近红外光谱无损定量分析吡嗪酰胺片

近红外光谱;径向基神经网络;吡嗪酰胺;定量分析     

近红外光谱-径向基神经网络在异烟肼片无损定量分析中的应用

应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。     

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.     

Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy

A new acetolactate synthase (ALS)-inhibiting herbicide, propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273), was applied to oilseed rape (Brassica napus L.) leaves in different leaf positions. Visible/near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of ALS activity and protein content in rapeseed leaves. Partial least squares (PLS) analysis was the calibration method with comparison of different spectral preprocessing by Savitzky-Golay (SG) smoothing, standard normal variate (SNV), first and second derivative. The best PLS models were obtained by first-derivative spectra for ALS, whereas original spectra for soluble, non-soluble and total protein contents. Simultaneously, certain latent variables (LVs) were used as the inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models. All LS-SVM models outperformed PLS models and BPNN models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias in validation set by LS-SVM were 0.998, 0.715 and 0.079 for ALS, 0.999, 33.084 and 1.178 for soluble protein, 0.997, 42.773 and 6.244 for non-soluble protein, 0.999, 59.562 and 7.437 for total protein, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the determination of ALS activity and protein content of rapeseed leaves. The results would be helpful for further on field analysis of using Vis/NIR spectroscopy to monitor the growing status and physiological properties of oilseed rape.     

Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy

Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.     

Discrimination of ethylene/vinyl acetate copolymers with different composition and prediction of the content of vinyl acetate in the copolymers and their melting points by near-infrared spectroscopy and chemometrics

Near-infrared (NIR) diffuse reflectance spectra have been measured by use of a rotating drawer for pellets of 12 kinds of ethylene/vinyl acetate (EVA) copolymers with vinyl acetate (VA, the comonomer) varying in the 7–44 wt % range. They are unambiguously discriminated from one another by a score plot of the principal component analysis (PCA) Factor 1 and 2, based upon the NIR spectra pretreated by multiplicative scatter correction (MSC). Principal component (PC) weight loadings for Factor 1 show that the discrimination relies largely upon bands due to the overtone and combination modes arising from the VA unit. We have found one “outlier” in the score plot and elucidated its spectral characteristics based upon PC weight loadings for Factor 2. Partial least-squares (PLS) regression has been applied to propose calibration models which predict the VA content in EVA. The models have been prepared for three kinds of pretreatment, the first derivative, the second derivative, and MSC; and four kinds of wavelength regions. The NIR spectra in the 1100–2200 nm region after the MSC treatment has given the best correlation coefficient and standard error of prediction (SEP) of 0.998 and 0.70%, respectively. The calibration models, prepared by NIR diffuse reflectance spectroscopy for the pellet samples, are compared with previously reported models by NIR transmission spectroscopy for the flowing molten samples, and with those by Raman spectroscopy for the pellet samples. PLS regression has also allowed us to predict melting points of the copolymers with the correlation coefficient and SEP of 0.997 and 0.78°C, respectively. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1529–1537, 1998     

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm “Baseline Correction Combined Partial Least Squares (BCC-PLS)”, which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7–19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33–68%).     

Application of thermal analysis to the study of antituberculosis drugs–excipient compatibility

First-line drugs (rifampicin, RIF; isoniazid, INH; ethambutol, ETA; and pyrazinamide, PZA) recommended in conventional treatment of tuberculosis were analyzed in 1:1 w/w binary mixtures with microcrystalline cellulose MC 101 (CEL) and lactose supertab^® (LAC) by differential scanning calorimetry (DSC), thermogravimetry (TG), differential thermal analysis (DTA), and Fourier transformed infrared analysis (FTIR) as part of development of fixed dose combination (FDC) tablets. Evidence of interaction between drug and pharmaceutical excipients was supposed when peaks disappearance or shifting were observed on DTA and DSC curves, as well as decreasing of decomposition temperature onset and TG profiles, comparing to pure species data submitted to the same conditions. LAC was showed to interact with RIF (absence of drug fusion and recrystallization events on DSC/DTA curves); INH (thermal events of the mixtures different from those observed for drug and excipient pure in DSC/DTA curves); PZA (decrease on drug fusion peak in DSC/DTA curves), and ETA (shift on drug onset fusion and absence of pure LAC events on DSC/DTA curves). In all cases, an important decrease on the temperature of drug decomposition was verified for the mixtures (TG analysis). However, FTIR analysis showed good correlation between theoretical and experimental drug-LAC spectra except for INH–LAC mixture, evidencing high incompatibility between these two species and suggesting that those interactions with PZA and RIF were thermally induced. No evidence of incompatibilities in CEL mixtures was observed to any of the four-studied drugs.     

Quantitative Analysis of Catalpol in Chinese Patent Medicine Lixin Pill by Near-infrared Diffuse Reflectance Spectroscopy

Lixin pill is a typical Chinese patent medicine with anti-rheumatic heart disease activity that has been widely used in clinical practice. Therefore it is very important to detect the concentration of catalpol, as the main component of the active ingredient. Near-infrared reflectance（NIR） spectroscopy was used to study the content of catalpol in the unprocessed Chinese patent medicine of Lixin pills. NIR is applied to quantitatively analyze 77 sam- ples, which were randomly divided into a calibration set containing 61 samples and a prediction set containing 16 samples. To get a satisfying result, partial least squares（PLS） regression was utilized to establish quantitative models. In PLS regression, the values of coefficient of determination（R2） and root mean square error of cross-validation （RMSECV） of PLS regression are 0.9419 and 0.0216, respectively. The process of establishing model, parameters of model, and prediction results were also discussed in detail（root mean square error of prediction is 0.0164）. The over- all results show that NIR spectroscopy can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in the Chinese patent medicine of Lixin pills. The prediction set suggests that this quantitative analysis model has excellent generalization ability and prediction precision. Accordingly, the result can provide tech- nical support for the further analysis of catalpol in unprocessed Lixin pill. Moreover, this study supplied technical support for the further analysis of other Chinese patent medicine samples.     

Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy

This paper indicates the possibility to use near infrared (NIR) spectroscopy as a rapid method to predict quantitatively the content of caffeine and total polyphenols in green tea. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model is chosen according to the lowest root mean square error of cross-validation (RMSECV) in training. The correlation coefficient R between the NIR predicted and the reference results for the test set is used as an evaluation parameter for the models. The result showed that the correlation coefficients of the prediction models were R = 0.9688 for the caffeine and R = 0.9299 for total polyphenols. The study demonstrates that NIR spectroscopy technology with multivariate calibration analysis can be successfully applied as a rapid method to determine the valid ingredients of tea to control industrial processes.     

PLS-ANN算法-NIR光谱非破坏性Norvasc药物有效成分的定量分析

采用偏最小二乘(PLS)结合人工神经网络(ANN)算法解析Norvasc(络活喜)药片的近红外(NIR)漫反射光谱, 实现了对其中有效成分苯磺酸氨氯地平的非破坏定量测定. 设计了最佳的PLS-ANN模型, 分别讨论了最佳波长范围、 导数光谱及输入层和隐含层节点数对预测结果的影响. 以HPLC法的测定结果作标准, 苯磺酸氨氯地平浓度预测值的相对误差RE＜3.5%, 该方法可用于Norvasc药品实际生产中的质量控制.     

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.     

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

New approach for chemometrics algorithm named region orthogonal signal correction (ROSC) has been introduced to improve the predictive ability of PLS models for biomedical components in blood serum developed from their NIR spectra in the 1280-1849 nm region. Firstly, a moving window partial least squares regression (MWPLSR) method was employed to locate the region due to water as a region of interference signals and to find the informative regions of glucose, albumin, cholesterol and triglyceride from NIR spectra of bovine serum samples. Next, a novel chemometrics method named searching combination moving window partial least squares (SCMWPLS) was used to optimize those informative regions. Then, the specific regions that contained the information of water, glucose, albumin, cholesterol and triglyceride were obtained. When an interested component in the bovine serum solution, such as glucose, albumin, cholesterol or triglyceride is being an analyte, the other three interests and water are considered as the interference factors. Thus, new approach for ROSC has employed for each specific region of interference signal to calculate the orthogonal components to the concentrations of analyte that were removed specifically from the NIR spectra of bovine serum in the region of 1280-1849 nm and the highest interference signal for model of analyte will be revealed. The comparison of PLS results for glucose, albumin, cholesterol and triglyceride built by using the whole region of original spectra and those developed by using the optimized regions suggested by SCMWPLS of original spectra, spectra treated OSC for orthogonal components of 1-3 and spectra treated ROSC using selected removing the highest interference signals from the spectra for orthogonal components of 1-3 are reported. It has been found that new approach of ROSC to remove the highest interference signal located by SCMWPLS improves of the performance of PLS modeling, yielding the lower RMSECV and smaller number of PLS factors.     

Improving the sensitivity of NIR spectroscopy with an enrichment technique: determining a trace analyte of ethyl carbamate.

An exploration was made to develop a determination method of a low-concentration analyte by NIR spectroscopy. An absorber, silica gel was employed to extract and enrich a low-concentration analyte of ethyl carbamate. The solid absorber with the enriched analyte was measured by NIR spectroscopy in the range of 800 - 2500 nm. Afterwards, PLS regression was performed between the NIR spectra and the concentrations of the analyte for quantitative analysis of the low-concentration analyte. The spectra of 20 solid samples of analyte-absorbed silica gel showed a good correlation with the concentrations of ethyl carbamate in the samples. A leave-one-out cross validation was applied to evaluate the prediction ability of PLS models built with the full spectra, spectra in the region of 1920 - 1970 nm and the region of 2250 - 2430 nm, respectively. The values of the root-mean-square error of the cross validation (RMSECV) were about 0.1 mg L(-1) (0.1 ppm).     

近红外光谱测定人参与西洋参的主要皂甙总量

采用近红外光谱测定人参与西洋参的主要皂甙总量.采集人参与西洋参的漫反射光谱,分别对光谱进行正交信号校正(OSC)与常规预处理,建立了对应的偏最小二乘(PLS)回归模型.与常规最优预处理方法相比,OSC能很好地消除人参与西洋参的品种差异,显著提高了光谱与皂甙含量的相关系数,同时降低了PLS建模因子数,提高了模型的稳健性与...     

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。     

高粱籽粒中多酚类物质的傅立叶变换近红外光谱分析

利用高效液相色谱(HPLC)法测定高粱籽粒中阿魏酸、原儿茶醛和花青素的含量,比色法测定总酚、总黄酮、缩合单宁的含量;运用偏最小二乘法建立NIR光谱与HPLC法和比色法分析值之间的多元校正模型,预测高粱籽粒中主要酚类物质的含量.结果表明,各成分近红外预测值与实测值之间的校正模型相关系数(R)、内部交叉验证均方差(RMSECV)、最佳主因子数分别为:总酚0.9737, 0.288, 4;总黄酮0.9660, 0.00671, 8;缩合单宁0.9558, 0.0289, 6;阿魏酸0.9818, 0.0391, 6;原儿茶醛0.9979, 0.0118, 5;花青素0.9977, 0.0523, 4;预测相对偏差(RSEP)分别为:总酚6.99%、总黄酮4.54%、 缩合单宁7.13%、阿魏酸2.68%、原儿茶醛5.46%、 花青素5.81%.结果表明,模型对样品NIR的预测值与其相应的化学值有较好的相关性,此模型可用来预测高粱籽粒中的各酚类物质的含量,在高粱优质育种和品质分析中具有广泛的应用价值.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.