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Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A natural plant anti-ultraviolet finishing agent was prepared by uniform design of three plant extracts : Amaranthus purslane, Solanum annuum and Phellodendron cedar. The ultraviolet absorbable components contained in the three plants were tested and analyzed. The fabrics were finished according to the uniform design scheme and the related indexes were tested. The effects of each component on UPF, TUVA and TUVB were analyzed by DPS regression software. The results showed that there were ultraviolet absorbable components in all three plants by infrared spectrum analysis, and the optimum volume ratio of UV-resistant finishing agent was 19.82%, 74.46%, 5.72%, respectively. The UPF, TUVA and TUVB of cotton fabric treated with optimum proportion were 61.02%, 1.39% and 1.61%, respectively. [ABSTRACT FROM AUTHOR]

Nitrogen is a key plant nutrient and its deficiency or surplus could inhibit plant growth and reduce crop yield. Over the past decade, remote sensing has been increasingly used to diagnosis nitrogen deficiency in crop. Taking rice grown in cold region in northeast China as an example, this paper studies the relationship between nitrogen content in japonica rice and the difference between spectral reflectance based on data measured from field. This relationship was used to inversely estimate nitrogen deficiency in the rice based on hyperspectral images. In our analysis, the nitrogen content producing the highest yield was defined as standard nitrogen content and its associated spectral reflectance was defied as standard spectral reflectance. The difference between real nitrogen content and the standard nitrogen content, as well as the difference between the real spectral reflectance the standard spectral reflectance, were calculated respectively. The difference in spectral reflectance was dimensionally reduced using the discrete wavelet multi-scale decomposition, continuous projection method (successive projections algorithm, SPA) and vegetation index construction. The characteristic bands screened by SPA were 459、460、475、 671、723、874 和 996 nm. Analysis showed that when the discrete wavelet multi-scale decomposition was used to reduce the dimension, the Sym8 wavelet mother function worked best when it was decomposed at the seventh layer. Comparing DVI, NDVI and RVI vegetation index found that the determination coefficient of the DVI index and nitrogen deficiency was significantly higher than that of NDVI and RVI index. The three indexes were used as input to the partial least squares (PLSR), the extreme learning machine (ELM) and genetic algorithm optimization extreme learning machine (GA-ELM). The GA-ELM model was most accurate with the R² being 0.7062 for the training set and 0.7594 for the verification set; their associated RMSE was 0.5099mg/g and 0.4276mg/g respectively. The GA-ELM model based on the optimal vegetation index was least accurate, with the R² for the training set and the verifying set being 0.6615 and 0.6509 respectively; their associated RMSE was 0.4415mg/g and 0.5312mg/g. Overall, GA-ELM improved stability and predictability of the model compared with PLSR and ELM. It can thus be used as a new method to detect nitrogen content in rice leaf, and has important implication in precision fertilization. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid and accurate estimation of crude protein (CP) content in pasture plays an important role in the monitoring and management of the forage growth on a wide range of grassland. Crude protein contents of pastures are ideal for characterization using hyperspectral data. In view of the limitation of ground and satellite remote sensing, in this paper, we attempted to achieve an accurate estimation of CP content in forage by unmanned aerial vehicle (UAV)-based hyperspectral remote sensing images with high spatial resolution of the pasture canopy. Although the hyperspectral data of the forage have a large number of bands, the reflectance of the canopy spectrum at each band contains information of various parameters which are from atomic level to plant community level. So, when estimating a physicochemical parameter using the spectral data, we may achieve low prediction accuracy because the spectra are affected by other parameters. Compared with the original spectra, multi-granularity spectral features can provide more sensitive features for inversion of chemical parameters. More importantly, multi-granularity spectral features can extract some hidden weak spectral information, which is of great significance for inversion of low-content physical and chemical indicators. However, in current inversion methods of pasture CP content, there is a lack of effective utilization of spectral multi-granularity information. In view of this, we first proposed a novel multi-granularity spectral feature extraction approach named multi-granularity spectral segmentation (MGSS) to segment each canopy spectrum into multiple spectral features. Second, by using the sequential forward selection method, sensitive components of each feature under a granularity can be selected. Finally, based on these selected components of different features, the inversion models of pasture CP content can be established using two regression methods, i.e., the stepwise multivariate linear regression (SMLR) and the partial least squares regression (PLSR). Taking a typical meadowland in Qinghai Plateau as an example, the detailed experimental analyses have been conducted. Results showed that under the same quantity sensitive components for MGSS and sensitive bands for the raw spectra, on the estimation accuracy of pasture CP content, MGSS was superior to the raw spectra. So the validity of MGSS in improving the accuracy of hyperspectral estimation of CP content in forage was verified. Specifically, under Granularity 23 (G23) of MGSS, the PLSR model achieved the best performance. Its determining coefficient (R²) was 0.937 which was 0.06 higher than that of the optimal model of the raw spectra. And the root mean square error (RMSE) and the mean relative error (MRE) were 1.906 (g/m²) and 8.82%, respectively, which were 0.75 (g/m²) and 1.37 percentage points lower than those of the optimal model of raw spectra. Moreover, on the single and combined components sensitive to CP content in forage, there were three characteristics among the selected components of MGSS, i.e., the agglomeration within the Red Edge range, the dispersion of non-Red Edge range, and the sparsity of strongly sensitive components, which can be helpful for selecting sensitive components. In conclusion, the proposed MGSS achieved the high performance estimation of CP content in forage by UAV hyperspectral imagery. And compared with the raw spectra, MGSS had better performance. This study provides a new technical means for the accurate estimation of CP content in grasslands in large areas. [ABSTRACT FROM AUTHOR]

A detailed study on a high-iron red mud from Shandong province in the perspective of process mineralogy were conducted through chemical analysis, XRD, optical microscope, laser particle size analysis, energy spectrum analysis and BPMA automatic analyzer. The results show that the red mud has a fine particle size, and the chemical composition and mineral ( phase) composition are complex. The main minerals ( phases) in red mud are particulate silicon slag, hematite, iron-aluminum oxide, limonite, as well as trace amounts of sodium aluminosilicate, iron-titanium oxide, quaitz, gibbsite, bauxite, feldspar, and mica, etc. Iron occurs in the form of hematite, limonite, iron-aluminum oxide and particulate silicon slag. There is a large amount of particulate silicon slag in red mud, but the mineral ( phase) particle size is fine. The key to taking full advantage of the red mud is how to make use of iron, aluminum, titanium and sodium in the particulate silicon slag. [ABSTRACT FROM AUTHOR]

For the practical engineering problems,the dynamic transmission error measurement of a two-stage cylindrical gear-bevel gear high-speed transmission system with a straight bevel gear pair and a zero degree spiral bevel gear pair is carried out based on the test method. The measured dynamic transmission error data are filtered,and the dynamic performance parameters of high speed bevel gear are obtained with filter analysis. The results show that: When using zero-degree spiral bevel gear,the low-frequency of its dynamic transmission error is much lower than that of the straight bevel gear. And with the use of zero degree spiral bevel gear,the amplitude of high-frequency of its dynamic transmission error is much larger than the amplitude of that of the straight bevel gear. The high frequency band determines the dynamic characteristics of the transmission system. Therefore,it can be concluded that when using zero degree spiral bevel gears,the dynamic performance of the system will be inferior to that of straight bevel gear.

Taking the last gears of the mine reducer named JS75 as the research object,by using the gear microscopic modification module of MASTA,the comparison and analysis of the contact spots,transmission error and harmonic amplitude before and after modifying are carried out. The determination of optimum modification method and amount is completed. The test of vibration and noise and spectrum analysis are carried out on the reducer under no- load and load by using M + P testing system. It is proved that the modification of the heavy load gears in last stage is very effective to control the vibration and noise of the transmission system,and the high speed spiral bevel gears has a very important influence on the dynamic performance of the reducer.

The spectrum of the image reflects the distribution of image energy in the frequency domain. The ratio of the high and low frequency components of the spectrum reflects the distribution of edge information and smooth information in the image. Through statistical analysis of the spectral distribution of dear and blurred images, this paper proposes an image clarity and fuzzy discrimination method based on sub-band spectral information. First, the spectrum energy outside the frequency radius is calculated, and the characteristic curve of the residual energy of the spectrum is obtained. Secondly, according to the residual energy curve, the normalized fuzzy judgment parameters are obtained. When the fuzzy parameter is less than a certain threshold, the image is judged to be a blurred image. The experimental results show that the parameter proposed in this paper can quantitatively measure the degree of image blur, and has the advantages of fast and simple judgment process and high classification discrimination. [ABSTRACT FROM AUTHOR]

In order to fully explore useful spectral and image information of hyperspectral images, a method of extracting the deep feature of hyperspectral images based on stacked auto-encoder (SAE) was investigated in this study, then the extracted deep feature was used to establish support vector regression (SVR) model to realize non-destructive detection of protein content in rice. Firstly, 420 rice samples (70 in each group) were placed in 6 groups at different storage time (0, 24, 48, 72, 96 and 120 h) under the condition of high temperature (45 ℃) and high humidity (95% relative humidity). Secondly, the hyperspectral images (400-1 000 nm) of 6 group rice samples with different protein content were collected by the hyperspectral image acquisition system. After hyperspectral images collection, the Kjeldahl method for determination of nitrogen was used to detect the protein content of rice samples. According to the chemical detection results, the protein content of the rice samples decreased from 7.78 to 7.62 g/(100 g) with the increase of storage time. Thirdly, in order to separate samples from background, the threshold segmentation method was used to obtain the sample mask in ENVI software, then the mask was applied to the hyperspectral image containing only rice samples. The rice sample area was chosen as region of interest (ROI), and the average spectral data and image information of ROI was extracted respectively. Finally, SAE was used to extract deep feature of spectral data, image information and fusion data. 1) For spectral information, savitzky-golay (SG) was used to pre-process the obtained spectral data, and SAE was used to extract the deep feature, then SVR was used to establish the prediction model, and grid search method was used to optimize the kernel parameter g and penalty factor c in SVR. The results showed that the optimal scale of SAE was 478-400-290-70, and R²c (determinant coefficient of calibration set), R²p (determinant coefficient of prediction set), RMSEC (root mean square error of calibration set), RMSEP (root mean square error of prediction set) using deep feature extracted by SAE were 0.976 2, 0.939 2, 0.068 6 g/(100 g), 0.115 3 g/(100 g), respectively. 2) For image information, the RGB images in each band were extracted first, the size was 100 pixels×100 pixels. There was much redundant information in the original images, so we unified them to the gray images with 28 pixels×28 pixels, then flattened and converted them to one-dimensional column vector, after deep feature extraction by SAE on the vector, the prediction results of the model built with SVR showed that the optimal scale of SAE was 784-700-480-30, and R²c R²p, RMSEC, RMSEP modeled using deep feature extracted by SAE were 0.915 4, 0.821 0, 0.051 0 g/(100 g), 0.111 8 g/(100 g), respectively. 3) For the fusion data of spectral data and image information, the initial dimension was 1 262, the fusion data combined all the redundant information of spectral and image, so dimension reduction becomes critical. After feature extraction by SAE, the dimension was reduced to the low level and the efficiency of network training was improved. The optimal scale of SAE was 1262-550-450-30, and R²c, R²p, RMSEC, RMSEP of the model build with deep feature extracted by SAE were 0.971 0, 0.964 4, 0.077 2 g/(100 g), 0.085 1 g/(100 g), respectively. Compared with using spectral data or image information alone, the prediction effect of the model build with the fusion data was improved obviously. To summarize, the method in the paper fully fused the spectral data and image information of hyperspectral image, and then deep feature extracted by SAE improved the prediction accuracy of the model established by SVR effectively, which provides a theoretical basis for non-destructive detection of protein content in rice [ABSTRACT FROM AUTHOR]

One photoinitiator based on the 3, 4-ethylenedioxythiophene ( EDOT) was synthesized through introducing an acetylehienyl and an acetyl group respectively into two active sites of the EDOT. The structure was characterized by ¹HNMR spectroscopy. UV-Vis absorption spectroscopy, thermal stability were also tested. We also measured its photoinitiating activity with real-time ITIR technology. The results show that the photoinit iator has good absorption in t he range of 365-405 nm, which is better than 2-isopropylthioxanthone (ITX) and suitable for UV-LED photocuring systems. Meanwhile, its thermal stability is better than ITX and the photoinitiating activity is comparable to ITX. [ABSTRACT FROM AUTHOR]

The U-typed reduction chamber is the core reaction device of the iron suspension roaster. In order to study the fluidization characteristics, a cold experiment system of U-typed reduction chamber was established. Based on the experimental data collected by pressure fluctuation method, the following conclusions were drawn. Aeration velocity and particle size were the main factors affecting the initial fluidization velocity, while the initial bed height had little effect. With the increasing of initial bed height and aeration velocity, the pressure fluctuation in fluidization chamber became lower, but opposite result was obtained for particle size. Besides, power spectrum analysis revealed that the formation, coalescence and fragmentation of bubbles were the fundamental reasons for pressure fluctuation. The results would provide theoretical support for the design and optimization of the operating parameters during suspension magnetization roasting experiments. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the storage process, honeysuckle easily undergoes discoloration and mildew under the influence of temperature, humidity and microorganisms, which leads to a significant decrease of its medicinal efficacy and economic value, and even harms the health of consumers. Hence, it is necessary to monitor the quality of honeysuckle during storage. Chlorogenic acid (CGA), as the main active ingredient, is an important indicator to evaluate the quality of honeysuckle. In order to realize rapid and effective detection of CGA content in honeysuckle, 500 hyperspectral images of honeysuckle during different storage periods were collected by hyperspectral imaging (HSI) system, and then CGA content values were measured by high performance liquid chromatography (HPLC) method. Average spectral information extracted from the hyperspectral images and corresponding CGA values were used to build HSI detection models. Because of the non-uniformity of sample surface, baseline drift of instrument, random noise and light scattering, the collected hyperspectral images contained some redundant information, which could reduce the accuracy of modeling. In order to improve the prediction accuracy and efficiency of the model, six spectral preprocessing methods were used to improve the signal-to-noise ratio of the original spectrum, including Savizky-Golay filter (SG), moving average, standard normal variable (SNV), baseline correction (BC), multiplicative scatter correction (MSC), orthogonal signal correction (OSC). Comparing the effects of different pretreatment methods by establishing partial least squares regression (PLSR) models, the SNV-PLSR model obtained the best prediction accuracy with determination coefficient (R2) of 0.976 6 and root mean square error (RMSE) of 0.271 1% in prediction set, and SNV was identified as the best pretreatment method for further analysis. In order to simplify the calibration model, the uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), the combination of UVE and CARS (UVE-CARS), and the combination of UVE and SPA (UVE-SPA) were used to extract characteristic wavelengths from the pre-processed spectrum by SNV method. And UVE, CARS, SPA, UVE-CARS and UVE-SPA selected 192, 51, 17, 26, 9 characteristic wavelengths from the full spectrum. Then, based on the full spectrum data and the selected characteristic variables by five variable screening methods, the linear PLSR and the non-linear BP neural network model were established. The performance of all the models were evaluated by the index of R2 for calibration set and prediction set, (RMSE) for calibration set and prediction set. The results showed that UVE-CARS algorithm could effectively eliminate useless information variables from full spectrum, and 26 characteristic wavelengths were selected from full spectrum by UVE-CARS algorithm, and the established model based on UVE-CARS algorithm had high accuracy, which was considered as the best feature wavelength screening method. The prediction results of the non-linear BP model were better than that of the linear PLSR model. In all BP model, the prediction accuracy of UVE-CARS-BP was the highest with R2 of 0.978 4 and RMSE of 0.250 3% in prediction set, respectively, and it was proved that the non-linear model was more suitable for the prediction of CGA content in honeysuckle. In conclusion, HSI technology combined with SNV-UVE-CARS-BP model can realize the rapid and non-destructive prediction of CGA content in honeysuckle during storage. [ABSTRACT FROM AUTHOR]

Nitrogen (N) and magnesium (Mg) elements play important role in the growth of cucumber plants, N and Mg deficiencies in cucumber plants drastically affects the quality and most importantly yield of agricultural products. In the published papers, chlorophyll content was used as an indicator for diagnosing N deficiency and Mg deficiency. However, leaf with low chlorophyll content appears both in N deficient and Mg deficient plans, which makes it is difficult to simultaneously detect N and Mg deficiencies using chlorophyll content. In this study, new indicators based on chlorophyll distribution features of the whole cucumber leaves were proposed for diagnostics of N and Mg deficiencies. N deficient, Mg deficient and control cucumber plants were cultured in a greenhouse with special nutrient supply. The content of N and Mg nutrient elements in N deficient, Mg deficient and control leaves were determined to test the nutrient status of cucumber plants in N deficient, Mg deficient and Control groups. 100 fresh cucumber leaves were collected and used as samples for detecting a chlorophyll distribution map. Firstly, hyperspectral images of cucumber leaves in the calibration set were collected and chlorophyll content of the cucumber leaves was determined using high performance liquid chromatography technology. Chlorophyll content calibration models were built using the hyperspectral images and chlorophyll content. Secondly, the hyperspectral images and chlorophyll content of cucumber samples in testing set were used to test the chlorophyll content calibration models, and the chlorophyll content calibration model with the best performance was selected as the optimal calibration model. The chlorophyll content distribution maps of N deficient, Mg deficient and control cucumber leaves were measured using the optimal chlorophyll content calibration model. After hyperspectral image collecting, hyperspectral image data of N deficient, Mg deficient and control leaves were obtained. Then, the spectral data of every pixel in the hyperspectral images was extracted and substituted in the optimal chlorophyll content calibration model to calculate the chlorophyll content at each pixel. The chlorophyll content of all pixels were displayed in two dimension spastically, then the chlorophyll content distribution maps of N deficient, Mg deficient and control leaves were obtained. The chlorophyll content distribution maps of 25 N deficient cucumber leaves, 25 Mg deficient cucumber leaves and 25 control cucumber leaves were determined. Compared with the distribution map of chlorophyll content in the control leaves, N deficiency led to the decrease of chlorophyll content in the whole leaf, and Mg deficiency led to the decrease of chlorophyll content in the area between the main veins. According to these results, two chlorophyll distribution features, the average and standard deviation of chlorophyll content at every pixels in a chlorophyll distribution map, were extracted for diagnosing N deficiency and Mg deficiency. Result showed that an average of chlorophyll content (11.5 mg/g) could be used as a threshold value to diagnose N deficiency, and the diagnostic rates for the calibration set and prediction set were 100% and 90%, respectively. A standard deviation of chlorophyll content (2.20 mg/g) could be used as a threshold value to diagnose Mg deficiency, and the diagnostic rates for the calibration set and prediction set were 93.3% and 90%, respectively. The result indicated that the extracted features could reflect the characteristic of N and Mg deficient cucumber leaves and could be employed to diagnose N and Mg deficiency nondestructively. [ABSTRACT FROM AUTHOR]

Tomato has become the most cultivated and consumed vegetable crop in the world, and China has become one of the largest producers and consumers of tomatoes in the world. The pectin content in cell wall of tomato during ripening is closely related to fruit hardness, which is one of the important indicators to determine the maturity and reflect the quality of tomato. The requirement of tomato maturity classification and evaluation promotes the development of non-destructive, fast and accurate detection methods of tomato hardness.Hyperspectral imaging integrates spectroscopy and imaging technology in an analysis system, which transfers tomato maturity assessment from subjective, manual classification and measurement methods. Hyperspectral imaging has been widely used in the rapid acquisition of information to classify, detect or identify the quality of various fruits. A novel method for tomato hardness detection based on hyperspectral imaging and modified interval Random Frog was proposed in this paper. Firstly, hyperspectral images of 120 tomato samples in different mature periods were captured by hyperspectral imaging system covering near-infrared region (865.11nm-1 711.71nm). And the hardness data of tomato was obtained by texture analyzer. Secondly, the spectral data were pretreated by multiplicative scatter correction (MSC) and normalized preprocessing to eliminate noise and improve signal-to-noise ratio. The validity of the characteristic wavelength plays a crucial role in the prediction performance of the model. Therefore, we need an effective method to extract the effective wavelength to improve the accuracy of the model. Interval random frog (iRF) algorithm considers all possible spectral wavelengths and ranks all the wavelengths based on selected probability. But one of the disadvantages of this method is large number of iterations and slow convergence. In view of above disadvantages, the traditional iRF algorithm was optimized in terms of constructing initial variable subset method. A modified interval Random Frog (miRF)was proposed to extract the characteristic wavelength effectively. Finally, a prediction model was developed based on partial least squares regression (PLSR) method to detect tomato hardness. The results indicated that the convergence efficiency and accuracy of miRF has a significantly improvement compared with the iRF method. The iRF has selected 100 feature bands, accounting for 40.65% of the full band, and its runtime was 32.1min. miRF has selected 47 feature bands, accounting for 19.1% of the full band, and its runtime was 1.6 min. It can be seen that miRF greatly reduces the running time of the algorithm. The characteristic wavelengths selected by iRF and miRF methods were mainly distributed in 1 582 nm-1 655 nm, followed by 1 160 nm-1 190 nm and 1 353 nm-1 383 nm, indicating that above regions were sensitive bands to tomato hardness. In order to prove the effectiveness of the proposed algorithm, the results of miRF-PLSR were compared with those of iRF-PLSR and SPA-PLSR. The prediction set correlation coefficients (RP) of the SPA-PLSR model and the iRF-PLSR model were 0.803 9 and 0.936 6 respectively. And the RP of miRF-PLSR model was 0.968 5. The root mean square error (RMSEP) of the SPA-PLSR model and the iRF-PLSR model were 0.007 7 kg/mm2 and 0.004 4 kg/mm2respectively. And the RMSEP of miRF-PLSR model was 0.004 0 kg/mm2. The experiments results show that the miRF-PLSR model has the best prediction results in all models. [ABSTRACT FROM AUTHOR]

To overcome currently existing problems of poor scalability and high price of virtual oscilloscopes, a virtual oscilloscope is developed by using compressed sensing signal processing technology and STM32, field programmable gate array (FPGA) and other modules combined with personal computers (PCs). It has 50 MB bandwidth and 100 MS/s sampling frequency. It is connected to a PC via a high-speed universal serial bus ( USB ) interface. Besides the waveform display and measurement functions of traditional oscilloscopes, it also has flexible and programmable software extensions, such as the signal generator, the spectrum analysis, and the amplitude-frequency measurement. Compared with the existing virtual oscilloscope, the developed virtual oscilloscope has many advantages, such as various functions, low cost, high expandability and easy portability. [ABSTRACT FROM AUTHOR]

Soil salinization is a worldwide environmental problem with severe economic and social consequences. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. In this study, the spectra characteristics and salinization parameters of the soils in the different layers in northern Ningxia Yinchuan were measured. Based on the soil science and geostatistics methods, the sensitive wavelengths and the best transformation forms of spectral reflectance to salinity parameters (soil pH value, electric conductivity(EC) and salt ions) in 0-5 cm and 0-20 cm were selected respectively, and then the soil salinity parameters monitoring model was established. The results showed that: 1) The variation trend of soil spectral characteristic curves of five different types and different salinization degrees was similar. Saline soil had the highest spectral reflectance, and slightly SO42- type soil had the lowest reflectance. Salinized soils had good spectral response characteristics in visible and near infrared spectra region. The reflectance had the most closely related to the content of SO42- in all salinity parameters (coefficient of correlation was 0.910 4) of 0-5cm layer. There were non-significant relationships between reflectance and the contents of CO32-, HCO3- and Cl-. The average coefficient of correlation of reflectance and SO42- in 0-20 cm layer was decreased 0.232 2 than in 0-5 cm. However, the average coefficient of correlations of reflectance and Cl-, K+, HCO3-, EC were increased 0.433 1, -0.343 3, 0.303 2, 0.296 2, and got significant level. 2) After the spectral reflectance were transformed in different methods, the correlation between the most sensitive wavelengths and each salinity parameters were enhanced to some extent, especially after the (R)′ (first order differential conversion) and (CR)′ (the first order differential after continuous removal). In 0-5 cm layer, (R)′ was the optimal transformation forms of reflectance for pH value, SO42-, K+, Mg2+, and the (CR)′ was best for EC, CO32-, HCO3-, Cl-, Na+ and Ca2+. In 0-20 cm layer, (CR)′ was the optimal index for soil pH value, [lg(1/R)]′ was the optimal index for EC, HCO3-, SO42-, Na+, Ca2+ were, and (R)′ were the best one for CO32-, K+, Mg2+. In addition, there are different sensitive wavelengths in different soil layers about the same salinity parameters. 3) In the models of PLSR (Partial least squares regression), the average determination coefficient (R2) between sensitive wavelengths of 10 salinity parameters were 0.820 8 and 0.890 7 in 0-5cm and 0-20 cm soil layer, respectively. The determination coefficient between sensitive reflectance and SO42- was 0.967 6 in 0-5 cm layer, and it was higher 0.077 6 than in 0-20 cm layer. The numbers of sensitive wavelengths reduced and R2 decreased that used PLSR method to established prediction model than used the SR (step-wised regression) method, but the R2 of the SR method also got the significant level. The results conformed that the prediction accuracy of models for SO42-, CO32- in 0-20 cm were lower than in 0-5 cm. However, the prediction ability of models for other salinity parameters in 0-20 cm was stronger than in 0-5 cm. The study provide some beneficial references for regional soil salinity prediction and configuration of plant structure. [ABSTRACT FROM AUTHOR]

In order to reveal the mapping relationship between internal excitation and vibration characteristics of planetary transmission mechanism, a vibration acceleration measurement system of planetary transmission mechanism was established by designing a vibration test scheme, and the vibration characteristics of typical gearshift 2 500 r/min planetary transmission mechanism were measured and analyzed. The results show that the vibration root mean square (RMS) value of planetary transmission mechanism is correlated with the position of measuring point. The measuring point is in the meshing position of planetary gear, and its vibration amplitude is larger. The vibration RMS value increases with the increase in the torque. The frequency spectrum analysis results show that the vibration energy mainly concentrates on the meshing frequency and multiple frequencies of planetary gears, and the axial frequency spectrum is similar to the radial frequency spectrum. The maximum amplitude of meshing vibration is obtained by measuring the vibration of planetary transmission mechanism and analyzing its frequency spectrum. [ABSTRACT FROM AUTHOR]

Navel orange is a very popular fruit in China, which is mainly cultivated along the Yangtze River. Navel oranges are classified into different grades based on external quality and internal quality before they are sold. Soluble solids content is one of the main indices for evaluating the internal quality of navel orange. Therefore, it is very important to improve the detection accuracy of soluble solids content in production. So far, visible and near infrared spectroscopy (Vis-NIR) is one of the most widely used and effective techniques in internal quality assessment of fruits. In this study, 199 Fukumoto navel oranges were taken as experimental samples. The transmission spectra of navel oranges of three positions including pedicle upwards (P1), pedicle horizontal (P2) and pedicle downward (P3) were acquired by using a special visible and near infrared transmission spectrum measurement system designed by ourselves. The average spectra (P4) and weighted spectra (P5) of P1, P2 and P3 were calculated. The transmission spectra, including P1, P2, P3, P4 and P5 were preprocessed by multivariate scattering correction, standard normal variate transformation, first derivative and second derivative respectively. The best pretreatment results were obtained based on first derivative after comparative study. Then the spectra data preprocessed by first derivative were divided into 30 to 50 intervals with step length of 5, and backward interval partial least squares was used to select the optimal band combination. Good results observed when P1, P2, P3, P4 and P5 were divided into 35, 40, 30, 35 and 40 intervals, in which 161, 180, 114, 308 and 170 variables were retained. On this basis, competitive adaptive re-weighted sampling (CARS) was used to select feature variables. After running CARS for 20 times in each selection, 24, 23, 18, 39 and 22 variables were kept respectively. Finally, Five PLS models were established, including P1-PLS, P2-PLS, P3-PLS, P4-PLS and P5-PLS. Among the P1-PLS, P2-PLS and P3-PLS models, P2-PLS model was the best one, as the value of correlation coefficients of prediction was 0.924 and the value of root mean square error of prediction was 0.404. This model can be realized by adjusting the navel oranges to pedicle horizontal in modeling. P4-PLS model and P5-PLS model had achieved good prediction results, as the value of correlation coefficients of prediction was higher than 0.91 and the value of root mean square error of prediction was lower than 0.43. P4-PLS model was based on the average spectra of P1, P2 and P3, and had potential to be realized by rolling the navel oranges in actual application. However, P5-PLS model was based on weighted spectra of P1, P2 and P3, which was difficult to realize in on-line detection. This study can provide a reference for the development of on-line detection equipment for the assessment of internal content of substances in navel orange. [ABSTRACT FROM AUTHOR]

Nano cuprous oxide(Cu2O)powders were prepared by plasma induced cathode discharge electrolysis in CuSO4 solution， and the components and morphology of the prepared particles were investigated by various concentrations， electrolysis voltage and time. The results showed that a higher concentration of solution had a positive effect on the formation of Cu2O phase. As the discharge time extended， the particles aggregated and the size increased from 100nm to 400nm. Increasing the voltage was beneficial for the refinement of particles from 200nm to 40nm.Emission spectrum analysis indicated that there existed hydroxyl radicals and Cu atoms in plasma emission area， which was suggested that the formation mechanism of Cu2O. Theoretical calculation showed that excitation temperature， the electric field intensity and the arc column jet velocity was 9563K， 2.4×105V/m， and 73m/s， respectivively. [ABSTRACT FROM AUTHOR]

Land degradation (desertification) in arid region, as one of the global ecological challenges, is crucial to food security, environmental quality and regional natural resource management. Fortunately, the development of remote sensing technology greatly improves the ability of land degradation information extraction and assessment, which can effectively reveal conditions of land degradation in arid region and provide the scientific basis for land degradation trend prediction and formulating the corresponding preventing measures. In fact, the essence of land degradation is the decrease of ecological service following the change of type, quantity, structure and function of land use/cover which emerges from interactions of the coupled human-environment (H-E) factors at multiple spatio-temporal scales, leading the difficulty of classification and assessment. This prompts an attempt to reduce the complexity of the assessment by identifying a limited suite number of processes and variables which makes the problem tractable at particular scale like the interaction process of soil and vegetation, the core of the degradation. Based the standard spectral endmember space of GF-1 satellite, this paper innovatively established three levels evaluation system of land degradation state in arid areas, including land use/cover structure, degradation type and degradation degree. Secondly, we realized dryland multi-temporary linear spectral mixture analysis. The remote sensing images of GF-1/WFV endmember fractions time series were applied to characterize the quality attributes of the underlying surface and organize the classification knowledge, and then to complete the fine land cover/use classification of Minqin county in 2015. Based on land use/cover mapping in arid region, the time series EMs including vegetation (GV), sand land (SL), saline land (SA), dark surface (DA) were combined to obtain and express the variables reflecting the ecosystem function, and then, were organized with decision tree (DT) for degradation state mapping. Finally, the results of land degradation state were assessed using photos of the site landscape and the measured data of the sampling sites. The results showed that: 1) The MESMA method could decompose four stable EMs types, which were consistent with the physical components of the surface cover in study area. The model could effectively simulate the spectral information of study area. More importantly, it solved the problem that the bands number of GF-1 WFV was few and the information mining was insufficient. And which provides a basis for establishing knowledge for decision tree classification and assessment. 2) The time series EMs in standard spectral endmember space could perfectly highlight the temporal interaction characteristics details between vegetation types and habitat and the ability of remote sensing to identify land degradation types and degree, with corresponding accuracy of 87.5% and 78.7%, respectively. 3) For Minqin, a typical dryland system, the sandification process and the sand-salinization process were the dominant land degradation processes. Light sandification and moderate sandification were the dominant degradation degrees. Through the relative evaluation results of land degradation types and degree, it helps divide the key control areas, ecological conservation areas and the use of decompression areas. The research result contributes to the sustainable development of regional management and control of ecological fragile areas, land space development and ecological environment protection. General remote-sensing technical framework for land degradation assessment was confirmed that it have the potential to be applied to the study of land degradation assessment in arid regions across time and regions. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flotation tests， Zeta potential measurements， infrared spectrum analyses， EDLVO calculations and SEM analyses were used to investigate the effect of citric acid on entrainment behavior of fine chlorite in hematite reverse flotation. Flotation results illustrated that citric acid promotes entrainment behavior of fine chlorite， and is conducive to removing chlorite in concentrate. Zeta-potential tests and infrared spectrum analyses indicated that adsorption of citric acid on the surfaces of chlorite and hematite increases their electronegativity and enhances the electrostatic repulsion between hematite and chlorite particles. EDLVO calculations demonstrated that the attraction between hematite and fine chlorite is transformed into repulsion after interacting with citric acid， and the calculation results are consistent with those of SEM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is hot research to evaluate the quality of heavy oil asphalt by infrared spectroscopy. Bv Fourier transform infrared spectroscopy (FTIR) and separation analysis, the 90# A grade asphalt and the SBS modified asphalt were investigated. The results show that the correlation between infrared spectrum analysis technology and conventional asphalt detection method is 99. 6%. The 90 #A asphalt has 9 obvious feature peaks from 3 000 cm-1 to 650 cm-1, and the SBS modified asphalt has 11 obvious feature peaks from 2 950 cm-1 to 650 cm-1 with corresponding characteristic group for each characteristic peak. The asphalt materials have the same characteristic group with the same technical indicators. By comparing and analyzing the characteristic peaks of asphalt infrared spectroscopy, the asphalt quality can be quickly judged, and the experimental time of testing and evaluating the index of asphalt material can be shortened. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of the aero-engine safe and reliable using, the rotational speed, rotor eccentricity and rotational speed ratio of an aero-engine dual rotor tester were studied. A simplified complete machine finite element model with high and low pressure rotor intermediate bearings and the elastic deformation of the casing was established, the validity of the theoretical model was evaluated by comparing the model with the data of a dual rotor system in the literature, the Newmark-β numerical integration method was used for solving nonlinear dynamic responses of systems. The results indicate that the vibration response amplitude of high pressure rotor and low pressure rotor is distinct difference when the eccentric position is different. The amplitude response is complicated when the eccentricity is applied to the high pressure rotor part. The motion pattern of the rotor system is significantly influenced by the ratio of high and low pressure speed to rotation ratio, the system may do cycle, almost cycle or chaotic motion. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As vast agricultural byproduct and an important source of lignocellulosic biomass in China, straws of maize, cotton and wheat are now being developed as renewable energy resources to address a serious energy shortage and environmental issues related to other energy sources. It is necessary to improve the comprehensive utilization efficiency of the three types of straw. Although maize (monocotyledon), cotton (dicotyledon) and wheat (monocotyledon) belong to therophyte herbage from the perspective of phytology, but their straw forms are totally different. For the sake of the difference among them, the utilization cannot be generalized without regard to the effect of chemical composition, in particular the characteristics of lignin. It is composed of phenyl propane monomers connected by the ether and carbon-carbon bonds, containing a variety of active groups. There are three types of phenylpropane, p-hydroxy phenyl, guaiacyl and syringyl, which correspond to hydroxy phenyl lignin (H-lignin), syringyl lignin (S-lignin) and guaiacyl lignin (G-lignin), respectively. Lignin within plants has different shares of the three constitutional units. The heterogeneity of linkage types among the phenyl propane monomers has different effects on the pretreatment for the utilization and removal of lignin. Previous researches have shown that the relative ratio of G-lignin/S-lignin (G/S value) is relevant to the ease or complexity of delignification. Therefore, the higher the ratio, the harder the delignification is. In this paper, three samples of each kind of straw were collected, coarse grinded, degreased, dewaxed, and fine grinded, in sequence. Then the milled wood lignin (MWL) was isolated from the straws of cotton, corn and wheat. After the all procedures above, MWL was studied with FT-IR, by potassium bromide pellet technique. Normalization was conducted with 9 spectra obtained, at the wavelength of 1 506 cm-1 (to make A1506=1) as spectral pretreatment. Then the spectra were compared in 3 aspects: the relative G/S ratio represented by A1265/A1329, the identification of lignin types based on plant taxonomy, and the differences and similarities comparing with the MWL from eucalyptus. The results indicate that there is more guaiacyl and less syringyl in the MWL isolated from the samples, the relative ratio of G/S among the three kinds of straws was not significant difference, and no evident regularity is found. Secondly, the identification results based on plant taxonomy show that the MWL from cotton straw is the type of GS1, while that from corn and wheat straw belong to the type of HGS; the content of basic units of lignin in mole percent in these straws have following laws: for H-lignin, corn straw＞ wheat straw＞＞cotton straw; for G-lignin, cotton straw＞corn straw＞wheat straw; for S-lignin, corn straw＞wheat straw＞ cotton straw. Thirdly, the MWL isolated from straw of corn and wheat share the generality of gramineous plants, while the MWL from cotton straw is closer to ligneous plants, which have distinguish peak at 1 265 and 1 226 cm-1. [ABSTRACT FROM AUTHOR]

A partial failure vibration signal model of spur gear was established starting from gear drive mechanism and considering both amplitude modulation and frequency modulation effects of meshing vibration due to partial spur gear damage. Based on partial failure model of spur gear, structure feature of planetary gear and vibration signal generation and changing rule were analyzed, and partial failure vibration signal model of planetary gear was established. Load test of rocker arm with different gear partial failures and spectrum analysis of collected vibration signal were conducted, the results show that the model of gear fault vibration signal model can objectively and accurately reflect failure state of rocker gear, and using the method of spectrum analysis can achieve partial failure diagnosis of rocker gear.

In order to promote application of acoustic emission monitoring technology in effect evaluation of coal seam hydraulic fracturing, coal seam hydraulic fracturing process was monitored by use of coal and rock dynamic disaster acoustoelectric monitor, and characteristics of acoustic emission signals collected in the process were analyzed through fourier transformation and wavelet packet analysis. The field test results show that main frequency of effective acoustic emission signals of coal seam hydraulic fracturing is in 470-1 700 Hz, which is lower than main frequency of underground noise interference signal. Energy distribution of the effective acoustic emission signals of coal seam hydraulic fracturing is concentrated in 0-5 000 Hz with proportion 86.5%-95.4%, and the energy distribution of effective signals is concentrated in low frequency band such as 312.5-625, 625-937.5, 937.5-1 250, 1 875-2 187.5 Hz.

In view of problem that it is difficult to extract vibration signal and carry out fault diagnosis of coal mine electromechanical equipment, intrinsically safe mine-used portable vibration recorder was applied to collect vibration signal of shearer cutting unit, and spectrum analysis and Hilbert demodulation technique were used to demodulate fault characteristic frequency of the vibration signal, so as to determine fault location, which provides reliable basis for fault diagnosis and maintenance of shearer.

In order to study the effect of carbon dioxide phase change cracking process on the structure, the response spectrum analysis was carried out on the data obtained from field tests. First of all, the velocity signal is subjected to fast Fourier transform to analyze its spectral characteristics; then, the velocity signal is directly differentiated, the low-pass denoising is performed by using ensemble empirical mode decomposition(EEMD), and the reconstructed signal of the main characteristic frequency band is extracted to obtain a clear acceleration-time history curve; the acceleration curve after the denoising is used as the input signal to study the response spectrum of the carbon dioxide phase change cracking signal with different damping ratios. The results show that the frequency of carbon dioxide phase change cracking signal is concentrated in low frequency, the peak velocity and the main frequency are within the safe allowable range, and it is suitable for vibration sensitive environment; EEMD can effectively inhibit the high frequency oscillation caused by direct differentiation of the velocity signal, and the signal to noise ratio can reach up to 25 dB; the carbon dioxide phase change cracking response spectrum is simple, and the peak corresponding period is small, which is conducive to structural safety; the peak values of the relative displacement and the relative velocity response spectrums decrease with the increase of damping ratio, but the peak corresponding cycle is constant; the peak values of the absolute acceleration and the standard acceleration response spectrums are equal at non-zero damping ratio, which is not affected by damping ratio; attention is paid to the period in the response spectrum that has a large influence on the structure, and the stability of the structure can be guaranteed to some extent. [ABSTRACT FROM AUTHOR]

Chlorophyll is a good indicator of plant nutrition stress, photosynthetic ability and aging process. The use of hyperspectral remote sensing technology to monitor the chlorophyll status of soybean leaves is of great significance for soybean growth diagnosis and fertilization regulation. However, the information weakly correlated to chlorophyll is very easy to be neglected in the selecting process, which leads to the loss of effective information containing chlorophyll. Therefore, it is important to determine the weak information variables in leaf spectra so as to reveal the rules of hyperspectral response of chlorophyll. This study collected 80 sets of soybean leaf samples at seed-filling period in Jianghan Plain. The leaf hyperspectral data were measured by ASD HandHeld2 type spectrometer and SPAD (soil and plant analyzer development) value was measured by SPAD 502 chlorophyll meter in laboratory. The concentration gradient method was used to divide the whole sample set (80 samples) into a calibration set (54 samples) and a validation set (26 samples). Then the correlation between SPAD value and soybean leaf reflectance was analyzed, and as well as for every two spectral wavelength variables. The characteristic wavelengths of leaf spectrum were extracted from full bands based on iteratively retains informative variables (IRIV) method. Finally Partial Least Squares Regression (PLSR) model and Support Vector Machine (SVM) model were calibrated to estimate soybean leaf SPAD values by using characteristic variables and full spectral variables, respectively. The performance of the SPAD estimation model was tested using the determination coefficients (R2), root mean squared error (RMSE), and relative percent deviation (RPD). The results showed that the SPAD values of soybean leaves correlated strongly with the spectral wavelength variables in the visible bands, especially in the band of 500-650 nm and 690-730 nm, which were significantly negatively correlated with the spectral wavelength variables (P=0.01). The correlation between visible wavelength variables and near-infrared wavelength variables of soybean leaves was weak, and the correlation between internal variables of two bands was high, especially the collinear problem of near-infrared bands was prominent. Then 9 characteristic wavelength variables of soybean chlorophyll were determined based on IRIV algorithm. Among them, 5 strong information variables and 3 weak information variables were extracted in the visible bands, and 1 weak information variable was extracted in the near-infrared bands. The SVM model was better than the PLSR model. The R2 of validation set in PLSR model was 0.52, but in SVM model was higher than 0.59, so the SPAD value and the reflectance tended to be nonlinear correlated. IRIV algorithm can effectively determine the characteristic wavelength variables of SPAD value of soybean leaves. The estimation ability based on IRIV characteristic wavelength variables model was better than that based on full spectral variables model and strong information variables model. The IRIV-SVM performance was the best, and the R2 and RPD in validation set were 0.73 and 1.82, respectively. This study attempts to demonstrate the importance of weak information variables in the spectra and provides a theoretical basis for revealing the hyperspectral response mechanism of the leaves. [ABSTRACT FROM AUTHOR]

In the transmission to the monitoring point of coal and rock rupture microseism signal by engineering geological media, gob area is often came across, which leads to the mutation of original microseism signal and causes the accuracy decreased and difficulty increased in inversing deformation and fracture of coal or rock conditions and forecasting rock-burst according to microseism signal. To solve this problem, this paper adopted a laboratory simulation test method to study the effects of different width and different height of goaf on microseism signal propagation law and the time domain and frequency domain analysis on such a microseism signal was done. The results of study show that the average speed and energy of the signal are significantly reduced when microseism signal meets the goaf in the transmission; the effect of change of the goaf width on microseism signal is more obvious than change of the goaf height; when passing through the goaf took on low frequency characteristic and the signal center frequency, the bandwidth and the most value all dropped, furthermore, the signal spectrum reduction was more obvious with the goaf becoming bigger. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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BACKGROUND: The drug and thermal effects of moxa-wool moxibustion have been extensively studied in the clinical trials; however, the infrared radiation effects during the moxibustion are rarely reported. OBJECTIVE: To learn the spectrum characteristics of moxa sticks in the traditional moxibustion. METHODS: The herba violae, bog rush, tobacco and moxa were chosen as experimental materials. Furthermore, the moxa sticks of different storage years (1, 3, 10 years), proportions (1: 3, 1:5, 1:10), and places of production (Nanyang, Qiai) were compared. A spectral measurement platform was built by using an optical power meter to analyze the spectral characteristics of the ten kinds of materials above. RESULTS AND CONCLUSION: For the different materials, the herba violae and bog rush could not burn after crushed and the infrared radiation intensity of tobacco was too low, but a strong infrared emission was from the moxa with a wide range of spectrum from ultraviolet to far infrared ray. It was also found that the year of storage, proportion, place of production as well as production process had significant effects on the radiation intensity of the moxa stick. The spectrum ascended at visible-light spectrum region, reached its peak at near-infrared region, and then descended at mid-infrared and far-infrared regions. We analyzed the stability characteristics and relative total intensity of moxa stick based on statistics and mathematics. The results showed that the stability characteristics were proportional to the proportion of moxa wool, and the year of storage had stronger effect on the relative total intensity compared with the proportion of moxa wool. Herein, we systematically analyzed the spectral characteristics of different moxa sticks, thereby providing the scientific basic data for the study on the optical radiation of moxa-wool moxibustion [ABSTRACT FROM AUTHOR]

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Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)