Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Applications of Raman spectroscopy in pharmaceutical analysis

As Raman spectroscopy enables rapid, non-destructive measurements, the technique appears a most promising tool for on-line process monitoring and analysis in the pharmaceutical industry. This article gives a short introduction to Raman spectroscopy and presents several applications in the pharmaceutical field.     

In-situ surface-enhanced Raman scattering and FT-Raman spectroscopy of black prints

The black inkjet and laser prints were analysed with regard to application in forensic analysis of questioned documents. The purpose of this work was to study spectral properties and compare the suitability of surface-enhanced Raman scattering (SERS) with Fourier transform Raman spectra of prints. This work aimed to find optimal surface-enhanced Raman spectroscopic approach for the future analysis of documents using statistical methods. In this work, we analysed eight prints of four laser and four inkjet devices. The samples were measured using two dispersive Raman devices; (DXR Raman microscope with excitation line 532 nm, Foram 685-2 spectrometer − 685 nm) and FT-Raman device (Bruker Spectrometer MultiRAM with excitation line 1064 nm). The silver nanoparticles (AgNPs) colloid for SERS experiment were synthesised and checked by UV–vis spectroscopy and scanning electron microscopy (SEM). The remarkable differences caused by centrifugation of silver colloid were observed just in the SEM images. The main contribution of this paper is to propose the novel approach achieving sufficient SERS signal intensity of black prints using the both, laser and inkjet printers. Moreover, this method is based on just a single metal colloid, and the analysis can be performed in-situ, i.e. directly on the printed sample surface. We consider the SERS could by highly promising and universal for applications in the forensic analysis of printed documents with the combination of statistical method when conventional methods are not effective.     

Quantitative analysis by comprehensive two-dimensional gas chromatography using interval Multi-way Partial Least Squares calibration

A new approach for target quantitative analysis for comprehensive two-dimensional gas chromatography (GC × GC), interval Multi-way Partial Least Square (iNPLS) is presented and evaluated in this paper. In iNPLS, the two-dimensional chromatogram is split in small sections; each of these pieces is treated as an independent new chromatogram. Separated conventional NPLS calibration models for the concentration of the target analyte are built for each of the pieces of the whole chromatogram, and the best model is selected for quantitative analysis. An algorithm for iNPLS running on MatLab platform was written, preliminarily evaluated with using solutions of model compounds with different chemical properties and subsequently applied to quantify some allergens in perfume samples. The results were found to be adequate, and good precision and accuracy was obtained even for poorly resolved peaks.     

Two-dimensional correlation spectroscopy and multivariate curve resolution for the study of lipid oxidation in edible oils monitored by FTIR and FT-Raman spectroscopy

Accelerate oxidative degradation of six vegetable oils was monitored using FTIR and FT-Raman spectroscopy. Two-dimensional correlation spectroscopy and multivariate curve resolution alternating least squares (MCR-ALS) were applied to the analysis of the data. The use of hetero-spectral two-dimensional correlation of FTIR and FT-Raman data allowed the use of well established band assignments to interpret less clearly assigned spectral features. With a moving window approach it was possible to obtain simplified two-dimensional correlation maps and to detect compounds evolving with different kinetic. Simultaneous analysis of the oxidation experiments of the six different oils monitored by both spectroscopic techniques was performed using MCR-ALS. Although a complete resolution of the data was not possible, the spectral changes occurring during the oxidative degradation of the oils were described with a five-component model. The two fundamentally different chemometric approaches lead to coincident results.     

Validation of a direct non-destructive quantitative analysis of amiodarone hydrochloride in Angoron formulations using FT-Raman spectroscopy

Raman spectroscopy was applied for the direct non-destructive analysis of amiodarone hydrochloride (ADH), the active ingredient of the liquid formulation Angoron^®. The FT-Raman spectra were obtained through the un-broken as-received ampoules of Angoron^®. Using the most intense vibration of the active pharmaceutical ingredient (API) at 1568 cm⁻¹, a calibration model, based on solutions with known concentrations, was developed. The model was applied to the Raman spectra recorded from three as-purchased commercial formulations of Angoron^® having nominal strength of 50 mg ml⁻¹ ADH. The average value of the API in these samples was found to be 48.56 ± 0.64 mg ml⁻¹ while the detection limit of the proposed technique was found to be 2.11 mg ml⁻¹. The results were compared to those obtained from the application of HPLC using the methodology described in the European Pharmacopoeia and found to be in excellent agreement. The proposed analytical methodology was also validated by evaluating the linearity of the calibration line as well as its accuracy and precision. The main advantage of Raman spectroscopy over HPLC method during routine analysis is that it is considerably faster and no solvent consuming. Furthermore, Raman spectroscopy is non-destructive for the sample. However, the detection limit for Raman spectroscopy is much higher than the corresponding for the HPLC methodology.     

Complete quality analysis of commercial surface-active products by Fourier-transform near infrared spectroscopy

Using proper calibration data Fourier-transform near infrared spectroscopy is used for developing multivariate calibrations for different analytical determinations routinely used in the surfactants industry. Four products were studied: oleyl-cetyl alcohol polyethoxylated, cocamidopropyl betaine (CAPB), sodium lauryl sulfate (SLS) and nonylphenol polyethoxylated (NPEO). Calibrations for major as well as very low concentrated compounds were achieved and every model was validated through linearity, bias, accuracy and precision tests, showing good results and the viability of NIR spectroscopy as a full quality control method for this products. Duplicate and complete analysis on a single sample takes at most 3 min, requiring neither sample preparation nor the use of reagents. The analytical reference procedures involved in this work represent the typical ones used in the industry and the NIR method shows good results in the analysis of components with weight concentrations less than 1%.     

Monitoring of pigmented and wooden surfaces in accelerated ageing processes by FT-Raman spectroscopy and multivariate control charts

Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected.The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events.     

Quality control of cosmetic mixtures by NIR spectroscopy

Cosmetic preparations typically consist of mixtures of various compounds of natural origin or their derivatives. Their analysis is made rather difficult by their usually high complexity and is utterly impossible with a single analytical method; also, there is usually little to be gained by determining every individual component of the mixture. Rather, analyses are aimed at ensuring a proper balance between the contents of each component and thus require the use of methods capable of delivering global information. The combined use of near-infrared (NIR) spectroscopy and multivariate spectral processing chemometric techniques has enabled the development of effective methods for establishing the composition of complex samples with acceptable levels of analytical properties, such as accuracy, precision and throughput. In this work, we developed partial least squares calibration models for the determination of each component in a cosmetic mixture, and global indices (viz. the hydroxyl value), simply from the NIR spectrum of the sample. The models thus obtained are accurate enough for use in quality control analyses of cosmetic preparations and provide an effective alternative to existing conventional global methods. Experimental setup for measurement     

Quantitative surface analysis by X-ray photoelectron spectroscopy and X-ray excited auger electron spectroscopy

It is shown that X-ray excited KLL Auger electron spectra allow it to describe measured signal strengths similarly to X-ray photoelectron signals, thus offering valuable information on the quantitative surface composition of a solid sample. The principal equation and corresponding fundamental parameters are discussed. As a result Auger spectra of C, N, O, F, and Na can be easily used in a multiline approach for quantitative analysis. LMM and MNN spectra give rise to more problems, due to their more complicated structure, uncertainties with regard to the background and the influence of Coster-Kronig transitions. These problems are overcome by the use of empirical ratios of the strongest lines of 2p/LMM or 3d/MNN. Since these ratios are independent of sample composition, they allow it to transform the Auger signal into the corresponding photoelectron signal, provided that a standard sample has been measured. Thus a true additional information is obtained and moreover difficulties in cases of photoelectron spectra with overlapping lines from other chemical elements can be overcome.Dedicated to Professor Günther Tölg on the occasion of his 60th birthday     

多组分烟用香精定量分析方法的研究进展

综述了近年来烟用香精、香料各组分化合物的定量分析方法的研究进展。面积归一化法、内标法、外标法和标准加入法等常用色谱定量分析方法主要用于烟用香精、香料中目标化合物或单个组分的测定;多标定量法、特征峰-内标定量法和多元校正法能深入剖析烟用香精复杂体系中各组分含量(引用文献48篇)。     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

Non-destructive analysis of museum objects by fibre-optic Raman spectroscopy

Raman spectroscopy is a versatile technique that has frequently been applied for the investigation of art objects. By using mobile Raman instrumentation it is possible to investigate the artworks without the need for sampling. This work evaluates the use of a dedicated mobile spectrometer for the investigation of a range of museum objects in museums in Scotland, including antique Egyptian sarcophagi, a panel painting, painted surfaces on paper and textile, and the painted lid and soundboard of an early keyboard instrument. The investigations of these artefacts illustrate some analytical challenges that arise when analysing museum objects, including fluorescing varnish layers, ambient sunlight, large dimensions of artefacts and the need to handle fragile objects with care. Analysis of the musical instrument (the Mar virginals) was undertaken in the exhibition gallery, while on display, which meant that interaction with the public and health and safety issues had to be taken into account. Experimental set-up for the non-destructive Raman spectroscopic investigation of a textile banner in the National Museums of Scotland     

Comparative analysis of smokeless gunpowders by Fourier transform infrared and Raman spectroscopy

Fourier Transform Infrared (FTIR) and Raman spectroscopic techniques were used to perform a comparative study of the spectral profiles of single-base, double-base and triple-base smokeless gunpowders. Preliminary results based on visual comparison of the spectra point out that spectra obtained by both vibrational techniques were useful for a rapid identification of gunpowders containing dinitrotoluene as one of the major components and triple-base gunpowders. Additionally, the Raman spectra of gunpowders with diphenylamine in its primary composition showed a characteristic band, assigned to 2-nitro-diphenylamine, allowing the identification of this type of gunpowders.     

激光诱导击穿光谱对U元素的定量分析

铀矿是核领域最重要的矿产资源之一,快速、有效勘探铀矿资源能促进核领域平稳、健康发展。激光诱导击穿光谱(LIBS)技术具备多目标元素现场快速检测的优点,能实现铀矿资源准确、快速的现场分析。本工作基于LIBS技术对铀矿中U元素进行了定量分析,对比了偏最小二乘(PLS)和随机森林(RF)两种机器学习算法的定量效果。结果显示,RF模型的定量线性相关系数为0.996,对三个验证集的相对误差分别是22.33%、12.79%和12.04%;PLS模型的定量线性相关系数为0.997,对三个验证集的相对误差分别是4.33%、6.63%和6.85%。对比结果表明,本研究中的PLS模型定量准确度更高,同RF算法相比,PLS算法更适用于铀矿中U的LIBS定量分析。     

Comparison of multivariate calibration models for glucose,urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L⁻¹ for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L⁻¹ for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.     

Comparison of infrared attenuated total reflection and Raman spectroscopy in the quantitative analysis of diclofenac sodium in tablets

The quantification of diclofenac sodium (DS) in tablets was performed using partial least squares (PLS) models based on FTIR ATR (Fourier transform infrared attenuated total reflection) and FT-Raman spectra. Separate calibration models were built for two groups of tablets, standard and sustained release, containing different excipients. To compare the predictive ability of these models the relative standard errors of prediction (RSEP) were calculated. In the case of DS determination from the Raman data, RSEP error values in the range of 2.4-2.8% (2.7-2.9%) for the calibration (validation) data sets were obtained. For ATR models constructed using spectra registered three times for each sample, RSEP errors in the range of 3.6-3.7% (4.2-4.3%) were found. These errors decreased to 2.8% (3.0%) when spectra collected six times were applied. Five commercial products containing 25, 50, 75 and 100 mg of DS per tablet were quantified. Concentrations derived from the elaborated models correlated strongly with the results of reference analyses and gave recoveries of 99.1-101.3% and 99.1-101.7% for the ATR and Raman data, respectively. Although both spectroscopic techniques can be used as fast and convenient alternatives to the standard pharmacopeial methods of DS quantification in solid dosage forms, in the case of the ATR technique, it is necessary to repeat measurements at least a few times to obtain acceptable quantification errors.     

Mutlivariate calibration with Raman data using fast principal component regression and partial least squares methods

Linear and non-linear calibration methods (principal component regression (PCR), partial least squares regression (PLS), and neural networks (NN)) were applied to a slightly non-linear Raman data set. Because of the large size of this data set, recently introduced linear calibration methods, specifically optimised for speed, were also used. These fast methods achieve speed improvement by using the Lanczos decomposition for the singular value decomposition steps of the calibration procedures, and for some of their variants, by optimising the models without cross-validation (CV). Linear methods could deal with the slight non-linearity present in the data by including extra components, therefore, performing comparably to NNs. The fast methods performed as well as their classical equivalents in terms of precision in prediction, but the results were obtained considerably faster. It, however, appeared that CV remains the most appropriate method for model complexity estimation.     

Surface-enhanced Raman spectroscopy for classification of testosterone propionate and nandrolone residues in chicken

There is a critical need for a rapid and simple screening method of androgens in chicken. In this study, we evaluated surface-enhanced Raman spectroscopy (SERS) coupled with multivariate techniques for the classification of two androgens (i.e., testosterone propionate and nandrolone) in chicken from 294 samples. Raw Raman spectra were pretreated by using the methods of baseline correction, normalization and second derivative. Support vector machines (SVM) model using the score values of the first four principal components as the inputs was developed to classify all the chicken samples into the four groups (i.e., control, nandrolone, testosterone propionate, and testosterone propionate combined with nandrolone groups) with accuracy of 96.9%. Furthermore, the particle swarm optimization (PSO) was adopted to automatically optimize the penalty parameter C and the kernel parameter g of SVM model for improving the classification accuracy. The experimental results demonstrated that SERS, in combination with multivariate methods, could be utilized as a rapid and simple classification assay of androgens in chicken and exhibited great potential in practical applications as a screening tool to better serve customers.     

Simultaneous determination of antimony(III) and antimony(V) by UV-vis spectroscopy and partial least squares method (PLS)

This paper describes a procedure for the speciation of antimony by UV-vis spectroscopy using pyrogallol as complexing agent. A partial least squares (PLS) regression was performed to resolve highly overlapping spectrophotometric signals obtained from mixtures of Sb(III) and Sb(V). The relative error in absolute value was less than 5% when concentrations of several mixtures were calculated. The minimum concentration determined was 3.96 × 10⁻⁵ mol dm⁻³ and 3.98 × 10⁻⁵ mol dm⁻³ for Sb(V) and Sb(III), respectively. The analysis of the possible effect of the presence of foreign ions in the solution was performed and the procedure was successfully applied to the speciation of antimony in pharmaceutical preparations and aqueous samples.