Evaluation and comparison of mid-infrared,Raman and near-infrared spectroscopies for characterization and determination of the compositions in fully biodegradable poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) blends combined with chemometrics

In this work, mid-infrared (MIR), Raman and near-infrared (NIR) spectroscopies were evaluated and compared for characterization and determination of the compositions in poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) (PLA/PPC/PBAT) blends via chemometrics. Qualitative analysis of MIR, Raman, and NIR spectra of the three compositions was performed. Partial least squares (PLS) models were developed based on each spectroscopy for quantitative determination of the concentrations. The data suggested that MIR and Raman have an advantage over NIR in terms of qualitative recognition of the three compositions. The data also showed that Raman and NIR succeeded in determining the concentrations, while the concentration determined via MIR was inaccurate. Hence, Raman is the optimal analytical tool for qualitative characterization and quantitative determination of the compositions in fully biodegradable PLA/PPC/PBAT blends. The characteristic bands in the Raman spectra clearly identify PLA, PPC, and PBAT to be 392 cm^?1 (δ CCO), 948 cm^?1 (ν C?O?C) and 1600 cm^?1 (ν C ? C in benzene ring), respectively. The optimal calibration models based on Raman for PLA, PPC, and PBAT exhibited root mean square error of prediction (RMSEP) values of 3.140%, 3.576%, and 2.538%, respectively.     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band intensity ratio of the 380 and 1,096 cm^?1 bands. For calibration purposes, 80.5% crystalline and 120-min milled (0% crystalline) Whatman CC31 and six cellulose mixtures produced with crystallinities in the range 10.9–64% were used. When intensity ratios were plotted against crystallinities of the calibration set samples, the plot showed a linear correlation (coefficient of determination R ² = 0.992). Average standard error calculated from replicate Raman acquisitions indicated that the cellulose Raman crystallinity model was reliable. Crystallinities of the cellulose mixtures samples were also calculated from X-ray diffractograms using the amorphous contribution subtraction (Segal) method and it was found that the Raman model was better. Additionally, using both Raman and X-ray techniques, sample crystallinities were determined from partially crystalline cellulose samples that were generated by grinding Whatman CC31 in a vibratory mill. The two techniques showed significant differences. In the second approach, successful Raman PLS regression models for crystallinity, covering the 0–80.5% range, were generated from the ten calibration set Raman spectra. Both univariate-Raman and WAXS determined crystallinities were used as references. The calibration models had strong relationships between determined and predicted crystallinity values (R ² = 0.998 and 0.984, for univariate-Raman and WAXS referenced models, respectively). Compared to WAXS, univariate-Raman referenced model was found to be better (root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values of 6.1 and 7.9% vs. 1.8 and 3.3%, respectively). It was concluded that either of the two Raman methods could be used for cellulose I crystallinity determination in cellulose samples.     

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.     

Quantitative determination of acetylsalicylic acid and acetaminophen in tablets by FT-Raman spectroscopy

A procedure for quantitative determination of acetylsalicylic acid and acetaminophen in pharmaceuticals by PLS (partial least squares) and PCR (principal component regression) treatment of FT (Fourier transform)-Raman spectroscopic data is proposed. The proposed method was tested on powdered samples. Three chemometric models were built: the first, for samples consisting of an active substance diluted by lactose, starch and talc; the second, in which a simple inorganic salt was applied as an internal standard and additions were not taken into account; and the third, in which a model was constructed for a commercial pharmaceutical, where all constituents of the tablet were known. By utilising selected spectral ranges and by changing the chemometric conditions it is possible to carry out fast and precise analysis of the active component content in medicines on the basis of the simplified chemometric models. The proposed method was tested on five commercial tablets. The results were compared with data obtained by intensity ratio and pharmacopoeial methods. To appraise the quality of the models, the relative standard error of predictions (RSEPs) were calculated for calibration and prediction data sets. These were 0.7-2.0% and 0.8-2.3%, respectively, for the different PLS models. Application of these models to the Raman spectra of commercial tablets containing acetylsalicylic acid gave RSEP values of 1.3-2.0% and a mean accuracy of 1.2-1.7% with a standard deviation of 0.6-1.2%.     

基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

Rapid quality assessment of Gentianae Macrophyllae Radix based on near-infrared spectroscopy and capillary electrophoresis

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near-infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside, and roburic acid were considered quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside, and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM.     

Rapid and Nondestructive Analysis of Bacillus Calmette–Guerin Polysaccharide Nucleic Acid Injection by near-Infrared Spectroscopy with Chemometrics

A novel quantitative analytical method using near-infrared (NIR) spectroscopy combined with chemometrics has been developed to determine the polysaccharides and nucleic acids for routine quality analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections. A Monte-Carlo method was used to detect and discard outliers and to improve the predictive ability of the model. Various other spectral preprocessing methods such as smoothing, derivative, multiplicative scattering correction, standard normal variables, and orthogonal signal correction methods were used to remove noise and other irrelevant information from the spectra. Sample-set partitioning based on joint x–y distance method was utilized to divide the sample measurements into calibration and validation datasets. The optimal wavelength variables were determined by competitive adaptive weighted sampling. The model was established and cross-validated using partial least square regression. The root mean square errors of cross-validation for polysaccharides and nucleic acids were determined to be 0.0382 and 5.218, and the root mean square errors of prediction were 0.0229 and 6.282. The overall results show that NIR spectroscopy combined with chemometry is effective for the quantitative analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

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.     

Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm^?1, assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

Fluorescence spectroscopic determination of triglyceride in human serum with window genetic algorithm partial least squares

Fluorescence spectrum, as well as the first and second derivative spectra in the region of 220–900 nm, was utilized to determine the concentration of triglyceride in human serum. Nonlinear partial least squares regression with cubic B‐spline‐function‐based nonlinear transformation was employed as the chemometric method. Window genetic algorithms partial least squares (WGAPLS) was proposed as a new wavelength selection method to find the optimized spectra wavelengths combination. Study shows that when WGAPLS is applied within the optimized regions ascertained by changeable size moving window partial least squares (CSMWPLS) or searching combination moving window partial least squares (SCMWPLS), the calibration and prediction performance of the model can be further improved at a reasonable latent variable number. SCMWPLS should start from the sub‐region found by CSMWPLS with the smallest root mean squares error of calibration (RMSEC). In addition, WGAPLS should be utilized within the region of smallest RMSEC whether it is the sub‐region found by CSMWPLS or region combination found by SCMWPLS. Moreover, the prediction ability of nonlinear models was better than the linear models significantly. The prediction performance of the three spectra was in the following order: second derivative spectrum < original spectrum < first derivative spectrum. Wavelengths within the region of 300–367 nm and 386–392 nm in the first derivative of the original fluorescence spectrum were the optimized wavelength combination for the prediction model. Copyright © 2012 John Wiley & Sons, Ltd.     

Simultaneous spectrophotometric determination of uranium, nitric acid and nitrous acid by least-squares method in PUREX process

Multi-wavelength linear regression spectrophotometry combined with method of least squares for simultaneous determination of uranium, nitric acid and nitrous acid in PUREX (Plutonium/URanium EXtraction) process was developed. The molar absorbance matrix was calibrated with absorbance data measured in the wavelength range of 350–500 nm for a series of standard solutions by linear least-squares regression. This method used information from the absorption spectra of U(VI)–nitrous acid–nitric acid solutions to determine U(VI), nitrous acid and nitric acid. In the range of 0.95–74.1 g/L U(VI), 5 × 10^?4–2 × 10^?3 mol/L nitrous acid and 3–5 mol/L aqueous nitric acid solution, the measuring precision for determination of U(VI), nitrous acid and nitric acid was 0.46, 4.09, and 0.68 % respectively. In the solution of 30 % TBP–kerosene, the measuring precision for determination of U(VI) and nitrous acid was 0.42 and 4.2 % respectively in the range of 0.95–74.1 g/L U(VI) and 5 × 10^?4–2×10^?3 mol/L nitrous acid. The spectrophotometric method can be valuable for monitoring and controlling of both species in PUREX process operation, thanks to its simplicity, efficiency and accuracy.     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

Determining the Kinematic Viscosity of Lubricant Oils for Gear Motors by Using the Near Infrared Spectroscopy (NIRS) and the Wavelength Selection

This paper reports the chemometric predictive models developed for near infrared spectroscopy (NIRS) for the quantitative determination of the kinematic viscosity (37.1–93.1 cSt) of lubricant oils for gear motors. The gear motor is a complete motive force system that consists of an electric motor and a reduction gear train integrated into one easy-to-mount and configure package. The method used for measuring the viscosity of the lubricating oil was ASTM D445, the Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids. A comparison was made among several multivariate calibration techniques and algorithms for pre-processing and variable selection of data, including partial least squares, interval partial least squares (iPLS), a genetic algorithm (GA), and a successive projections algorithm. Finally, the results obtained for the root mean square errors of prediction in cSt and relative average error were, respectively, 1.86 and 2.97% (GA) and 2.36 and 2.97% (iPLS). The method proposed in this study is a useful alternative for the determination of the kinematic viscosity in oils for gear motors.     

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000-4000 cm⁻¹ with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.     

Sequential automated fusion/extraction chromatography methodology for the dissolution of uranium in environmental samples for mass spectrometric determination

The ability to predict the amount of time that a light petroleum mixture has been weathered could have many applications, such as aiding forensic investigators in determining the cause and intent of a fire. In our study, an evaporation chamber that permits control of airflow and temperature was constructed and used to weather a model nine-component hydrocarbon mixture. The composition of the mixture was monitored over time by gas chromatography and a variety of chemometric models were explored, including partial least squares (PLS), nonlinear PLS (PolyPLS) and locally weighted regression (LWR or loess). A hierarchical application of multivariate techniques was able to predict the time for which a sample had been exposed to evaporative weathering. A classification model based on partial least squares discriminant analysis (PLS-DA) could predict whether a sample was relatively fresh (< 12 h exposure time) or highly weathered (>20 h exposure time). Subsequent regression models for these individual classes were evaluated for accuracy using the root mean square error of prediction (RMSEP). Prior to regression model calculation, y-gradient generalized least squares weighting (GLSW) was used to preprocess the data by removing variance from the X-block, which was orthogonal to the Y-block. LWR was found to be the most successful regression method, whereby fresh samples could be predicted to within 40 min of exposure and highly weathered samples predicted to within 5.6h. These results suggest that our hierarchical chemometric approach may also allow us to estimate the age of more complicated light petroleum mixtures, such as gasoline.     

Chemometric Techniques Applied for Classification and Quantification of Binary Biodiesel/Diesel Blends

In this paper, three different types of biodiesel, which were synthesized from peanut, corn, and canola oils, were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Different biodiesel/diesel blends containing 2–90% (V/V) of each biodiesel type were prepared and analyzed by near infrared spectroscopy (NIR). In the next step, the chemometric methods of hierarchical clusters analysis (HCA), principal component analysis (PCA), and support vector machines (SVM) were used for exploratory analysis of the different biodiesel samples, and the SVM was able to give the best classification results (correct classification of 50 peanut and 50 corn samples, and only one misclassification out of 49 canola samples). Then, partial least squares (PLS) and multivariate adaptive regression splines (MARS) models were evaluated for biodiesel quantification. Both methods were considered equivalent for quantification purposes based on the values smaller than 5% for the root mean square error of calibration (RMSEC) and root mean square of validation (RMSEP), as well as Pearson correlation coefficients of at least 0.969. The combination of NIR to the chemometric techniques of SVM and PLS/MARS was proven to be appropriate to classify and quantify biodiesel from different origins.     

Chemometric detection of acetaminophen in pharmaceuticals by infrared spectroscopy combined with pattern recognition techniques: comparison of attenuated total reflectance-FTIR and Raman spectroscopy

This paper presents and discusses the building of discriminant models from attenuated total reflectance (ATR)-FTIR and Raman spectra that were constructed to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The datasets, containing 11 spectra of pure substances and 21 spectra of various formulations, were processed by partial least squares (PLS) discriminant analysis. The models found in the present study coped greatly with the discrimination, and their quality parameters were acceptable. A root mean square error of cross-validation was in the 0.14-0.35 range, while a root mean square error of prediction was in the 0.20-0.56 range. It was found that standard normal variate preprocessing had a negligible influence on the quality of ATR-FTIR; in the Raman case, it lowered the prediction error by 2. The influence of variable selection with the uninformative variable elimination by PLS method was studied, and no further model improvement was found.     

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。