Spegtrophotometrig Determination of α -Tocopherol Acetate in Soft Capsules

Abstract

Two spectrophotometric methods have been described for the determination of α -tocopherol acetate (vitamin E) in soft capsules. The first method applies the orthogonal function method under least squares. The quadratic coefficients calculated over the wavelength range 277 - 304 nm at 4-nm intervals in chloroform were reproducible and independent of ethyl oleate concentration. The second method applies the second derivative (D₂) to correct for irrelevant absorbance due to ethyl oleate The peak trough amplitude at 271 - 287 nm was in linear correlation to the concentration of vitamin E. The recoveries obtained using the proposed methods agreed with those obtained using the official proceduce.     

Near‐infrared spectroscopy and partial least squares‐class modeling (PLS‐CM) for metabolomics fingerprinting discrimination of intervention breakfasts ingested by obese individuals

Near‐infrared spectroscopy has been used in nutritional metabolomics fingerprinting for the assessment of the intake of intervention breakfasts prepared with four different vegetable oils that were previously subjected to a deep frying process of 20 cycles for 5 min at 180°C. The target oils were an extra virgin olive oil and three varieties of refined sunflower oil. Of the three latter, one of them was used as such, other was spiked with a synthetic oxidation inhibitor (dimethylsiloxane) and, finally, the last one was enriched with an extract of phenolic compounds from olive pomace, the antioxidant properties of which are well known. Urine sampled from individuals before intake and 2 and 4 h after intake was directly analyzed by NIRS to obtain fingerprint characteristics of the metabolome composition. The resulting urinary patterns were combined for statistical analysis by unsupervised and supervised approaches. Partial least squares‐class modeling enabled to develop class‐models for each intervention breakfast, thus achieving discrimination of urinary fingerprints from individuals after breakfast intake. The models were statistically characterized by estimation of sensitivity and specificity parameters for the training and evaluation (validation) steps. The application of variable importance in projection algorithm enabled to detect the spectral regions with higher significance to explain the variability observed in the partial least squares class‐models. Quantitative differences of variable importance in projection scores discriminated among the different classes under study. Copyright © 2013 John Wiley & Sons, Ltd.     

Improved methods for fluorescence background subtraction from Raman spectra

Raman spectroscopy has attracted interest as a non‐invasive optical technique to study the composition and structure of a wide range of materials at the microscopic level. The intrinsic fluorescence background can be orders of magnitude stronger than the Raman scattering, and so, background removal is one of the foremost challenges for quantitative analysis of Raman spectra in many samples. A range of methods anchored in instrumental and computational programming approaches have been proposed for removing fluorescence background signals. An enhanced adaptive weighting scheme for automated fluorescence removal is reported, applicable to both polynomial fitting and penalized least squares approaches. Analysis of the background fitting results for ensembles of simulated spectra suggests that the method is robust and reliable and can significantly improve the background fit over the range of signal, shot noise and background parameters tested, while reducing the subjective nature of the process. The method was also illustrated by application to experimental data generated from aqueous solutions of bulk protein fibrinogen mixed with dextran. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

激光诱导击穿光谱结合偏最小二乘法定量分析脐橙中Cd含量

基于激光诱导击穿光谱(LIBS)技术对赣南脐橙中Cd元素进行定量分析. 利用LIBS获取样品中Cd元素的特征谱线信息, 并结合原子吸收分光光度计测量样品中Cd元素的真实含量.采用五点平滑法和中心化法对样品光谱数据进行预处理, 基于偏最小二乘法(PLS)对其中的39个样品建立Cd元素的定量分析模型, 在该模型的基础上预测另外13个样品的Cd含量, 并对PLS模型进行对比验证. PLS模型中拟合曲线的相关系数为0.9806, 12个样品的验证结果的相对误差为10.94%.研究结果表明, 激光诱导击穿光谱技术能够准确的检测农产品中重金属含量, 为农产品的安全检测提供技术方法. 关键词： 激光诱导击穿光谱 Cd 定量分析 偏最小二乘法     

Orthogonal projection to latent structures solution properties for chemometrics and systems biology data

Partial least squares (PLS) is a widely used algorithm in the field of chemometrics. In calibration studies, a PLS variant called orthogonal projection to latent structures (O‐PLS) has been shown to successfully reduce the number of model components while maintaining good prediction accuracy, although no theoretical analysis exists demonstrating its applicability in this context. Using a discrete formulation of the linear mixture model known as Beer's law, we explicitly analyze O‐PLS solution properties for calibration data. We find that, in the absence of noise and for large n, O‐PLS solutions are simpler but just as accurate as PLS solutions for systems in which analyte and background concentrations are uncorrelated. However, the same is not true for the most general chemometric data in which correlations between the analyte and background concentrations are nonzero and pure profiles overlap. On the contrary, forcing the removal of orthogonal components may actually degrade interpretability of the model. This situation can also arise when the data are noisy and n is small, because O‐PLS may identify and model the noise as orthogonal when it is statistically uncorrelated with the analytes. For the types of data arising from systems biology studies, in which the number of response variables may be much greater than the number of observations, we show that O‐PLS is unlikely to discover orthogonal variation whether or not it exists. In this case, O‐PLS and PLS solutions are the same. Copyright © 2011 John Wiley & Sons, Ltd.     

Impact of fat and muscle in energy dispersive X‐ray diffraction‐based identification of heroin using multivariate data analysis

As for the detection of drug body packing, skin is a typical interference factor. In this paper, multivariate data analysis was proposed to analyze the impact of fat and muscle on heroin identification based on the profile of spectra of energy dispersive X‐ray diffraction. In the space of principal components, the results showed that different pure samples (heroin, muscle, fat) clustered in different areas, whereas the location of mixture samples moved between locations of pure samples. The impact of fat and muscle lies in moving the feature points between pure materials in the space of principal components. Furthermore, the model of heroin covered by fat and muscle of different thicknesses was set up, and a linear relationship was proven to be suitable. Our findings indicate that multivariate data analysis would be a promising method in the detection of drug body packing. Copyright © 2011 John Wiley & Sons, Ltd.     

基于最小二乘法的长周期实物期权精确估值迭代模拟算法

提出了一种基于最小二乘法的长周期实物期权精确估值迭代模拟算法,并通过一个商用通信卫星在轨服务投资决策的算例对该算法的实现进行了说明.算法将一个需要一次进行大量运算的问题转变为一个需要进行多次运算但每次运算的计算量相对较小的问题,能够很好地解决在缺乏并行计算的条件下大量模拟运算所面临的计算资源瓶颈问题,不仅能够得到较为精确的实物期权价值的点估计值和区间估计值,也便于推导最优的投资策略.     

Statistical properties of the state obtained by solving a nonlinear multivariate inverse problem

This article takes a statistical approach to solving a multivariate state‐space problem where many data are nonlinearly related to a state vector. The state is unknown and to be predicted, but the problem can be ill posed. A state‐space model quantifies the variability of the physical process (state equation) and of the measurements related to the process (measurement equation). The resulting posterior distribution is then maximized, yielding the predicted state vector. Statistical properties of the predicted state vector, in particular its first two moments with respect to the joint distribution, are approximated using the delta method. These are then applied to the problem of retrieving, from satellite data, a profile of CO₂ values in a column of the atmosphere. Copyright © 2012 John Wiley & Sons, Ltd.