Determination of marker constituents in radix Glycyrrhizae and radix Notoginseng by near infrared spectroscopy

High-performance liquid chromatographic (HPLC) methods were developed for the determination of glycyrrhizin in radix Glycyrrhizae and ginsenosides Rb₁, Rb₂, Rc, Rd, Re, Rf and Rg₁ in radix Notoginseng. These methods were used as reference methods for near-infrared (NIR) spectroscopy. Spectroscopic calibrations were developed for the determination of glycyrrhizin, the total content of ginsenosides and the individual major ginsenosides Rb₁, Rd, Re and Rg₁. Standard errors of cross validation (SECV) were 1.22 mg g^–1 for glycyrrhizin (concentration range 21.3–34.1 mg g^–1) and 0.99 mg g^–1 for the sum of ginsenosides (concentration range 55.3–¶71.1 mg g^–1). The corresponding coefficients of determination (R²) were 0.94 and 0.98, respectively. The SECVs were generally less than a factor of 2.5 of the repeatability standard deviation of the HPLC methods.     

Determination of ascorbic acid in pharmaceutical preparations by near infrared reflectance spectroscopy

We developed a method for determination of ascorbic acid in pharmaceutical preparations containing various excipients by using near infrared diffuse reflectance spectroscopy and two different calibration methods, viz. stepwise multiple linear regression (SMLR) and partial least-squares (PLS) regression, which provided comparable results and resulted in prediction errors of 1-2%. However, the PLS method provided somewhat better results with the more complex samples.     

PLS-BP法近红外光谱技术同时测定鲜乳中四种主成分

基于近红外光谱技术,将偏最小二乘法(Partial Least Squares,PLS)和单隐层的反向传播网络(Back-Propagation Network,BP)联用并测定了鲜乳中4种主成分含量.用PLS法将原始数据压缩为主成分,取前3个主成分的14个数据输入网络,以Kolmogorov定理为依据,经过实验确定中间层的神经元个数为29,初始训练迭代次数为1000,建立了脂肪、蛋白质、乳糖、牛乳总固体4种主成分含量的预测校正模型.PLS-BP模型对样品4个组分含量的预测决定系数(R2)分别为:0.961、0.974、0.951、0.997;本研究为近红外光谱技术在鲜乳多组分快速检测提供了新思路.     

Measuring moisture in cheese by near infrared absorption spectroscopy

Data in the literature indicate that the measurement of moisture in cheese by near infrared transmittance (NIT) is more accurate than by reflectance (NIR). The accuracy of the NIT measurement (SEP) was calculated for Edam, Gouda, Brie, Colby, and Cheddar. A range of SEP values (0.12-0.35) was obtained using different methods of calibration for different types of samples. There was close agreement between these results, as they related to the precision of the reference method. The calibration validation technique described as Standard Error of Cross Validation (SECV) generated results that compared very closely to the equivalent SEP values for independent sample sets.     

Determination of hexacelsian by infrared spectroscopy

Hexacelsian has been determined by infrared spectroscopy with KBr discs and K(4)Fe(CN)(6) as internal standard. A KBr particle size of <40 mum gave better homogenization of the sample-KBr mixture than a particle size in the 40-70 mum range. For determinations of hexacelsian in synthetic samples containing amorphous phase or celsian, calibration curves were constructed. A least-squares fit yielded correlation coefficients of 0.998 and 0.997.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

Compositional analysis of Miscanthus giganteus by near infrared spectroscopy

Fourier transform near infrared spectroscopy was applied to ball-milled and dried whole plant Miscanthus × giganteus samples in combination with partial least square regression analysis for prediction of main constituents of the biomass. The developed models with 172 calibration samples had an R² in the range of 0.96–0.99. For the first time, the acetyl content was modeled for Miscanthus. An independent calibration set of 58 samples revealed a low root mean square error of prediction of 0.414 % for extractives, 0.485 % for glucan, 0.249 % for xylan, 0.061 % for arabinan, 0.050 % for acetyl, 0.198 % for Klason lignin, 0.226 % for total ash and 0.133 % for ash after extraction, an indication of a high level of accuracy. The results showed major improvement over previously reported models, which was attributed to the smaller particle size used. The models are a valuable tool for the fast monitoring of the composition of M. × giganteus in e.g. plant breeding studies.     

Quantitative determination of acyclovir in plasma by near infrared spectroscopy

The aim of this study was to assess the feasibility of near infrared spectroscopy (NIRS) for analysis of acyclovir in plasma. This methodology was based on the direct measurement of the transmission spectra of liquid samples and a multivariate calibration model (partial least squares, PLS) to determine the acyclovir concentration in plasma sample. The PLS calibration set was built on using the spiked samples by mixing different amounts of acyclovir. Concentration of acyclovir in the plasma samples was calculated employing a 6-factors PLS calibration using the spectral information in the range of 6102-5450 cm^− 1. The root mean square errors of prediction (RMSEP) found was 1.21 for acyclovir. The developed PLS-NIRS procedure allows the determination of 120 samples/h does not require any sample pretreatment and avoids waste generation.     

Identification of amber and imitations by near infrared reflection spectroscopy

Imitations of amber have been prepared since a long time, but the number of imitations is increasing since modern polymers are available. At present, many imitations are based on a few synthetic polymers and even experts have some problems in distinguishing between real and falsed amber without destruction of the material. IR-Spectroscopy or pyrolysis combined with gas chromatography/mass spectrometry are very efficient methods in identifying amber and imitations but these methods need a sample preparation. By means of near infrared reflection spectroscopy it is possible to identify amber and imitations without any sample preparation in a short time. Optimization of the method and the application are discussed.     

Determination of water in furfural by infrared spectroscopy

The IR absorption band at 5250 cm^–1 is used to determine 0–5% water in furfural. The accuracy of the determination is not less than 5%. The method is suitable for any kind of furfural. The analytical results are unaffected by the presence of formic, acetic, and pyromucic acids, and methanol and ethanol.     

Discrimination of Scrophulariae Radix according to geographical origin and determination of active constituents by near infrared spectroscopy (NIRS)

The potential of near infrared reflectance spectroscopy (NIR) was investigated for its ability to non-destructively discriminate the geographic origins of Scrophularia spp., Andong, Uisung and China. Application of principal component analysis to NIR spectra leads to a clear separation of Andong sample from the others. Moreover, the contents of two neuroprotective constituents of Scrophularia spp., 8-O-(E-p-methoxycinnamoyl)-harpagide (HG), and E-p-methoxycinnamic acid (MCA), were determined by HPLC-DAD. Partial least squares (PLS) regression of NIR spectra combined with these analytical reference data yield the development of calibration models for the contents of the two constituents. The correlation coefficients of prediction models for HG and MCA were > 0.87. These outcomes indicated that the NIRS could be useful for the discrimination of Scrophularia spp.     

近红外光谱法分析土壤中的有机质和氮素

应用近红外光谱技术测定土壤中的全氮、有机质、碱解氮，分别测定了2mm、0.15mm粒度的风干土在4000cm^-1～12000cm^-1波数范围的近红外光谱，用偏最小二乘法建立数学模型来进行含量预测，结果表明近红外光谱与土壤有机质、全氮、碱解氮具有良好的相关性，2mm风干土碱解氮建模的决定系数R^2为92.39，相对标准偏差为7.5%；2mm风干土全氮建模的决定系数R^2为88，相对标准偏差为8.2%;0.15mm的全氮建模的决定系数R2为89.86，相对标准偏差为7.2%;0.15mm风干土有机质建模的决定系数R^2为96.41，相对标准偏差为8.3%。因此，用近红外光普法测定土壤有机质、全氮、碱解氮的含量是可行的。     

Fundamentals and applications of near infrared spectroscopy in spectroelectrochemistry

Near infrared spectroscopy as a tool for in situ spectroelectrochemical investigations of electrochemical systems is reviewed with particular attention to experimental approaches and typical results from all parts of chemistry and applied chemistry     

Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy

A relationship was established between the organic matter content in soils determined by conventional chemical measurements and by diffuse reflectance spectra in the near infrared region (1000-2500 nm). Radial basis function networks (RBFN) with regularized forward selection to control the model complexity were used for non-parametric regression, resulting in a RMSEP of 0.25%. The observed results using RBFN were better than those obtained by partial least squares regression (PLS) and multi-layer perceptron (MLP) feed-forward networks with a back-propagation learning algorithm. RBFN is a suitable tool to model this complex system, with additional advantages over MLP, since the training procedure is less dependent on the initial conditions.     

Characterization of estuarine sediments by near infrared diffuse reflectance spectroscopy

It has been developed a partial least squares near infrared (PLS-NIR) method for the determination of estuarine sediment physicochemical parameters. The method was based on the chemometric treatment of first order derivative reflectance spectra obtained from samples previously lyophilized and sieved through a lower than 63 μm grid. Spectra were scanned from 833 to 2976 nm, averaging 36 scans per spectrum at a resolution of 8 cm⁻¹, using chromatographic glass vials of 9.5 mm internal diameter as measurement cells. Models were built using reference data of 31 samples selected through the use of a hierarchical cluster analysis of NIR spectra of sediments obtained from the Ria de Arousa estuary and prediction parameters were established from a validation set of 50 samples of the same area. pH, redox potential (Eh), carbon (C), nitrogen (N) and hydrogen (H) content together with Sn, Pb, Cd, As, Sb and total Cr and also acid soluble, reducible and oxidable Cr fractions were employed as characteristic parameters of the studied sediments. Standard error of prediction values for C and N content were of the order of 4 and 1.3 mg g⁻¹ for H. Prediction errors for pH and Eh were 0.15 units and 37 mV, respectively, thus indicating the good prediction capabilities of the method. Regarding trace metal concentrations PLS-NIR provided prediction error levels for unknown samples around 20% for Sn, Pb, As and Sb and root mean square errors of prediction around 40% for concentration levels of 400 ng g⁻¹ Cd and 100 μg g⁻¹ Cr. For the different extractable fractions of Cr the residual prediction deviation varied from 1.3 to 1.7 but relative errors found for samples of the validation set were only useful for screening purposes.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

Simultaneous determination of 15 marker constituents in various radix Astragali preparations by solid-phase extraction and high-performance liquid chromatography

An improved quality control method was developed to simultaneously determine 15 major constituents (eight flavonoids and seven saponins) in various radix Astragali preparations, using SPE for pretreatment of samples, HPLC with diode-array and evaporative light scattering detectors (DAD-ELSD) for quantification in one run, and HPLC-ESI-TOF/MS for definite identification of compounds in preparations. Optimum separations were obtained with a ZORBAX C(18) column, using a gradient elution with 0.3% aqueous formic acid and ACN. This established method was fully validated with respect to linearity, precision, repeatability, and accuracy, and was successfully applied to quantify the 15 compounds in 19 commercial samples, including 3 dosage forms, i. e., oral solution, injection, concentrated granule, and its processed products of radix Astragali. The results demonstrated that many factors might result in significant differences in quality of the final preparations, including crude drugs, pretreatment processes, manufacturing procedure, storage conditions, etc. Then the developed method provided a reasonable and powerful manner to ensure the efficacy, safety, and batch-to-batch uniformity of radix Astragali products by standardizing each procedure, and thus should be proposed as quality control for the clinical use and modernization of herbal preparations.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.