Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Determination of Fipronil in Acetamiprid Formulation by Attenuated Total Reflectance-Mid-Infrared Spectroscopy Combined with Partial Least Squares Regression

There is a high demand for rapid determination of fipronil in pesticide preparations because it has been restricted and even prohibited in many countries. An infrared-based methodology was developed for this analyte in acetamiprid formulations by attenuated total reflectance mid-infrared spectroscopy. The quantitative calibration models of fipronil were established by partial least squares regression. The determination coefficients (R²) of the model were above 0.99 while both the root mean square error of prediction and root mean square error of calibration were below 0.0011, which showed the partial least squares model accurately predicted fipronil concentrations in acetamiprid. The accuracy was further demonstrated by comparison with another two models' results of low (<1.0%, w/w) and high concentration sample sets (1.0%–4.5%, w/w). These results demonstrate the potential of infrared spectroscopy to quickly detect fipronil in acetamiprid.     

Development of a novel RP‐HPLC method for the efficient separation of aripiprazole and its nine impurities

The development of an RP‐HPLC method for the separation of aripiprazole and its nine impurities was performed with the use of partial least squares regression, response surface plot methodology, and chromatographic response function. The HPLC retention times and computed molecular parameters of the aripiprazole and its nine impurities were further used for the quantitative structure–retention relationship (QSRR) study. The QSRR model, R²: 0.899, Q²: 0.832, root mean square error of estimation: 4.761, root mean square error of prediction: 6.614, was developed. Very good agreement between the predicted and observed retention times (t_R) for three additional aripiprazole impurities (TC1–TC3) indicated the high prediction potential of the QSRR model for t_R evaluation of other aripiprazole impurities and metabolites. The developed HPLC method is the first reported method for the efficient separation of aripiprazole and its nine impurities, which could be used for the analysis of an additional three aripiprazole impurities (TC1–TC3).     

Selection of individual variables versus intervals of variables in PLSR

The selection abilities of the two well‐known techniques of variable selection, synergy interval‐partial least‐squares (SiPLS) and genetic algorithm‐partial least‐squares (GA‐PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA‐PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA‐PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA‐PLS was not significant. For real data of less overlapped components (metabolite dataset), GA‐PLS that tends to select far fewer variables did not give significantly better root mean square error of cross‐validation (RMSECV), cross‐validated R² (Q²), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA‐PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA‐PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantitative analysis of two adulterants in Cynanchum stauntonii by near-infrared spectroscopy combined with multi-variate calibrations

Authentication of traditional Chinese medicines (TCMs) has become important because they can be adulterated with relatively cheap herbal medicines similar in appearance. Detection of such adulterated samples is needed because their presence is likely to reduce the pharmacological potency of the original TCM and, in the worst cases, the samples may be harmful. The aim of this study was to develop a rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN), in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants ?? two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of 800?C2500 nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of 5 mass %, but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLS or RBF-ANN.     

Rapid Simultaneous Determination of Andrographolides in Andrographis paniculata by Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing.     

Esters flash point prediction using artificial neural networks

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4‐5‐8‐5‐1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R²) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set). © 2012 Wiley Periodicals, Inc.     

Pareto calibration with built-in wavelength selection

In multivariate regression, it is often reported that wavelength selection can improve results. Improvement is often solely based on bias measures such as the root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV), R² for the calibration and validation, etc. In recent studies, it has been shown that when variance measures are included, Pareto optimal models can be determined. However, variance measures used to date do not provide the ability to choose wavelength subset models relative to full wavelength models when wavelength subset models may be the Pareto models. In this paper, simplex optimization is used with a more complete variance measure to generate Pareto optimal models. The standard basis set is used as well a basis set that includes the range and null space of the calibration spectra. Results show that it is possible to identify Pareto optimal models and if a wavelength subset is best, these are the models found. Regression coefficients for non-essential wavelengths are zero to near zero.     

近红外光谱结合特征变量筛选方法用于农药乳油中毒死蜱含量的测定

为提高毒死蜱农药乳油中有效成分近红外光谱定量分析模型的精度和稳定性。采用联合区间偏最小二乘法(siPLS)结合遗传算法(GA)筛选特征变量,由交互验证法确定最佳主成分因子数及筛选的变量数。结果表明,从全光谱区优选出81个变量,主成分因子数为11时,能建立性能最优的模型,模型预测集的决定系数R_p~2为0.972,预测均方根误差(RMSEP)为0.353%。研究表明,利用siPLS结合GA方法优选特征变量,能大幅度地消除农药乳油光谱变量间的冗余信息和无关信息,降低模型的复杂度,提高农药有效成分预测模型的精度及稳定性。     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Kohonen classification applying ‘missing variables’ criterion to evaluate the p‐boronophenylalanine human‐body‐concentration decreasing profile of boron neutron capture therapy patients

The irradiation dose in tumor and healthy tissue of a boron neutron capture therapy (BNCT) patient depends on the boron concentration in blood. In most treatments, this concentration is experimentally determined before and after irradiation but not while irradiation is being carried out because it is troublesome to take the blood samples when the patient remains isolated in the irradiation room. A few models are used to predict the boron profile during that period, which until now involves a biexponential decay. For the prediction of decay concentration profiles of the p‐boronophenylalanine (BPA) in the human body during BNCT treatment, a Kohonen‐based neural network method is suggested. The results of various (20 × 20 × 40 Kohonen network) models based on different trainings on the data set of 67 concentration sets (profiles) are described and discussed. The prediction ability and robustness of the modeling method were tested by the leave‐one‐out procedure. The results show that the method is very robust and mostly independent of small variations. It can yield predictions, root mean squared prediction error (RMSPE), with a maximum of 3.30 µg g⁻¹ for the present cases. In order to show the abilities and limitations of the method, the best and the few worst results are discussed in detail. It should be emphasized that one of the main advantages of this method is the automatic improvement in the prediction ability and robustness of the model by feeding it with an increasing number of data. Copyright © 2011 John Wiley & Sons, Ltd.     

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.