Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture

Qinzhou, China

Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture

Qinzhou, China
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Shen F.,Zhejiang University | Shen F.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | Wu J.,Shaoxing Testing Institute of Quality Technical Supervision | Ying Y.,Zhejiang University | And 3 more authors.
Food Chemistry | Year: 2013

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. © 2013 Elsevier Ltd. All rights reserved.


Zhu F.,Zhejiang University | Zhang H.,Zhejiang University | Shao Y.,Zhejiang University | He Y.,Zhejiang University | And 2 more authors.
Food and Bioprocess Technology | Year: 2014

A nondestructive and rapid method using near-infrared (NIR) hyperspectral imaging was investigated to determine the spatial distribution of fat and moisture in Atlantic salmon fillets. Altogether, 100 samples were studied, cutting out from different parts of five whole fillets. For each sample, the hyperspectral image was collected with a pushbroom NIR (899-1,694 nm) hyperspectral imaging system followed by chemical analysis to measure its reference fat and moisture contents. Mean spectrum were extracted from the region of interest inside each hyperspectral image. The quantitative relationships between spectral data and the reference chemical values were successfully developed based on partial least squares (PLS) regression with correlation coefficient of prediction of 0.93 and 0.94, and root mean square error of prediction of 1.24 and 1.06 for fat and moisture, respectively. Then the PLS models were applied pixel-wise to the hyperspectral images of the prediction samples to produce chemical images for visualizing fat and moisture distribution. The results were promising and demonstrated the potential of this technique to predict constituent distribution in salmon fillets. © 2013 Springer Science+Business Media New York.


Xu X.,Zhejiang University | Xu X.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | Liu X.,Zhejiang University | Liu X.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | And 4 more authors.
Biosensors and Bioelectronics | Year: 2013

This report illustrates a promising one-step and label-free optical biosensor for determination of aflatoxin B1 (AFB1) that is most commonly found in foods and highly dangerous even at very low concentrations. In this research, gold nanorods (GNRs) were employed as a sensing platform, which showed high stability under high ionic strength conditions without addition of any stabilizing agent. GNR-AFB1-BSA (bovine serum albumin) conjugates aggregated after mixing with free antibodies, resulting in significant changes in absorption intensity. At the same time the existence of AFB1 molecules in samples caused dispersion of nanorods, as a result of competitive immune-reaction with antibodies. By taking advantages of the competitive dispersion of GNRs, the developed method could effectively reduce false results caused by undesirable aggregation, which is a big problem for spherical gold nanoparticles. Absorption intensity of UV-vis spectra served as the sensing indicator, with dynamic light scattering (DLS) measurement as another sensing tool. The designed biosensing system could detect AFB1 in a linear range from 0.5 to 20ngmL-1, with a good correlation coefficient of 0.99. And the limit of detection (LOD) was 0.16ngmL-1, indicating an excellent sensitivity with absorbance result. The recoveries of the spiked AFB1 in real peanut samples ranged from 94.2% to 117.3%. Therefore the proposed nano-biosensor was demonstrated to be sensitive, selective, and simple, providing a viable alternative for rapid screening of toxins in agriculture products and foods. © 2013 Elsevier B.V.


Yu K.,Zhejiang University | Zhao Y.,Zhejiang University | Li X.,Zhejiang University | Li X.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | And 5 more authors.
Computers and Electronics in Agriculture | Year: 2014

Detection of crack defect in fresh jujube is a critical process to guarantee jujube quality and meet processing demands of fresh jujube fruit. This study presented a novel method for identification of fresh jujube crack feature using hyperspectral imaging in visible and near infrared (Vis/NIR) region (380-1030. nm) combined with image processing. Hyperspectral image data of samples were used to extract the characteristic wavebands by chemometrics, which integrated the method of partial least squares regression (PLSR), principal component analysis (PCA) of spatial hyperspectral image (SPCA) and independent component analysis (ICA) of spatial hyperspectral image (SICA). On the basis of the selected wavebands, least-squares support vector machine (LS-SVM) discrimination models were established to correctly distinguish between cracked and sound fresh jujube. The performance of discriminating model was evaluated using receiver operating characteristics (ROC) curve analysis. The results demonstrated that PLSR-LS-SVM discrimination model with a high accuracy of 100% had the optimal performance of "area"=1 and "std"=0. For acquiring rich crack feature information, SPCA was also carried on images at the five characteristic wavebands (467, 544, 639, 673 and 682. nm) selected by PLSR. Finally, the SPC-4 image was explored to identify the location and area of crack feature through a developed image processing algorithm. The results revealed that hyperspectral imaging combined with image processing technique could achieve the rapid identification of crack features in fresh jujube. © 2014 Elsevier B.V.


Tang W.,Zhejiang University | Tang W.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | Wu J.,Zhejiang University | Wu J.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture
Analytical Methods | Year: 2014

An amperometric method for the detection of organophosphorus pesticide based on acetylcholinesterase inhibition is reported. A silver electrode is used and shows an inherent advantage in determination of pesticides, which can monitor the activity of the enzyme in real time by electrochemical oxidization of thiocholine at low potential. Cyclic voltammetry and amperometry were used to evaluate the performance of silver electrode for thiocholine detection. Spectrophotometry was conducted to optimize the parameters for the enzymatic reaction. Cyclic voltammetry shows the advantages of silver over others (platinum, glassy carbon and gold) electrodes, when detecting thiocholine at low potentials. The silver electrode could linearly respond to thiocholine in the range from 5.2 × 10-7 to 2.6 × 10-5 M at 0.08 V. The measurement displayed a detection limit of 6.2 ppb. The analytical feasibility was investigated further via determination of paraoxon in Chinese cabbages and Fuji apples, which shows recovery rates of 92.05% and 106.11%, respectively. The results indicate the capability of the proposed method for pesticides monitoring in real samples. © 2014 The Royal Society of Chemistry.


Fan K.,Zhejiang University | Fan K.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | Wu J.,Zhejiang University | Wu J.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture
Analytical Methods | Year: 2013

A new carbon paste electrode based on the use of an ionic liquid as the binder is introduced. The composite electrode consists of graphite and n-octylpyridinium hexafluorophosphate (an ionic liquid). The behavior of the n-octylpyridinium hexafluorophosphate carbon paste electrode (OPFP-CPE) towards [Fe(CN)6]3-/4- was tested. This novel electrode has conductivity similar to glassy carbon electrode (GCE). It shows low background current and well defined voltammograms for the [Fe(CN)6] 3-/4-. The oxidation of nitrite was studied on the OPFP-CPE in aqueous solution using cyclic voltammetry (CV), differential pulse voltammetry (DPV), and chronoamperometry (CA). The proposed method was used for determination of nitrite in food samples (ham sausage) containing nitrite. Nitrite can be determined in the ranges of 1.0 × 10-4 to 1.0 × 10-3 M by CV, 1.0 × 10-6 to 2.0 × 10-4 M by DPV and 7.6 × 10-6 to 1.4 × 10 -4 M by CA, with the detection limits of 3.0 × 10-5 M, 1.0 × 10-7 M, and 4.0 × 10-7 M (calculated by 3σ) for CV, DPV, and CA, respectively. The recovery of spiked nitrite to the ham sausage extract was from 90.00% to 109.76%. © 2013 The Royal Society of Chemistry.


Fu X.,Zhejiang University | Fu X.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | Ying Y.,Zhejiang University | Ying Y.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture
Critical Reviews in Food Science and Nutrition | Year: 2016

In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food hazards, food authentication, and traceability. Near infrared (NIR) spectroscopy and imaging techniques have gained wide acceptance in many fields because of their advantages over other analytical techniques. Following a brief introduction of NIR spectroscopy and imaging basics, this review mainly focuses on recent NIR spectroscopy and imaging applications for food safety evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; (3) physical hazards detection; (4) new technology-induced food safety concerns; and (5) food traceability. The review shows NIR spectroscopy and imaging to be effective tools that will play indispensable roles for food safety evaluation. In addition, on-line/real-time applications of these techniques promise to be a huge growth field in the near future. © 2016, Copyright © Taylor & Francis Group, LLC.


Xiang R.,Zhejiang University | Xiang R.,China Jiliang University | Ying Y.,Zhejiang University | Ying Y.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2013

As one of the key problem for the robotic harvesting, the real-time recognition and localization of fruits and vegetables in field have drawn wide attention. First, the development of common hardware and algorithms for the recognition of fruits and vegetables was reviewed. For the image segmentation of fruits and vegetables, researches on image preprocessing, color feature selecting, image segmentation designing and image post processing were summarized and analyzed. For the recognition of fruits and vegetables, researches on common algorithms, recognition algorithms for clustered objects and for occluded objects were reviewed. Second, active and passive range finder methods which have been used commonly in the localization of fruits and vegetables were analyzed and compared. Moreover, stereo matching methods used in the passive range finder methods for fruits and vegetables were analyzed and compared, too. Finally, existing problems in researches on the recognition and localization for fruits and vegetables in field were analyzed, and research prospects were also presented.


Zhang X.,Zhejiang University | Liu F.,Zhejiang University | He Y.,Zhejiang University | He Y.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture | And 2 more authors.
Sensors (Switzerland) | Year: 2012

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380-1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. © 2012 by the authors; licensee MDPI, Basel, Switzerland.


Zhang X.,Zhejiang University | He Y.,Zhejiang University | He Y.,Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture
Industrial Crops and Products | Year: 2013

In this study we developed a technique to early and rapidly estimate seed yield using hyperspectral images of oilseed rape leaves in the visible and near infrared (VIS-NIR) region (380-1030. nm). Hyperspectral images of leaves were acquired four times from field trials in China between seedling until pods stage. Seed yield data on individual oilseed rape plants were collected during the local harvest season in 2011. Partial least square regression (PLSR) was applied to relate the average spectral data to the corresponding actual yield. We compared four PLSR models from four growing stages. The best fit model with the highest coefficients of determination (RP2) of 0.71 and the lowest root mean square errors (RMSEP) of 23.96 was obtained based on the hyperspectral images from the flowering stage (on March 25, 2011). The loading weights of this resulting PLSR model were used to identify the most important wavelengths and to reduce the high dimensionality of the hyperspectral data. The new PLSR model using the most relevant wavelengths (543, 686, 718, 741, 824 and 994. nm) performed well (RP2=0.71, RMSEP = 23.72) for predicting seed weights of individual plants. These results demonstrated that hyperspectral imaging system is promising to predict the seed yield in oilseed rape based on its leaves in early growing stage. © 2012 Elsevier B.V..

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