Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems

The use of Fourier transform near infrared (FT-NIR) spectroscopy for simultaneous determination of multiple properties in an active pharmaceutical ingredient (API) fermentation process is described, together with procedures for developing accurate NIR calibrations with a performance independent of scale and the specific bioreactor used. Measurements were made in situ, by insertion of transflection probes into pilot and industrial bioreactors providing direct contact with the fermentation culture media. The ultimate goal was to establish methods for real time process monitoring aimed at enhanced process supervision, fault detection diagnosis and control of bioreactors. The in situ acquired spectra were related to lab results of samples taken from the reactors during the course of the manufacturing process. Suitable spectral wavenumber regions were selected and calibration models based on partial least squares (PLS) were developed. The root mean square errors of prediction for API content, viscosity, nitrogen source and carbon source concentration were all within acceptable ranges as compared to the off-line lab measurements, respectively, 0.03% (w/w), 150 cp, 0.01% (w/w), and 0.4% (w/w).     

Spectrophotometric simultaneous determination of nitroaniline isomers by orthogonal signal correction-partial least squares

The simultaneous determination of nitroaniline isomer mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removes the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200–500 nm range for 21 different mixtures of nitroaniline isomers. Calibration matrices were containing 1–21, 1–15 and 1–18 μg ml⁻¹ of m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The RMSEP for m-nitroaniline, o-nitroaniline and p-nitroaniline with OSC and without OSC were 0.6567, 0.2692, and 0.3134, and 1.3818, 1.2181, and 0.3953, respectively. This procedure allows the simultaneous determination of nitroaniline isomers in real matrix samples and good reliability of the determination was proved.     

Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR

This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1-649.9 cm(-1). Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Orthogonal signal correction-partial least squares method for simultaneous spectrophotometric determination of cypermethrin and tetramethrin

The simultaneous determination of cypermethrin and tetramethrin mixtures by using spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200-350 nm range for 25 different mixtures of cypermethrin and tetramethrin. Calibration matrices were containing 0.1-12.9 and 0.1-13.8 microg mL(-1) for cypermethrin and tetramethrin, respectively. The RMSEP for cypermethrin and tetramethrin with OSC and without OSC were 0.0884, 0.0614 and 0.2915, 0.2309, respectively. This procedure allows the simultaneous determination of cypermethrin and tetramethrin in synthetic and real samples good reliability of the determination was proved.     

Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression

Abstract  This work describes a quantitative spectroscopic method for the analysis of ternary mixtures of ceratine (CER), creatinine (CRE), and uric acid (UA) using multivariate data models based upon ultraviolet spectroscopy. By multivariate calibration methods, such as partial least squares regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. In this study, the calibration model is based on absorption spectra in the 200–260 nm range for 36 different mixtures of CER, CRE, and UA. The unrelated information was removed by the orthogonal signal correction (OSC) method and the results were proved. Evaluation of the prediction errors for the prediction set reveals the OSC-treated data give substantially lower root mean square error of prediction (RMSEP) values than original data. The RMSEP for CER, CRE, and UA with OSC were 1.1686, 0.2195, and 0.3726, and without OSC were 1.9057, 0.3482, and 0.6164, respectively. This procedure allows the simultaneous determination of CER, CRE, and UA in synthetic and real samples. Graphical abstract        

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.     

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.     

Simultaneous spectrophotometric determination of cobalt,copper and nickel using 1-（2-thiazolylazo）-2-naphthol by chemometrics methods

The simultaneous determination of cobalt, copper and nickel using 1-（2-thiazolylazo）-2-naphthol （first figure of this article） by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares （PLS） regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction （OSC） is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 550-750-nm range for 21 different mixtures of cobalt, copper and nickel. Calibration matrices were formed from samples containing 0.05-1.05, 0.05-1.30 and 0.05-0.80 μg·mL^-1 for cobalt, copper and nickel, respectively. The root mean square error of prediction （RMSEP） for cobalt, copper and nickel with OSC and without OSC were 0.007, 0.008, 0.011 and 0.031,0.037, 0.032 μg· mL^-1, respectively. This procedure allows the simultaneous determination of cobalt, copper and nickel in synthetic and real samples and good reliability of the determination was proved.     

三维荧光二阶校正法用于未知混合物中苯酚含量的测定

将三维荧光光谱技术与秩消失因子分析、广义秩消失因子分析和交替三线性分解3种二阶校正方法相结合,建立了测定未知混合物中苯酚含量的三维荧光二阶校正新方法。设定在激发波长240~280 nm和发射波长280~360 nm范围内测定未知混合物中苯酚的三维荧光光谱,构建三维响应数据阵,运用基于三线性分解的二阶校正算法进行解析。结果表明,当模拟样品的组分数为2时,秩消失因子分析、广义秩消失因子分析和交替三线性分解3种方法测定苯酚的预测均方根误差分别为0.33,1.18和0.15,平均回收率分别为101.6%,115.6%和101.9%;当组分数为3时,3种方法的预测均方根误差则分别为1.61,1.80和0.51,平均回收率分别为134.2%,133.9%和107.1%;将其分别应用于实际样品中苯酚的测定,结果满意,且交替三线性分解法的测定结果优于秩消失因子分析法和广义秩消失因子分析法。     

Quantitative analysis of topical gels and ointments by FT-Raman spectroscopy

A method for quantitative determination of ibuprofen (IBU), naproxen (NAP), methyl salicylate (MES) and menthol (MNT) in commercial topical gels and ointments using partial least squares (PLS) models based on FT-Raman spectra is described. The calculated relative standard errors of prediction (RSEP) were found to be in the range of 2.1–3.2% for the calibration and validation data sets. Two commercial topical gels containing 5.0% of IBU and 10% of NAP (w/w), as well as one ointment containing 15% of MES and 10% of MNT (w/w) as active pharmaceutical ingredients (APIs), were successfully quantified using the developed models with recoveries in the 99.2–101.5% range. The proposed procedure can be used as a fast, reliable and economic method for the quantification of APIs in topical gels and ointments.     

Near infrared and Raman spectroscopy as Process Analytical Technology tools for the manufacturing of silicone-based drug reservoirs

Using near infrared (NIR) and Raman spectroscopy as PAT tools, 3 critical quality attributes of a silicone-based drug reservoir were studied. First, the Active Pharmaceutical Ingredient (API) homogeneity in the reservoir was evaluated using Raman spectroscopy (mapping): the API distribution within the industrial drug reservoirs was found to be homogeneous while API aggregates were detected in laboratory scale samples manufactured with a non optimal mixing process. Second, the crosslinking process of the reservoirs was monitored at different temperatures with NIR spectroscopy. Conformity tests and Principal Component Analysis (PCA) were performed on the collected data to find out the relation between the temperature and the time necessary to reach the crosslinking endpoints. An agreement was found between the conformity test results and the PCA results. Compared to the conformity test method, PCA had the advantage to discriminate the heating effect from the crosslinking effect occurring together during the monitored process. Therefore the 2 approaches were found to be complementary. Third, based on the HPLC reference method, a NIR model able to quantify the API in the drug reservoir was developed and thoroughly validated. Partial Least Squares (PLS) regression on the calibration set was performed to build prediction models of which the ability to quantify accurately was tested with the external validation set. The 1.2% Root Mean Squared Error of Prediction (RMSEP) of the NIR model indicated the global accuracy of the model. The accuracy profile based on tolerance intervals was used to generate a complete validation report. The 95% tolerance interval calculated on the validation results indicated that each future result will have a relative error below ±5% with a probability of at least 95%. In conclusion, 3 critical quality attributes of silicone-based drug reservoirs were quickly and efficiently evaluated by NIR and Raman spectroscopy.     

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.     

Simultaneous spectrophotometric determination of mercury and palladium with Thio-Michler's Ketone using partial least squares regression and orthogonal signal correction

A simple, novel and sensitive spectrophotometric method was described for simultaneous determination of mercury and palladium. The method is based on the complex formation of mercury and palladium with Thio-Michler's Ketone (TMK) at pH 3.5. All factors affecting on the sensitivity were optimized and the linear dynamic range for determination of mercury and palladium found. The simultaneous determination of mercury and palladium mixtures by using spectrophotometric method is a difficult problem, due to spectral interferences. By multivariate calibration methods such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 360-660 nm range for 25 different mixtures of mercury and palladium. Calibration matrices were containing 0.025-1.60 and 0.05-0.50 microg mL(-1) of mercury and palladium, respectively. The RMSEP for mercury and palladium with OSC and without OSC were 0.013, 0.006 and 0.048, 0.030, respectively. This procedure allows the simultaneous determination of mercury and palladium in synthetic and real matrix samples good reliability of the determination.     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

Simultaneous Spectrophotometric Determination of Benzyl Alcohol and Diclofenac in Pharmaceutical Formulations by Chemometrics Method

Diclofenac sodium (DS) is a drug with analgesic, antipyretic, and anti‐inflammatory properties. It is present in numerous pharmaceutical preparations. In injectable forms, it is usually accompanied by benzyl alcohol (BA) as an excipient, which is used as a blocking anesthetic (4%) and an antiseptic (4–10%). In this work a spectrophotometric methodology was applied in order to determine benzyl alcohol and diclofenac in injectable formulations by applying a multivariate calibration method. By a multivariate calibration method such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. In this study, the concentration model is based on absorption spectra in the 230–320 nm range for 25 different mixtures of benzyl alcohol and diclofenac. Calibration matrix contains 10–95 and 1–50 μg mL^?1 for benzyl alcohol and diclofenac, respectively. The root mean square errors of prediction (RMSEP) for benzyl alcohol and diclofenac were 3.0776 and 1.7557, respectively. The proposed method was validated by using a set of synthetic sample mixtures and subsequently applied to simultaneous determination of benzyl alcohol and diclofenac in two different pharmaceutical formulations.     

GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms

The non-linear regression technique known as alternating conditional expectations (ACE) method is only applicable when the number of objects available for calibration is considerably greater than the number of considered predictors. Alternating conditional expectations regression with selection of significant predictors by genetic algorithms (GA-ACE), the non-linear regression technique presented here, is based on the ACE algorithm but introducing several modifications to resolve the applicability limitations of the original ACE method, thus facilitating the practical implementation of a very interesting calibration tool. In order to overcome the lack of reliability displayed by the original ACE algorithm when working on data sets characterized by a too large number of variables and prior to the development of the non-linear regression model, GA-ACE applies genetic algorithms as a variable selection technique to select a reduced subset of significant predictors able to accurately model and predict a considered variable response. Furthermore, GA-ACE actually provides two alternative application approaches, since it allows either the performance of prior data compression computing a number of principal components to be subsequently subjected to GA-selection, or working directly on original variables.In this study, GA-ACE was applied to two real calibration problems, with a very low observation/variable ratio (NIR data), and the results were compared with those obtained by several linear regression techniques usually employed. When using the GA-ACE non-linear method, notably improved regression models were developed for the two response variables modeled, with root mean square errors of the residuals in external prediction (RMSEP) equal to 11.51 and 6.03% for moisture and lipid contents of roasted coffee samples, respectively. The improvement achieved by applying the new non-linear method introduced is even more remarkable taking into account the results obtained with the best performance linear method (IPW-PLS) applied to predict the studied responses (14.61 and 7.74% RMSEP, respectively).     

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

利用近红外光谱技术对食用植物油中反式脂肪酸(Trans fatty acids,TFA)含量进行快速定量检测,并通过波段选择、预处理方法、变量筛选及建模方法对TFA含量预测模型进行优化.采用AntarisⅡ傅里叶变换近红外光谱仪在4000~10000 cm-1光谱范围采集98个食用植物油样本的近红外透射光谱,然后采用气相色谱法测定TFA的真实含量.首先,对样本原始光谱进行波段、预处理方法优选;在此基础上,采用竞争自适应重加权法(Competitive adaptive reweighted sampling,CARS)筛选TFA相关的重要变量,最后应用主成分回归、偏最小二乘和最小二乘支持向量机方法分别建立食用植物油中TFA含量的预测模型.研究结果表明,近红外光谱技术检测食用植物油中的TFA含量是可行的,优化后的最佳预测模型的校正集和预测集R2分别为0.992和0.989,RMSEC和RMSEP分别为0.071%和0.075%.最佳预测模型所用的变量仅26个,占全波段变量的0.854%.此外,与全波段偏最小二乘预测模型相比,其预测集R2由0.904上升为0.989,RMSEP由0.230%下降为0.075%.由此表明,模型优化非常必要,CARS能有效筛选TFA相关的重要变量,极大减少建模变量数,从而简化预测模型,并较大提高预测模型的精度和稳定性.     

Spectrophotometric simultaneous determination of manganese(II) and iron(II) in pharmaceutical by orthogonal signal correction-partial least squares

The simultaneous determination of manganese(II) and iron(II) mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used to remove the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 450-600 nm range for 21 different mixtures of manganese(II) and iron(II). Calibration matrices were containing 0.05-1.2 and 0.1-2.3 microg mL(-1) Mn(II) and Fe(II), respectively. The RMSEP for manganese(II) and iron(II) with OSC and without OSC were 0.0316, 0.0291, and 0.0907, 0.115, respectively. This procedure allows the simultaneous determination of manganese(II) and iron(II) in synthetic and real matrix samples with good reliability of the determination.