An Improved Weighted Partial Least Squares Method Coupled with Near Infrared Spectroscopy for Rapid Determination of Multiple Components and Anti-Oxidant Activity of Pu-Erh Tea.

Background: Pu-erh tea is a unique microbially fermented tea, which distinctive chemical constituents and activities are worthy of systematic study. Near infrared spectroscopy (NIR) coupled with suitable chemometrics approaches can rapidly and accurately quantitatively analyze multiple compounds in samples. Methods: In this study, an improved weighted partial least squares (PLS) algorithm combined with near infrared spectroscopy (NIR) was used to construct a fast calibration model for determining four main components, i.e., tea polyphenols, tea polysaccharide, total flavonoids, theanine content, and further determine the total antioxidant capacity of pu-erh tea. Results: The final correlation coefficients R square for tea polyphenols, tea polysaccharide, total flavonoids content, theanine content, and total antioxidant capacity were 0.8288, 0.8403, 0.8415, 0.8537 and 0.8682, respectively. Conclusions: The current study provided a comprehensive study of four main ingredients and activity of pu-erh tea, and demonstrated that NIR spectroscopy technology coupled with multivariate calibration analysis could be successfully applied to pu-erh tea quality assessment.

[Determination of Impurity Elements in Rosin with Inductively Coupled Plasma Mass Spectrometry].

An analytical method for the content determining of impurity elements in rosin was established. After being dissolved and diluted in ethanol, the rosin was analyzed directly with inductively coupled plasma mass spectrometry (ICP-MS). Some experiment conditions were optimized, such as the RF power, carrier gas flow, sample amount, and increase the temperature of plasma center channel, to improve the ionization efficiency. Proper oxygen concentration was added in plasma to make sure organic carbon been complete burned, resulting in carbon deposits eliminate and signal stable. Though equipment turn and isotopes selection can effectively eliminate interferences caused by oxide and doubly charged ions mass spectrums. A large amount of isobars and polyatomic ion interference were avoided. The collision reaction cell (CRC) technology was used to further eliminate the mass spectra interference, with He collision model and H2 reaction model, respectively. The results showed that detection limits of different impurity elements were within the range from 0.002 to 0.035 µg·g-1, and the recoveries of real added sample were between the range from 94.00% to 106.00%, and relative standard deviation is less than 3.34%. This method enables to detect rosin dissolved in ethanol directly and needs no special sample preparation equipment. This simple, fast and accurate approach provides us a new analysis method for the quality valuation of impurity elements in rosin.

[Study on the Detection of Trace Sulphur in Diesel with Inductively Coupled Plasma Optical Emission Spectroscopy].

The method to detect trace sulphur content in diesel with inductively coupled plasma optical emission spectrometry (ICP-OES) was established. Kerosene sample was directly diluted, and then trace sulphur content in the solution was analyzed directly with ICP-OES. High concentrations of organic solvents with high saturation vapor pressure can cause the increasing of the loading of plasma, which would interfere the determination. In order to keep the working stability of equipment, operating parameters, such as RF power, loading gas velocity, were being optimized. First, the axially-viewed technology was adopted to increase the sensitivity of determination. Second, the analysis spectral lines of sulfur are located in ultraviolet area; therefore the Ar was adopted as protective air, and increase Ar clean time to keep the pure of sight source. Third, the condensation and deposition of high concentrations of carbon could be avoided effectively by introducing O2 to plasma. Some detailed processes were used to further keep the stability of determinate signals such as: using element Y as internal standard to correct matrix effectives and compensate the errors caused by the spectral line shift, some automatic software were used to correct signal background. The results showed that the detection limits was 0.2 µg·L-1, the relative standard deviation (RSD) was in range of 1.6%~2.1%, and the recovery of the methods was in range of 97.4%~101.8%. The method has the following advantages: simple sample pretreatment, fast analyzing speed, low detection limitation, high precision and accuracy, as well as wide linear dynamic range. It can be used for rapid analysis of trace sulfur in diesel.

[Direct Determination of Heavy Metal Elements in Propolis by Inductively Coupled Plasma Mass Spectrometry].

In current study, a method was established for simultaneous quantitative analysis of Cr, Ni, As, Cd, Sb, Sn, Hg and Pb in propolis by using inductively coupled plasma mass spectrometry. Before analyzed by ICP-MS, the propolis was diluted with n-propanol/xylene (70 : 30, φ). Organic sample can remain stable by diluting with n-propanol/xylene, result from long-time sample dispersion in the solution. Carbon accumulation on the sampling cone, which comes from the high carbon content of butter, will clog the orifice and decrease analysis sensitivity. Thus a small amount of oxygen was added into the argon gas line to eliminate carbon. ORS was used to eliminate the polyatomic interferences caused by the high salty matrixes. The effects of the He collision gas flow rate on estimating detection limits of Cr, Ni and As were investigated. The matrix effects and the instrument drift have been calibrated with Rh as internal standard element. The results show that the detection limits is in the range of 20.8-102.7 ng x L(-1), the recovery is in the range of 92.0%-109.0%, and the RSD is less than 3.5%. This method was simple, sensitive and precise to simultaneously analyze 8 heavy metal elements in propolis.

[Analysis of Trace Inorganic Elements in Castor Oil by Inductively Coupled Plasma Mass Spectrometry].

A method for the determination of Na, Mg, Si, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Hg and Pb in castor oil after direct dilution with ethanol by inductively coupled plasma mass spectrometry (ICP-MS) was established. The sample was diluted by ethanol before ICP-MS determination. The condensation and deposition of high concentrations of carbon in mass cone interface and ion lens, which will decrease the sensitivity of element analysis, were avoided effectively by introducing O2 to plasma. The mass spectral interferences were eliminated by octopole reaction system (ORS). The matrix effects were calibrated to using Sc, Ge, Rh and Ir as internal standard elements. Au standard solution, which could form amalgam alloy with Hg, was dropped to eliminate the memory effect of Hg. The results show that the correlation coefficient for analyte is no less than 0.999 5, the detection limits is in the range of 0.06 - 20.1 ng x L(-1), the recovery is in the range of 990.4% - 110.2%, and the RSD is less than 4.8%. This method was very fast, simple and accurate to simultaneously analyze multi-elements in castor oil.

[Subcellular distribution of trace elements in wound granulation tissue of severe burn patients by inductively coupled plasma mass spectrometry].

A method for simultaneous and quantitative determination of Cr, Mn, Fe, Co, Cu, Zn, Se and Cd elements in the subcellular fractions of nuclei, mitochondria, lysosome, microsome and cytosol of wound granulation tissue of severe burn patients by octopole reaction system (ORS) inductively coupled plasma mass spectrometry (ICP-MS) was established. Using differential centrifugation, the sample is separated into different subcellular fractions. The subcellular fraction was digested by HNO3 + H2O2 with microwave digestion followed by dilution with ultrapure water then the above 8 trace elements in the solution were analyzed directly by ICP-MS. In the presented method, using ORS eliminates the polyatomic interferences caused by the matrixes. Rh as internal standard element was used to compensate matrix effect and signal drift. The detection limits of the 8 elements are in the range of 0.72-33.05 ng x L(-1), and the RSD is less than 8.4%. The results showed that the levels of some elements in subcellular fractions of wound granulation tissues were significantly different from those of normal skin tissues. ORS-ICP-MS is a useful tool for simultaneous determination of multi-elements in wound granulation tissue of severe burn patients, and could be widely used in other biological samples analysis.

[Study on the determination of trace elements in bitter almond by inductively coupled plasma mass spectrometry].

Samples of bitter almond were digested by microwave digestion, and trace elements amounts of B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Ba and Pb in sample solutions were determined by inductively coupled plasma mass spectrometry (ICP-MS). HNO3 + H2O2 was used to achieve the complete decomposition of the organic matrix in a closed-vessel microwave oven. The working parameters of the instrument were optimized. The result showed that the relative standard deviation (RSD) was less than 4.79% for all the elements, and the recovery was 90.00%-109.30% by adding standard recovery experiment. This method was simple, sensitive and precise and can perform simultaneo multi-elements determination for bitter almond, which could satisfy the sample examination request and provide scientific rationale for determining inorganic elements in bitter almond.

[Study on the determination of 28 inorganic elements in sunflower seeds by ICP-OES/ICP-MS].

The present paper describes a simple method for the determination of trace elements in sunflower seeds by using inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma spectrometry (ICP-MS). HNO3 + H2O2 were used to achieve the complete decomposition of the organic matrix in a closed-vessel microwave oven. The contents of 10 trace elements (Al, B, Ca, Fe, K, Mg, Na, Si, P and S) in sunflower seeds were determined by ICP-OES while 18 trace elements (As, Ba, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sr, Sn, Sb, Ti, V and Zn) were determined by ICP-MS. The rice reference material (GBW10045) was used as standard reference materials. The results showed a good agreement between measured and certified values for all analytes. The concentrations of necessary micro elements Ca, K, Mg, P and S were higher. This method was simple, sensitive and precise and can perform simultaneous multi-elements determination of sunflower seeds.

[Determination of impurity elements in edible phosphate by ICP-MS].

An octopole reaction system (ORS) inductively coupled plasma mass spectrometry (ICP-MS) was developed for the determination of Mg, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, As, Cd and Pb in edible phosphate. The sample was digested by HNO3 followed by dilution with ultrapure water, then the above 12 impurity elements in the solution were analyzed directly by ICP-MS The use of ORS can eliminate the interference of polyatomic ions dramatically. Sc, Y, In and Bi were used to correct the matrix interference and drift. The detection limits of the 12 elements are in the range of 0.004-0.362 microg x L(-1). This method is rapid, simple and applicable for the analysis of trace elements in edible phosphate.

[Direct determination of heavy metal elements in sweeteners by inductively coupled plasma mass spectrometry].

An analysis method of microwave digestion and inductively coupled plasma-mass spectrometry (ICP-MS) with octopole reaction system (ORS) was established for the determination of 10 heavy metal elements including Cr, Co, Ni, Cu, As, Cd, Sn, Sb, Hg and Pb in sweetener. Samples were decomposed by HNO3 and H2O2 followed by dilution with ultrapure water then the above 10 heavy metal elements in the solution were analyzed directly by ICP-MS. The use of ORS can eliminate the interference of polyatomic ions dramatically. 45Sc, 89Y, 115In and 209Bi as internal standard elements were used to compensate matrix effect and signal drift. The optimum conditions for the determination was tested and discussed. Under the optimal conditions, the detection limits of the 10 elements were in the range of 0.003-0.038 MICROg x L(-1), the recovery of the samples was in the range of 93.0%-106.6% and the relative standard deviation (RSD) < or = 3.4%, which showed that the method was very precise. The technique was applied for the quality control and safety evaluation of sweetener.

Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method.

Polysaccharide is the main active compound of Lilium, and showed many activities, such as hypoglycemic, antioxidant, immune-modulatory. There are three types' Lilium in China market, i.e. Lilium lancifolium Thunb (JD), Lilium davidiivar. Unicolor Salisb (L. davidii var)(LZBH), and Lilium brownii F.E. Brown var. viridulum Baker (BH). Near infrared spectroscopy (NIR) technique has become popular in the fields of quality control, due to its advantages, such as fast, non-destructive, and can detect several ingredients, simultaneously. In this study, a classification model was established based on NIR technique and random forest method to accurately distinguish three types' Lilium species, and the classification accuracy reached 94.37%. Furthermore, taking the effects of neighbor wavelength into account, a new weighted partial least square algorithm was proposed to establish an accurate and quantitative model for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R2) and low root mean square errors of prediction (RMSEP) were, 0.9455 and 0.9098, respectively. The obtained results showed that combined NIR technique with chemometrics was an effective and green method for quality control.

Application of near infrared spectroscopy for rapid determination the geographical regions and polysaccharides contents of Lentinula edodes.

In this study, a calibration model based on Near-infrared spectroscopy (NIR) technique and chemometrics method was developed for rapid and non-destructive detecting the polysaccharide contents of lentinula edodes samples collected from different regions. The polysaccharide contents of these samples were firstly determined by standard phenol-sulphruic acid method. Then, NIR spectra of these samples were collected by using Fourier transform infrared spectrometry. Based on these experimental data, a random forest method was further used to distinguish the regions of these samples, with a classification accuracy of 96.6%. After that, a rapid, accurate, and quantitative model was established for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R2) and low root mean square errors of prediction (RMSEP) were, 0.925 and 0.720, respectively. These results showed that combined NIR technique with chemometrics was an effective and green method for lentinula edodes quality control.

[Determination of impurity elements in high-purity aluminium using inductively coupled plasma-mass spectrometry after matrix extraction and separation].

To establish the method for the determination of 7 trace impurity elements including Co, Ni, Cu, Mo, Cd, Pb and Bi in high-purity aluminium by inductively coupled plasma-mass spectrometry (ICP-MS), the impurity elements were extracted from the aluminium matrix in the form of chelates with ammonium pyrrolidine dithiocarbamate (APDC) into methyl isobutylketone (MIBK). The instrument parameters, and conditions of separation and interference of matrix were discussed in detail. The optimal detection conditions were determined. The detection limits range from 0. 011 to 0. 052 microg x L(-1) depending on the elements, recoveries of samples are in the range of 92. 2%-103. 0%, and the precision is RSD<2. 3%. This method is accurate, quick and convenient. It is used in the determination of impurity elements in high-purity aluminium with satisfactory results.

[Determination of trace impurities in high purity titanium dioxide by high resolution inductively coupled plasma mass spectrometry].

An analytical method using high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) for the rapid simultaneous determination of twenty six elements (Be, Na, Mg, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Mo, Cd, Sn, Sb, Ba, Ce, Nd, Sm, Pt, Pb and Bi) in high purity titanium dioxide was described. Samples were decomposed by (NH4)2SO4 and H2SO4. Most of the spectral interferences could be avoided by measuring in the high-resolution mode. The matrix effects due to the presence of excess sulfuric acid and Ti were evaluated. The optimum conditions for the determination were tested and discussed. The standard addition method was employed for quantitative analysis. The detection limits are 0.004-0.63 microg x g(-1), the recovery ratio is 87.6%-106.4%, and the RSD is less than 3.5%. The method is accurate, quick and convenient. It has been applied to the determination of trace impurities in high purity titanium dioxide with satisfactory results.

[The matrix effects of organic acid compounds in ICP-MS].

The matrix effects arising from oxalic acid, lactic acid, tartaric acid and citric acid in inductively coupled plasma mass spectrometry (ICP-MS) were investigated. It has been proved that the sensitivity of analytes can be significantly enhanced by adding small amounts of organic acid compounds with adjusted nebulizer gas flow-rate, especially for the elements with ionization potential between 9 and 11 eV. The tartaric acid has higher enhancement effect on the signal intensity of the hard-to-ionize elements than oxalic acid, lactic acid and citric acid. The mechanism of the enhancement was investigated. The method has been used to determine Be, Zn, As, Se, Sb and Hg in water standard reference materials (SRM). The analytical results are very close to the certified values.

Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features.

Identification and characterization of B-cell epitopes in target antigens was one of the key steps in epitopes-driven vaccine design, immunodiagnostic tests, and antibody production. Experimental determination of epitopes was labor-intensive and expensive. Therefore, there was an urgent need of computational methods for reliable identification of B-cell epitopes. In current study, we proposed a novel peptide feature description method which combined peptide amino acid properties with chemical molecular features. Based on these combined features, a random forest (RF) classifier was adopted to classify B-cell epitopes and non-epitopes. RF is an ensemble method that uses recursive partitioning to generate many trees for aggregating the results; and it always produces highly competitive models. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for current method were 78.31%, 80.05%, 72.23%, 0.5836, and 0.8800, respectively. These results showed that an appropriate combination of peptide amino acid features and chemical molecular features with a RF model could enhance the prediction performance of linear B-cell epitopes. Finally, a freely online service was available at http://sysbio.yznu.cn/Research/Epitopesprediction.aspx.

Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC.

As the most frequent drug target, G-protein coupled receptors (GPCRs) are a large family of seven transmembrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. It plays important roles in a variety of cellular functions, including protein folding, protein trafficking and localization, cell-cell interactions and epitope recognition. Therefore, investigating the exact position of glycosylation site in GPCR sequence can provide useful clues for drug design and other biotechnology applications. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is a significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. In this article, we presented an effective method to recognize the sites of human GPCRs by combining amino acid hydrophobicity with ensemble support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values were 94.4, 89.7, 98.9%, 0.895 and 0.989, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs functional sites to facilitate drug discovery.

Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms.

Chromatography has been established as one of the most important analytical methods in the modern analytical laboratory. However, preprocessing of the chromatograms, especially peak alignment, is usually a time-consuming task prior to extracting useful information from the datasets because of the small unavoidable differences in the experimental conditions caused by minor changes and drift. Most of the alignment algorithms are performed on reduced datasets using only the detected peaks in the chromatograms, which means a loss of data and introduces the problem of extraction of peak data from the chromatographic profiles. These disadvantages can be overcome by using the full chromatographic information that is generated from hyphenated chromatographic instruments. A new alignment algorithm called CAMS (Chromatogram Alignment via Mass Spectra) is present here to correct the retention time shifts among chromatograms accurately and rapidly. In this report, peaks of each chromatogram were detected based on Continuous Wavelet Transform (CWT) with Haar wavelet and were aligned against the reference chromatogram via the correlation of mass spectra. The aligning procedure was accelerated by Fast Fourier Transform cross correlation (FFT cross correlation). This approach has been compared with several well-known alignment methods on real chromatographic datasets, which demonstrates that CAMS can preserve the shape of peaks and achieve a high quality alignment result. Furthermore, the CAMS method was implemented in the Matlab language and available as an open source package at http://www.github.com/matchcoder/CAMS.

Exploring the relationship between 5'AMP-activated protein kinase and markers related to type 2 diabetes mellitus.

The importance of 5'AMP-activated protein kinase (AMPK) in regulating glucose and fatty acid metabolism is increasing. Thus, it is regarded as a new pharmacological target for treatment of obesity, insulin resistance and type 2 diabetes mellitus (T2DM). In order to explore the relationships between AMPK and diabetes mellitus, urines samples from four groups of C57 mice, i.e., the normal male and female C57 mice, female C57-AMPK gene knocked-out mice, and male C57-AMPK gene knocked-out mice, were studied by coupling GC/MS with a powerful machine learning method, random forest. The experimentation has been designed as two steps: firstly, the normal male and female mice were compared with male and female C57-AMPK gene knocked-out mice, respectively; then the differences between male C57-AMPK gene knocked-out mice and female C57-AMPK gene knocked-out mice were further detected. Finally, not only the differences between the normal C57 mice and C57-AMPK gene knocked-out mice were observed, but also the gender-related metabolites differences of the C57-AMPK gene knocked-out mice were obviously visualized. The results obtained with this research demonstrate that combining GC/MS profiling with random forest is a useful approach to analyze metabolites and to screen the potential biomarkers for exploring the relationships between AMPK and diabetes mellitus.

Using random forest to classify T-cell epitopes based on amino acid properties and molecular features.

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.