Study of ε-caprolactone polymerization by NIR spectroscopy

Near-infrared (NIR) spectroscopy is proposed for the in-line quantitative and kinetic study of the polymerization of ε-caprolactone and eventually to facilitate real-time control of the manufacturing process. Spectra were acquired with a fibre-optic probe operating in transflectance mode immersed in the reactor. The NIR data acquired were processed using a multivariate curve resolution alternating least squares (MCR-ALS) algorithm. The proposed method allows calculation of the concentration and spectral profiles of the species involved in the reaction. The key point of this method is the lack of reference concentrations needed to perform the MCR-ALS method. The use of an extended spectral matrix using both process and pure analyte spectra solves the rank deficiency. The concentration profiles obtained were used to calculate a kinetic fitting of the reaction, but the method was improved by applying kinetic constraints (hard modelling). The rate constants of batches at different temperatures and the energy of activation for this reaction were calculated. Whenever possible, the hard modelling combined with the MCR-ALS method improves the fit of the experimental data: the results show good correlation between the NIR and reference data and allow the collection of high-quality kinetic information on the reaction (rate constants and energy of activation).     

Combined kinetics and iterative target transformation factor analysis for spectroscopic monitoring of reactions

Obtaining rate constants and concentration profiles from spectroscopy is important in reaction monitoring. In this paper, we combined kinetic equations and Iterative Target Transformation Factor Analysis (ITTFA) to resolve spectroscopic data acquired during the course of a reaction. This approach is based on the fact that ITTFA needs a first guess (test vectors) of the parameters that will be estimated (target vectors). Three methods are compared. In the first, originally proposed by Furusj? and Danielsson, kinetic modelling is only used to provide the initial test vectors for ITTFA. In the second the rate constant used to provide the test vectors is optimised until a best fit is reached. In the third, a guess of the rate constant is used to provide the test vectors to ITTFA. The outcome of ITTFA is then used to fit the kinetic model and obtain a new guess of the rate constant. With this constant new concentration profiles are generated and provided to the ITTFA algorithm as new test vectors, in an iterative manner, minimising the residuals of the predicted dataset, until convergence. The second and third methods are new implementations of ITTFA and are compared to the first, established, method. First order (both one and two step) and second order reactions were simulated and instrumental noise was introduced. An experimental second order reaction was also employed to test the methods.     

Curing reaction of glycidylthioether resins: Kinetic model study by near infrared spectroscopy and multivariate curve resolution

The reactivity of sulfur‐based epoxy monomers was studied by monitoring of a model system involving phenylglycidylthioether and aniline. The reaction was carried out under isothermal conditions and monitored in situ by near infrared spectroscopy. Using multivariate curve resolution‐alternating least squares made it possible to obtain the concentration and spectral profiles of each species throughout the reaction. To obtain the kinetic rate constants, the values of the recovered concentration profiles were fitted to a kinetic model proposed for the reaction. Reactivity was evaluated by comparing the concentration profiles and kinetic rate constants obtained with the same parameters obtained for phenylglycidylether/aniline as a reference system. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 4846–4856, 2006     

New strategy to identify radicals in a time evolving EPR data set by multivariate curve resolution-alternating least squares

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra.     

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.     

Extraction of the contribution of kinetic profile for reaction of a chemical species in unknown matrices by standard addition method

A new and simple strategy is applied to resolve kinetic profile for the reaction of an analyte in unknown matrices, using standard addition method (SAM). The proposed method uses kinetic spectrophotometric data obtained by standard addition of analyte into unknown mixtures followed by the reaction of analyte with a proper reagent. The proposed method extracts kinetic profile for the reaction of an analyte by averaging the kinetic profiles obtained by subtraction of kinetic profiles after and before standard addition. The rate constant can be obtained using computational curve fitting. The performance of method was evaluated by using synthetic data as well as several experimental data sets. The proposed method can be applied to obtain kinetic profiles of the reactions in the presence of additive interference as well as multiplicative interferences. Hydroxylation reaction of diphenylcarbazide (DPCI) in the presence of diphenylcarbazone (DPCO) as a real system at various pHs was also studied by the present method. The rate constant and the order of the hydroxylation reaction were determined from extracted kinetic profiles.     

A Multivariate Curve Resolution Approach to the Study of the Degradation Kinetics of Valsartan under Photolytic and Acid Conditions

The analysis of UV‐spectrophotometric data with second‐order chemometrics techniques, including multivariate curve resolution with alternating least‐squares (MCR‐ALS) and hybrid hard‐ and soft MCR (HS‐MCR), was examined as an alternative tool for studying the kinetics of drug degradation under stress conditions, employing valsartan (VAL) as a model drug. Despite small structural and spectroscopic differences between VAL and its degradation products, MCR‐ALS and HS‐MCR were able to detect the generation of two photoneutral degradation products (DP‐1 and DP‐2) and a single acid hydrolysis product (DP‐3), providing good approximations to their pure spectra and concentration profiles, from which estimations of the kinetic profiles and rate constants were obtained. Kinetic models based on first‐order reactions explained the degradation processes. MCR‐ALS and HS‐MCR analyses yielded similar rate constants; however, the latter was capable of more properly fitting the experimental data to a kinetic model in the case of drug photolysis. The results were confirmed by comparison with data obtained by HPLC analysis of the degraded samples.     

Kinetic analysis of reactions of Si-based epoxy resins by near-infrared spectroscopy, 13C NMR and soft-hard modelling

Soft- and hard-modelling strategy was applied to near-infrared spectroscopy data obtained from monitoring the reaction between glycidyloxydimethylphenyl silane, a silicon-based epoxy monomer, and aniline. On the basis of the pure soft-modelling approach and previous chemical knowledge, a kinetic model for the reaction was proposed. Then, multivariate curve resolution-alternating least squares optimization was carried out under a hard constraint, that compels the concentration profiles to fulfil the proposed kinetic model at each iteration of the optimization process. In this way, the concentration profiles of each species and the corresponding kinetic rate constants of the reaction, unpublished until now, were obtained. The results obtained were contrasted with ¹³C NMR. The joint interval test of slope and intercept for detecting bias was not significant (α = 5%).     

Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion

Leccinum rugosiceps is an edible mushroom belonging to genus Leccinum of Boletaceae. Its fruiting bodies are richer in nutrients than many vegetables and fruit. The model of support vector machine was established for the discrimination of L. rugosiceps from regions based on rapid and low-cost ultraviolet and infrared spectroscopies. The mid-level data fusion was performed by support vector machine. Compared to a single spectroscopic technique, mid-level data fusion provided higher accuracy by selecting the most significant variance from data matrixes based on partial least squares discriminant analysis. The accuracy of the classification of samples in the calibration and test sets were 85.00 and 94.74%, higher than separate measurements by ultraviolet or infrared spectroscopy. This approach has applications for authentication and quality assessment of L. rugosiceps.     

An approach for simultaneous estimation of reaction kinetics and curve resolution from process and spectral data

Parameter estimation of reaction kinetics from spectroscopic data remains an important and challenging problem. This study describes a unified framework to address this challenge. The presented framework is based on maximum likelihood principles, nonlinear optimization techniques, and the use of collocation methods to solve the differential equations involved. To solve the overall parameter estimation problem, we first develop an iterative optimization‐based procedure to estimate the variances of the noise in system variables (eg, concentrations) and spectral measurements. Once these variances are estimated, we then determine the concentration profiles and kinetic parameters simultaneously. From the properties of the nonlinear programming solver and solution sensitivity, we also obtain the covariance matrix and standard deviations for the estimated kinetic parameters. Our proposed approach is demonstrated on 7 case studies that include simulated data as well as actual experimental data. Moreover, our numerical results compare well with the multivariate curve resolution alternating least squares approach.     

Kinetic approach for the enzymatic determination of levodopa and carbidopa assisted by multivariate curve resolution-alternating least squares

A combination of kinetic spectroscopic monitoring and multivariate curve resolution-alternating least squares (MCR-ALS) was proposed for the enzymatic determination of levodopa (LVD) and carbidopa (CBD) in pharmaceuticals. The enzymatic reaction process was carried out in a reverse stopped-flow injection system and monitored by UV-vis spectroscopy. The spectra (292-600 nm) were recorded throughout the reaction and were analyzed by multivariate curve resolution-alternating least squares. A small calibration matrix containing nine mixtures was used in the model construction. Additionally, to evaluate the prediction ability of the model, a set with six validation mixtures was used. The lack of fit obtained was 4.3%, the explained variance 99.8% and the overall prediction error 5.5%. Tablets of commercial samples were analyzed and the results were validated by pharmacopeia method (high performance liquid chromatography). No significant differences were found (α = 0.05) between the reference values and the ones obtained with the proposed method. It is important to note that a unique chemometric model made it possible to determine both analytes simultaneously.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Simultaneous kinetic spectrophotometric determination of Cu(II), Co(II) and Ni(II) using partial least squares (PLS) regression

A partial least squares (PLS-1) calibration model based on kinetic—spectrophotometric measurement, for the simultaneous determination of Cu(II), Ni(II) and Co(II) ions is described. The method was based on the difference in the rate of the reaction between Co(II), Ni(II) and Cu(II) ions with 1-(2-pyridylazo)2-naphthol in a pH 5.8 buffer solution and in micellar media at 25°C. The absorption kinetic profiles of the solutions were monitored by measuring the absorbance at 570 nm at 2 s intervals during the time range of 0–10 min after initiation of the reaction. The experimental calibration matrix for the partial least squares (PLS-1) model was designed with 30 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.1-2 μg mL⁻¹ for each cation. The proposed method was successfully applied to the simultaneous determination of Cu(II), Ni(II) and Co(II) ions in water and in synthetic alloy samples.      

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

Multicomponent kinetic determinations using multivariate calibration techniques

Multivariate calibration techniques for use in multicomponent kinetic-based determinations are reviewed. Multivariate calibration is a chemometric tool that continues to grow in popularity among analytical chemists. Multicomponent kinetic methods depend on differences in rates of reactions or processes to distinguish among the components. Kinetic profiles or a combination of kinetic profiles and spectra are commonly used. Because of their ability to process large quantities of data, multivariate calibration techniques are well suited for kinetic-based determinations. The concepts and principles of multivariate calibration are discussed first. Classical least squares regression, principal component regression, partial least squares regression and artificial neural networks are the multivariate calibration techniques considered here in detail. Recent examples of the application of these techniques to multicomponent kinetic determinations are reviewed. Both single and multiwavelength kinetic data are considered.     

多元曲线分辨结合在线红外用于3-氨基-4-氨基肟基呋咱的合成反应机理

利用红外光谱在线监测丙二睛、亚硝酸钠和盐酸羟胺合成3-氨基-4-氨基肟基呋咱的反应过程,采用多元曲线分辨-交替最小二乘法（MCR-ALS）、直观推导式演进特征投影法(HELP)等化学计量学方法对反应过程所获得的实时红外光谱数据矩阵进行解析,得到了各组分纯物质的浓度变化曲线和对应的红外光谱,并将多元曲线分辨 交替最小二乘法与直观推导式演进特征投影法的分析结果进行比较,得出可相互验证的一致结论,据此推出该反应合理的反应机理。 2种方法得到的反应物与生成物的光谱与原光谱的相似度近似于1,说明该解析方法具有准确性和可靠性。 结果表明,化学计量学结合红外光谱可有效的应用于3-氨基-4-氨基肟基呋咱合成过程的机理推断。     

Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Model-based analysis for kinetic complexation study of Pizda and Cu(II)

In the present work, the multivariate kinetic complexation of a new synthesized ligand, 1-(2'-hydroxyl cyclohexyl)-3'-[aminopropyl]-4-[3'-aminopropyl]piperazine (Pizda) and Cu(2+) in 50% ethanol-water solution is investigated using the UV-vis stopped-flow technique and state-of-the-art multi-wavelength numerical analysis. Model-based least squares fitting analysis or hard modeling is a specific part of chemometrics which is based on mathematical relationships for describing the measurements. Some recent developments include the incorporation of the effects of non-ideal experimental conditions into the fitting algorithm so it can substantially simplify experimental procedures. In this study no buffers are required because pH changes are taken into computations. Some 21 multi-wavelength kinetic measurements, taken at various initial concentrations of [H(+)] were analyzed globally, i.e. simultaneously applying an all inclusive reaction mechanism and a common set of species spectra. Using numerical analysis, the pH of the experimental solutions was allowed to vary as a consequence of the proceeding reactions. This enabled the complete kinetic analysis of the formation and dissociation of Cu(Pizda)(n+). Here protonation equilibria have been directly incorporated into the rate law, so thus variable pH values have been allowed during each measurement. Using the independently estimated stability constants (from spectrophotometric and potentiometric measurements) for the Cu(Pizda)(n+) complexes, a total of six rate constants and one protonation constant could be elucidated. The results of the analysis include the concentration distribution and spectra of all chemical species involved in the reaction. A low standard deviation and residual profiles obtained validate the results.     

Uncommon multivariate statistical methods for environmental studies: A review

In this paper we describe the characteristics and the applications of the multivariate methods for spectroscopic and chromatographic techniques independent component analysis (ICA) and two-dimensional correlation spectroscopy (2DCOS) focused to their use in environmental studies. In our opinion, these methods are important because they allow to characterize environmental samples with different aims and scopes from those generally obtained by means of more common multivariate methods such as principal component analysis (PCA) and partial least squares (PLS). The new insights of these methods in recent environmental studies are reviewed and debated.