is acceptably flat given the χ(2)-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks--even if the window is so large that the baseline is inaccurately removed--because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ(2) statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.     

非线性光学晶体三硼酸铋的拉曼光散射

结构分析表明非线性光学晶体三硼酸铋BiB3 O6的晶胞参数为a1=0 .7116nm ,a2 =0 .4993nm ,a3 =0 .65 0 8nm ,β =10 5 .62° ,每个初基晶胞含有二个分子。在不同几何配置下的激光拉曼光谱观察到了晶体中硼氧四面体和硼氧三角形的特征晶格振动和振动模的分裂。拉曼光谱表明晶体的内部结构有很强的各向异性 ,这就是晶体非线性光学性质的来源