Characterization of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis

A method is proposed for monitoring the radix rehmanniae proparate processing procedure and determining the endpoint of the process using attenuated total reflectance (ATR) FT-IR through nonnegative independent component analysis (ICA). In the proposed method, ATR FT-IR spectra of the samples were firstly measured at different steaming periods. Then, nonnegative ICA was used for direct estimation of the feature spectra of the pure components in the mixture without pre-separation and other prior information. The estimated independent components (ICs) and their variation of the relative concentrations were used to characterize the processing procedure and determine the endpoint. The results show that the estimated three ICs are consistent with that of the chemical components in the mixtures, i.e. catalpol/rehmaionoside, glucose, and other compounds that nearly keep invariant during the processing procedure. The endpoint determined by the IR-ICA method is 15 h, which was located in the range obtained by expert sensory analysis, whereas the endpoint determined by the traditional sensory analysis is 14 ∼ 17 h and even 14 ∼ 20 h, which showed the significant deviation of the endpoints determined by different operators. Figure Characterisation of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis     

Determination of endpoint of procedure for radix rehmanniae steamed based on ultraviolet spectrophotometry combination with continuous wavelet transform and kernel independent component analysis

A method for determination of the endpoint of the procedure for radix rehmanniae steamed was proposed based on UV spectrophotometry combination with continuous wavelet transform and kernel independent component analysis (UV-CWT-KICA). In the proposed method, the raw UV spectra of the rehmanniae samples during steamed procedure were measured. The raw UV spectral data were firstly pretreated by CWT for elimination of the noise signal and enrichment of the spectral resolution, then the independent components (ICs) were estimated from the mixed CWT coefficient matrix. The results show that the ICs are chemical significance with their relative concentrations gradually decreasing or increasing during the first steamed period, and the endpoint of the steamed procedure can be determined by inspection of the relative concentration profiles, at which the ICs should be approached maximum or minimum. Furthermore, the estimated ICs of rehmanniae samples from different areas or with different grades are similar, and the relative concentration of the similar ICs in different groups are increasing or decreasing before the first 14 h, and nearly steady or some decreasing after 16 h. Based on the variations of the relative concentration profiles of the ICs, the endpoint of the steamed procedure can be determined as 15 h, while that determined by sensory analysis is 14-20 h. The proposed UV-CWT-KICA method can avoid the higher deviations of the endpoints that were determined by sensory analysis. It provides an alternative approach for determination of the endpoint of the procedure for processing traditional Chinese medicine (TCM).     

Processing of NMR,UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.     

A simple procedure for hydrodynamic injection in flow injection analysis applied to the atomic absorption spectrometry of chromium in steels

The proposed technique for hydrodynamic injection is based on commutation in a simple flow-injection system with only one peristaltic pump. Precise results (usually r.s.d. < 1%) are attained even when injected volumes are small (1–10 μl). Dead-volume effects are associated with the mechanical flexibility of the system and with diffusion; differences between effective sample size and the dimensions of the injection duct are emphasized, particularly for small volumes. The proposed technique is checked for a spectrophotometric model system and confirmed by the atomic absorption spectrometry of chromium in steels. With a sampling rate of 120 h^?1, the precision is better than 99% for chromium contents in the 0.0–30.0% (w/w) range. The results agree well with those obtained by i.c.p./a/e/s. and conventional a.a.s.     

X-ray fluorescence spectrometry applied to the analysis of environmental samples

X-ray fluorescence spectrometry is an expanding technique in the field of environmental analysis for both air and water pollution studies. Its capabilities now include the determination of gaseous contaminants in air in addition to the established area of elemental analysis, covering airborn particulates and soluble trace elements in water.     

Mass spectrometry applied to the analysis of estrogens in the environment

Environmental analytical chemistry has recently changed focus from analysis of non-polar, persistent contaminants (e.g. polychlorinated biphenyls and dioxins) to more polar and labile compounds that interfere with biological processes. For example, natural and synthetic estrogens and their metabolites have been detected in sewage treatment plant effluents at nanogram/liter concentrations that are similar to those at which both total sex reversal and intersex (containing both testes and ova) is induced in fish exposed to these compounds in laboratory experiments. The development of techniques for the analysis of natural and synthetic estrogens in biological fluids (i.e. serum and urine) has been a priority in the biomedical field. However, the recent recognition that estrogen hormones are contaminants in the environment that may contribute to endocrine disruption has focused attention on the need for highly sensitive and specific techniques that are applicable for trace analysis in complex environmental matrices. Three optimized mass spectrometric protocols have been developed for the determination and quantitation of steroid hormones in environmental matrices using gas chromatography/tandem mass spectrometry (GC/MS/MS), liquid chromatography/mass spectrometry selected ion monitoring, (LC/MS - SIM) and liquid chromatography/tandem mass spectrometry (LC/MS/MS). The advantages and disadvantages of each method are presented.     

Multivariate calibration techniques applied to the spectrophotometric analysis of one-to-four component systems

The UV spectrophotometric analysis of a multicomponent mixture containing paracetamol, caffeine, tripelenamine and salicylamide by using multivariate calibration methods, such as principal component regression (PCR) and partial least-squares regression (PLS), was described. The calibration set was based on 47 reference samples, consisting of quaternary, ternary, binary and single-component mixtures, with the aim to develop models able to predict the concentrations of unknown samples containing as many as one-to-four components. The calibration models were optimized by an appropriate selection of the number of factors as well as wavelength ranges to be used for building up the data matrix and excluding any information about the interfering excipients included in pharmaceutics. The PCR and PLS models were compared and their predictive performance was inferred by a successful application to the assays of synthetic mixtures and pharmaceutical formulations.     

Fast and eco-friendly method using atmospheric solids analysis probe mass spectrometry to characterize orange varieties

Orange fruit is one of the most popular types of fruit in the world, and its juice is the main product of its processing. This study aimed to evaluate a simple, fast, and eco-friendly methodology, atmospheric solids analysis probe mass spectrometry (ASAP-MS), to assess the chemical profile of four oranges varieties (Valencia, Folha Murcha, Pera, and Iapar). The oranges' varieties were evaluated for the physicochemical composition (extraction yield, pH, total titratable acidity, total soluble solids [TSS], °Brix), ratio (TSS/TA), and bioactive compounds (ASAP-MS analysis). The characterization of oranges resulted in great values of oranges yield according to the varieties (44.00–48.10% [w/w], adequate and characteristic acidity [0.73–1.35%], soluble solids content (10.24–13.80°Brix), pH (3.30–3.96), and ratio (7.59–19.90) level for this fruit. This powerful method showed that all analysis procedures were simple, fast, and easy because there is no need to prepare the sample and the analysis time lasted 2 min. Besides, results obtained exhibited a vast array of chemical groups. Principal component analysis (PCA) defined and distinguished the varieties of the orange. Therefore, ASAP-MS and PCA showed that they are very attractive candidates for routine analysis to monitor the varieties of the orange with its pronounced advantages, besides being contributing to the environment because it does not use any quantities of organic solvents. This methodology was applied for the first time to this type of sample.     

SEM analysis and gas permeability test to characterize polysulfone membrane prepared with polyethylene glycol as additive

Phase inversion method is applied to prepare flat sheet asymmetric polymeric membranes from homogeneous solution of 12 wt% polysulfone (PSf) with two different solvents--N-methyl-2-pyrrolidone (NMP) and dimethyl acetamide (DMAc). 5.0 wt% polyethylene glycol (PEG) of three different molecular weight (400, 6000, and 20,000 Da) is used as the polymeric additives in the casting solution. Membranes are characterized by two different techniques viz. scanning electron microscopy (SEM) and gas permeation tests. Finally, the results of both the techniques are compared with those calculated from pure water permeation tests using Hagen-Poiseuille equation. It is found that though the values obtained from all the techniques vary from each other, their trend with increase in molecular weight of PEG seems to be the same. It is seen that when molecular weight of PEG increases from 400 to 20,000 Da, the mean pore size of the prepared membranes decreases, while the porosity and pore density show an increasing trend; the pressure normalized gas flux rises significantly and the thickness of the top layer of the prepared membrane sheet increases.     

Fundamental and applied investigations in plasma-source mass spectrometry for elemental analysis

The current status of plasma source-mass spectrometry (PS-MS) is reviewed. An overview of interference effects that exist, alternative plasma sources available, and mass spectrometer interface studies is provided. A discussion of current and future development areas in plasma source mass spectrometry is also included.     

A simple program for weighted linear regression applied to activation analysis data processing

A simple ALGOL program for activation analysis data handling is presented The program, although designed in principle for processing single-channel spectrometry data, may also be used for multichannel spectrometry, on condition that the peak area is computed separately. The calculations of instrumental error and standard deviation are carried out. The outliers are tested, and the regression line diagram with the related observations are plotted by the program.     

A post-modification approach to independent compo-nent analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

Independent component analysis (ICA) has demonstrated its power to extract mass spectra from over-lapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is pro-posed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.     

Size exclusion chromatography/mass spectrometry applied to the analysis of polysaccharides.

A novel method for analysing polysaccharide materials is described which employs size-exclusion chromatography (SEC) followed by detection by on-line electrospray ionisation mass spectrometry (ESI-MS) and off-line matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS). It is demonstrated through SEC/ESI ion trap mass spectrometry that the formation of multiply charged oligomer ions, which bind up to five sodium cations, allows the rapid analysis of polysaccharide ions with molecular weights in excess of 9 kDa. MALDI spectra generated from fractionation of the effluent collected from the same SEC separation are shown to be in good agreement with the ESI spectra with respect to molecular weight distributions and types of ions generated. ESI and MALDI mass spectra of samples obtained from sequential graded ethanol precipitation and SEC fractionation of acid and enzymatically digested arabinoxylan polysaccharides show important structural differences between polysaccharide fragments. In addition, a comparison is made between the mass spectra of native and permethylated SEC-separated fragments of acid and enzymatically treated arabinogalactan. Linkage information of the permethylated arabinogalactan oligomers can be rapidly established through the use of on-line SEC/ESI-MS( n) experiments.     

Independent component analysis and its applications in signal processing for analytical chemistry

Independent component analysis (ICA) is a statistical method the goal of which is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. In an ICA procedure, the estimated independent components (ICs) are identical to or highly correlated to the spectral profiles of the chemical components in mixtures under certain circumstances, so the latent variables obtained are chemically interpretable and useful for qualitative analysis of mixtures without prior information about the sources or reference materials, and the calculated demixing matrix is useful for simultaneous determination of polycomponents in mixtures. We review commonly used ICA algorithms and recent ICA applications in signal processing for qualitative and quantitative analysis. Furthermore, we also review the preprocessing method for ICA applications and the robustness of different ICA algorithms, and we give the empirical criterion for selection of ICA algorithms in signal processing for analytical chemistry.     

General dissolution procedure for the analysis of aluminium alloys by atomic absorption spectrometry

Alloys are dissolved in nitric acid followed by hydrofluoric acid. Borax is added to complex the excess of fluoride and to act as spectroscopie buffer. Results obtained for up to 12 elements in 7 standard alloys are accurate.     

Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression

A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.     

Comparison of atmospheric pressure photoionization and electrospray ionization mass spectrometry for the analysis of UV filters

A comparison was made between the electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) tandem mass spectrometric (MS/MS) responses of eleven ultraviolet (UV) filters. Four of the target compounds were favourably ionized in negative ion mode, and the other seven compounds in positive ion mode. For nine of the compounds APPI generated a similar response to that of ESI, but the APPI signal‐to‐noise (S/N) ratios were 1.3–60 times higher. The two most polar of the UV filter compounds (PBSA and BP‐4) were more efficiently ionized by ESI, offering higher signal intensities and lower detection limits. APPI was, however, less susceptible to ion suppression than ESI when real samples were injected. In order to optimize the APPI conditions different dopant solvents were examined to enhance the efficiency of the photoionization process. Among the evaluated dopants, toluene was selected as the best compromise. At a toluene flow rate of 10% of the solvent flow rates the ionization response increased by a factor of 40–50 over the use of no dopant for the compounds in positive ion mode and by more than 300 for the compounds in negative ion mode. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry

Discrete wavelet transform (DWT) provides a well-established means for spectral denoising and baseline elimination to enhance resolution and improve the performance of calibration and classification models. However, the limitation of a fixed filter bank can prevent the optimal application of conventional DWT for the multiresolution analysis of spectra of arbitrarily varying noise and background. This paper presents a novel methodology based on an improved, second-generation adaptive wavelet transform (AWT) algorithm. This AWT methodology uses a spectrally adapted lifting scheme to generate an infinite basis of wavelet filters from a single conventional wavelet, and then finds the optimal one. Such pretreatment combined with a multivariate calibration approach such as partial least squares can greatly enhance the utility of Raman spectroscopy for quantitative analysis. The present work demonstrates this methodology using two dispersive Raman spectral data sets, incorporating lactic acid and melamine in pure water and in milk solutions. The results indicate that AWT can separate spectral background and noise from signals of interest more efficiently than conventional DWT, thus improving the effectiveness of Raman spectroscopy for quantitative analysis and classification.