MiRNA-506 presents multiple tumor suppressor activities by targeting EZH2 in nasopharyngeal carcinoma.

OBJECTIVE: MiR-506 has been reported to be associated with multiple malignancies, but its roles in nasopharyngeal cancer (NPC) are not fully understood. Our objective is to demonstrate its effects on NPC and the underlying mechanisms. METHODS: Totally fifteen pairs of NPC and adjacent non-tumorous tissues were collected for the detection of miR-506 and enhancer of zeste homolog 2 (EZH2) expression. Dual luciferase reporter assay was employed for verifying the relationship between miR-506 and EZH2. The flow cytometry and MTT assays were employed to explore the effects of miR-506 and EZH2 on the cell apoptosis and proliferation, respectively. Wound closure and transwell assays were used to evaluate the cell migration and invasion abilities. Western blotting or RT-qPCR assays were applied to detect the alterations of miR-506, EZH2 and epithelial-mesenchymal transition (EMT)-related markers. Morphological changes of cells with EMT were assessed by light microscopy. RESULTS: MiR-506 was significantly decreased and EZH2 was obviously increased in NPC tissues. Overexpression of miR-506 decreased the EZH2 level, promoted apoptosis, inhibited proliferation, invasion and migration of NPC cells. Accordingly, miR-506 overexpression attenuated EMT process of NPC cells as demonstrated by the alterations of EMT-related markers and the morphological changes. In addition, the luciferase assay proved that miR-506 directly targeted EZH2. Furthermore, the overexpression of EZH2 reversed the tumor-suppressive effects induced by miR-506 mimics. CONCLUSION: MiR-506 acted as a tumor suppressor to promote apoptosis and inhibit invasion and migration via directly targeting EZH2. MiR-506 can be a candidate target for gene therapy against NPC.

Two-Dimensional Excitonic Metal-Organic Framework: Design, Synthesis, Regulation, and Properties.

A family of two-dimensional (2D) Zn-based metal-organic frameworks (MOFs) with exitonic emission have been successfully synthesized under hydrothermal conditions. When isophthalic acid ligands with different substitutions are introduced, the crystal structures and fluorescence properties are significantly changed. Hirshfeld surface calculation is used to study the nuances of diverse substitutions during the construction of all of the crystals. The solid fluorescence results indicate that there are obvious two-channel emissions, including intralayer excimers and interlayer trapped excitons, both in 1 and 2 with a double-layer structure and in 3 with a single-layer structure, mainly exhibiting intralayer emission. Furthermore, the fluorescence changes on morphology transformations are explored after mechanical exfoliation consisting of grinding and ultrasonicaation of MOFs 1-3. The regulation and control of crystal structure and morphology can suppress emission based on interlayer excitons, achieving adjustment of the overall emitting color. To the best of our knowledge, this is the first report of 2D bilayer MOFs with dual-channel emissions, which provides a new structural model for synthesizing new exciton materials.

[Hyperspectral Technique for Estimating the Shelf-Lives of Fresh Spinach Leaves Covered with Film].

In order to prolong the shelf-life of fruits and vegetables, plastic films have been covered on them to improve water retention and keep external bacteria away. It is of great significance to estimate the quality of packaged fruits and vegetables accurately by predicting the shelf-life of them. In this research, hyperspectral technology combined with chemometric methods were employed to estimate the shelf-life of fresh spinach leaves in the same environment. Hyperspectral data covering the range of Vis-NIR (380～1 030 nm) and NIR (874～1 734 nm) were acquired from 300 spinach leaves (75 dishs) which were stored in 4 ℃ among 5 periods (0 d, 2 d, 4 d, 6 d, 8 d). Meanwhile, the chlorophyll contents of all spinach leaves were determined. The mean spectra of 300 spinach leaves (200 leaves in training set and 100 leaves in prediction set) were extracted. And then, principal component analysis (PCA) on the training set of 200 spectra from 5 periods of shelf-life displayed apparent cluster. Partial least-squares discriminant analysis (PLS-DA) models were established according to spectral datas and the virtual levels that we ascribed to the different storage periods previously. The total discriminant accuracy rates of prediction set were 83% (VIS-NIR) and 81% (NIR), respectively. The result indicated that the classification and prediction on the shelf-life of fresh spinach can be realized with hyperspectral technology combined with chemometric methods, which offered a theoretical guidance to evaluate the quality of packaged spinach for consumers, and provided technical supports for the development of instruments used for testing the shelf-life of fruits and vegetables in further study.

[Discriminate the Rape Sclerotinia at Early Stage Based on Confocal Raman Spectroscopy].

Oilseed rape(Brassica napus L. ) is a principal source of edible oil for human consumption and it feeds livestock as a by product with high energy and protein. However, oilseed plants often suffer from the invasion of various diseases, which could affect the yield and quality of the rapeseeds. Rape sclerotinia rot caused by the fungus sclerotinia sclerotiorum (Lib. ) de Bary may severely affect the growth of oilseed rape. Therefore, searching non-invasive detection methods of detection plant disease at early stage is crucial for monitoring growing conditions of crops. Confocal Raman spectroscopy in the region of 500～2 000 cm(-1) coupled with chemometrics methods were employed to discriminate the rape sclerotinia disease at early stage on the oilseed rape leaves. A total of 60 samples(30 healthy plant leaves and 30 infected leaves) were used to acquire the Raman spectra and wavelet transform was applied to remove the fluorescence background. Regression coefficients of the partial least squares-discriminant analysis(PLS-DA) were used to select the 8 characteristic peaks based on the whole Raman spectra. 983，1 001, 1 205, 1 521, 1 527, 1 658, 1 670 and 1 758 cm(-1) were employed to establish PLS-DA discriminate models and recognition accuracy was 100%. The results showed Raman spectra combined with chemometrics method is promising for detecting rape sclerotinia infection in the oilseed rape leaves at early stage. This study provided a theoretical reference for researching the interaction between the fungus and plants and early detecting of disease infection.

Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging.

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

[Study on the Visualization of the Biomass of Chlorella sp., Isochrysis galbana, and Spirulina sp. Based on Hyperspectral Imaging Technique].

Effective cultivation of the microalgae is the key issue for microalgal bio-energy utilization. In nutrient rich culture conditions, the microalge have a fast growth rate, but they are more susceptible to environmental pollution and influence. So to monitor the the growth process of microalgae is significant during cultivating. Hyperspectral imaging has the advantages of both spectra and image analysis. The spectra contain abundant material quality signal and the image contains abundant spatial information of the material about the chemical distribution. It can achieve the rapid information acquisition and access a large amount of data. In this paper, the authors collected the hyperspectral images of forty-five samples of Chlorella sp., Isochrysis galbana, and Spirulina sp., respectively. The average spectra of the region of interest (ROI) were extracted. After applying successive projection algorithm (SPA), the authors established the multiple linear regression (MLR) model with the spectra and corresponding biomass of 30 samples, 15 samples were used as the prediction set. For Chlorella sp., Isochrysis galbana, and Spirulina sp., the correlation coefficient of prediction (r(pre)) are 0.950, 0.969 and 0.961, the root mean square error of prediction (RMSEP) for 0.010 2, 0.010 7 and 0.007 1, respectively. Finally, the authors used the MLR model to predict biomass for each pixel in the images of prediction set; images displayed in different colors for visualization based on pseudo-color images with the help of a Matlab program. The results show that using hyperspectral imaging technique to predict the biomass of Chlorella sp. and Spirulina sp. were better, but for the Isochrysis galbana visualization needs to be further improved. This research set the basis for rapidly detecting the growth of microalgae and using the microalgae as the bio-energy.

[Study on Soil Elements Detection with Laser-Induced Breakdown Spectroscopy: A Review].

Laser-induced breakdown spectroscopy (LIBS), as a kind of atomic emission spectroscopy, has been considered to be a future new tool for chemical analysis due to its unique features, such as no need of sample preparation, stand-off or remote analysis. What's more it can achieve fast and multi-element analysis. Therefore, LIBS technology is regarded as a future "SurperStar" in the field of chemical analysis and green analytical techniques. At present, rapid and accurate detection and prevention of soil contamination (mainly in pollutants of heavy metals and organic matter) is deemed to be a concerned and serious central issue in modern agriculture and agricultural sustainable development. In this paper, the reseach achievements and trends of soil elements detection based on LIBS technology were being reviewed. The structural composition and foundmental of LIBS system is first briefly introduced. And the paper offers a review of on LIBS applications and fruits including the detection and analysis of major element, nutrient element and heavy metal element. Simultaneously, some studies on soil related metials and fields are briefly stated. The research tendency and developing prospects of LIBS in soil detection are presented at last.

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil.

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil.

Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease.

Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380-1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS.

The effect of temperature on the biodegradation properties of municipal solid waste.

The aim of this study is to analyse the effect of temperature on the biodegradation and settlement properties of municipal solid waste by using bioreactors. Three kinds of controlled temperature were performed during the biodegradation test; the variation of weight, leachate and biogas production were carefully monitored. The degradation test indicated that more leachate leaked out owing to the external compression and polymer hydrolysis reaction in the aerobic phase, which could lead to the decrease of biodegradation rate in the anaerobic phase. A proper temperature range in favour of enhancing biodegradation of refuse was obtained, which ranged from 22 °C to 45 °C. Finally, an empirical equation of biodegradation ratio was proposed, which incorporated the temperature effect. In the end, the validation of this proposed model is verified, and is proved to be reasonable for predicting degradation velocity in landfills.

[Study on the Nondestructive Detection Methods for Dynamica Change of Lipid Content in Chlorella sp.].

Microalgae based biodiesel production requires a large amount of lipid accumulation in the cells, and the accumulation is greatly influenced by the environment. Therefore, it is necessary to find fast and non-destructive methods for lipid change detection. In this paper, Chlorella sp. was adopted as the objective, which was cultured under different light condition consisted of red and blue lights with different proportion. We applied the visible near-infrared spectroscopy (Vis/NIRs) technique to detect the dynamic change of lipid during the microalgae growth processes and utilized hyperspectral imaging technology for visualization of lipid distribution in the suspension. The transmittance and reflectance spectra of microalgae were acquired with Vis/NIRs and hyper-spectroscopy, respectively. In the comparison of the transmittance and reflectance spectra, they showed some different characteristics. Meanwhile it also varied in terms of the number and the area of feature wavelengths obtained by successive projections algorithm (SPA) based on the different spectra. But the established multiple linear regression (MLR) model for lipid content prediction had similar results with rpre of 0.940, RMSEP of 0.003 56 and rpre of 0.932, RMSEP of 0.004 23, respectively. Based on the predictive model, we obtained the spectra and analyzed the lipid dynamic change in microalgae in one life cycle. In the life cycle, the lipid content in Chlorella sp. was relatively stable from the beginning of inoculation to exponential phase, the increase and accumulation of lipid phenomenon occurred in the late exponential phase. Combined with the MLR model and the hypersepctral images, we studied the visualization result of microalgae suspension in the steady phase. The stimulated images showed that the microalgae with higher lipid content appeared gathering. This study compared the difference and the feasibility of the Vis/NIRs and hyperspectral imaging technique in lipid content detection applied in microalgae growing microalgae. The results are meaningful for the fast and non-destructive detection of the growth information of microalgae. It has boththeoretical and practical significance for developing microalgal culture and harvest strategy in practice.

[Study on Non-Detective Determination of ß-Carotene Content in Loquats with Raman Spectroscopy].

In china, researches on Raman spectroscopy in terms of foodstuff mainly focus on carbohydrates, fatty acids, proteins and vitamins. Conventional methods for determining the carotenoids content require the extraction of the samples as well as other cleanup steps. In this work, Raman spectroscopy is applied to get the measured value form loquats with different mature stage which are compared with the reference value get from High Performance Liquid Chromatography (HPLC)，in order to find new, fast, and nondestructive calibration methods for quantification of ß-carotene content in loquat fruits. Least Squares Support Vector Machine and Partial least squares data processing methods are used to analyze the Raman spectra while PLS model has a prediction quality with the correlation coefficient of 0.845; the root-mean-square error of 0.022 µg·g(-1) and LS-SVM model has a better prediction quality with the correlation coefficient of 0.910 with the root-mean-square error of 0.058 µg·g(-1).

Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries.

Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380-1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.

[Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions].

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 hyperspectral data 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 those characteristic 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 reflect the 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 in pepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.

[Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect feature of 86 Hawthorn could be recognized. Lastly, the detect precision of bruised, insect damage and two-defect samples is 95.65%, 86.67% and 100%, respectively. This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise, insect damage, calyx, and stem-end in hawthorn fruit in qualitative analysis and feature detection which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant.

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

Application of microwave digestion/AAS in detecting crankshaft bearing knock.

The present study was carried out to detect crankshaft bearing knock using atomic absorption spectrometry (AAS) in an innovative way. Lubricating oil of MAGOTAN 2.0 with mileage of 1000-28000Km and sampling interval of 1000Km changed into atomic vapor in the heat after digesting with microwave. Hollow --cathode lamp made of the same element with metal content under test would radiate characteristic radiation with certain wavelength. A part of atomic vapor was launched with ground state atom after heating with graphite furnace. Concentration-absorbance working curve was finished with standard series sample after absorbance was measured. Finally, element content under test in oil was obtained based on the work curve. Database of primary element (Cu and Pb) content of lubricating oil in the same engine with different mileage was established. Results showed that Cu, Pb content fluctuates with different mileage in a certain range. In practical engineering applications, primary metal content in lubricating oil of engine crankshaft bearing was measured and compared with content variation trend chart. This new method not only helps automobile maintenance personnel to diagnose crankshaft bearing knock under no-disintegration situation but also is benefit for reducing the maintenance cost of automobile greatly and improving diagnostic accuracy of crankshaft bearing knock.

[Study on SPAD visualization of pumpkin leaves based on hyperspectral imaging technology].

Visible/near-infrared (380 approximately 1 030 nm) hyperspectral imaging technique was used to realize SPAD visualization of pumpkin leaves in the present study. Downy mildew could be diagnosed rapidly according to significant positive correlation between downy mildew epidemic and chlorophyll content. Leaves uninfected and infected with different level downy mildew were used to acquire hyperspectral images and extract spectral information. Competitive adaptive reweighted sampling (CARS) was applied to select optimal wavelengths and finally 10 optimal wavelengths were obtained. Partial least squares regression (PLSR) was employed to establish SPAD prediction model. Results showed that, through the analysis of calibration of 48 samples and prediction of 23 samples, CARS-PLSR could obtain good results with Rc= 0. 918, RMSECV= 3. 932; Rcv- 0. 846, RMSECV = 5. 254; Rp = 0. 881, and RMSEP= 3. 714. Regression model was gained based on the relationship between SPAD and spectral of pumpkin leaves. While SPAD of each pixel was calculated with PLSR regression equation, then SPAD distribution map of pumpkin was visualized using imaging processing technology. Final downy mildew infection could be diagnosed based on SPAD distribution map. This study provided a theoretical reference for effective monitoring plant growth and downy mildew epidemic.

A study of shear strength properties of municipal solid waste in Chongqing landfill, China.

The aim of this study is to analyze the effect of biodegradation on the shear strength of municipal solid waste (MSW), leachate, and biogas production. The direct shear (DS) test shows that the shear strength of waste in the initial stages is mainly depended on its composition and inter-structure. After the waste has been in a landfill for 30 days, the waste's increased biodegradation exhibited a great influence on the waste's shear strength. The increase of moisture content in the waste mass might cause a decrease of its shear strength. Tri-axial tests under consolidation-drained (CD) condition show that the shear strength of the cohesion and friction angle for degraded samples increased when the defined axial strain increased from 5 to 20 %. The cohesion varied from 35.90 to 66.42 kPa and the drained friction angle ranged between 29° and 38°. The measurements of shear strength properties are utilized to assess the slope stability of landfills.

[Study on identification the crack feature of fresh jujube using hyperspectral imaging].

Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.