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Zhang L.,Chinese Academy of Agricultural Sciences | Zhang L.,Laboratory of Risk Assessment for Oilseeds Products Wuhan | Zhang L.,Quality Inspection and Test Center for Oilseeds Products | Zhang L.,Hubei Collaborative Innovation Center for Green Transformation of Bio Resources | And 18 more authors.
Chemometrics and Intelligent Laboratory Systems | Year: 2016

Highly predictive multivariate calibration model depends on samples in training set. In this study, we introduced an outlier detection method and developed its improvement for shorter run time. Improved Monte-Carlo outlier detection (IMCOD) was proposed to establish cross-prediction models for determining normal samples, which were subsequently used to analyze the distribution of prediction errors for all of dubious samples together. Four real datasets were employed to illustrate and validate the performance of IMCOD. After sample selection for training set of NIR of soy flour samples, the Root Mean Square Error of Prediction (RMSEP) of PLS model decreased from 1.4811 to 0.7650. This method benefits the establishment of a good model for QSAR and NIR datasets. © 2015 Elsevier B.V. Source


Qin D.-F.,Hubei University | Li T.,Hubei University | Dai C.,Hubei University | Dai C.,Hubei Collaborative Innovation Center for Green Transformation of Bio Resources | Dai C.,Hubei Province Key Laboratory of Regional Development and Environmental Response
Chinese Journal of Applied Ecology | Year: 2015

This study explored whether the degree of pollen limitation was affected by the experimental level (a single flower or inflorescence) and pollen quality (self-pollen or outcross-pollen) of supplemental pollination in Sagittaria trifolia. The results showed that the experimental level caused varying degree of pollen limitation. Compared with the inflorescence level, pollination at the single flower level led to a redistribution of resources among flowers, therefore affecting seed numbers. Pollen quality also played a vital role in the estimation of pollen limitation. Compared with self-pollen, supplemental pollination with outcross-pollen resulted in significantly more seeds and a higher germination rate. This proved that in the research system the reproduction was limited by pollen quality rather than quantity. Our study revealed that both experimental level and pollen quality had effects on the estimation of pollen limitation. It was suggested that in future studies we should evaluate pollen limitation at the inflorescence or whole plant level, and also consider comparing self- and outcross-pollen when applicable. © 2015, Editorial Board of Chinese Journal of Applied Ecology. All right reserved. Source


Zhang J.,Hubei University | Zhang J.,Hubei Collaborative Innovation Center for Green Transformation of Bio Resources | Li Z.H.,Hubei University | Zhou P.Q.,Huazhong University of Science and Technology | And 3 more authors.
International Journal of Environmental Technology and Management | Year: 2015

During the past ten years (2000-2009), a continuous survey has been conducted to assess the concentration of trace metals in the water from three parts of Daye Lake. The results indicate that: 1) concentration of three trace metals exceed the Chinese drinking water standard measures; out of these As exceeded significantly by 70% followed by Pb and Cd which are 66.67% and 13.34% respectively; 2) all the water in Daye Lake had been polluted by trace metals and had high potential health risk as a drinking water resource for human beings; among the three research areas of Daye Lake: Sanli Qi Lake had the highest health risk (58.8 × 10-4), followed by Yinjia Lake (24.12 × 10-4), while Main Lake had the lowest health risks (3.84 × 10-4); 3) no significant correlations between these trace metals was observed (p < 0.5) which suggest they originated from diverse pollution sources. Copyright © 2015 Inderscience Enterprises Ltd. Source


Zhang L.,Chinese Academy of Agricultural Sciences | Zhang L.,Laboratory of Risk Assessment for Oilseeds Products Wuhan | Zhang L.,Quality Inspection and Test Center for Oilseeds Products | Zhang L.,Hubei Collaborative Innovation Center for Green Transformation of Bio Resources | And 17 more authors.
RSC Advances | Year: 2015

Developing a method of identifying oil authenticity is becoming critical for protecting customers' rights as adulteration of edible oils is a particular concern in food quality. Since adulterants in edible oils are usually unknown, the authenticity identification is a one-class classification problem in chemometrics. In this study, a one-class classification model was built to identify the authenticity of peanut oils by fatty acid profiles. Based on previous studies, 28 fatty acids were identified and quantified for peanut oils. The authenticity identification model was built by one-class partial least squares (OCPLS) classifier for peanut oils. Subsequently, the established model was validated by independent test sets. The results indicated that the OCPLS classifier could effectively detect adulterated oils and was therefore employed for authenticity assessment. Moreover, counterfeit oils adulterated with different levels of other edible oils were simulated by the Monte Carlo method and employed to test the lowest adulteration level of this one-class classifier. As a result, the model could identify peanut oils and sensitively detect adulteration of edible oils with other vegetable oils at adulteration level of more than 4%. © 2015 The Royal Society of Chemistry. Source


Zhang J.,Hubei University | Zhang J.,Hubei Collaborative Innovation Center for Green Transformation of Bio Resources | Manske G.,University of Bonn | Zhou P.Q.,Huazhong University of Science and Technology | And 3 more authors.
Environment, Development and Sustainability | Year: 2016

Overuse of nitrogen (N) fertilizers in agriculture activities has caused severe water pollution in China. The lack of data at producer level hampers decision makers in the development and implementation of efficient policies to curb excessive N-fertilizer use. In a survey of 300 farm households in the Liangzihu Lake basin, we identified factors associated with farmers’ decisions on N-fertilizer use and application rate. Household survey and multiple linear regression models indicate that the average application rate in the study region is 229 kg N ha−1, which exceeds the recommended rate for maximum profit for cereal crops (maize, wheat, and rice) in China of 150–180 kg N ha−1. High N-application rates are associated with low farmland productivity (coefficient = −15.66, p = 0.02), a high share of off-farm income (coefficient = 27.14, p = 0.003), and a low education level of the household head (coefficient = −10.83, p = 0.039). Neither physical infrastructure nor access to input markets appears to be related to N-application rates. It may be concluded that excessive use of N in agriculture of Central China is mainly a problem of insufficient awareness and high share of off-farm income. © 2016 Springer Science+Business Media Dordrecht Source

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