NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.     

复杂样品近红外光谱定量分析模型的构建方法

针对复杂样品近红外光谱分析中校正集的设计问题, 探讨了标准样品参与复杂样品建模的可行性. 通过标准样品和复杂基质样品共同构建的偏最小二乘(PLS)模型, 考察了波段筛选和建模参数对预测结果的影响. 结果表明, 采用PLS方法建立定量模型时, 校正集样品性质应该尽量与预测集样品相似, 当样品的性质相差较大时, 适当增加校正集样品的差异性可使模型具有更强的预测能力. 同时, 波段优选对提高预测结果的准确性具有重要的意义.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Treating NIR data with orthogonal discrete wavelet transform: Predicting concentrations of a multi-component system through a small-scale calibration set

Through randomly arranging samples of a calibration set, treating their NIR spectra with orthogonal discrete wavelet transform, and selecting suitable variables in terms of correlation coefficient test (r-test), it is possible to extract features of each component in a multi-component system respectively and partial least squares (PLS) models based on these features are capable of predicting the concentration of every component. What is perhaps more important, with the proposed strategy, the predictive ability of the model is at least not impaired while the size of the calibration set can be obviously reduced. Therefore, it provides a more economical, rapid, as well as convenient approach of NIR quantitative analysis for multi-component system. In addition, all important factors and parameters related to the proposed strategy are discussed in detail.     

Resolution of mixtures of three nonsteroidal anti-inflammatory drugs by fluorescence using partial least squares multivariate calibration with previous wavelength selection by Kohonen artificial neural networks

A spectrofluorometric method for the quantitative determination of flufenamic, mefenamic and meclofenamic acids in mixtures has been developed by recording emission fluorescence spectra between 370 and 550 nm with an excitation wavelength of 352 nm. The excitation–emission spectra of these compounds are deeply overlapped which does not allow their direct determination without previous separation. The proposed method applies partial least squares (PLS) multivariate calibration to the resolution of this mixture using a set of wavelengths previously selected by Kohonen artificial neural networks (K-ANN). The linear calibration graphs used to construct the calibration matrix were selected in the ranges from 0.25 to 1.00 μg ml⁻¹ for flufenamic and meclofenamic acids, and from 1.00 to 4.00 μg ml⁻¹ for mefenamic acid. A cross-validation procedure was used to select the number of factors. The selected calibration model has been applied to the determination of these compounds in synthetic mixtures and pharmaceutical formulations.     

Improvement of multivariate calibration techniques applied to 1-to-N component mixtures through an optimized experimental design

This work describes a novel experimental design aimed at building a calibration set constituted by samples containing a different number of components. The algorithm performs a reiteration process to maintain the number of samples at the lower value as possible and to ensure an homogeneous presence of all the concentration levels. The mixture design was applied to a drug system composed by one-to-four components in different combination. The resolution of the system was performed by three multivariate UV spectrophotometric methods utilizing principal component regression (PCR) and partial last squares (PLS1 and PLS2) algorithms. The calibration set was composed by 61 references on four concentration levels, including 15 samples for each quaternary, ternary and binary composition and 16 one-component samples. The calibration models were optimized through a careful selection of number of factors and wavelength zones, in such a way as to remove interferences from instrumental noise and excipients present in the pharmaceutical formulations. The prediction power of the regression models were verified and compared by analysis of an external prediction set. The models were finally used to assay pharmaceutical specialities containing the studied drugs in one-to-four formulations.     

Optical coefficient‐based multivariate calibration on near‐infrared spectroscopy

The time and expense of calibration development limit the feasibility of NIR spectroscopy for many industrial applications, with a major portion of the costs being related to creation of a sufficient set of calibration samples. Net analyte signal (NAS) and generalized least squares (GLS) pre‐processing have been proposed in the literature as methods to simplify multivariate calibration by reducing the quantity of calibration samples by orthogonalizing or shrinking interference signals. Synthetic calibration has also been reported as a method to combine interference signals with pure component spectra to generate virtual calibration models, thereby reducing the number of real calibration samples required. The goals of this paper were to (1) compare theoretical and practical differences between NAS and GLS pre‐processing and (2) explore the potential of simplified NIR calibrations, both empirical and synthetic, constructed using optical coefficient‐based signal processing on predicting chemical compositions of pharmaceutical powder mixtures. A reduced calibration dataset including only one pharmaceutical powder mixture composition and pure component spectra was used for both empirical and synthetic calibrations. Absorption and reduced scattering coefficients, obtained from spatially‐resolved spectroscopy, were used herein as interference signals in NAS/GLS pre‐processing for both calibrations. As a result, NAS and GLS were shown to be equivalent in both theoretical and practical senses. After optical coefficient‐based signal processing, simplified calibrations, both empirical and synthetic, were demonstrated to have similar model performance as generic pre‐processing methods such as SNV and derivative, while requiring fewer principal components and achieving a lower prediction error. Copyright © 2010 John Wiley & Sons, Ltd.     

段式正交信号校正方法及在小麦近红外光谱数据分析中的应用

针对光谱数据峰宽、局部效应显著、含有噪音、变量个数多及彼此间常存在严重的复共线性等问题,改进和设计一种光谱数据局部校正方法:基于窗口平滑的段式正交信号校正方法,并将之结合偏最小二乘回归,以实现光谱数据的预处理及定量分析。通过NIPALS算法初始化将滤去的正交成分,以近邻分段方式进行逐个波长点的正交信号校正。而后将去噪后的光谱矩阵作为新的自变量阵,通过偏最小二乘回归构建其与性质参变量间的校正模型。通过小麦近红外漫反射光谱数据的应用实验结果表明,本方法正交成分估计稳定,去噪明显,模型的预报性能优于其它方法,PLS成分数减少,模型更加简洁。     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

Multivariate estimation between mid and near-infrared spectra of hexafluoroisopropanol-water mixtures.

Multivariate regression based on partial least squares (PLS2) was applied to estimating one spectral dataset from another set having an intrinsic relationship with each other. An estimation was successfully carried out between mid-infrared (IR) spectra in the range of 2980 - 3800 cm(-1) and that of near-infrared (NIR) spectra in the range of 6000 - 7500 cm(-1) for hexafluoroisopropanol (HFIP)-water mixtures. The result demonstrates that, after building a suitable regression model, not only NIR spectra, but also well-resolved IR spectra of HFIP-water mixture can be estimated properly in this way. The use of IR and NIR spectroscopy together with PLS2 regression will not only alleviate laborious and costly measurements, but also open a way to provide easier assignments of generally weak and highly overlapped NIR spectral bands.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties

A mixture design of experiment approach was followed to explore formulation effects on the technological properties of wheat flours optimized for industrial bread-making purposes. Ten different flour mixtures were investigated by means of near infrared spectroscopy (NIRS) to obtain information on flour performance in a critical phase such as dough leavening. For each mixture, a laboratory-scale bread making experiment was carried out according to a standardized recipe and the leavening phase of each dough sample was monitored by means of NIRS at different times. Parallel factor analysis (PARAFAC) was used to highlight the existence of differences among the mixtures on the basis of NIR spectrum variability with respect to the leavening time. Additionally, the relationship among the 3-way NIR dataset and some parameters measured on the baked bread loaves (dimensions, volume, weight) was investigated by means of the n-way extension of partial least squares regression (nPLS), in order to evaluate product properties from its leavening step and mixture formulation. The results give better insight on the relationships among wheat flour formulation and its performance in the leavening phase and as far as some properties of the final product are concerned, thus offering a way to monitor the leavening phase and give information on its influence on the final product properties.     

LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate

A novel near infrared (NIR) modeling method—Laplacian regularized least squares regression (LapRLSR) was presented, which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable.     

遗传算法用于偏最小二乘方法建模中的变量筛选

利用全局搜索方法－遗传算法（genetic algorithms,GA)对近红外光谱分析中的波长变量进行筛选,再用偏最小二乘方法（patrial least squares,PLS）建立分析校正模型。对两类样品的近红外光谱分析应用实例表明,这种选取变量进行校正的方法,不仅简化、优化了模型,而且增强了所建模型的预测能力,尤其适用于单纯PLS较以校正关联的体系。     

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.     

Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy

Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and aftertaste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting-PLS (IPW-PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW-PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra.