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Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496–1000 nm with coefficients of determination (R2) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R2 of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Near-infrared hyperspectral imaging together with versatile chemometric algorithms including invasive weed optimization (IWO) were employed for optimizing fast classification of bacterial colonies on agar plates. Hyperspectral images of colonies from six strains of bacteria were collected, and classification models were established by applying partial least squares-discriminant analysis and support vector machine (SVM) on the original as well as difference spectra. The parameters of SVM models were optimized by comparing genetic algorithm, particle swarm optimization and the proposed IWO. The results showed that difference spectra amplified the variations among the spectra of the six strains thus potential for improving classification accuracy. The best full wavelength classification model was IWO-SVM model which produced overall correct classification rates (OCCRs) of 100.0% and 97.0% for calibration and prediction, respectively. Besides, competitive adaptive reweighted sampling (CARS), GA and successive projections algorithm (SPA) were utilized to select important wavelengths to establish simplified models. Among them, the simplified IWO-SVM model based on the feature wavelengths selected by CARS gave the best classification rates of 97.2% and 96.0% for calibration and prediction, respectively. The study demonstrated that IWO was a useful tool for optimizing calibration models thus potential for usage in many other applications.

The potential of near-infrared spectroscopy in classifying individual bacterial strains from different species was investigated in this study. Bacterial samples in liquid nutrient culture were collected periodically (0, 6 and 12 h) during incubation and their spectra were acquired in the near-infrared (NIR) range of 1000–2500 nm. Spectral transforms, including absorbance (A), transmittance (T) and Kubelka-Munk (KM) units were explored in order to enhance classification performance. Partial least squares discriminant analysis (PLS-DA), radial basis function neural network (RBF) and support vector machine (SVM) were used in classification model development. The results illustrated that nonlinear methods such as SVM and RBF neural network outperformed PLS-DA, where the overall correct classification rates (OCCRs) were both above 96%. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and random forest (RF) were employed to reduce spectral redundancy and to identify important wavelengths for simplifying models. The RF model yielded the best predictions as indicated by the shortest modeling time and the excellent OCCRs (100%) for both calibration and prediction. The overall results demonstrated the suitability of NIR spectroscopy with RF for the simultaneous classification of water-borne pathogenic strains from different species.

The movement and deposition patterns of pesticide liquids in complex airflow field were difficult to determine, and thus led to some problems such as low utilization efficiency of pesticide and reduce quality of agricultural products. By using CFD technology, simulation test was carried out to study the droplet deposition characteristics within range of -1.5m to 4m in the downwind direction of UAV based on multi-feature fusion. The simulation boundary conditions and simulation parameters of 3-dimentional air field space were defined according to the spraying parameters measured from the UAV. The results showed that: droplet deposition was distributed in parabolic form. The deposition of droplets decreased with the increase of airflow velocity and spray height, and deposits scale was gradually moved away from the nozzle. When airflow velocity was greater than 3m/s, or spray height was more than 1.3m, it was not suitable for spraying. The deposition increased with the increase of droplet size, and deposits scale reduced. The change of spray angle caused a certain influence on distribution of droplet deposition, and it was a main factor that affects deposition characteristic. The result would provide a theoretical reference for spraying parameters determination and operating conditions selection.

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria&ndash, Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

In this paper,the electromagnetic responses and potential applications of THz metamaterials are reviewed through the focus on fabrication,unit structure design,and material selection,respectively.It describes different kinds of fabrication technologies for obtaining metamaterials with special electromagnetic responses such as magnetic resonance and reconfigurable tunability,which is helpful for further understanding of electromagnetic resonances in metamaterials.The paper analyzes the electromagnetic response characteristics in detail and points out that the unit structure design can be used to obtain desired electromagnetic characteristics,such as anisotropy,bianisotropy,polarization modulation,multiband response,broadband response,asymmetric transmission,optical activity,and perfect absorption,etc.The dependence of electromagnetic responses upon surrounding dielectrics can be used not only to control resonant frequency by a proper substrate selection,but also for sensing applications.Furthermore,the introduction of functional materials with controllable dielectric properties by external optical field,electrical field,magnetic field and temperature has the potential to achieve tunable metamaterials,which is highly desirable for THz functional devices.Finally,the opportunities and challenges for further developments of THz metamaterials are briefly introduced.

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

The overall qualities of chestnuts are greatly affected by worm-eaten chestnuts, as they lead to a reduction of profits. Hence a fast, accurate and non-destructive method for sorting chestnuts is in great demand. In this study, the technology of machine vision was employed to grade chestnuts. A recognition method to identify worm-eaten chestnuts is presented based on the edge image of the wormhole. First, by applying a Sobel operator, binary images were gained through extracting the edges of the gray images, which were preprocessed with the denoising method of a Wiener filter. The binary image contained both the edge of the contour and the wormhole. The wormhole edges were obtained through separating the wormhole edge in light of the character that the gray degree of pixels in the neighborhood of the wormhole edge is lower than the threshold value set. Second, through morphological dilating and eroding, the denoised wormhole edge images were obtained. The connected component of the binary images of the wormhole edge were labeled, and the first three longest components were considered as feature values of the worm channel, which were then normalized. Third, the normalized data were input to a back-propagation (BP) neural network for training, where the hidden layer was 7. And only three steps were needed for iteration. When the model was utilized for prediction, the recognition rate was as high as 100%. The results showed that the proposed worm-eaten chestnut recognition method is accurate and fast, and it can provide a basis for on-line detection. Since the gray degree of the wormhole region is close to the normal region, this study used an enhanced boundary detection method to extract the edge of the worm channel solely, rather than the normally used region segmentation.

Gated power supply is a key control circuit unit of image intensifier gated imaging. It has a great significance for the reduction of gated LLL image intensifier volume and the improvement of image intensifier imaging quality to carry out the research of small volume cathode high-voltage pulse circuit of gated power supply. By analyzing the photocathode gated operational principle of LLL image intensifier, this paper is to design a small volume cathode high-voltage pulse circuit for the gated image intensifier, and it gives the circuit block diagram and experimental results of gated power supply's cathode high-voltage pulse.

The image intensifier is special auto-gating power supply is an important component of the LLL image intensifier. It is the image intensifier tubes provide cathode, micro channel plate (MCP), anode the required voltage. It can realize automatic brightness control and image intensifier cathode protection function. The purpose of this paper is to design a cathode pulse control circuit which can protect the gating mode of auto-gating power supply and combining the independent cathode gating technology.

To solve the damping problem of water hammer wave in the modeling method of water diversion system of hydropower station, this paper introduces the feedback regulation technology from head to flow, that is: A fixed water head is taken out for flow feedback, and the following conclusions are obtained through modeling and simulation. Adjusting the feedback coefficient F of the water head to the flow rate can change the damping characteristic of the system, which can simulate the attenuation process of the water shock wave in the true water diversion pipeline. Even if a small feedback coefficient is introduced, the damping effect of the system is very obvious, but it has little effect on the amplitude of the first water shock wave after the transition process. Therefore, it is feasible and reasonable to introduce water head to flow rate feedback coefficient F in hydraulic turbine diversion system.

Purpose: Cancer stem–like cells have been well accepted to be involved in recurrence and metastasis of cancers, but the prognostic potential of biomarkers integrating with metastasis and cancer stem–like cells for colorectal cancer is unclear. Experimental Design: We identified three proteins, CLIC4, ERp29, and Smac/DIABLO, from metastatic cancer stem–like cells of colorectal cancer and verified the proteins' role in metastatic behaviors. The proteins were detected by IHC in colorectal cancer tumors and matched colonic mucosa from patients with colorectal cancer who underwent radical surgery in the training cohort. The associations between proteins expression levels and five-year disease-specific survival (DSS) were evaluated to predict the survival probability in the training cohort of 421 cases and the validation cohort of 228 cases. Results: A three-protein panel including CLIC4, ERp29, and Smac/DIABLO, which was generated from multivariate analysis by excluding clinicopathologic characteristics from the training cohort, distinguished patients with colorectal cancer into very low-, low-, middle-, and high-risk groups with significant differences in five-year DSS probability (88.6%, 63.3%, 30.4%, 11.4%; P < 0.001). The panel is independent from tumor–node–metastasis staging system and histologic grading to predict prognosis, and also enables classification of validation cohort into four risk stratifications (five-year DSS probability is 98.2%, 80.2%, 25.6%, and 2.7%; P < 0.001). Conclusions: CLIC4, ERp29, and Smac/DIABLO integrated into a novel panel based on cancer stem–like cells in association with metastasis stratify the prognostic risks of colorectal cancer. Prediction of risks with molecular markers will benefit clinicians to make decisions of individual management with postoperative colorectal cancer patients. Clin Cancer Res; 20(14); 3809–17. ©2014 AACR.

Measurement of soil total nitrogen (STN) is urgently important concerning requirement of precise and quantitative fertilizer application. To overcome the shortage of routine chemical detection, vis- short wavelength near infrared spectroscopy (Vis-SWNIRS) was employed as an accurate, cheap and timely alternative. Kennard-Stone algorithm was utilized for sample set partitioning, where 22 of the 32 samples were selected as calibration set, and the remaining 10 were included in validation set. No outliers were discovered under criterion based on spectral Mahalanobis distance and Dixon testing. Partial least squares regression (PLSR) was used to build STN detection model based on Vis-SWNIRS with cross validation by leave-one-out method. As a result, 5 principal components turned to be optimal considering model performance, where correlation coefficients for calibration and validation were 0.9724 and 0.8691, respectively, with PRESS (Prediction Residual Error Sum of Square) of 0.0684. It is feasible to detect STN by Vis-SWNIR spectroscopy.

In recent years, the carbon nanotube (CNT) emitters used for ion sources or gas sensors have been investigated, and the progress of several approaches such as field ionization and field desorption sources has been reported. However, a major concern for these applications is possible loss of CNTs caused by field evaporation, which can shorten the lifetimes of CNT-based emitters used for high electric field ion sources. So in CNT-based field emitter technology, emitter lifetime and degradation will be key parameters to be controlled. However, up to now only very few investigations in this direction have been conducted. The reason for this might lie in the fact that one often considers that the threshold value of field evaporation for a kind of material ( 40 V/nm) is much higher than the field of ionization or desorption ( 10 V/nm) according to the metal material characteristics (such as the threshold values of field evaporation for tungsten and molybdenum are 54 V/nm and 45 V/nm, respectively). In this work, the carbon nanotube thin-film (the density of CNTs is about 2.5108/cm2) is fabricated by screen-printing method, and the field evaporation behavior of CNT thin-film is studied experimentally in an ultrahigh vacuum system typically operating at a pressure of lower than 10-9 Torr after a 4-hour bake-out at ~200℃. Unlike the vertically aligned CNT array having higher electric field around the edge of the array because of the shielding effect, the printed CNT thin-film has more uniform distribution of electric field and is very easy to relize the mass production. The results show that the prepared CNT thin-film has quite obvious field evaporation behavior (some contaminants have deposited on the surface of grid after field evaporation, and energy-dispersive X-ray spectroscopy elemental mapping result of the grid indicates that the contaminants consist mainly of carbon elements), with turn-on field in a range of 10.0-12.6 V/nm, ion current could reach up to hundreds of pA. Meanwhile, the results with scanning electron microscope analysis and field electron emission measurement indicate that the CNT distribution turns into more non-uniform distribution after field evaporation; even some CNTs are directly dragged away from the substrate by the strong field. The field evaporation of CNT thin-film also leads to field electron emission onset voltage increasing from 240 V to 300 V, field enhancement factor decreasing from 8300 to 4200, and threshold field of field evaporation rising from 10.0 V/nm to 12.6 V/nm. However, the repeatability of sample treated by the field evaporation brings about an improvement to a certain extent. It could be understood in this way: upon applying a positive voltage, the most protruding parts, which have the strongest emissive capability, are evaporated first, which leads to the declined field enhancement factor; the parts of CNTs which have relatively weak emissive capability are not evaporated. So the uniformity of electric field is improved through reducing the difference in field enhancement factor rather than surface morphology between carbon nanotubes. The field evaporation of CNT thin-film is also a process which improves the uniformity of electric field. Therefore, the stability and repeatability of the field electron emission for carbon nanotube thin-film are improved naturally.