Determination of polyacetylenes and ginsenosides in Panax species using high performance liquid chromatography

A new HPLC method was developed to separate and identify three polyacetylenes (panaxynol, panaxydol and 1,8-heptadecadiene-4,6-diyne-3,10-diol) found in Panax species. The mobile phase was a linear gradient of 2 : 1 : 3 to 2 : 1 : 1 (v/v/v) methanol/acetonitrile/water in 40 min. HPLC analysis was performed at a flow rate of 1.5 ml/min with UV detection at 254 nm. The contents of the polyacetylenes and ginsenosides in Panax ginseng (white ginseng and red ginseng), P. quinquefolium, P. japonicus, and P. noteginseng were determined using these methods. The species containing the highest polyacetylene content (0.080%) was P. quinquefolium cultivated in Nagano, Japan. Meanwhile, the species with the highest ginsenoside content (9.176%) was P. noteginseng cultivated in Yunnan, China.     

Development of Near Infrared Spectroscopy for Rapid Quality Assessment of Red Ginseng

Near infrared spectroscopy(NIRS) was developed as a rapid analysis method for the qualitative and quantitative assessment of the quality of red ginseng. Discriminant analysis(DA) based on principal component analysis and Mahalanobis distance was used to distinguish red ginseng from counterfeits non-destructively. The result shows that the proposed method could distinguish red ginseng from counterfeits correctly and no misclassified sample was found in both training and test sets. The partial least squares(P...     

Flow injection mass spectroscopic fingerprinting and multivariate analysis for differentiation of three Panax species

This study describes the use of spectral fingerprints acquired by flow injection(FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min.     

Illuminating the flesh of bone identification – An application of near infrared spectroscopy

Near infrared (NIR) reflectance spectroscopy coupled with chemometric analysis was evaluated as a non-destructive tool to discriminate skull bone samples from different animal species. In total 70 skull bones from animals of three classes (mammalians, avian and reptiles) were scanned in the wavelength range between 950 to 1650 nm. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse the NIR spectra of the skull samples. Correct classification rates of 96% and 81% were obtained for the classification of skull bone samples according to avian and mammalian classes, respectively. Overall, a 91% correct classification rate was obtained for the classification of skull samples according to the class (mammalian and avian). This study demonstrates the potential of NIR spectroscopy coupled with chemometric as data processing, as a means of a rapid, non-destructive classification technique for skull bone samples.     

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...     

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.     

气相色谱-三重四极杆质谱分析人参炮制品中的挥发性成分

人参炮制的化学成分变化研究主要集中在皂苷和糖类,本文首次从挥发性成分角度阐释了人参不同炮制品的物质基础。利用气相色谱-质谱联用(GC-MS/MS)方法,对鲜参、生晒参和红参中挥发性成分及其衍生规律进行研究。采用TG-5SILMS非极性气相色谱柱,以He为载气,通过NIST MS Spectral Database对挥发性成分进行检测并鉴定。鲜参、生晒参、红参中分别检出30、33和34种挥发性成分,其中生晒参中(-)-斯巴醇含量为鲜参含量的31.98倍,辛醛等8种挥发性成分为鲜参中含量的3倍以上,红参中有环癸等10种挥发性成分为鲜参中含量的3倍以上。生晒参和红参中各有4种挥发性成分在鲜参中未检出。     

染料茜素红电解过程分析

用覆盖ＳｎＯ２／Ｓｂ２Ｏ３活怀层的钛作阳极,对染料茜素红水溶液进行电降解,测定了不同ｐＨ值下、不同电解时间的紫外可见吸收光谱,并用主成分分析、潜交量投影、遗传算法以及渐进因子分析先进化学计量学方法解析得到中间型体的动力学曲线和纯光谱,最后对茜素红水溶液电降解的机理做了初步探索。     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

一类用化学指纹特征鉴别中药材真伪的方法

提出用Fisher因子表征中药材质量模式,根据类间距离最大、类内距离最小原则,计算色谱图中各峰的权值,并以各峰权值为变换向量从各峰面积值中计算出Fisher因子,用于中药材质量模式分类.以红参主根的真伪鉴别为实例,从定量评价、目视比较和聚类分析等方面将Fisher因子、主成分及指纹峰进行分类效果对比研究.结果表明,Fisher因子能更好地表征红参化学模式特征,分类准确性最高.因此,用本文方法提取的Fisher因子是一类能表征中药材质量模式的化学指纹特征参量,适宜用作鉴别中药材真伪.     

Multi-experimental characterization of selected medical plants growing in the Czech Republic

Complex analysis of the impact of post-production conditions on antioxidant status, content of selected polyphenols, color, and concentration of selected metals was evaluated in the group of ten most popular medical plants conventionally produced in the Czech Republic. Ultraviolet visible near infrared spectroscopy (UV–VIS–NIR), electron paramagnetic resonance (EPR), high-performance liquid chromatography (HPLC), and inductively coupled plasma optical emission spectrometry (ICP-OES) were employed for these purposes. The entire data set of the obtained experimental characteristics was processed by multivariate statistical methods to assess the influence of the way of processing on sample properties. On the basis of 11 characteristics identified by the analysis of variance (ANOVA) as the most significantly different, successful differentiation of medical plant samples was achieved according to post-harvest treatment, families, and individual medical plant species by means of canonical discrimination method, reaching 100, 89.5, and 100% correctness, respectively. The importance of the sample processing on their properties and on the concentration of target naturally containing compounds with health-promoting and antioxidant properties was thus proved unambiguously.     

Development of new analytical methods for the determination of caffeine content in aqueous solution of green coffee beans

Background

This study was conducted to develop fast and cost effective methods for the determination of caffeine in green coffee beans. In the present work direct determination of caffeine in aqueous solution of green coffee bean was performed using FT-IR-ATR and fluorescence spectrophotometry. Caffeine was also directly determined in dimethylformamide solution using NIR spectroscopy with univariate calibration technique.

Results

The percentage of caffeine for the same sample of green coffee beans was determined using the three newly developed methods. The caffeine content of the green coffee beans was found to be 1.52 ± 0.09 (% w/w) using FT-IR-ATR, 1.50 ± 0.14 (% w/w) using NIR and 1.50 ± 0.05 (% w/w) using fluorescence spectroscopy. The means of the three methods were compared by applying one way analysis of variance and at p = 0.05 significance level the means were not significantly different. The percentage of caffeine in the same sample of green coffee bean was also determined by using the literature reported UV/Vis spectrophotometric method for comparison and found to be 1.40 ± 0.02 (% w/w).

Conclusion

New simple, rapid and inexpensive methods were developed for direct determination of caffeine content in aqueous solution of green coffee beans using FT-IR-ATR and fluorescence spectrophotometries. NIR spectrophotometry can also be used as alternative choice of caffeine determination using reduced amount of organic solvent (dimethylformamide) and univariate calibration technique. These analytical methods may therefore, be recommended for the rapid, simple, safe and cost effective determination of caffeine in green coffee beans.

     

Near-infrared (NIR) hyperspectral imaging and multivariate image analysis to study growth characteristics and differences between species and strains of members of the genus Fusarium

Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96?h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47?% pixels correctly predicted). For F. subglutinans, 78-100?% pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60-80?%. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Distinguishing Ontario ginseng landraces and ginseng species using NMR-based metabolomics

The use of ¹H-NMR-based metabolomics to distinguish and identify unique markers of five Ontario ginseng (Panax quinquefolius L.) landraces and two ginseng species (P. quinquefolius and P. ginseng) was evaluated. Three landraces (2, 3, and 5) were distinguished from one another in the principal component analysis (PCA) scores plot. Further analysis was conducted and specific discriminating metabolites from the PCA loadings were determined. Landraces 3 and 5 were distinguishable on the basis of a decreased NMR intensity in the methyl ginsenoside region, indicating decreased overall ginsenoside levels. In addition, landrace 5 was separated by an increased amount of sucrose relative to the rest of the landraces. Landrace 2 was separated from the rest of the landraces by the increased level of ginsenoside R_b1. The Ontario P. quinquefolius was also compared with Asian P. ginseng by PCA, and clear separation between the two groups was detected in the PCA scores plot. The PCA loadings plot and a t-test NMR difference plot were able to identify an increased level of maltose and a decreased level of sucrose in the Asian ginseng compared with the Ontario ginseng. An overall decrease of ginsenoside content, especially ginsenoside R_b1, was also detected in the Asian ginseng’s metabolic profile. This study demonstrates the potential of NMR-based metabolomics as a powerful high-throughput technique in distinguishing various closely related ginseng landraces and its ability to identify metabolic differences from Ontario and Asian ginseng. The results from this study will allow better understanding for quality assessment, species authentication, and the potential for developing a fully automated method for quality control.

Liquid chromatography/mass spectrometry of malonyl-ginsenosides in the authentication of ginseng

Different negative ion electrospray (ES) source conditions are required to concentrate the ion current in [M-H](-) for malonylated and non-malonylated ginsenosides. However, both can be ionised optimally in a single liquid chromatography/mass spectrometry (LC/MS) analysis by employing switchable voltages in the post-source ion optics of a quadrupole ion trap mass spectrometer. Coupled with automatic MS/MS scanning and post-acquisition neutral loss data analysis, this method provides a means of profiling the malonylated and acetylated ginsenosides in ginseng extracts. Analyses revealed numerous malonylated ginsenosides that could be partially characterised by serial MS/MS experiments. The ratio of mRb(1) to other isomeric forms present and to mRb(2) and mRc appears to show consistent differences among Panax ginseng (Asian ginseng), P. quinquefolius (American ginseng) and P. notoginseng (Sanchi ginseng). The ratio of malonylated to non-malonylated ginsenosides is reduced in the red form of Asian ginseng compared with the white form and there is a concomitant increase in the levels of the corresponding acetylated ginsenosides. The ability to analyse malonylated ginsenosides is an important contribution to the range of chemical characteristics that can be used to authenticate the different species of ginseng and will assist in quality control and standardisation.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.