The Experts below are selected from a list of 6420 Experts worldwide ranked by ideXlab platform

Pei Chen - One of the best experts on this subject based on the ideXlab platform.

  • Multi-component determination and Chemometric Analysis of Paris polyphylla by ultra high performance liquid chromatography with photodiode array detection.
    IEEE Journal of Solid-state Circuits, 2016
    Co-Authors: Pei Chen, Shuang-cheng Ma
    Abstract:

    : Multi-source Analysis of traditional Chinese medicine is key to ensuring its safety and efficacy. Compared with traditional experimental differentiation, Chemometric Analysis is a simpler strategy to identify traditional Chinese medicines. Multi-component Analysis plays an increasingly vital role in the quality control of traditional Chinese medicines. A novel strategy, based on Chemometric Analysis and quantitative Analysis of multiple components, was proposed to easily and effectively control the quality of traditional Chinese medicines such as Chonglou. Ultra high performance liquid chromatography was more convenient and efficient. Five species of Chonglou were distinguished by Chemometric Analysis and nine saponins, including Chonglou saponins I, II, V, VI, VII, D, and H, as well as dioscin and gracillin, were determined in 18 min. The method is feasible and credible, and enables to improve quality control of traditional Chinese medicines and natural products.

  • Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and Chemometric Analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and Chemometric Analysis

  • use of fuzzy chromatography mass spectrometric fcms fingerprinting and Chemometric Analysis for differentiation of whole grain and refined wheat t aestivum flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %.

Ping Geng - One of the best experts on this subject based on the ideXlab platform.

  • Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and Chemometric Analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and Chemometric Analysis

  • use of fuzzy chromatography mass spectrometric fcms fingerprinting and Chemometric Analysis for differentiation of whole grain and refined wheat t aestivum flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %.

Bing-ren Gu - One of the best experts on this subject based on the ideXlab platform.

  • Combination of quantitative Analysis and Chemometric Analysis for the quality evaluation of three different frankincenses by ultra high performance liquid chromatography and quadrupole time of flight mass spectrometry
    Journal of Separation Science, 2015
    Co-Authors: Chao Zhang, Hong-yu Jin, Run-tao Tian, Lei Sun, Shuang-cheng Ma, Bing-ren Gu
    Abstract:

    Frankincense has gained increasing attention in the pharmaceutical industry because of its pharmacologically active components such as boswellic acids. However, the identity and overall quality evaluation of three different frankincense species in different Pharmacopeias and the literature have less been reported. In this paper, quantitative Analysis and Chemometric evaluation were established and applied for the quality control of frankincense. Meanwhile, quantitative and Chemometric Analysis could be conducted under the same analytical conditions. In total 55 samples from four habitats (three species) of frankincense were collected and six boswellic acids were chosen for quantitative Analysis. Chemometric analyses such as similarity Analysis, hierarchical cluster Analysis, and principal component Analysis were used to identify frankincense of three species to reveal the correlation between its components and species. In addition, 12 chromatographic peaks have been tentatively identified explored by reference substances and quadrupole time-of-flight mass spectrometry. The results indicated that the total boswellic acid profiles of three species of frankincense are similar and their fingerprints can be used to differentiate between them.

Junhu Cheng - One of the best experts on this subject based on the ideXlab platform.

  • pork biogenic amine index bai determination based on Chemometric Analysis of hyperspectral imaging data
    Lwt - Food Science and Technology, 2016
    Co-Authors: Weiwei Cheng, Junhu Cheng
    Abstract:

    Abstract Biogenic amine index (BAI) is a sensitive indicator of meat freshness and quality. This study investigated the use of Chemometric methods for analyzing hyperspectral imaging (HSI) data between 400 nm and 1000 nm to rapidly and non-destructively determine BAI values in pork. Partial least square regression (PLSR) model established using full wavelengths showed good results. In order to simplify the calibration model, four new PLSR and multiple linear regression (MLR) models based on the two sets of feature-related wavelengths selected by successive projections algorithm (SPA) and regression coefficients (RC) were built and compared. The optimized simplified model (RC-MLR) yielded excellent results with R2P of 0.957 and RMSEP of 4.866 mg/kg, which was thus used to visualize BAI value corresponding to each pixel of the image using pseudo color. In addition, the mechanisms of HSI for BAI determination were discussed. The established models used to determine BAI values were based on physiochemical changes associated with BAI generation in meat rather than direct detection of the BAI contents. The overall results of this study demonstrated that HSI data can be utilized to predict BAI values in pork based on Chemometric Analysis.

  • recent applications of spectroscopic and hyperspectral imaging techniques with Chemometric Analysis for rapid inspection of microbial spoilage in muscle foods
    Comprehensive Reviews in Food Science and Food Safety, 2015
    Co-Authors: Junhu Cheng
    Abstract:

    Muscle food is one of the most perishable food products because of its vulnerability to microbial spoilage, which can result in critical food safety problems. Traditional techniques for detection and evaluation of microbial spoilage in muscle foods are tedious, laborious, destructive, and time-consuming. In recent years, spectroscopic and imaging technologies have shown great potentials for the assessment of food quality and safety due to their nondestructive, noninvasive, cost-effective, and rapid responsive nature. This review focuses on the applications of several valuable spectroscopic techniques including visible and near-infrared spectroscopy, Fourier transform infrared spectroscopy, fluorescence spectroscopy, Raman spectroscopy, and hyperspectral imaging for the rapid and nondestructive detection of microbial spoilage in common muscle foods such as meat, poultry, fish, and related products. Combined with Chemometric Analysis, such as spectral preprocessing and modeling methods, these potential technologies have been successfully developed for the determination of total viable count, aerobic plate count, Enterobacteriaceae, Pseudomonas, Escherichia coli, and lactic acid bacteria loads in muscle foods. Moreover, the advantages and disadvantages of these techniques are discussed and some perspectives about future trends are also presented.

James M. Harnly - One of the best experts on this subject based on the ideXlab platform.

  • Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and Chemometric Analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and Chemometric Analysis

  • use of fuzzy chromatography mass spectrometric fcms fingerprinting and Chemometric Analysis for differentiation of whole grain and refined wheat t aestivum flour
    Analytical and Bioanalytical Chemistry, 2015
    Co-Authors: Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria, Pei Chen
    Abstract:

    A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with Chemometric Analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components Analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %.