Yong He

Zhejiang University, Hang-hsien, Zhejiang Sheng, China

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Publications (424)361.9 Total impact

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    ABSTRACT: Classification is a critical step to make full use of the hyperspectral data. The most current approaches perform well for analyzing the macro texture, but they often fail to deal with the micro texture. Thus, this study proposes a general framework for the material with micro texture based on Hyperspectral Image (HSI) technique. In this framework, Local Response Pattern (LRP) is firstly proposed to describe 2D image texture to preserve more structural information and keep less sensitive to image conditions. Then, LRP is extended to represent HSI with Texture Enhancement (TE) by considering opponent relationships between pairs of bands. After that, Discriminated Locality Preserving Projection (DLPP) is proposed to reduce data dimension in a linearizing nonlinear manifold way. Finally, experiments on the hyperspectral images of fresh and frozen-thawed fish fillets are conducted. The results demonstrate that the proposed framework is efficient in terms of both recognition rates and robustness.
    Information Sciences 04/2015; 299. DOI:10.1016/j.ins.2014.12.025 · 3.89 Impact Factor
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    ABSTRACT: NaOH pretreatment is a convenient and effective method which is widely used in rice straw anaerobic digestion. But the mechanism of the alkaline (NaOH) hydrolysis of biopolymers compositions and polymeric cross-linked network structures ofrice straw cell wall need further study. This paper firstly studied the effect and mechanism of alkali pretreatment on anaerobic digestion and biogas production of rice straw by using a combination of confocal Raman microscopy and transmission electron microscope. First, the original rice straw and the rice straw pretreated by NaOH were taken for mapping scanning by confocal Raman microscopy withmicron-scale spatial resolution. Then principal component analysis was adoptedto extract main information of Raman spectra, it could be found that the two types of samples were respectively presented with ray-like distribution in the first two principal component space, which were with cumulative contribution of 99%. And there was a clear boundary between the two types of samples without any overlapping, indicating that there was a significant difference of Raman spectralcharacteristic between original rice leaf and rice leaf pretreated by NaOH. Further analysis of the loading weights of the first two principal components showed that the Raman peaks at 1 739, 1 508 and 1 094 cm-1 were the important bands, and these three Raman peaks were attributed to the scattering of hemicellulose, cellulose and lignin respectively. Following, chemical imaging analysisof hemicellulose, cellulose and lignin were achieved by combining these Raman peaks and microscopic image information. It could be found that the NaOH pretreatment resulted in a loss of dense spatial uniformity structure of tissue and great decreases of the contents of these three ingredients, particularly lignin. It can be concluded that it is feasible to non-destructively measure hemicellulose, lignin and cellulose in rice straw tissue by confocal Raman microscopy, and toachieve chemical imaging analysis of the three ingredients in tissue, and this research will be much help for revealing the promotion mechanism of NaOH pretreatment for the rice straw fermentation and biogas production.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2015; 35(3). DOI:10.3964/j.issn.1000-0593(2015)03-0657-06 · 0.27 Impact Factor
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    ABSTRACT: In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectraldata of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves’ spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then thosecharacteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of R C=0.970, R CV=0.965, R P=0.934, meanwhile evaluation parameters of NC predicted model were R C=0.857, R CV=0.806, R P=0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflectthe NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC inpepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2015; 35(3). DOI:10.3964/j.issn.1000-0593(2015)03-0746-05 · 0.27 Impact Factor
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    ABSTRACT: Nitrogen is a necessary and important element for the growth and development of fruit orchards. Timely, accurate and nondestructive monitoring of nitrogen status in fruit orchards would help maintain the fruit quality and efficient production of the orchard, and mitigate the pollution of water resources caused by excessive nitrogen fertilization. This study investigated the capability of hyperspectral imagery for estimating and visualizing the nitrogen content in citrus canopy. Hyperspectral images were obtained for leaf samples in laboratory as well as for the whole canopy in the field with ImSpector V10E (Spectral Imaging Ltd., Oulu, Finland). The spectral datas for each leaf sample were represented by the average spectral data extracted from the selected region of interest (ROI) in the hyperspectral images with the aid of ENVI software. The nitrogen content in each leaf sample was measured by the Dumas combustion method with the rapid N cube (Elementar Analytical, Germany). Simple correlation analysis and the two band vegetation index (TBVI) were then used to develop the spectra data-based nitrogen content prediction models. Results obtained through the formula calculation indicated that the model with the two band vegetation index (TBVI) based on the wavelengths 811 and 856 nm achieved the optimal estimation of nitrogen content in citrus leaves (R 2=0.607 1). Furthermore, the canopy image for the identified TBVI was calculated, and the nitrogen content of the canopy was visualized by incorporating the model into the TBVI image. The tender leaves, middle-aged leaves and elder leaves showed distinct nitrogen status from highto low-levels in the canopy image. The results suggested the potential of hyperspectral imagery for the nondestructive detection and diagnosis of nitrogen status in citrus canopy in real time. Different from previous studies focused on nitrogen content prediction at leaf level, this study succeeded in predicting and visualizing the nutrient content of fruit trees at canopy level. This would provide valuable information for the implementation of individual tree-based fertilization schemes in precision orchard management practices.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2015; 35(3). DOI:10.3964/j.issn.1000-0593(2015)03-0715-04 · 0.27 Impact Factor
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    ABSTRACT: This paper reports a combined experimental and numerical investigation of the pyrolysis characteristics of coal, biomass, and coal–biomass blends. Coal and straw were grounded and pressed into spherical particles with diameter of 8 mm, and blended coal–straw particles were prepared through mixing pulverized coal and straw before pressed into particles. Sample particles were suspended in the center of a single-particle reactor system and devolatilized under different temperatures. The analysis of the time history of the residual mass of particles of coal, straw, and coal–straw blends suggested an absence of synergistic effect between the coal and the straw. In addition, a one-dimensional, time-dependent particle model; based on the chemical percolation devolatilization (CPD) and bio-CPD models; was developed to simulate the pyrolysis of coal and straw particles. The model predictions agree will with the measured data. An extended CPD model was proposed to explain the co-pyrolysis characteristics of coal–biomass blends. Encouraging agreement was found between the predicted and the experimental results of pyrolysis of coal–straw blends.
    Fuel 01/2015; 139:356–364. DOI:10.1016/j.fuel.2014.08.069 · 3.41 Impact Factor
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    ABSTRACT: The intent of present work was to develop a valid method for detection of defective features in loquat fruits based on hyperspectral imaging. A laboratorial hyperspectral imaging device covering the visible and near-infrared region of 380–1,030 nm was utilized to acquire the loquat hyperspectral images. The corresponding spectral data were extracted from the region of interests of loquat hyperspectral images. The dummy grades were assigned to the defective and normal group of loquats, separately. Competitive adaptive reweighted sampling (CARS) was conducted to elect optimal sensitive wavelengths (SWs) which carried the most important spectral information on identifying defective and normal samples. As a result, 12 SWs at 433, 469, 519, 555, 575, 619, 899, 912, 938, 945, 970, and 998 nm were selected, respectively. Then, the partial least squares discriminant analysis (PLS-DA) model was established using the selected SWs. The results demonstrated that the CARS-PLS-DA model with the discrimination accuracy of 98.51 % had a capability of classifying two groups of loquats. Based on the characteristics of image information, minimum noise fraction (MNF) rotation was implemented on the hyperspectral images at SWs. Finally, an effective approach for detecting the defective features was exploited based on the images of MNF bands with “region growing” algorithm. For all investigated loquat samples, the developed program led to an overall detection accuracy of 92.3 %. The research revealed that the hyperspectral imaging technique is a promising tool for detecting defective features in loquat, which could provide a theoretical reference and basis for designing classification system of fruits in further work.
    Food and Bioprocess Technology 11/2014; 7(11). DOI:10.1007/s11947-014-1357-z · 3.13 Impact Factor
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    ABSTRACT: Background Acute rejection (AR) remains a life-threatening complication after orthotopic liver transplantation (OLT) and there are few available diagnostic biomarkers clinically for AR. This study aims to identify intestinal microbial profile and explore potential application of microbial profile as a biomarker for AR after OLT. Methods The OLT models in rats were established. Hepatic graft histology, ultrastructure, function, and intestinal barrier function were tested. Ileocecal contents were collected for intestinal microbial analysis. Results Hepatic graft suffered from the ischemia-reperfusion (I/R) injury on day 1, initial AR on day 3, and severe AR on day 7 after OLT. Real-time quantitative polymerase chain reaction results showed that genus Faecalibacterium prausnitzii and Lactobacillus were decreased, whereas Clostridium bolteae was increased during AR. Notably, cluster analysis of denaturing gradient gel electrophoresis (DGGE) profiles showed the 7AR and 3AR groups clustered together with 73.4% similarity, suggesting that intestinal microbiota was more sensitive than hepatic function in responding to AR. Microbial diversity and species richness were decreased during AR. Phylogenetic tree analysis showed that most of the decreased key bacteria belonged to phylum Firmicutes, whereas increased key bacteria belonged to phylum Bacteroidetes. Moreover, intestinal microvilli loss and tight junction damage were noted, and intestinal barrier dysfunction during AR presented a decrease of fecal secretory immunoglobulin A (sIgA) and increase of blood bacteremia, endotoxin, and tumor necrosis factor-α. Conclusion We dynamically detail intestinal microbial characterization and find a high sensitivity of microbial change during AR after OLT, suggesting that intestinal microbial variation may predict AR in early phase and become an assistant therapeutic target to improve rejection after OLT.
    Transplantation 10/2014; 98(8):844-852. DOI:10.1097/TP.0000000000000334 · 3.78 Impact Factor
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    ABSTRACT: Spectral unmixing is a critical issue in multi-spectral data processing, which has the ability to identify the constituent components of a pixel. Most of the hyperspectral unmixing current methods are based on Linear Mixture Model (LMM) and have been widely used in many scenarios. However, both the noise contained in the LMM and the requirement of essential prior knowledge strongly limit their practical applications. In order to address these issues, this paper proposes an iterative approach named CBIGMM. It utilizes infinite Gaussian mixture model to describe the hyperspectral data, which is robust to the noise due to the intrinsic randomness of the Gaussian components; and extracts the endmembers and their corresponding abundance in a fully unsupervised way without prior knowledge. A set of experiment is conducted on both synthetic and real data set from pesticide-contaminated vegetables. The results and analyses show CBIGMM outperforms other methods in addressing hyperspectral unmixing problem.
    Expert Systems with Applications 10/2014; DOI:10.1016/j.eswa.2014.09.059 · 1.97 Impact Factor
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    ABSTRACT: The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel x 15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1,002, 1,005, 1,015, 1,019, 1,022, 1,103, 1,106, 1,167, 1,237 and 1,409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavelengths was taken as the input variables to build effective wavelengths based models. The results indicated that the classification accuracy of the four models based on full-spectral were over 90%, the optimal models were extreme learning machine and random forest, and the classification accuracy achieved 100%. The classification accuracy of effective wavelengths based models declined slightly but the input variables compressed greatly, the efficiency of data processing was improved, and the classification accuracy of EW-ELM model achieved 100%. ELM performed well both in full-spectral model and in effective wavelength based model in this study, it was proven to be a useful tool for spectral analysis. So rapid and nondestructive recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning is feasible, and it provides a new method for on line batch variety recognition of Chinese cabbage seeds.
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    ABSTRACT: Visible and near infrared (Vis-NIR) hyperspectral imaging system was carried out to rapidly determinate the content and estimate the distribution of nitrogen (N) in oilseed rape leaves. Hyperspectral images of 420 leaf samples were acquired at seedling, flowering and pod stages. The spectral data of rape leaves were extracted from the region of interest (ROI) in the wave- length range of 380-1,030 nm. Different spectra preprocessing including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivatives were applied to improve the signal to noise ratio. Among 471 wavelengths, only twelve wavelengths (467, 557, 665, 686, 706, 752, 874, 879, 886, 900, 978 and 995 nm) were selected by successive projections algorithm(SPA) as the effective wavelengths for N prediction. Based on these effective wavelengths, partial least squares(PLS) and least-squares support vector machines (LS-SVM) calibration models were established for the determination of N content. Reasonable estimation accuracy was obtained, with Rp of 0.807 and RMSEP of 0.387 by PLS and Rp of 0.836 and RMSEP of 0.358 by LS-SVM, respectively. Considering the simple structure and satisfying results of PLS model, SPA-PLS model was used to generate the distribution maps of N content in rape leaves. The concentrations of N were calculated at each pixel of hyperspectral images at the selected effective wavelengths by inputting its correspond- ing spectrum into the established SPA-PLS model. Different colour represented the change in N content in the rape leaves under different fertilizer treatments. By including all pixels within the selected ROI, the average N status can be displayed in more detail and visualised. The visualization of N distribution could be helpful to understanding the change in N content in rape leaves during rape growth period and facilitate discovering the difference of N content within one sample as well as among the samples from different fertilising plots. The overall results revealed that hyperspectral imaging is a promising technique to detect N content and distribution within oilseed rape leaves rapidly and nondestructively.
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    ABSTRACT: Visible/near-infrared spectroscopy was applied to determine the content of catalase (CAT) and peroxidase (POD) in barley leaves under the herbicide stress of propyl 4-(2-(4, 6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate (ZJ0273). The spectral data of the barley leaves in the range of 500-900 nm were preprocessed by moving average with 11 points. Seven outlier samples for CAT and 8 outlier samples for POD were detected and removed by Monte Carlo-partial least squares (MCPLS). PLS, least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models were built for both CAT and POD. ELM model obtained best results for CAT, with correlation coefficient of calibration (Rc) of 0.916 and correlation co-efficient of prediction (Rp) of 0.786. PLS model obtained best prediction results for POD, with Rc of 0.984 and Rp of 0.876. Successive projections algorithm (SPA) was applied to select 8 and 19 effective wavelengths for CAT and POD, respectively. PLS, LS-SVM and ELM models were built using the selected effective wavelengths of CAT and POD. ELM model performed best for CAT and POD prediction, with Rc of 0.928 and Rp of 0.790 for CAT and Rp of 0.965 and Rp of 0.941 for POD. The prediction results using the full spectral data and the effective wavelengths were quite close, and the prediction performance for POD was much better than the prediction performance for CAT, and the studies should be further explored to build more precise and more robust models for CAT and POD determination. The overall results indicated that it was feasible to use visible/near-infrared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ0273.
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    ABSTRACT: Near-infrared hypserspectral imaging technology was applied for the discrimination of a variety of life states, the judgment of being alive or death. Discrimination models were built based on spectral data of pieris rapaes acquired during different life states. The wavelengths from 951.5 to 1 649.2 nm were used for analysis after the removal of spectral region with obvious noises at the beginning and the end. And the spectra data of 951.51 649.2 nm were preprocessed by different pretreatment methods. To discriminate the state of being alive or death of pieris rapaes, discrimination models were built based on the spectral data processed by different pretreatment methods. Results showed that the discriminant accuracy can approach or attain 100%. Thus the method was proved to be useful for the discrimination of the state of being alive or death of pieris rapaes. After the spectral data were preprocessed by moving average (MA) algorithm, 17 characteristic wavelengths were extracted based on weighted regression coefficient (B w) and 20 were extracted based on successive projections algorithm (SPA) to identify the state of being alive or death of pieris rapaes. Four classification methods based on characteristic wavelengths, including partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor algorithm (KNN), back propagation neural network (BPNN) and support vector machine (SVM) were used to build discriminant models for identifying the state of being alive or death of pieris rapaes. The discriminant accuracy all can approach or attain 100%.
    Guang pu xue yu guang pu fen xi = Guang pu 08/2014; 34(8). DOI:10.3964/j.issn.1000-0593(2014)08-2225-04 · 0.27 Impact Factor
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    ABSTRACT: Near-infrared spectroscopy combined with chemometrics was used to investigate the feasibility of identifying different brands of soymilk powder and the counterfeit soymilk powder products. For this purpose, partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA) and back-propagation neural network (BPNN) were employed as pattern recognition methods to class ify soymilk powder samples. The performances of different pretreatments of raw spectra were also compared by PLS-DA. PLS-DA models based on De-trending and multiplicative scatter correction (MSC)combined with De-trending(MSC+De-trending) spectra obtained best results with 100% prediction accuracy, respectively. Six and seven optimal wavenumbers selected by chi-loading weights of the best two PLS-DA models were used to build LDA and BPNN models. Results showed that BPNN performed best and correctly classified 100% of the soymilk powder samples for both the calibration and the prediction set. The overall results indicated that NIR spectroscopy could accurately identify branded and counterfeit soymilk powder products.
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    ABSTRACT: The feasibility of protein determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy (MIR) was studied in the present paper. Wavenumbers 3 581-689 cm(-1) were used for quantitative analysis of protein content after removing of the part of obvious noises. Five points Savitzky-Golay smoothing was applied to pretreat the MIR spectra and partial least squares (PLS) model was built based on the pretreated spectra. The full spectra PLS model obtained poor performance with the ratio of prediction to deviation (RPD) of only 1.77. Successive projections algorithm (SPA) was applied to select 7 sensitive wavenumbers from the full spectra, and PLS model, multiple linear regression (MLR), back-propagation neural network (BPNN) and extreme learning machine (ELM) model were built using the selected sensitive wavenumbers. SPA-PLS model and SPA-MLR model obtained relatively worse results than SPA-BPNN model and SPA-ELM model. SPA-ELM obtained the best results with correlation coefficient of prediction (R(p)) of 0.899 5, root mean square error of prediction (RMSEP) of 1.431 3 and RPD of 2.18. The overall results indicated that MIR combined with chemometrics could be used for protein content determination of shiitake mushroom, and SPA could select sensitive wavenumbers to build more accurate models instead of the full spectra.
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    ABSTRACT: This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROD inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.
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    ABSTRACT: Scanning positive column of atmospheric-pressure glow discharge (AGD) plasma is proposed as an eco-friendly and cost-effective strategy toward the instantaneous (2 s) reduction of graphene oxide (GO) paper, further leading to a reduced graphene oxide (rGO) paper with unimpeded liquid permeation for high performance supercapacitors. Unlike previous finding that AGD plasma has no effect on GO, a well-designed AGD plasma containing scanning positive column region is demonstrated to be capable of converting GO paper to rGO paper. With the synergy of energetic electrons (electron density 1.03 × 1016 cm–3) and surface touch heating (translational temperature 800 K), restoration of graphene’s excellent electrical properties is realized, evidenced by a substantially (5 orders of magnitude) improved electrical conductivity. Moreover, the ultrafast reduction of oxygen functionalities results in the creation of exposed open channels and the expansion of graphene layers, leading to a rGO paper supercapacitor electrode with unimpeded liquid permeation. Taking the above advantages, the as-obtained rGO paper exhibits obviously better capacitive and rate performance than the parent GO and chemically reduced rGO counterparts in terms of higher specific capacitance, better charge/discharge rate response, and faster ion diffusion, holding a great promise for energy storage applications.
    The Journal of Physical Chemistry C 06/2014; 118(25):13493–13502. DOI:10.1021/jp5037734 · 4.84 Impact Factor
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    ABSTRACT: Healthy tea and tea infected by anthracnose were first studied by confocal Raman microscopy to illustrate chemical changes of cell wall in the present paper. Firstly, Raman spectra of both healthy and infected sample tissues were collected with spatial resolution at micron-level, and ultrastructure of healthy and infected tea cells was got from scanning electron microscope. These results showed that there were significant changes in Raman shift and Raman intensity between healthy and infected cell walls, indicating that great differences occurred in chemical compositions of cell walls between healthy and infected samples. In details, intensities at many Raman bands which were closely associated with cellulose, pectin, esters were reduced after infection, revealing that the content of chemical compounds such as cellulose, pectin, esters was decreased after infection. Subsequently, chemical imaging of both healthy and infected tea cell walls were realized based on Raman fingerprint spectra of cellulose and microscopic spatial structure. It was found that not only the content of cellulose was reduced greatly after infection, but also the ordered structure of cellulose was destroyed by anthracnose infection. Thus, confocal Raman microscopy was shown to be a powerful tool to detect the chemical changes in cell wall of tea caused by anthracnose without any chemical treatment or staining. This research firstly applied confocal Raman microscopy in phytopathology for the study of interactive relationship between host and pathogen, and it will also open a new way for intensive study of host-pathogen at cellular level.
    Guang pu xue yu guang pu fen xi = Guang pu 06/2014; 34(6). DOI:10.3964/j.issn.1000-0593(2014)06-1571-06 · 0.27 Impact Factor
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    ABSTRACT: Propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy) benzylamino)benzoate (ZJ0273) is a new herbicide which inhibits acetolactate synthase (ALS). The ZJ0273 is considered as safe for the environment and exhibits a satisfactory effect on weed control in the rapeseed field. ALS is the key enzyme of reactions in the biosynthesis of total amino acids (TAAs) especially branched-chain amino acids (BCAAs). This study reports the effect of ZJ0273 on BCAAs and TAAs in rapeseed leaves using near-infrared spectroscopy (NIRS) techniques. A decrease in TAAs and BCAAs contents was observed as the herbicide dosages were increased along with leaf senescence. The wavelengths 2,416 and 1,340 nm were selected to develop the NIRS model for detecting BCAAs and TAAs, and correlation coefficients of model’s prediction set were 0.9823, 0.9764, 0.9831, and 0.9968 for valine, isoleucine, leucine, and TAAs, respectively. The results indicated that 100 mg/L ZJ0273 was a safe dosage for oilseed rape as it did not show a significant effect on the contents of amino acids compared to other higher dosages (500 and 1,000 mg/L).
    Acta Physiologiae Plantarum 06/2014; 36(8). DOI:10.1007/s11738-014-1591-z · 1.52 Impact Factor
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    ABSTRACT: Variations in syngas composition could bring a challenge for its combustion with both high efficiency and low emission. In this study, the effect of CO content on the laminar burning velocity of typical syngas was determined by the heat flux method and by kinetic simulations. For the 0% H2 in syngas, the laminar burning velocity increased monotonically with CO content until its maximum value and then dropped rapidly with further increase of CO content, while for the 25% H2 case, the laminar burning velocity increased almost linearly with CO content. Based on the kinetic simulations, consumption rate changes of CO and OH and the discrepancy of the heat release rate in the preheat zone contribute to these trends. At sufficient OH, the increase in the reaction rate between OH and CO corresponds to a faster heat release in the preheat zone, whereas at insufficient OH, oxidation of CO by OH is inhibited and the heat release process is delayed, decelerating the release rate and decreasing the laminar burning velocity.
    International Journal of Hydrogen Energy 06/2014; 39(17). DOI:10.1016/j.ijhydene.2014.03.216 · 2.93 Impact Factor
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    ABSTRACT: This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874-1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.
    PLoS ONE 05/2014; 9(5):e98522. DOI:10.1371/journal.pone.0098522 · 3.53 Impact Factor

Publication Stats

3k Citations
361.90 Total Impact Points

Institutions

  • 1970–2015
    • Zhejiang University
      • • State Key Lab of Clean Energy Utilization
      • • School of Biosystems Engineering and Food Science
      • • School of Medicine
      • • Department of Mathematics
      Hang-hsien, Zhejiang Sheng, China
  • 2013
    • Zhejiang Medical University
      • First Affiliated Hospital
      Hang-hsien, Zhejiang Sheng, China
    • Lund University
      • Division of Combustion Physics
      Lund, Skåne, Sweden
  • 2009
    • Hangzhou Normal University
      Hang-hsien, Zhejiang Sheng, China
    • Zhejiang University of Technology
      Hang-hsien, Zhejiang Sheng, China
  • 2008
    • Xiamen University
      • Department of Physics
      Xiamen, Fujian, China
    • Zhejiang Technical Institute of Economics
      Hang-hsien, Zhejiang Sheng, China
  • 2004
    • University of Pannonia, Veszprém
      • Department of Mathematics
      Gyulafirátót, Veszprém, Hungary