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1
INGLING, C. R., JONATHAN P. BARLEY, and NADEEM GHANI. "Chromatic Content of Spectral Lights." Vision Research 36, no. 16 (August 1996): 2537–51. http://dx.doi.org/10.1016/0042-6989(95)00308-8.
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Qi, Haixia, Bingyu Zhu, Lingxi Kong, Weiguang Yang, Jun Zou, Yubin Lan, and Lei Zhang. "Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves." Applied Sciences 10, no. 7 (March 26, 2020): 2259. http://dx.doi.org/10.3390/app10072259.
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The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.
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Chudnovsky, A. A., E. Ben-Dor, and H. Saaroni. "Reflectance spectroscopy of indoor settled dust in Tel Aviv, Israel: comparison between the spring and the summer seasons." Advances in Geosciences 12 (July 4, 2007): 51–57. http://dx.doi.org/10.5194/adgeo-12-51-2007.
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Abstract. The influence of mineral and anthropogenic dust components on the VIS-NIR-SWIR spectral reflectance of artificial laboratory dust mixtures was evaluated and used in combination with Partial Least Squares (PLS) regression to construct a model that correlates the dust content with its reflectance. Small amounts of dust (0.018–0.33 mg/cm2) were collected using glass traps placed in different indoor environments in Tel Aviv, Israel during the spring and summer of 2005. The constructed model was applied to reflectance spectroscopy measurements derived from the field dust samples to assess their mineral content. Additionally, field samples were examined using Principal Component Analysis (PCA) to identify the most representative spectral pattern for each season. Across the visible range of spectra two main spectral shapes were observed, convex and concave, though spectra exhibiting hybrid shapes were also seen. Spectra derived from spring season dust samples were characterized mostly by a convex shape, which indicates a high mineral content. In contrast, the spectra generated from summer samples were characterized generally by a concave shape, which indicates a high organic matter content. In addition to this seasonal variation in spectral patterns, spectral differences were observed associated with the dwelling position in the city. Samples collected in the city center showed higher organic content, whereas samples taken from locations at the city margins, near the sea and next to open areas, exhibited higher mineral content. We conclude that mineral components originating in the outdoor environment influence indoor dust loads, even when considering relatively small amounts of indoor settled dust. The sensitive spectral-based method developed here has potentially many applications for environmental researchers and policy makers concerned with dust pollution.
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Danner, Martin, Katja Berger, Matthias Wocher, Wolfram Mauser, and Tobias Hank. "Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies." Remote Sensing 11, no. 10 (May 14, 2019): 1150. http://dx.doi.org/10.3390/rs11101150.
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Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5°, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R² = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.
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Zhang, H., T. Q. Song, K. L. Wang, G. X. Wang, H. Hu, and F. P. Zeng. " Prediction of crude protein content in rice grain with canopy spectral reflectance." Plant, Soil and Environment 58, No. 11 (October 31, 2012): 514–20. http://dx.doi.org/10.17221/526/2012-pse.
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Non-destructive and rapid monitoring methods for crude protein content (CPC) in rice grain are of significance in nitrogen diagnosis and grain quality monitoring, and in enhancing nutritional management and use efficiency. In this study, CPC and canopy spectra in rice were measured based on rice field experiment. Key spectral bands were selected by principal component analysis (PCA) method, and the predicted models were built by multiple linear regressions (MLR), artificial neural network (ANN) and partial least squares regression (PLSR). The results showed that there is a significant correlation between CPC content and key spectral bands. The results of prediction for the three models were in order of PLSR > ANN > MLR with correlation values of 0.96, 0.92 and 0.90, respectively, for the validation data. Therefore, it is implied that CPC in rice (grain quality) could be estimated by canopy spectral data.
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JACQUES, STEVEN L. "SPECTRAL IMAGING AND ANALYSIS TO YIELD TISSUE OPTICAL PROPERTIES." Journal of Innovative Optical Health Sciences 02, no. 02 (April 2009): 123–29. http://dx.doi.org/10.1142/s1793545809000528.
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An introduction to the basics of spectral imaging as applied to biological tissues is presented. An example of a spectral image of a face is used to demonstrate the data and spectral analysis that specify the melanin content (M), blood content (B), tissue oxygen saturation (S), water content (W), fraction of scattering due to Rayleigh scattering (f) and due to Mie scattering (1 - f), and the reduced scattering coefficient at 500-nm wavelength (μ′s 500 nm). The sensitivity of reflectance spectra to variation in the various parameters is illustrated.
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Rickett, James. "Estimating attenuation and the relative information content of amplitude and phase spectra." GEOPHYSICS 72, no. 1 (January 2007): R19—R27. http://dx.doi.org/10.1190/1.2399451.
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Seismic attenuation affects both the amplitude and phase of seismic waves. Algorithms to estimate attenuation are split among those that use amplitude information (e.g., spectral-ratio method), those that use phase information (e.g., rise-time method), and those that use a combination of both (e.g., time-domain algorithms). In this study, I explore the relative information provided by amplitude and phase spectra. To do this, I show how the difference in phase spectra between waveforms recorded at two depth levels can be used to estimate attenuation. This phase-difference method is analogous to the method of spectral ratios, but uses phase information rather than amplitude information. Under the simplifying assumption that the noise in both log-amplitude and phase spectra can be modeled as uncorrelated Gaussian random variables with equal variance, the posterior variances in the attenuation estimates from the spectral-ratio and phase-difference methods can be compared directly. It turns out that over typical seismic bandwidths and typical levels of attenuation, the relative uncertainty in estimates of attenuation from phase spectra is approximately twice the relative uncertainty in estimates of attenuation from log-amplitude spectra. Including phase and amplitude information simultaneously (as opposed to just amplitude information) reduces the relative uncertainty by only about 10% over seismic bandwidths. This reduction in uncertainty is not large, but may be significant depending on the sensitivity of the application.
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Li, Huan, Changkuan Zhang, Ying Zhang, Dong Zhang, Jay Gao, and Zheng Gong. "Predicting water content using linear spectral mixture model on soil spectra." Journal of Applied Remote Sensing 7, no. 1 (July 8, 2013): 073539. http://dx.doi.org/10.1117/1.jrs.7.073539.
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Jödicke, Katrin, Robin Zirkler, Timo Eckhard, Werner Hofacker, and Bernd Jödicke. "High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems." ChemEngineering 4, no. 1 (February 1, 2020): 8. http://dx.doi.org/10.3390/chemengineering4010008.
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In modern fruit processing technology, non-destructive quality measuring techniques are sought for determining and controlling changes in the optical, structural, and chemical properties of the products. In this context, changes inside the product can be measured during processing. Especially for industrial use, fast, precise, but robust methods are particularly important to obtain high-quality products. In this work, a newly developed multi-spectral imaging system was implemented and adapted for drying processes. Further it was investigated if the system could be used to link changes in the surface spectral reflectance during mango drying with changes in moisture content and contents of chemical components. This was achieved by recovering the spectral reflectance from multi-spectral image data and comparing the spectral changes with changes of the total soluble solids (TSS), pH-value and the relative moisture content xwb of the products. In a first step, the camera was modified to be used in drying, then the changes in the spectra and quality criteria during mango drying were measured. For this, mango slices were dried at air temperatures of 40–80 °C and relative air humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectral imaging system. The quality criteria were then predicted from spectral data. It could be shown that the newly developed multi-spectral imaging system can be used for quality control in fruit drying. There are strong indications as well, that it can be employed for the prediction of chemical quality criteria of mangoes during drying. This way, quality changes can be monitored inline during the process using only one single measuring device.
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Igbinosun, Osazonamen J., Adam P. Bruckner, and Stephen E. Wood. "In Situ Measurements of Water Content for Sub-Surface Planetary Applications Using Near-Infrared Internal Reflection Spectroscopy (IRS) with a Multimode Optical Fiber." Applied Spectroscopy 74, no. 2 (August 23, 2018): 160–67. http://dx.doi.org/10.1177/0003702818781868.
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Results and analysis of internal reflection spectral absorbance experiments are reported for near-infrared (NIR) spectra obtained using an optical fiber sensor system. We present a preliminary study to diagnose the efficacy of our fiber optic system to observe and distinguish various phases of water, i.e., ice, liquid, and adsorbed. This study was motivated by the need for a technique capable of obtaining soil water content measurements in real time and in situ, at low humidity conditions for simulation studies of planetary bodies such as Mars. Spectral signatures were observed for the solid, liquid, and adsorbed phases of water. For all phases, peak absorbance at λ ≈1.45 and 1.94 μm was observed despite slight peak shifting due to dispersion effects. Dispersion effects commonly obscure spectra obtained with internal reflection spectroscopy for particular spectral regions. Here we report a spectral region with minimal distortions. Internal reflection spectra were compared directly to transmission spectra with only slight variations. Spectral matching was performed to determine sample penetration depths for unknown incidence angles. In general, relative absorbance and spectral shifting can distinguish spectra of the various phases of water.
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Holt, Lori L., Andrew J. Lotto, and Keith R. Kluender. "Neighboring spectral content influences vowel identification." Journal of the Acoustical Society of America 108, no. 2 (August 2000): 710–22. http://dx.doi.org/10.1121/1.429604.
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Grzywny, Tomasz, Hyunchul Park, and Renming Song. "Spectral heat content for Lévy processes." Mathematische Nachrichten 292, no. 4 (November 15, 2018): 805–25. http://dx.doi.org/10.1002/mana.201800035.
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Cięszczyk, Sławomir. "A Multi-Band Integrated Virtual Calibration-Inversion Method for Open Path FTIR Spectrometry." Metrology and Measurement Systems 20, no. 2 (June 1, 2013): 287–98. http://dx.doi.org/10.2478/mms-2013-0025.
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Abstract This paper addresses problems arising from in situ measurement of gas content and temperature. Such measurements can be considered indirect. Transmittance or natural radiation of a gas is measured directly. The latter method (spectral radiation measurement) is often called spectral remote sensing. Its primary uses are in astronomy and in the measurement of atmospheric composition. In industrial processes, in situ spectroscopic measurements in the plant are often made with an open path Fourier Transform Infrared (FTIR) spectrometer. The main difficulty in this approach is related to the calibration process, which often cannot be carried out in the manner used in the laboratory. Spectral information can be obtained from open path spectroscopic measurements using mathematical modeling, and by solving the inverse problem. Determination of gas content based on spectral measurements requires comparison of the measured and modeled spectra. This paper proposes a method for the simultaneous use of multiple lines to determine the gas content. The integrated absorptions of many spectral lines permits calculation of the average band absorption. An inverse model based on neural networks is used to determine gas content based on mid-infrared spectra at variable temperatures.
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Cassells, J. A., R. Reuss, B. G. Osborne, and I. J. Wesley. "Near Infrared Spectroscopic Studies of Changes in Stored Grain." Journal of Near Infrared Spectroscopy 15, no. 3 (June 2007): 161–67. http://dx.doi.org/10.1255/jnirs.727.
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The potential for near infrared (NIR) spectroscopy to be used to detect quality changes in stored grain was investigated. Wheat, barley and canola were stored at different temperatures and moisture content for a period of 12 months. NIR reflectance spectra of the samples recorded prior to storage were contrasted against the spectra of the samples stored under various conditions by calculating the root mean squares of the point-for-point spectral differences. The ability of NIR to indicate whether there were changes occurring in the grain was determined by the spectral differences being greater than the differences due to repacking. Changes in NIR spectra were low in grain stored at low temperatures and moisture content, but increased in grain stored under more adverse conditions. For wheat and barley stored for 12 months at 30°C and 14% moisture content, spectral contrasts increased to 1294 and 790 microabsorbance units, respectively. Changes in spectral contrast of canola were higher with contrasts of canola stored for 12 months at 30°C and 8% moisture content reaching 2700 microabsorbance units. In order to confirm that the changes seen in the contrast were due to changes in the grain and not due to the drift in the NIR instrument, a stable chemical standard (polyethylene) was used as a control. The results show that spectral differences can be used to monitor the post-harvest maturity of wheat and barley. Spectral changes observed in standard cells containing wheat and barley decreased after six months. The continual high rate of change observed in spectral differences of canola makes it unsuitable for use in standard cells.
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Martinez, L. J., and A. Ramos. "Estimation of chlorophyll concentration in maize using spectral reflectance." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 65–71. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-65-2015.
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The objective of this research was to identify the relationship between chlorophyll contents and spectral measurements in a maize crop. Spectral measurements were taken and the chlorophyll content was determined in leaf samples in a field experiment with different N rates. Analysis of variance showed differences in the chlorophyll content and spectral indices for the treatments and strong correlations between some reflectance indices and chlorophyll content. Due to a relationship between the N and chlorophyll contents, the results proved to be important for making decisions related to the nitrogen supply for crops, which is crucial in agriculture.
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Devaux, M. F., D. Bertrand, P. Robert, and M. Qannari. "Application of Principal Component Analysis on NIR Spectral Collection after Elimination of Interference by a Least-Squares Procedure." Applied Spectroscopy 42, no. 6 (August 1988): 1020–23. http://dx.doi.org/10.1366/0003702884430443.
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In NIR spectroscopy, multidimensional analyses such as Principal Component Analysis (PCA) may be applied to examine the similarity between spectra of natural products. However, such an approach is often limited by the effect of spectral interference due to water or particle size distribution of the samples. In the present work, the advantage of the elimination of such spectral interference before performing PCA was investigated. Unwanted component spectra were eliminated by a least-squares procedure. They were first orthogonalized and normalized by the Gram-Schmidt orthogonalization method. The subtraction coefficients were then assessed, similarly to principal component (PC) scores, by projection of the NIR spectra on the orthogonalized component spectra, and PCA was performed on the corrected spectra. This method was applied on an illustrative collection of wheat semolina conditioned in three levels of water content. Water was the component to be eliminated and had been previously modeled by two spectral patterns. These spectral patterns were used as the unwanted component spectra. PCA was applied independently before and after spectral correction of the collection of spectra and graphs obtained by the two procedures were compared. The squared correlation coefficient of the 3 first PC scores with water content was 0.979 before correction, with the 3 groups of water content appearing clearly on PCA graphs. After correction, the corresponding squared correlation coefficient for the 7 first PC scores was 0.016. PCA graphs obtained with corrected spectra also showed that the water effect was completely eliminated. At this moment, samples were separated according to their technological nature. The procedure developed may be useful in pattern recognition study and for automatic clustering of NIR spectra. It may also be applied in fields other than NIR spectroscopy.
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Xiong, Yiong, and Yue Ming Yue. "New Spectral Indices as Indicators of Carotenoids Content." Applied Mechanics and Materials 220-223 (November 2012): 2983–86. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2983.
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Carotenoids play a key role in adaptation of plants to light stress and other unfavorable ecological conditions. Remote sensing of carotenoids content still not well developed due to the absorptions overlap of chlorophyll a and carotenoids in the visible spectral range. In this study, for accurate estimation of carotenoids contents, an assumption of absorption in [450, 550nm] is proposed. That is, the spectral reflectance features in this range are comprehensive effects of chlorophyll (chl) and carotenoids (car) absorption. And the effects of chlorophyll and carotenoids were multiplicative in [450, 550nm] and a new spectral index was defined. The results showed that the new spectral index was strongly related to carotenoids and could be applied as the indicator of carotenoids content.
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Wang, Qiyuan, Yanling Zhao, Feifei Yang, Tao Liu, Wu Xiao, and Haiyuan Sun. "Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators." Remote Sensing 13, no. 13 (July 5, 2021): 2634. http://dx.doi.org/10.3390/rs13132634.
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Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.
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Zhang, Junyi, Hong Sun, Dehua Gao, Lang Qiao, Ning Liu, Minzan Li, and Yao Zhang. "Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis." Remote Sensing 12, no. 17 (August 24, 2020): 2741. http://dx.doi.org/10.3390/rs12172741.
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The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models.
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Hewson, Robert D., Thomas J. Cudahy, Malcolm Jones, and Matilda Thomas. "Investigations into Soil Composition and Texture Using Infrared Spectroscopy (2–14 μm)." Applied and Environmental Soil Science 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/535646.
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The ability of thermal and shortwave infrared spectroscopy to characterise composition and texture was evaluated using both particle size separated soil samples and natural soils. Particle size analysis and separation into clay, silt, and sand-sized soil fractions was undertaken to examine possible relationships between quartz and clay mineral spectral signatures and soil texture. Spectral indices, based on thermal infrared specular and volume scattering features, were found to discriminate clay mineral-rich soil from mostly coarser quartz-rich sandy soil and to a lesser extent from the silty quartz-rich soil. Further investigations were undertaken using spectra and information on 51 USDA and other soils within the ASTER spectral library to test the application of shortwave, mid- and thermal infrared spectral indices for the derivation of clay mineral, quartz, and organic carbon content. A nonlinear correlation between quartz content and a TIR spectral index based on the 8.62 μm was observed. Preliminary efforts at deriving a spectral index for the soil organic carbon content, based on 3.4–3.5 μm fundamental H–C stretching vibration bands, were also undertaken with limited results.
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Fu, Chengbiao, Heigang Xiong, and Anhong Tian. "Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content." Mathematics 6, no. 12 (December 14, 2018): 330. http://dx.doi.org/10.3390/math6120330.
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The study of field spectra based on fractional-order differentials has rarely been reported, and traditional integer-order differentials only perform the derivative calculation for 1st-order or 2nd-order spectrum signals, ignoring the spectral transformation details between 0th-order to 1st-order and 1st-order to 2nd-order, resulting in the problem of low-prediction accuracy. In this paper, a spectral quantitative analysis model of soil-available phosphorus content based on a fractional-order differential is proposed. Firstly, a fractional-order differential was used to perform a derivative calculation of original spectral data from 0th-order to 2nd-order using 0.2-order intervals, to obtain 11 fractional-order spectrum data. Afterwards, seven bands with absolute correlation coefficient greater than 0.5 were selected as sensitive bands. Finally, a stepwise multiple linear regression algorithm was used to establish a spectral estimation model of soil-available phosphorus content under different orders, then the prediction effect of the model under different orders was compared and analyzed. Simulation results show that the best order for a soil-available phosphorus content regression model is a 0.6 fractional-order, the coefficient of determination (), root mean square error (RMSE), and ratio of performance to deviation (RPD) of the best model are 0.7888, 3.348878, and 2.001142, respectively. Since the RPD value is greater than 2, the optimal fractional model established in this study has good quantitative predictive ability for soil-available phosphorus content.
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Bertram, Hanne C., Knud E. Bach Knudsen, Anja Serena, Anders Malmendal, Niels Chr Nielsen, Xavier C. Fretté, and Henrik J. Andersen. "NMR-based metabonomic studies reveal changes in the biochemical profile of plasma and urine from pigs fed high-fibre rye bread." British Journal of Nutrition 95, no. 5 (May 2006): 955–62. http://dx.doi.org/10.1079/bjn20061761.
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This study presents an NMR-based metabonomic approach to elucidate the overall endogenous biochemical effects of a wholegrain diet. Two diets with similar levels of dietary fibre and macronutrients, but with contrasting levels of wholegrain ingredients, were prepared from wholegrain rye (wholegrain diet (WGD)) and non-wholegrain wheat (non-wholegrain diet (NWD)) and fed to four pigs in a crossover design. Plasma samples were collected after 7 d on each diet, and 1H NMR spectra were acquired on these. Partial least squares regression discriminant analysis (PLSDA) on spectra obtained for plasma samples revealed that the spectral region at 3·25 parts per million dominates the differentiation between the two diets, as the WGD is associated with higher spectral intensity in this region. Spiking experiments and LC–MS analyses of the plasma verified that this spectral difference could be ascribed to a significantly higher content of betaine in WGD plasma samples compared with NWD samples. In an identical study with the same diets, urine samples were collected, and1H NMR spectra were acquired on these. PLS-DA on spectra obtained for urine samples revealed changes in the intensities of spectral regions, which could be ascribed to differences in the content of betaine and creatine/creatinine between the two diets, and LC–MS analyses verified a significantly lower content of creatinine in WGD urine samples compared with NWD urine samples. In conclusion, using an explorative approach, the present studies disclosed biochemical effects of a wholegrain diet on plasma betaine content and excretion of betaine and creatinine.
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A. Al-Jubouri, Hanan, and Sawsan M. Mahmmod. "SPECTRAL ALGORITHM FOR CONTENT-BASED IMAGE RETRIEVAL." Journal of Engineering and Sustainable Development 24, no. 05 (September 1, 2020): 37–49. http://dx.doi.org/10.31272/jeasd.24.5.6.
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Ng, Gerald, and H. Steven Colburn. "Median plane localization: dependence on spectral content." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2285. http://dx.doi.org/10.1121/1.1572383.
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Osco, Lucas Prado, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Nayara Estrabis, Felipe Ianczyk, et al. "A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements." Remote Sensing 12, no. 6 (March 12, 2020): 906. http://dx.doi.org/10.3390/rs12060906.
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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.
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Rustioni, Laura, and Davide Bianchi. "Drought increases chlorophyll content in stems of Vitis interspecific hybrids." Theoretical and Experimental Plant Physiology 33, no. 1 (February 10, 2021): 69–78. http://dx.doi.org/10.1007/s40626-021-00195-0.
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AbstractPlants respond to drought stress through different physiological mechanisms. To highlight a specific adaptation strategy related to stem photosynthetic activity, woody tissue pigmentation was studied. In this work, 25 Vitis hybrids were studied in two experimental vineyards: one of them with sufficient water availability and the other showing drought stress. 1500 reflectance spectra of woody tissues were collected. Beside the spectral elaboration and interpretation, indexes for the pigment quantification were calculated. The content and the proportion in chlorophylls (a and b) in stems were significantly modified in response to water stress. Genotypes were classified based on their plasticity/elasticity and adaptive/susceptible behavior. The cluster analysis produced 4 main groups based on their average spectra and on the spectral variations between watered and stress conditions. The most interesting genotypes concerning this trait were pointed out. Nevertheless, this adaptation mechanism could also be due to an increased detoxification necessity related to other physiological disfunctions caused by water stress. A multi-parameter approach is encouraged for phenotyping of abiotic stress tolerant plant selection. In the next future, this work will support the release of new drought tolerant rootstocks for viticulture.
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Gavriely, N., and D. W. Cugell. "Airflow effects on amplitude and spectral content of normal breath sounds." Journal of Applied Physiology 80, no. 1 (January 1, 1996): 5–13. http://dx.doi.org/10.1152/jappl.1996.80.1.5.
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Even though it is well known that breath-sound amplitude (BSA) increases with airflow, the exact quantitative relationships and their distribution within the relevant frequency range have not yet been determined. To evaluate these relationships, the spectral content of tracheal and chest wall breath sounds was measured during breath hold, inspiration, and expiration in six normal men. Average spectra were measured at six flow rates from 0.5 to 3.0 l/s. The areas under the spectral curves of the breath sounds minus the corresponding areas under the breath-hold spectra (BSA) were found to have power relationships with flow (F), best modeled as BSA = k.F alpha, where k and alpha are constants. The overall mean +/- SD value of the power (alpha) was 1.66 +/- 0.35, significantly less than the previously reported second power. Isoflow inspiratory chest wall sound amplitudes were 1.99 +/- 0.70- to 2.43 +/- 0.65-fold larger than the amplitudes of the corresponding expiratory sounds, whereas tracheal sound amplitudes were not dependent on respiratory phase. Isoflow breath sounds from the left posterior base were 32% louder than those from the right lung base (P < 0.01). BSA-F relationships were not frequency dependent during expiration but were significantly stronger in higher than in lower frequencies during inspiration over both posterior bases. These data are compatible with sound generation by turbulent flow in a bifurcating network with 1) flow separation, 2) downstream movement of eddies, and 3) collision of fast-moving cores of the inflowing air with carinas, all occurring during inspiration but not during expiration.
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Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, P. Schluessel, S. M. Newman, J. P. Taylor, and W. Wu. "Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels." Atmospheric Chemistry and Physics Discussions 9, no. 2 (April 1, 2009): 8683–736. http://dx.doi.org/10.5194/acpd-9-8683-2009.
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Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an ultra-spectral satellite sensor with 8461 spectral channels. IASI spectra contain high information content on atmospheric, cloud, and surface properties. The instrument presents a challenge for using thousands of spectral channels in a physical retrieval system or in a Numerical Weather Prediction (NWP) data assimilation system. In this paper we describe a method of simultaneously retrieving atmospheric temperature, moisture, and cloud properties using all available IASI channels without sacrificing computational speed. The essence of the method is to convert the IASI channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Because the EOFs are orthogonal to each other, about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements.
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Merrelli, Aronne, and David D. Turner. "Comparing Information Content of Upwelling Far-Infrared and Midinfrared Radiance Spectra for Clear Atmosphere Profiling." Journal of Atmospheric and Oceanic Technology 29, no. 4 (April 1, 2012): 510–26. http://dx.doi.org/10.1175/jtech-d-11-00113.1.
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Abstract The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.
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Yang, Chwen-Ming. "Estimation of Leaf Nitrogen Content from Spectral Characteristics of Rice Canopy." Scientific World JOURNAL 1 (2001): 81–89. http://dx.doi.org/10.1100/tsw.2001.387.
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Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = �0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.
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Reeves, James B. "Near- versus Mid-Infrared Spectroscopy: Relationships between Spectral Changes Induced by Water and Relative Information Content of the Two Spectral Regions in Regard to High-Moisture Samples." Applied Spectroscopy 49, no. 3 (March 1995): 295–303. http://dx.doi.org/10.1366/0003702953963544.
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The objectives of this work were to examine similarities and differences in the near-infrared and mid-infrared spectral regions when one is working with high-moisture materials and to study spectral changes in these regions as a method to identify the relationship of spectral information in the near-IR to fundamental absorptions in the mid-IR. Near- and mid-infrared spectra were taken with a Digilab FTS-65 Fourier transform spectrometer. Liquids were examined by transmission and solids by reflectance. Results with solutions showed that less spectral distortion arises when one is subtracting water from mid- rather than from near-infrared spectra. It was also easier to produce high-quality spectra in the mid-infrared by using attenuated total reflectance than by using transmission in the near-infrared. While mid-infrared spectra showed changes (induced by water, pH, physical state, and ionic strength) similar to those found in the near-infrared, there appeared to be more information available in the mid-infrared, even in the presence of water.
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Liu, L., and K. Shang. "MINERAL INFORMATION EXTRACTION BASED ON GAOFEN-5’S THERMAL INFRARED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1157–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1157-2018.
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Gaofen-5 carries six instruments aimed at various land and atmosphere applications, and it’s an important unit of China High-resolution Earth Observation System. As Gaofen-5’s thermal infrared payload is similar to that of ASTER, which is widely used in mineral exploration, application of Gaofen-5’s thermal infrared data is discussed regarding its capability in mineral classification and silica content estimation. First, spectra of silicate, carbonate, sulfate minerals from a spectral library are used to conduct spectral feature analysis on Gaofen-5’s thermal infrared emissivities. Spectral indices of band emissivities are proposed, and by setting thresholds of these spectral indices, it can classify three types of minerals mentioned above. This classification method is tested on a simulated Gaofen-5 emissivity image. With samples acquired from the study area, this method is proven to be feasible. Second, with band emissivities of silicate and their silica content from the same spectral library, correlation models have been tried to be built for silica content inversion. However, the highest correlation coefficient is merely 0.592, which is much lower than that of correlation model built on ASTER thermal infrared emissivity. It can be concluded that GF-5’s thermal infrared data can be utilized in mineral classification but not in silica content inversion.
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Tillmann, Peter, and Christian Paul. "The Repeatability File—A Tool for Reducing the Sensitivity of near Infrared Spectroscopy Calibrations to Moisture Variation." Journal of Near Infrared Spectroscopy 6, no. 1 (January 1998): 61–68. http://dx.doi.org/10.1255/jnirs.122.
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The study presented here demonstrates the benefits of using a repeatability file for near infrared (NIR) spectroscopy calibration development. Calibrations were developed from rapeseed samples of a narrow moisture content (2.5–6.0%) and used on a validation set with an extended moisture content (3.6–13.0%). The elevated moisture content of the validation samples led to increased standard errors of prediction ( SEP) for the calibrations predicting oil, protein, GSL and C18:1 content. Two methods were investigated to reduce the moisture sensitivity of the NIR spectroscopy calibrations: (a) the exclusion of water bands in the NIR spectral region during calibration development and (b) the use of a repeatability file as proposed by Westerhaus (1990). The increased SEPs could be decreased by up to 66% by either the exclusion of the water bands or the use of a repeatability file containing spectra of moist and dry samples. The best results were obtained with a repeatability file using spectra from a single sample. A spectral interpretation is given.
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Li, Yuqi, Aditi Majumder, Dongming Lu, and M. Gopi. "Content-Independent Multi-Spectral Display Using Superimposed Projections." Computer Graphics Forum 34, no. 2 (May 2015): 337–48. http://dx.doi.org/10.1111/cgf.12564.
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Ferguson, Katy. "Measuring printer impulses using high frequency spectral content." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2583. http://dx.doi.org/10.1121/1.3588539.
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Hubbard, S. F., R. G. Petschek, and K. D. Singer. "Spectral content and dispersion of hyper-Rayleigh scattering." Optics Letters 21, no. 21 (November 1, 1996): 1774. http://dx.doi.org/10.1364/ol.21.001774.
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Yang, Xiguang, Ying Yu, and Mingze Li. "Estimating soil moisture content using laboratory spectral data." Journal of Forestry Research 30, no. 3 (April 3, 2018): 1073–80. http://dx.doi.org/10.1007/s11676-018-0633-6.
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Liu, Li, Li Yan, Yaocheng Xie, and Jie Xu. "Determination of Fiber Contents in Blended Textiles by NIR Combined with BP Neural Network." ISRN Textiles 2013 (May 19, 2013): 1–5. http://dx.doi.org/10.1155/2013/546481.
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Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.
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Ju, Y., and T. N. Farris. "Spectral Analysis of Two-Dimensional Contact Problems." Journal of Tribology 118, no. 2 (April 1, 1996): 320–28. http://dx.doi.org/10.1115/1.2831303.
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Contact problems can be converted into the spatial frequency domain using Fast Fourier Transform (FFT) techniques. Spectral analysis is used to develop an algebraic relationship between the surface displacement and the contact pressure. This relationship can be used to find the contact pressure or displacement for the contact of smooth surfaces or the complete contact of rough surfaces. In addition to providing rapid, robust solutions to contact problems, the algebraic relationship contains details of the relationship between surface displacement and contact pressure on different length scales. In particular, it is shown that the frequency composition of pressure is similar to that for slope of the surface displacement. Thus, the high frequency content of the surface profile gives rise to high localized contact pressure, in some cases singular pressure for complete contact. However, measurement limitations always lead to the omission of certain high frequency components of the surface profile. Assuming that the high frequency content of the surface profile obeys a power law, spectral analysis is also used to estimate partial contact parameters. This result relates the exponent of the power law to the contact pressure and implied surface integrity. It is concluded that spectral analysis can be combined with the FFT to provide a useful technique for classifying rough surface contacts.
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Tutuncu, Azra N., Augusto L. Podio, and Mukul M. Sharma. "An experimental investigation of factors influencing compressional‐ and shear‐wave velocities and attenuations in tight gas sandstones." GEOPHYSICS 59, no. 1 (January 1994): 77–86. http://dx.doi.org/10.1190/1.1443536.
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Results are presented for compressional and shear velocities and attenuations in fully brine‐saturated tight gas cores with porosities from 3 to 11.9 percent and clay contents from 1 to 38 percent. The influence of porosity, clay content, frequency, and stress on velocities and attenuations were examined using the amplitude spectra of P‐ and S‐waves in the frequency domain. Attenuations of samples were obtained using the spectral ratio method. For a few selected samples the attenuations were also measured using the length correlation method and these results were compared with the spectral ratio results. In tight gas sandstones, the attenuations obtained were 2 to 5 times greater than the attenuation obtained for Berea sandstone. In general, the presence of clay softens the rock grain contacts causing smaller values of compressional ([Formula: see text] and shear ([Formula: see text]) velocities as the clay content increases. However, the [Formula: see text] ratio was found to increase with clay content. Compressional‐and shear‐wave amplitude spectra exhibited a shift in peak frequency toward lower frequencies for samples with higher clay content when compared to clean samples. Velocities and attenuations were found to be frequency dependent, but the positive slope of both compressional and shear attenuations indicate that scattering starts to dominate at the lower frequency end of the ultrasonic measurements. Both [Formula: see text] and [Formula: see text] increased while both compressional and shear attenuations decreased when stress was increased.
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Xue, Yun, Bin Zou, Yimin Wen, Yulong Tu, and Liwei Xiong. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra." Sustainability 12, no. 11 (May 29, 2020): 4441. http://dx.doi.org/10.3390/su12114441.
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Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.
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Pereira, Gustavo Eduardo, Letícia Sequinatto, Jaime Antonio de Almeida, Alexandre Ten Caten, and Josie Moraes Mota. "VIS-NIR spectral reflectance for discretization of soils with high sand content." Semina: Ciências Agrárias 40, no. 1 (February 15, 2019): 99. http://dx.doi.org/10.5433/1679-0359.2019v40n1p99.
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The aim of this study is to evaluate the spatial distribution and relationships between the physicochemical attributes and radiometry of soils with high sand contents. One hundred surface horizon samples were collected for physicochemical and spectral analyses of the soil. The samples were selected spatially by the conditioned Latin hypercube method. The physicochemical analyses consisted of granulometry, organic carbon content, and iron oxides content, extracted using sodium dithionite-citrate-bicarbonate (DCB). The spectral response of the soils was analyzed in the 400 to 1000 nm range. The spectral curves were obtained from the samples of the surface horizons, which were categorized according to the attribute in question. The relationship between the soil physicochemical attributes and soil radiometry was evaluated through a Pearson's correlation. There was a tendency for the organic carbon content to decrease with an increase in soil depth, associated with the presence of soils with higher sand contents. For soils with iron contents lower than 80 g kg-1, there was an increase in the reflectance along the spectrum, whereas for soils with contents between 80 and 160 g kg-1, the reflectance decreased after 600 nm, with greater variation along the spectrum for soils with iron contents higher than 120 g kg-1. The diffuse reflectance spectroscopy could potentially allow for granulometric distinction between the soils evaluated.
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Zhang, Tian-jun, Lei Zhang, Shu-gang Li, Jia-lei Liu, Hong-yu Pan, and Xiang Ji. "Wave Velocity and Power Spectral Density of Hole-Containing Specimens with Different Moisture Content under Uniaxial Compression." Energies 11, no. 11 (November 15, 2018): 3166. http://dx.doi.org/10.3390/en11113166.
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The gas drainage borehole is a typical hole-containing structure, and its failure is similar to the hole-containing specimen. To study the characteristics of wave velocity and power spectral density of the hole-containing specimens with different moisture content, an ultrasonic test of the hole-containing specimens during destruction was carried out. A waveform with different moisture contents was recorded by the RSM-SY7 system. The wave velocity and power spectral density was calculated to analyze its relationship with moisture content and degree of damage. The results show that the wave velocity of the hole-containing specimens gradually decreases during the destruction. There were 3 stages of attenuation of wave velocity during the destruction, which were stable, slightly reduced, and rapidly decreasing stages. Changes in moisture content would cause changes of duration of the three stages. The power spectral density gradually decreases during the destruction. The moisture content affected the attenuation mode of the power spectral density and the amount of attenuation. In the detecting of the gas borehole, the wave velocity and the power spectral density can be used to locate the damage area. This research provides a theoretical basis for detection engineering.
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Wensheng, Wu, Niu Wei, and Luo Li. "Quantitative analysis of neutron-capture gamma-ray energy spectra using direct demodulation." GEOPHYSICS 79, no. 2 (March 1, 2014): D91—D98. http://dx.doi.org/10.1190/geo2013-0296.1.
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Because of the effects of background and measurement environment and multiplet effects of different elements, high-precision analysis of mixed capture [Formula: see text]-ray energy spectra of complicated formations remains challenging for geochemical elemental logging. The direct demodulation (DD) method makes full use of the measured data information, enabling physical constraints to be rationally applied to the spectral analysis process, and can yield high-precision elemental content from poor-statistics, low signal-to-noise ratio, and disturbed data. We construct mixed formations of different sandstones and limestones, mixed formations of sandstone and anhydrite, and more complicated mixed formations of multiple lithologies and employ Monte Carlo numerical simulations to obtain the neutron-capture [Formula: see text]-ray energy spectra of these mixed formations. We then employ the DD method and the weighted-least-squares (WLS) method to analyze quantitatively such mixed spectra, respectively, and compare the results with the actual contents of formation elements. The results indicate that the DD method offers higher precision spectral analysis compared with the results of the WLS method. The results for the capture [Formula: see text]-ray energy spectra of the formation for two actual wells also indicate that the DD method can be useful for spectral analysis in actual application.
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Soom, Andres, and Jern-Wen Chen. "Simulation of Random Surface Roughness-Induced Contact Vibrations at Hertzian Contacts During Steady Sliding." Journal of Tribology 108, no. 1 (January 1, 1986): 123–27. http://dx.doi.org/10.1115/1.3261131.
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Random normal contact vibrations, excited by surface irregularities swept through the contact region of Hertzian contacts during sliding, are studied using digital simulation techniques. The input disturbances are modeled as random time processes with specified spectral content in the spatial wavenumber and frequency domains. The Hertzian contact stiffness is modeled directly or through a bilinear approximation. The contact vibration spectra and resulting mean square contact loading are obtained from the simulations. A comparison with previous measurements shows good agreement between the simulation and experimental results.
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Ye, Wanjun, Yiqian Chen, Chong Gao, Tengfei Xie, Hongjun Jing, and Yousheng Deng. "Experimental Study on the Microstructure and Expansion Characteristics of Paleosol Based on Spectral Scanning." Journal of Spectroscopy 2021 (January 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6689073.
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To investigate the microstructure of paleosol and its expansion characteristics, the paleosol of the Zaosheng #3 tunnel of the Yinxi high-speed railway was studied. Based on X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), nuclear magnetic resonance (NMR), and scanning electron microscopy techniques (SEM), the microstructure of the paleosol was analyzed in terms of the mineral composition, formation elements, pore structure, and particle morphology. Five groups of undisturbed and remolded soils with different moisture contents were tested for the unloaded expansion rate and loaded expansion rate. The results show that the mineral components of the paleosol are mainly quartz, potash feldspar, calcite, and hematite, with the highest-content-component quartz accounting for 45.4% of the total content; the clay mineral composition of the paleosol has the highest content of montmorillonite at 12.3%. The elemental composition of the paleosol is dominated by Al, Si, Ca, and Fe, which form expansive mineral components such as quartz and montmorillonite, creating inherent conditions for expansibility of the paleosol. The T2 distribution curves of the undisturbed and remolded paleosol are composed of three peaks. The pore distribution of paleosol mainly includes medium pores, followed by large pores, and the contents of small pores and superlarge pores are very small. In terms of particle contact, the undisturbed soil is mostly in the form of “surface-surface” and “surface-edge” contact, and the remolded soil is mainly in the form of “point-surface” and “point-point” contact. The unloaded expansion rate of remolded soil is approximately twice that of undisturbed soil. The rate of loaded expansion of both soils decreases with increasing moisture content.
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Bodrogi, Peter, Xue Guo, and Tran Quoc Khanh. "Brightness In The Photopic Range: Psychophysical Modelling With Blue-sensitive Retinal Signals." Light & Engineering, no. 05-2020 (October 2020): 9–24. http://dx.doi.org/10.33383/2020-018.
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The brightness perception of a large (41°) uniform visual field was investigated in a visual psychophysical experiment. Subjects assessed the brightness of 20 light source spectra of different chromaticities at two luminance levels, Lv=267.6 cd/m2 and Lv=24.8 cd/m2. The resulting mean subjective brightness scale values were modelled by a combination of the signals of retinal mechanisms: S-cones, rods, intrinsically photosensitive retinal ganglion cells (ipRGCs) and the difference of the L-cone signal and the M-cone signal. A new quantity, “relative spectral blue content”, was also considered for modelling. This quantity was defined as “the spectral radiance of the light stimulus integrated with the range (380–520) nm, relative to luminance”. The “relative spectral blue content” model could describe the subjective brightness perception of the observers with reasonable accuracy.
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Lestander, Torbjörn A., and Paul Geladi. "NIR spectral information used to predict water content of pine seeds from multivariate calibration." Canadian Journal of Forest Research 35, no. 5 (May 1, 2005): 1139–48. http://dx.doi.org/10.1139/x05-046.
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It is possible to predict the water content (6%32%) of pine seeds single seeds at 30 degree-days and bulk samples at 45 degree-days using multivariate calibration models based on the near infrared (NIR) spectra (11002200 nm) of the seeds. One would expect the water peaks in the NIR spectra to contribute uniquely to the models, but this is not entirely the case. Different ways of studying models and spectra reveal that the contribution of the spectral information to the calibration models is complicated and nonlinear. Model parameters also show contributions from the biological processes in the seeds. Regression models between water content and NIR spectra using biorthogonal partial least squares (BPLS) showed that water content was associated with overtones of HOH, mainly around the peak of water at 19301940 nm, as expected. However, overtones of CH, C=O, and NH also influenced the BPLS models. This was caused by evolving biological phenomena, such as respiration and protein metabolism in imbibed seeds, and hence gave more complex regression models of seedwater interaction. There was also a difference in NH absorption that indicated enhanced protein metabolism at prolonged degree-days.
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Robert, P., D. Bertrand, M. Crochon, and J. Sabino. "A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples." Applied Spectroscopy 43, no. 6 (August 1989): 1045–49. http://dx.doi.org/10.1366/0003702894203723.
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Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.
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Barucci, M. A., P. H. Hasselmann, A. Praet, M. Fulchignoni, J. D. P. Deshapriya, S. Fornasier, F. Merlin, et al. "OSIRIS-REx spectral analysis of (101955) Bennu by multivariate statistics." Astronomy & Astrophysics 637 (May 2020): L4. http://dx.doi.org/10.1051/0004-6361/202038144.
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Contact. The NASA New Frontiers asteroid sample return mission Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) has provided a large amount of data on the asteroid (101955) Bennu, including high-quality spectra obtained by the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS). Aims. To better constrain the surface properties and compositional variations of Bennu, we studied the visible and near-infrared spectral behavior across the asteroid surface by means of a statistical analysis aiming to distinguish spectrally distinct groups, if present. Methods. We applied the G-mode multivariate statistical analysis to the near-infrared OVIRS spectra to obtain an automatic statistical clustering at different confidence levels. Results. The statistical analysis highlights spectral variations on the surface of Bennu. Five distinct spectral groups are identified at a 2σ confidence level. At a higher confidence level of 3σ, no grouping is observed. Conclusions. The results at a 2σ confidence level distinguish a dominant spectral behavior group (group 1, background) and four small groups showing spectral slope variations, associated with areas with different surface properties. The background group contains most of the analyzed data, which implies a globally homogeneous surface at the spectral and spatial resolution of the data. The small groups with redder spectra are concentrated around the equatorial ridge and are associated with morphological surface features such as specific craters and boulders. No significant variation is detected in the band area or depth of the 2.74 μm band, which is associated with hydrated phyllosilicate content. The spectral slope variations are interpreted as a consequence of different regolith particle sizes, and/or porosity, and/or space weathering, that is, the presence of more or less fresh material. The OSIRIS-REx mission primary sampling site, Nightingale, and a boulder known as the Roc, are redder than the background surface.