Author
Shouguo Zheng
Bio: Shouguo Zheng is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Breakdown voltage & Single-mode optical fiber. The author has an hindex of 3, co-authored 10 publications receiving 33 citations.
Papers
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TL;DR: In this paper, the authors investigated the advanced application of Raman spectroscopy (RS) and surface-enhanced RAMS (SERS) in the detection of plant diseases, which can effectively prevent the development and spread of diseases and ensure the agricultural yield.
Abstract: Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.
30 citations
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TL;DR: The ability to manufacture a reference tapered fiber lens with high symmetry at sub-wavelength scale with a wide range of geometry control is demonstrated, either for the length from several hundred nanometers to several hundred microns, or for the curvature radius on the endface of a single mode fiber.
Abstract: In numerous applications of optical scanning microscopy, a reference tapered fiber lens with high symmetry at sub-wavelength scale remains a challenge. Here, we demonstrate the ability to manufacture it with a wide range of geometry control, either for the length from several hundred nanometers to several hundred microns, or for the curvature radius from several tens of nanometers to several microns on the endface of a single mode fiber. On this basis, a scanning optical microscope has been developed, which allows for fast characterization of various sub-wavelength tapered fiber lenses. Focal position and depth of microlenses with different geometries have been determined to be ranged from several hundreds of nanometers to several microns. FDTD calculations are consistent with experimental results.
14 citations
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TL;DR: In this paper, the authors developed an approach for the fast and accurate determination of organophosphate pesticides by combining surface-enhanced Raman scattering (SERS) technology with chemometric methods.
Abstract: We have developed an approach for the fast and accurate determination of organophosphate pesticides by combining surface-enhanced Raman scattering (SERS) technology with chemometric methods. We measured the SERS spectra of three pesticides (methyl parathion, edifenphos and ethyl paraxon) and then processed the spectra using the following pre-processing algorithms: baseline subtraction; Savitzky–Golay first derivative, standard normal variate variance (SNV) transformation and multiplicative scatter correction. Principal components analysis and non-negative matrix factorization were subsequently adopted to obtain the main features of the spectra. The data were then used to develop classification models by support vector machines and random forest (RF) regression and the discrimination performance was evaluated through a five-fold cross-validation method. The experiments showed that the baseline subtraction method can perfectly eliminate the fluorescence background and baseline drift. Principal components analysis greatly shortens the training time on the premise of maintaining classification accuracy, although non-negative matrix factorization gives poorer results. SNV can improve the discriminant accuracy up to 4%, except for the use of non-negative matrix factorization, but multiplicative scatter correction and the first derivative method have a negative effect. The classification model of the highest accuracy (99.79%) is built by support vector machines with the spectra processed by SNV and principal components analysis; and the training process takes 1115 s. The training time (81 s) of the RF model developed with the spectra processed by SNV is much shorter than the former method, although the accuracy is similar. The classification accuracy of the model built with RF and different data always maintains a high level, suggesting that RF has excellent robustness. These results show that the most suitable method for the determination of organophosphate pesticides is a combination of SERS spectrometry and RF with SNV.
12 citations
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TL;DR: In this paper, the authors explore a novel and rapid route for fabricating silver nanoparticles on the distal facet of an optical fiber, which relies on the reaction between silver ions and organic free radicals initiated by the photolysis of photoinitiators.
Abstract: We explore a novel and rapid route for fabricating silver nanoparticles on the distal facet of an optical fiber. The reduction of neutral silver particles relies on the reaction between silver ions and organic free radicals initiated by the photolysis of photoinitiators. A similar approach has been extended to the fabrication of a silver nanoparticle array at the extremity of a fiber bundle. The size and number of silver nanoparticles can be tuned by controlling the laser exposure doses. The results of the synthesis can be envisaged via in situ measurement of their optical transmission rate after the photosynthesis process.
5 citations
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TL;DR: In this paper, self-assembly monolayers of faceted gold nanocrystals were employed as surface-enhanced Raman scattering (SERS) substrates for the examination of facet-dependent SERS performance.
Abstract: In this work, self-assembly monolayers of faceted gold nanocrystals were employed as surface-enhanced Raman scattering (SERS) substrates for the examination of facet-dependent SERS performance. In ...
59 citations
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01 Nov 2018
51 citations
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TL;DR: In this paper , the authors present modern advances in early plant disease detection based on hyperspectral remote sensing, identifying current gaps in the methodologies of experiments and a further direction for experimental methodological development is indicated.
Abstract: The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
45 citations
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TL;DR: A novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.
38 citations
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TL;DR: Using the evaluation framework, appropriate schemes can be found for several datasets, reducing the root mean square error of prediction (RMSEP) by 50%–60% compared with using the raw spectrum.
Abstract: Spectrum preprocessing is an essential component in the near‐infrared (NIR) calibration. However, it has mostly been configured arbitrarily in the literature and calibration applications. In this paper, a systematic evaluation framework was proposed to quantify the effect of preprocessing, where repeated cross‐validation and evaluation are involved. As many as 108 preprocessing schemes were gathered from the literature and were tested on 26 different NIR calibration problems. Using the evaluation framework, appropriate schemes can be found for several datasets, reducing the root mean square error of prediction (RMSEP) by 50%–60% compared with using the raw spectrum. However, the influence of preprocessing is highly data‐dependent, and no universal solution could be found. Taking the effectiveness and correlation into consideration, Savitzky‐Golay (SG), SG1D, and SG1D + vector normalization (VN)(/standard normal variate [SNV]) are worth testing first. Nevertheless, the heterogeneity at both the dataset level and sample level demonstrated the necessity of a complete evaluation. Our scripts are available at https://github.com/jiaoyiping630/spectrum-preprocessing.
33 citations