Insights into information contained in multiplicative scatter correction parameters and the potential for estimating particle size from these parameters

This paper investigates the nature of information contained in scatter correction parameters. The study had two objectives. The first objective was to examine the nature and extent of information contained in scatter correction parameters. The second objective is to examine whether this information can be effectively extracted by proposing a method to obtain particularly the mean particle diameter from the scatter correction parameters. By using a combination of experimental data and simulated data generated using fundamental light propagation theory, a deeper and more fundamental insight of what information is removed by the multiplicative scatter correction (MSC) method is obtained. It was found that the MSC parameters are strongly influenced not only by particle size but also by particle concentration as well as refractive index of the medium. The possibility of extracting particle size information in addition to particle concentration was considered by proposing a two-step method which was tested using a 2-component and 4-component data set. This method can in principle, be used in conjunction with any scatter correction technique provided that the scatter correction parameters exhibit a systematic dependence with respect to particle size and concentration. It was found that the approach which uses the MSC parameters gave a better estimate of the particle diameter compared to using partial least squares (PLS) regression for the 2-component data. For the 4 component data it was found that PLS regression gave better results but further examination indicated this was due to chance correlations of the particle diameter with the two of the absorbing species in the mixture.     

近红外光谱法快速分析有机肥料中有机质、总养分和pH值

建立了近红外光谱法结合偏最小二乘(PLS)法测定126种有机肥料中有机质、总养分和p H值的快速方法。采用K–S法分类,选取S–G平滑、S–G导数、多元散射校正和均值平均化4种前处理方法对粉碎后样品的近红外光谱信息进行预处理,以PLS法建立定量分析模型。结果表明,有机肥料中总养分的RC,SEC,RP,SEP,RPD分别为0.990,1.272%,0.985,1.084%,5.9;p H值的RC,SEC,RP,SEP,RPD分别为0.910,0.344%,0.737,0.428%,2.9。有机质项目根据国标方法分为小于40%、小于55%和大于55%3种样品进行分析,3种样品的RP分别为1.000,0.989,1.000;RPD分别为18.9,17.5,8.8。对比国标方法,有机质和总养分的测定精度满足实验室精确分析要求,p H值测定法可用于定量分析。NIR–PLS法实现了对有机肥料进行无损快速的检测分析。     

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).     

A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers

This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

Application of artificial neural networks to the nondestructive determination of ciprofloxacin hydrochloride in powder by short-wavelength NIR spectroscopy

The present study is aimed at providing a new short-wavelength near-infrared (NIR) spectroscopic method for the nondestructive quantitative analysis of ciprofloxacin hydrochloride in powder via artificial neural networks (ANNs). For this purpose, the NIR spectra of 90 experimental powder samples in the range 700–1100 mm were analyzed. Four different pretreatment methods—first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC)—were applied to three sets of the NIR spectra of the powder samples. Among all of the ANN models, the first-derivative model is found to be the best. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of the major components in pharmaceuticals. The degree of approximation as an evaluation criterion prevents the overfitting phenomenon occurring in ANNs. The text was submitted by the authors in English.     

Artificial neural networks for non-destructive determination of acetylspiramycin powder by short-wavelength NIR spectroscopy

The present study has aimed at providing new insight into short-wavelength near-infrared (SW-NIR) spectroscopy (780–1100 nm) for non-destructive quantitative analysis of acetylspiramycin (macrolide antibiotics) powder by using artificial neural networks (ANNs). Presently, it was shown the third vibrational overtone of the CH stretching band can be used to quantitatively determine constituents in pharmaceutical. The third overtone referred to as the SW-NIR region ranges from 780 nm to 1100 nm. In this paper, 156 experimental samples of acetylspiramycin powder were analyzed using ANNs in the 780–1100 nm region of SW-NIR spectra. Four different pretreated methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to three sets of SW-NIR spectra of powder samples. The results presented here demonstrate that the SW-NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis. Degree of approximation as an evaluation criterion of the network was employed, which proved the accuracy of the predicted results.     

Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy

The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.     

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.     

Horizontal attenuated total reflection applied to simultaneous determination of ash and protein contents in commercial wheat flour

Partial least-squares (PLS) regression was used to generate various models for the determination of both the protein and the ash contents of wheat flours by using spectroscopic data in the mid-infrared region obtained with a horizontal attenuated total reflectance (HATR) accessory. One hundred samples of wheat flour were used as purchased in the market: 55 for constructing the calibration model and 45 as external samples. The protein content varied between 8.85 and 13.23% and the ash content, between 0.330 and 1.287%, as determined by reference methods. Raw spectra and those corrected by multiplicative signal correction (MSC), first and second derivative spectra, were used as data for building the models. Different pre-treatments, such as mean centered and/or variance scaled (VS) methods, were tested and compared. Very good models were built as judged by the correlation coefficients (R²), root mean square error of calibration (RMSEC), root mean square error of validation (RMSEV) and root mean square error of prediction (RMSEP) that were obtained. Best results were achieved with MSC treated spectra.     

Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non‐destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in biologically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable selection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares (PLS) regression. Raman spectra of 90 artificial bio‐fluid samples with eight urine metabolites at near‐physiological concentrations were used to test these models. The combination of multiplicative scatter correction (MSC), continuous wavelet transform (CWT), randomization test (RT) and PLS modeling presented the best performance for all the metabolites. The correlation coefficient (R) between predicted and prepared concentration reached as high as 0.96.     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

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     

Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology

This study aims to establish a rapid quantitative analysis method for biochar based on near infrared spectroscopy (NIRS) technology. Near infrared spectra of 163 samples in the 10000–3800 cm^–1 (1000–2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were also analyzed. A partial least square (PLS) model for FC, VM and Ash was established after the model spectral ranges were optimized, the optimal factors were determined, and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC + SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R²_p of actual values vs prediction values for FC, VM and ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square error of prediction (RMSEP) was 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model had good accuracy and precision for both of FC and VM, and could be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model need to improve the precision for Ash analysis according to RPD value. This method provides a fast and effective technical means for the quantitative analysis of biochar components.     

Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients

For manufacturing of medicaments, all ingredients must be reliably identified. Wet chemistry methods for identification of cellulose ethers, used by the Pharmacopoea Europea, is time consuming and expensive. To distinguish microcristalline and powdered cellulose, only unspecific sedimentation properties are used. However, applications as well as technological and pharmacokinetic properties of cellulose and various cellulose ethers are different.

NIR reflectance spectroscopy speeds up the identification of excipients. So this technique causes fewer delay in manufacturing processes. The discrimination of powdered and microcristalline celluloses as well as cellulose and cellulose ethers is made possible by factor analysis and soft independent modelling of class analogies (SIMCA). The classification was improved by spectral pretreatment multiplicative scatter correction (MSC), derivation and wavelength selection. The discrimination of powdered and microcristalline celluloses is statistically highly significant, so the identification can be done reliably. Cellulose ethers can be quickly identified by NIR spectroscopy, although a large number of samples of different manufacturers and physical properties, for example viscosity, were used. The only exception is the discrimination of methylcellulose and cellulose ethers containing methyl and hydroxyalkyl substituents, which show identical spectra. But even for those excipients, the wet chemistry expenses can be reduced to one test. The developed strategy for data evaluation is quite general in nature, hence it can be applied to other pharmaceutical powders, excipients and active components as well.     

激光诱导击穿光谱结合CARS变量选择方法定量检测倍硫磷含量

利用双脉冲激光诱导击穿光谱(LIBS)技术对溶液中的倍硫磷含量进行定量检测。采用二通道高精度光谱仪采集不同浓度倍硫磷样品在206.28~481.77 nm波段的LIBS光谱,并对光谱进行多元散射校正(MSC)、标准正态变量变换(SNV)及3点平滑预处理,根据偏最小二乘(PLS)建模确定最优的预处理方法。在此基础上,利用竞争性自适应重加权算法(CARS)筛选与倍硫磷相关的重要变量,然后应用PLS回归建立溶液中倍硫磷含量的定量分析模型,并与单变量定量分析模型及未变量选择的PLS定量分析模型进行比较。结果表明,相比单变量定量分析模型及原始光谱PLS定量分析模型,CARS-PLS定量分析模型的性能更优,其模型的校正集和预测集的决定系数及平均相对误差分别为0.969 4、15.537%和0.995 9、5.016%。此外,与原始光谱PLS模型相比,CARS-PLS模型仅使用其中1.9%的波长变量,但预测集平均误差却由9.829%下降为5.016%。由此可见,LIBS技术检测溶液中的倍硫磷含量具有一定的可行性,且CARS方法能简化定量分析模型,提高模型的预测精度。     

Determination of linoleic acid in toothpaste by gas chromatography with flame ionization detection.

A new method for the determination of linoleic acid (LA) in toothpaste by a routine analysis has been proposed. Studies were based on the ISO 5509 procedure, which was modified for the purpose of LA determination in the toothpaste. Gas chromatography (GC) was employed for the qualitative and quantitative determination of linoleic acid methyl ester. The content of LA (5.31%) in sunflower oil added to the toothpaste composition (0.5%) was determined, and then the optimization studies for the determination of LA in the toothpaste samples were carried out. The relative standard deviation (RSD) of the procedure developed was 9.96% (n = 9). The quantitative analysis showed that the content of LA in the toothpaste samples studied was 0.0258 +/- 0.0011%. The detection limit of LA in toothpaste was approximately 0.001%.     

Orthogonal projection to latent structures combined with artificial neural network for quantitative analysis of phenoxymethylpenicillin potassium powder

A method for quantitative analysis of phenoxymethylpenicillin potassium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). Being a preprocessing method, O-PLS can remove systematic orthogonal variation from a given data set X without disturbing the correlation between X and the response set y. In this paper, O-PLS method was applied to preprocess the original spectral data of phenoxymethylpenicillin potassium powder, and the filtered data was used to establish the ANN model. In this model, the concentration of phenoxymethylpenicillin potassium as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare with O-PLS-ANN model, the calibration models that use the original spectra and different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the spectra were also designed. Experimental results show that O-PLS-ANN model is the best.     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

高效液相色谱法同时测定牙膏及漱口水中的23种致癌染料

建立了同时测定牙膏和漱口水中23种致癌染料的高效液相色谱(HPLC)检测方法.漱口水样品直接以乙醇溶解稀释,牙膏样品烘干后以乙醇超声提取.采用ZORBAX Eclipse XDB-Phenyl(150 mum x 4.6 mm x 5 pm)色谱柱进行分离,以2.5 mmol/L磷酸二氢四丁基铵水溶液(pH7.5)和甲...