Bleachability and characterization by fourier transform infrared principal component analysis of acetosolv pulps obtained from sugarcane bagasse

Sugarcane bagasse Acetosolv pulps were bleached by xylanase and the pulps classified by using Fourier transform infrared (FTIR) spectroscopy and principal component analysis (PCA). Pulp was treated with xylanase for 4–8 h with stirring at 30°C. Some samples were further extracted with NaOH for 1 h at 65°C. FTIR spectra were recorded directly from the dried pulp samples by using the diffuse reflectance technique. Reduction in kappa number of 69% was obtained after sequence xylanase (4 h)-alkaline extraction. During bleaching the viscosity decreased only 12%. FTIR-PCA showed that the first three principal components (PCs) explained more than 90% of the total variance of the pulp spectra. PC2×PC1 plot showed that the points related to pulps from sequence xylanase (4 h)-alkaline extraction are different from the other. This group isenlarged by plotting PC3×PC1 or PC3×PC2 containing all pulps submitted to alkaline extraction. PC2 and PC3 are the principal factor for differentiation of the pulps. These PCs suffer influence of the ester bands (1740 and 1244 cm⁻¹). On the other hand, the pulps bleached only with xylanase could not be differentiated from the nonbleached pulps.     

Kinetic study of a complex triangular reaction system in alkaline aqueous-ethanol medium using on-line transmission FTIR spectroscopy and BTEM analysis

The kinetics of the base-catalyzed reaction of methyl 4-hydroxybenzoate in aqueous-ethanol solvent medium was studied and analyzed via combined on-line transmission FTIR spectroscopy and Band-Target Entropy Minimization (BTEM) technique. This reaction is considered complex since it involves simultaneous hydrolysis and ethanolysis reactions of methyl 4-hydrozybenzoate (MP) to form ethyl 4-hydroxybenzoate (EP) as an intermediate and sodium 4-hydroxybenzoate as a final product. The pure component spectra of the reactive species involved in the reaction were reconstructed using BTEM technique. Their corresponding real concentrations were calculated and subsequently used for analyzing the kinetics of this triangular reaction system. The effects of temperature and solvent mixture compositions were studied. In general, the results show that the rates of both hydrolysis and ethanolysis reactions increase with temperature. Addition of ethanol to the solvent mixture also reduces the rates of the hydrolysis reactions. The effect of solvent mixture on the rate of ethanolysis reaction is more complex and influenced by at least two competing factors, namely the concentration of ethoxide ion in the solution and the stabilization effect on the reactant. The enthalpy and entropy activation parameters, ΔH^‡ and ΔS^‡, of both the hydrolysis and ethanolysis reactions were determined using the Eyring equation and the activation parameters confirm the associative nature in the elementary steps in these reactions. Finally, it is shown that the dominant synthetic pathway in this triangular system changes from direct hydrolysis of methyl 4-hydrozybenzoate to the indirect pathway via ethanolysis and then hydrolysis depending on the solvent mixture composition.     

Application of Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics for authentication of cod-liver oil

Some vegetable oils such as canola (CaO), corn (CO), soybean (SO), and walnut (WO) oils have similar color with cod liver oil (CLO), therefore, the presence of these oils was difficult to detect using naked eye. For this reason, Fourier transform infrared (FTIR) spectroscopy using horizontal attenuated total reflectance (HATR) as sampling accessory and in the combination with chemometrics was developed for detection and quantification of these vegetable oils as adulterants in CLO. The quantification of vegetable oils was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR), while the classification between pure CLO and CLOs adulterated with CaO, CO, SO, and WO was performed using discriminant analysis (DA). PLS with FTIR normal spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R²) higher than 0.99 and root mean square error of calibration (RMSEC) in the range of 0.04-0.82% (v/v). The PLS model was further used to predict the levels of these vegetable oils in independent samples for validation/prediction purpose. The root mean square error of prediction (RMSEP) values obtained were of 1.75% (v/v) (CaO), 1.39% (v/v) (CO), 1.35% (v/v) (SO), and 1.37% (v/v) (WO), respectively. The classification using DA revealed that the developed method can classify CLO and that mixed with these vegetable oils using 9 principal components.     

Multivariate design

In multivariate data analysis such as principal components analysis (PCA) and projections to latent structures (PLS), it is essential that the training set systems (objects) are selected to provide data with substantial information for model parametrization, and to represent properly any future situations where the multilvariate model is used for predictions. In the framework of multivariate projections (PCA, SIMCA and PLS), elementary concepts of statistical design (fractional factorials and composite designs) can be used with the latent variables (PC or PLS scores) as design variables. The plan of action thus becomes: (1) problem formulation (specify aim and model, make a conceptual division of the investigated system into subsystems); (2) collection of multivariate data for each type of subsystems; (3) estimation of the practical dimensionality of the data for each type of subsystems by PC or PLS analysis; (4) use of the PC or PLS scores (t) as design variables in the combination of subsystems to systems in the training set; (5) measurement of responses (Y); (6) analysis of data by PCA or PLS; (7) interpretation of results with possible feedback to steps 1, 2 or 3. The procedures are illustrated by two problems: a structure/activity relationship for a family of peptides, and optimization of an organic synthesis with respect to system variables (solvent, substrate, co-reactant_) and process variables (temperature, reactant concentrations).     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

Development and Application of Chemometric Methods in Near Infrared Spectral Analysis

In recent 10 years, like other disciplines influenced by the fast development of PC technique, chemometrics has been used in many analytical methods, especially in instrumental analysis. This article describes applications and comparison of multivariate linear regression (MLR), principal component analysis (PCA), principal component regression (PCR), partial least square (PLS), neural network (ANN), fuzzy and model recognition. A better calibration method can be a great help to improve the efficiency of the routine analytical work.     

Synthesis, characterization and hydrolytic degradation of linear and crosslinked poly[(glycino ethyl ester)(allyl amino)phosphazene]

Polyorganophosphazenes substituted by glycino ethyl ester and allylamine with different ratios were synthesized and their structures were characterized by ¹H NMR, ³¹P NMR and FTIR. Via the crosslink reaction, a novel biodegradable crosslinked polyorganophosphazene material was obtained. DSC and FTIR spectra indicated the occurrence of crosslink. Hydrolysis studies were also performed to compare the crosslinked polymers with linear ones. The co-substituted polyorganophosphazenes with more allylamine at pendant groups exhibited a lower degradation rate than poly[bis(glycino ethyl ester)phosphazene] and crosslinked polyphosphazenes had an even lower degradation rate. SEM photographs characterized the surface of polyphosphazenes films after hydrolytic degradation, confirming that uncrosslinked ones had outstanding hydrolytic evidences at the surface while the crosslinked ones only had sporadic small erosion holes, remaining much smoother.     

Magnesium chloride supported high-mileage catalysts for olefin polymerization. IV. FTIR and quantitative analysis of modifiers in the catalysts

Fourier transform infrared (FTIR) spectra were obtained for a typical MgCl₂-supported, high-mileage catalyst for propylene polymerization. When ball-milling MgCl₂ with ethyl benzoate (EB), the latter is incorporated into the support (I) by Lewis acid-base complexation involving both oxygen atoms of the ester. Reaction of (I) with p-cresol (PC) resulted in a material (II) that contains all the characteristic IR bands of PC. The reaction of (II) that contains all the characteristic IR bands of PC. The reaction of (II) with AlEt₃ (TEA) resulted in (III) whose spectrum supports the reaction observed by product analysis and NMR spectroscopy. There was no evidence of any reaction between TEA and EB. Further reaction of (III) with an excess of TiCl₄ caused substantial removal of the p-cresol moiety as shown by the diminution of its characteristic bands. Finally, activation with 3TEA-1MT (methyl-p-toluate) complexes gave spectra that revealed the presence of MT in the activated catalyst without any visage of p-cresol moiety. The nondestructive FTIR method, however, is not quantitative. Quantitative analysis of the organic components in the support materials (I), (II), and (III) and the catalysts was accomplished by hydrolysis of the inorganic components, extraction with ether, and analysis by gas chromatography. The results are in good agreement with composition deducted from elemental analysis and substantiate the FTIR conclusions.     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

FTIR, XRD, and DSC analysis of the rosemary extract effect on polyethylene structure and biodegradability

The purpose of this research study was evaluation of the utility of two common multivariate techniques, agglomerative cluster analysis (CA) and principal component analysis (PCA), as supplementary means of detecting incompatibilities, which can occur between active pharmaceutical ingredients and excipients at the preformulation stage of a solid dosage form. For the detection of incompatibilities between atenolol (beta blocker) and selected excipients (mannitol, lactose, starch, methylcellulose, β-cyclodextrin, meglumine, chitosan, polyvinylpyrrolidone and magnesium stearate), the thermogravimetry (TG), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) were chosen. The results have shown that compatibility between atenolol and an excipient can be identified in a CA dendrogram by two large clusters, from which one groups an excipient and physical mixtures with a high concentration of the excipient. Another cluster encompasses atenolol and mixtures with a high content of the drug. In the PCA plot, all samples are located along the first principal component axis (PC1), beginning from a single component located with the most negative PC1 value, through mixtures with gradually varying concentration of both ingredients, till the second component located close to the most positive PC1 values. The results have shown that CA and PCA fulfil their role as supporting techniques in the interpretation of the data acquired from the TG curves, and the obtained data are compatible with the results of DSC and FTIR analyses.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra

Fourier transform infrared spectroscopy (FTIR) has been studied many times in the context of identification of plant, fungal and bacterial species. Infrared spectra are commonly analyzed using multivariate statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least squares analysis (PLS) and discriminant analysis (DA). In this study, a univariate statistical method for analysis of variance (ANOVA) was used to reduce the number of variables before applying the multivariate methods. Analyzing variables using ANOVA or a combination of ANOVA with CA produced better results. Here, experiments were carried out by performing ANOVA using the first derivative of the spectra instead of the original spectra or its second derivative because using the first‐derivative variables led to improved distinction between species. Different results were obtained by applying different validation methods. The leave‐one‐out validation method gave higher results than the validation‐with‐training and validation sample sets, thus indicating the non‐objectivity of the leave‐one‐out validation method. Copyright © 2010 John Wiley & Sons, Ltd.     

FTIR investigation of spray-dried milk protein concentrate powders

Fourier transform infrared (FTIR) spectroscopy was used to examine the conformation of proteins in spray-dried milk protein concentrate (MPC) powders and to determine if the spectral changes could be related to nitrogen solubility of these powders. MPC samples (83–92% protein, dry basis) were prepared using a range of processing conditions and stored for 4 weeks at 21 °C. FTIR spectra were collected in the mid infrared (MIR) region between 4000 and 600 cm⁻¹. FTIR data was pre-processed to remove physical effects causing discrimination between samples using firstly second derivatives and normalization and secondly the extended multiplicative scatter correction (EMSC) technique. The FTIR spectral changes were subsequently assessed using second derivative spectroscopy and principal components analysis (PCA) in the amide I and II regions (1700–1400 cm⁻¹) and the fingerprint region (1800–700 cm⁻¹). PCA analysis showed that the different powder preparations could be separated on scores plots but the separation was not related to nitrogen solubility per se. However, changes in nitrogen solubility of individual MPC powders during storage could be correlated to changes in FTIR spectra. PCA analysis of FTIR spectra could generally discriminate between MPC powders that had lost significant nitrogen solubility (9–20%) and those in which nitrogen solubility was preserved on storage. There were changes in intensity and/or position of bands at 1630 cm⁻¹ when the solubility of a stored sample decreased substantially. The results of this work also show that EMSC data pre-processing for these samples gives comparable results when compared with more complicated data pre-processing for the removal of physical effects.     

Partial least squares modeling of combined infrared, H NMR and C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding ¹H and ¹³C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (D_LR), viscosity (V_LR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, ¹³C NMR and ¹H NMR spectra and on 3 sets of merged spectra, i.e., IR + ¹H NMR, IR + ¹³C NMR and IR + ¹H NMR + ¹³C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, ¹³C NMR and ¹H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input ¹³C NMR or ¹H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.     

Simultaneous Spectrophotometric Determination of Iron and Cobalt in Micellar Medium by Using a Principal Component Artificial Neural Network and Multivariate Calibration

A simple and reliable method for simultaneous spectrophotometric determination of iron(II) and cobalt(II) has been established. The method is based on complex formation with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in a micellar medium. Despite a spectral overlap, Fe²⁺ and Co²⁺ have been simultaneously determined with chemometric approaches involving principal component artificial neural network (PC‐ANN), principal component regression (PCR) and partial least squares (PLS). Various synthetic mixtures of iron and cobalt were assessed and the results obtained by the applications of these chemometric approaches were evaluated and compared. It was found that the PC‐ANN method afforded relatively better precision than that of PCR or PLS. The proposed method permits detection limits of 0.05 and 0.07 ng mL^?1 for Co and Fe, respectively. The influences of pH, ligand amount, solvent percentage and time on the absorbance were also investigated. The proposed method was also applied satisfactorily for the determination of Fe(II) and Co(II) in real and synthetic samples.     

Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis

In the present paper, the influence of different acquisition techniques (transmission, diffuse reflectance infrared Fourier transform and attenuated total reflectance) in the determination of nine coal properties related to combustion power plants has been studied. Raw coal samples of different origins were pooled for developing a correlation between the resultant spectra and the corresponding coal properties by multivariate analysis techniques. Thus, the existent collinearity in mid-infrared coal spectra led to the application of partial least squares regression (PLS), studying simultaneously the influence of different spectroscopic units as well as several spectral data mathematical pre-treatments. On the other hand, a principal component analysis (PCA) revealed a relationship between principal components and coal composition in both transmission and reflection techniques. Although the best accuracy and precision results were obtained for coal properties related to organic matter, the system was also able to differentiate coal samples attending to the presence of a specific mineral matter, kaolinite.