Non-destructive and clean prediction of aviation fuel characteristics through Fourier transform-Raman spectroscopy and multivariate calibration

The combination of Fourier transform (FT)-Raman spectroscopy and partial least squares (PLS) regression is proposed to be used in off-line kerosene quality control. Here, six important physico-chemical properties have been studied: Abel flash point, initial boiling point (IBP), 10% of distilled sample, final boiling point (FBP), total percentage of aromatic compounds (% aromatics) and viscosity. The Raman spectra were obtained directly from standard 2 ml glass vials ( mm), using a Bruker RFS 100 FT-Raman spectrometer, equipped with a 1064 nm Nd:YAG laser and a Ge detector, in back-scattering mode and accumulating 25 scans (150 s acquisition time) with a laser power of 300 mW. All Raman spectra were unit normalized in order to avoid the influence of the laser power into the regression models. Different Raman shift spectral ranges have been evaluated, the 193.5-1688.1 cm⁻¹ region being the most satisfactory one. Corrected standard errors of prediction of 1.9, 2.3, 2.8 °C, 0.19 cSt and 0.7% were obtained for flash point, IBP, FBP, viscosity and % aromatics, respectively. The influences of the glass vials, sample positioning and laser power have been studied, as well.     

LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate

A novel near infrared (NIR) modeling method—Laplacian regularized least squares regression (LapRLSR) was presented, which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable.     

Application of a chemometric method for simultaneous determination of acetaminophen and diclofenac in content-uniformity and drug-dissolution studies

A new, repeatable, and rapid method has been developed for resolution of binary mixtures of acetaminophen and diclofenac with minimum sample pretreatment and without separation of the analytes. The method, based on the PLS1 processing of absorbance data in the UV region, was successfully used for quantification of the drug content of three tablet preparations. The results obtained were in good agreement with HPLC recovery data. The method also enabled determination of drug-dissolution profiles of these commercial tablets, by simultaneous determination of both analytes during the dissolution test.     

The polynomial reconstruction problem: The first 50 years

The problem of reconstructing the characteristic polynomial of a graph of order at least 3 from the collection of characteristic polynomials of its vertex-deleted subgraphs was posed by Cvetkovi? in 1973 as a spectral counter part to the well-known Ulam's reconstruction conjecture. Over the last 50 years, this problem has received notable attention, many positive results have been obtained, but in the general case the problem is still unresolved. In particular, no counter example is found in literature. In this expository paper we survey classical and some more recent results concerning the polynomial reconstruction problem, discuss some related problems, variations and generalizations.     

A fitting algorithm for real coefficient polynomial rooting

A new algorithm for computing all roots of polynomials with real coefficients is introduced. The principle behind the new algorithm is a fitting of the convolution of two subsequences onto a given polynomial coefficient sequence. This concept is used in the initial stage of the algorithm for a recursive slicing of a given polynomial into degree-2 subpolynomials from which initial root estimates are computed in closed form. This concept is further used in a post-fitting stage where the initial root estimates are refined to high numerical accuracy. A reduction of absolute root errors by a factor of 100 compared to the famous Companion matrix eigenvalue method based on the unsymmetric QR algorithm is not uncommon. Detailed computer experiments validate our claims.     

Exhaled breath condensate: Determination of non-volatile compounds and their potential for clinical diagnosis and monitoring. A review

Exhaled breath condensate is a promising, non-invasive, diagnostic sample obtained by condensation of exhaled breath. Starting from a historical perspective of early attempts of breath testing towards the contemporary state-of-the-art breath analysis, this review article focuses mainly on the progress in determination of non-volatile compounds in exhaled breath condensate. The mechanisms by which the aerosols/droplets of non-volatile compounds are formed in the airways are discussed with methodological consequences for sampling. Dilution of respiratory droplets is a major problem for correct clinical interpretation of the measured data and there is an urgent need for standardization of EBC. This applies also for collection instrumentation and therefore various commercial and in-house built devices are described and compared with regard to their design, function and collection parameters. The analytical techniques and methods for determination of non-volatile compounds as potential markers of oxidative stress and lung inflammation are scrutinized with an emphasis on method suitability, sensitivity and appropriateness. The relevance of clinical findings for each group of possible non-volatile markers of selected pulmonary diseases and methodological recommendations with emphasis on interdisciplinary collaboration that is essential for future development into a fully validated clinical diagnostic tool are given.     

一种面向PX装置成品杂质含量检测的拉曼光谱分析法

面向对二甲苯(PX)成品的在线分析问题,为克服色谱分析法的不足,本文提出了一种基于拉曼光谱的PX成品杂质含量分析方法。为了克服激光中心波长与功率波动带来的不良影响,提出了激光中心波长自动校正与PX特征峰归一化方法;同时,结合相关分析获取了主要杂质的特征波段,并采用偏最小二乘算法进行定量分析。针对11个未知样本,PLS模型对甲苯(MB)、乙苯(EB)、间二甲苯(MX)、邻二甲苯(OX)的标准预测误差分别为0.03%、0.11%、0.02%、0.03%。实验结果表明,拉曼光谱法能够快速、准确且无损地测定PX成品中主要杂质的含量,为在线拉曼分析系统的开发奠定了方法基础。     

基于ELM算法的羊肉水分含量的快速无损检测

在近红外光谱900-1700nm的波长范围内采集南疆羊肉的光谱数据,来研究水分含量的快速无损检测.为减弱非目标因素对光谱的影响,采用SNV和去趋势法对光谱数据进行预处理.为降低建模的复杂度,去除共线性的影响,采用连续投影算法和相关系数法相结合选取8个特征波长变量,最后使用PLS和ELM算法分别进行建模.实验表明,与采用全光谱波段建模相比,采用特征波长变量建模,PLS和ELM算法的运行时间都大大缩短,并且在运行时间和预测精度上,ELM算法均优于PLS算法.ELM算法采用8个特征波段变量建模,预测精度达到0.9768,均方误差为4.4291e-04,相关系数为0.7603,运行时间可控制在1e-04s之下,这可为研发羊肉水分含量的便携式检测装置提供理论参考.