The Experts below are selected from a list of 5745 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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collaborative analysis on the marked ages of Rice Wines by electronic tongue and nose based on different feature data sets
Sensors, 2020Co-Authors: Huihui Zhang, Jun Wang, Wenqing Shao, Shanshan Qiu, Zhenbo WeiAbstract:Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of Rice Wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying Rice Wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying Rice Wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).
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application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying Rice Wines of different geographical origins
Analytica Chimica Acta, 2019Co-Authors: Jun Wang, Weilin Zhang, Luyi Zhu, Zhenbo WeiAbstract:Abstract In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of Rice Wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5' - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of Rice Wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of Rice Wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and “area method” was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the Rice Wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of Rice Wines, and ELM performed better.
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application of novel nanocomposite modified electrodes for identifying Rice Wines of different brands
RSC Advances, 2018Co-Authors: Zhenbo Wei, Weilin Zhang, Luyi Zhu, Yanan Yang, Jun WangAbstract:In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of Rice Wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The Rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the ‘area method’. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different Wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify Rice Wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.
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fabrication of conducting polymer noble metal nanocomposite modified electrodes for glucose ascorbic acid and tyrosine detection and its application to identify the marked ages of Rice Wines
Sensors and Actuators B-chemical, 2018Co-Authors: Zhenbo Wei, Weilin Zhang, Xize Xiao, Yana Yang, Jun WangAbstract:Abstract In previous studies, conductive polymers (CPs)/noble metal nanoparticles (NMNPs) composite materials modified electrodes were always applied to distinguish the trace amounts of specific analytes in complex liquid mixtures. In this paper, polymer sulfanilic acid (PABSA)/AuNPs/glassy carbon electrode (GCE), polymer acid chrome blue K (PACBK)/AuNPs/GCE and polymer aspartic acid (PASP)/PtNPs/GCE were fabricated for the identification of the wine age of Rice Wines with pattern recognitions. The sensitivity of those modified electrodes was exhibited by cyclic voltammetry, and the parameters of electrochemical behaviors was optimized and confirmed gradually. The original responses were recorded by chronoamperometry with multi-frequency rectangle pulse voltammetry and multi-frequency staircase pulse voltammetry, and the feature data correlated with the wine age were extracted from original responses by ‘area method’. Based on the feature data, principal component analysis (PCA, unsupervised method), locality preserving projections (LPP, semi-supervised method) and differential financial analysis (DFA, supervised method) were applied for the classification of Rice wine samples with different marked age, and DFA exhibited the most clear result; least squares support vector machines (LSSVM) and library for support vector machines (LIBSVM) were applied for the prediction of the wine ages, and LIBSVM worked better than LSSVM, the correlations based on the training and testing dataset were R 2 = 0.9999 and R 2 = 0.9998, respectively. In conclusion, the CPs/NMNPs/GCEs with pattern recognitions were powerful tools to identify the marked ages of Rice Wines.
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identification of the Rice Wines with different marked ages by electronic nose coupled with smartphone and cloud storage platform
Sensors, 2017Co-Authors: Zhebo Wei, Jun Wang, Xize Xiao, Hui WangAbstract:In this study, a portable electronic nose (E-nose) was self-developed to identify Rice Wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify Rice Wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR.
Zhenbo Wei - One of the best experts on this subject based on the ideXlab platform.
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collaborative analysis on the marked ages of Rice Wines by electronic tongue and nose based on different feature data sets
Sensors, 2020Co-Authors: Huihui Zhang, Jun Wang, Wenqing Shao, Shanshan Qiu, Zhenbo WeiAbstract:Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of Rice Wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying Rice Wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying Rice Wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).
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application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying Rice Wines of different geographical origins
Analytica Chimica Acta, 2019Co-Authors: Jun Wang, Weilin Zhang, Luyi Zhu, Zhenbo WeiAbstract:Abstract In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of Rice Wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5' - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of Rice Wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of Rice Wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and “area method” was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the Rice Wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of Rice Wines, and ELM performed better.
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application of novel nanocomposite modified electrodes for identifying Rice Wines of different brands
RSC Advances, 2018Co-Authors: Zhenbo Wei, Weilin Zhang, Luyi Zhu, Yanan Yang, Jun WangAbstract:In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of Rice Wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The Rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the ‘area method’. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different Wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify Rice Wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.
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fabrication of conducting polymer noble metal nanocomposite modified electrodes for glucose ascorbic acid and tyrosine detection and its application to identify the marked ages of Rice Wines
Sensors and Actuators B-chemical, 2018Co-Authors: Zhenbo Wei, Weilin Zhang, Xize Xiao, Yana Yang, Jun WangAbstract:Abstract In previous studies, conductive polymers (CPs)/noble metal nanoparticles (NMNPs) composite materials modified electrodes were always applied to distinguish the trace amounts of specific analytes in complex liquid mixtures. In this paper, polymer sulfanilic acid (PABSA)/AuNPs/glassy carbon electrode (GCE), polymer acid chrome blue K (PACBK)/AuNPs/GCE and polymer aspartic acid (PASP)/PtNPs/GCE were fabricated for the identification of the wine age of Rice Wines with pattern recognitions. The sensitivity of those modified electrodes was exhibited by cyclic voltammetry, and the parameters of electrochemical behaviors was optimized and confirmed gradually. The original responses were recorded by chronoamperometry with multi-frequency rectangle pulse voltammetry and multi-frequency staircase pulse voltammetry, and the feature data correlated with the wine age were extracted from original responses by ‘area method’. Based on the feature data, principal component analysis (PCA, unsupervised method), locality preserving projections (LPP, semi-supervised method) and differential financial analysis (DFA, supervised method) were applied for the classification of Rice wine samples with different marked age, and DFA exhibited the most clear result; least squares support vector machines (LSSVM) and library for support vector machines (LIBSVM) were applied for the prediction of the wine ages, and LIBSVM worked better than LSSVM, the correlations based on the training and testing dataset were R 2 = 0.9999 and R 2 = 0.9998, respectively. In conclusion, the CPs/NMNPs/GCEs with pattern recognitions were powerful tools to identify the marked ages of Rice Wines.
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Nickel and copper foam electrodes modified with graphene or carbon nanotubes for electrochemical identification of Chinese Rice Wines
Microchimica Acta, 2017Co-Authors: Zhenbo Wei, Weilin Zhang, Jun WangAbstract:The authors describe the application of two types of metallic foams modified with either graphene (GR) or carbon nanotubes (CNTs) as voltammetric electrodes in order to discriminate Rice Wines of different age and brand. Two types of bare metallic foams (bare Ni and Cu foam electrodes) were combined with GR or CNTs to give four types of modified metallic foams, referred to as GR/Ni, GR/Cu, CNT/Ni, and CNT/Cu foam electrodes. Cyclic voltammetry was applied to study the effects of GR and CNTs on the response of the electrodes. Multifrequency rectangle pulse voltammetry and multifrequency staircase pulse voltammetry were applied to generate potential waveforms, and chronoamperometric curves were recorded. Principal component analysis (PCA) allowed a classification of the Rice Wines, and characteristic regular distributions were identified in the PCA plots. Support vector machines (SVM) were found to perform better than partial least squares regression in predicting ages and brands of the Rice Wines in that all fit correlation coefficients were >0.9930. The SVM based leave-one-out cross-validation method proved to be the most powerful regression tool. The six types of foam electrodes perform very well in the classification and prediction of Rice Wines of different ages and brands. Graphical abstract Modified Ni and Cu foams were modified by grapheme and carbon nanotubes respectively. Those modified electrodes worked well to classify and predict Rice Wines of different ages and brands with the help of multi-frequency potential waveforms and pattern recognition methods.
Donghong Liu - One of the best experts on this subject based on the ideXlab platform.
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determination of biogenic amines in semi dry and semi sweet chinese Rice Wines from the shaoxing region
Food Control, 2012Co-Authors: Jianjun Zhong, Zhongxiang Fang, Guangfa Xie, Ningbo Liao, Jie Shu, Donghong LiuAbstract:Abstract The aim of this study was to investigate the levels of nine biogenic amines in two types of Chinese Rice wine (semi-dry and semi-sweet) from the Shaoxing region. Thirty-nine samples from different manufacturers were analyzed by reversed-phase high-performance liquid chromatography and ultraviolet detection after pre-column derivatization with benzoyl chloride. Serotonin and tyramine were the most prevalent amines (100%) followed by histamine, cadaverine and putrescine (95.2–88.1%). Spermine, spermidine, 2-phenylethylamine and tryptamine were not detected in any sample. The most prominent biogenic amine was serotonin followed by putrescine, tyramine, cadaverine and histamine, their mean contents being 53.3, 24.3, 19.2, 9.08 and 8.83 mg/L, respectively. The total biogenic amine contents were variable, ranging from 29.3 to 260 mg/L, with 115 mg/L on average. Significantly higher ( P r ≥ 0.75, P k -means clustering.
Zhongxiang Fang - One of the best experts on this subject based on the ideXlab platform.
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determination of biogenic amines in semi dry and semi sweet chinese Rice Wines from the shaoxing region
Food Control, 2012Co-Authors: Jianjun Zhong, Zhongxiang Fang, Guangfa Xie, Ningbo Liao, Jie Shu, Donghong LiuAbstract:Abstract The aim of this study was to investigate the levels of nine biogenic amines in two types of Chinese Rice wine (semi-dry and semi-sweet) from the Shaoxing region. Thirty-nine samples from different manufacturers were analyzed by reversed-phase high-performance liquid chromatography and ultraviolet detection after pre-column derivatization with benzoyl chloride. Serotonin and tyramine were the most prevalent amines (100%) followed by histamine, cadaverine and putrescine (95.2–88.1%). Spermine, spermidine, 2-phenylethylamine and tryptamine were not detected in any sample. The most prominent biogenic amine was serotonin followed by putrescine, tyramine, cadaverine and histamine, their mean contents being 53.3, 24.3, 19.2, 9.08 and 8.83 mg/L, respectively. The total biogenic amine contents were variable, ranging from 29.3 to 260 mg/L, with 115 mg/L on average. Significantly higher ( P r ≥ 0.75, P k -means clustering.
Fei Shen - One of the best experts on this subject based on the ideXlab platform.
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differentiation of chinese Rice Wines from different wineries based on mineral elemental fingerprinting
Food Chemistry, 2013Co-Authors: Fei Shen, Yibin Ying, Tao JiangAbstract:Abstract Discrimination of Chinese Rice Wines from three well-known wineries (“Guyuelongshan”, “Kuaijishan”, and “Pagoda”) in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese Rice wine.
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ageing status characterization of chinese Rice Wines using chemical descriptors combined with multivariate data analysis
Food Control, 2012Co-Authors: Fei Shen, Dongli Liu, Yibin YingAbstract:Abstract Wine ageing status identification is of great commercial and scientific interest, as wine quality and value are closely related to the organoleptic characteristics developed during the ageing process. In this study, Chinese Rice Wines from three well-known wineries (“Guyuelongshan”, “Kuaijishan” and “Pagoda”) were analyzed for 21 chemical parameters, including six conventional parameters, five sugars, lactic acid and nine macro-elements. Then the experimental data were subjected to multivariate statistical analysis to predict and classify samples of different ageing status (3, 9, 15, 21, and 33 months). Systematic differences between samples were revealed by a two-way analysis of variance (ANOVA) and principal component analysis (PCA). Discrimination model built by forward stepwise linear discriminant analysis (LDA) based on the 16 selected parameters achieved 88.5% accuracy in leave-one-out (LOO) cross-validation. The most five discriminant variables were Zn, Mn, alcohol, Cu and Al, respectively. When the discrimination was performed on the samples from each winery, the classification accuracy in LOO cross-validation was 97.7%, 91.1% and 78.0%, respectively. The results demonstrated that these chemical parameters have the potential to enable the authentication of ageing status of Rice wine.
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discrimination between shaoxing Wines and other chinese Rice Wines by near infrared spectroscopy and chemometrics
Food and Bioprocess Technology, 2012Co-Authors: Fei Shen, Yibin Ying, Yunfeng Zheng, Danting Yang, Tao JiangAbstract:Shaoxing Rice wine (also called Shaoxing wine) is the most well-known Chinese Rice wine in China. The common fraudulent practice in the commercialization of Chinese Rice wine is to sell Wines from different geographical origins under the denomination of Shaoxing Rice wine. In this study, the use of near-infrared (NIR) spectroscopy combined with chemometrics as a rapid tool for the discrimination of Chinese Rice wine from three geographical origins (“Fujian”, “non-Shaoxing”, “Shaoxing”) has been preliminarily investigated. NIR spectra were collected in transmission mode in the wavelength range of 800–2,500 nm. Discriminant models were developed by principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least-squares analysis (DPLS). The chemical properties of Chinese Rice wine were also investigated to find out the difference between samples from three varied origins. The results showed that good classification could be obtained after spectral pre-treatment. The percentage of samples correctly classified by both DA and DPLS methods in calibration and validation set was 97.2% and 100%, respectively. The results demonstrated that NIR could be used as a simple and rapid technique to distinguish Shaoxing Wines from non-Shaoxing Wines and Fujian Wines. To further validate the ability of NIR spectroscopy, more samples should be incorporated to build a more robust model.
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multivariate classification of Rice Wines according to ageing time and brand based on amino acid profiles
Food Chemistry, 2011Co-Authors: Fei Shen, Yibin Ying, Yunfeng Zheng, Qing ZhugeAbstract:Abstract Discrimination of Chinese Rice Wines according to ageing time and brand using amino acid profiles was presented in this study. Free amino acids (16) in 98 Rice Wines were simultaneously determined using high-performance liquid chromatograph-diode array detection (HPLC-DAD). Then the experimental data was subjected to multivariate analysis. Principal component analysis (PCA) was employed to differentiate samples from various ageing times (3, 9, 11 and 15 months) and brands (“ pagoda ”, “ kuaijishan ”, and “ guyuelongshan ”). Partial least square discriminant analysis (PLS-DA) and full (leave-one-out) cross-validation were used to develop classification models. The overall correct classification rate for different ageing times and brands was 99.7% and 94.9%, respectively. The proposed method shows an effective strategy for the detection of mislabelling of Rice Wines.
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determination of amino acids in chinese Rice wine by fourier transform near infrared spectroscopy
Journal of Agricultural and Food Chemistry, 2010Co-Authors: Fei Shen, Yibin Ying, Danting Yang, Xiaoying Niu, Geqing ZhuAbstract:Chinese Rice wine is abundant in amino acids. The possibility of quantitative detection of 16 free amino acids (aspartic acid, threonine, serine, glutamic acid, proline, glycine, alanine, valine, methionine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine) in Chinese Rice wine by Fourier transform near-infrared (NIR) spectroscopy was investigated for the first time in this study. A total of 98 samples from vintage 2007 Rice Wines with different aging times were analyzed by NIR spectroscopy in transmission mode. Calibration models were developed using partial least-squares regression (PLSR) with high-performance liquid chromatography (HPLC) by postcolumn derivatization and diode array detection as a reference method. To validate the calibration models, full cross (leave-one-out) validation was employed. The results showed that the calibration statistics were good (rcal>0.94) for all amino acids except proline, histidine, and arginine. The correlation coefficient in cross validation (rcv) was >0.81 for 12 amino acids. The residual predictive deviation (RPD) value obtained was >1.5 in all amino acids except proline and arginine, and it was >2.0 in 6 amino acids. The results obtained in this study indicated that NIR spectroscopy could be used as an easy, rapid, and novel tool to quantitatively predict free amino acids in Chinese Rice wine without sophisticated methods.
