scholarly journals Evaluation of MOPITT Version 7 joint TIR–NIR X<sub>CO</sub> retrievals with TCCON

2019 ◽  
Vol 12 (10) ◽  
pp. 5547-5572 ◽  
Author(s):  
Jacob K. Hedelius ◽  
Tai-Long He ◽  
Dylan B. A. Jones ◽  
Bianca C. Baier ◽  
Rebecca R. Buchholz ◽  
...  
Keyword(s):  
Bias Correction ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Small Region ◽  
Global Scale ◽  
Total Carbon ◽  
Along Track ◽  
Global Bias ◽  
Bias Corrections

Abstract. Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

scholarly journals Evaluation of MOPITT version 7 joint TIR-NIR X<sub>CO</sub> retrievals with TCCON

2019 ◽  
Author(s):  
Jacob K. Hedelius ◽  
Tai-Long He ◽  
Dylan B. A. Jones ◽  
Rebecca R. Buchholz ◽  
Martine De Mazière ◽  
...  
Keyword(s):  
Bias Correction ◽  
Mean Squared Error ◽  
Small Region ◽  
Global Scale ◽  
Total Carbon ◽  
Accuracy And Precision ◽  
Along Track ◽  
Global Bias ◽  
Better Than ◽  
Bias Corrections

Abstract. Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

scholarly journals A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio

2020 ◽  
Vol 12 (22) ◽  
pp. 3804
Author(s):  
B. G. Mousa ◽  
Hong Shu ◽  
Mohamed Freeshah ◽  
Aqil Tariq
Keyword(s):  
Soil Moisture ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Global Scale ◽  
Signal To Noise ◽  
Significance Levels ◽  
Land Data Assimilation ◽  
Land Data Assimilation System

In this research, we developed and evaluated a new scheme for merging soil moisture (SM) retrievals from both passive and active microwave satellite estimates, based on maximized signal-to-noise ratios, in order to produce improved SM products using least-squares theory. The fractional mean-squared-error (fMSE) derived from the triple collocation method (TCM) was used for this purpose. The proposed scheme was applied by using a threshold between signal and noise at fMSE equal to 0.5 to maintain the high-quality SM observations. In the regions where TCM is unreliable, we propose four scenarios based on the determinations of correlations between all three SM products of TCM at significance levels (i.e., p-values). The proposed scheme was applied to combine SM retrievals from Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) to produce SMAP+ASCAT and AMSR2+ASCAT SM datasets at a global scale for the period from June 2015 to December 2017. The merged SM dataset performance was assessed against SM data from ground measurements of international soil moisture network (ISMN), Global Land Data Assimilation System-Noah (GLDAS-Noah) and ERA5. The results show that the two merged SM datasets showed significant improvement over their parent products in the high average temporal correlation coefficients (R) and the lowest root mean squared difference (RMSE), compared with in-situ measurements over different networks of ISMN. Moreover, these datasets outperformed their parent products over different land cover types in most regions of the world, with a high overall average temporal R and the lowest overall average RMSE value with GLDAS and ERA5. In addition, the suggested scenarios improved SM performance in the regions with unreliable TCMs.

scholarly journals Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data

2019 ◽  
Vol 11 (3) ◽  
pp. 290 ◽  
Author(s):  
Tran Thi Ngoc Trieu ◽  
Isamu Morino ◽  
Hirofumi Ohyama ◽  
Osamu Uchino ◽  
Ralf Sussmann ◽  
...  
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Regression Method ◽  
Total Carbon ◽  
Multilinear Regression ◽  
Bias Correction Method ◽  
Empirical Correction ◽  
Systematic Biases ◽  
Temporal And Spatial ◽  
Global Bias

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5° 0.5° latitude longitude box centered at each TCCON site matched with TCCON XH2O data averaged over ±15 min of the GOSAT overpass time. Considering land data, the global bias changed from −1.3 ± 9.3% to −2.2 ± 8.5%, and station bias from −2.3 ± 9.0% to −1.7 ± 8.4%. In mixed land and ocean data, global bias and station bias changed from −0.3 ± 7.6% and −1.9 ± 7.1% to −0.8 ± 7.2% and −2.3 ± 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods.

scholarly journals Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Meryl L. McDowell ◽  
Gregory L. Bruland ◽  
Jonathan L. Deenik ◽  
Sabine Grunwald
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Spectral Classification ◽  
Sample Set

Subsetting of samples is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subsetting by soil total carbon (Ct) content, soil order, and spectral classification withk-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models forCtprediction. Our sample set was composed of various Hawaiian soils from primarily agricultural lands withCtcontents from <1% to 56%. Slight improvements in the coefficient of determination (R2) and other standard model quality parameters were observed in the models for the subset of the high activity clay soil orders compared to the models of the full sample set. The other subset models explored did not exhibit improvement across all parameters. Models created from subsets consisting of only lowCtsamples (e.g.,Ct< 10%) showed improvement in the root mean squared error (RMSE) and percent error of prediction for lowCtsoil samples. These results provide a basis for future study of practical subsetting strategies for soilCtprediction.

scholarly journals Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X<sub>CO<sub>2</sub></sub> measurements with TCCON

2016 ◽  
Author(s):  
Debra Wunch ◽  
Paul O. Wennberg ◽  
Gregory Osterman ◽  
Brendan Fisher ◽  
Bret Naylor ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Data Quality ◽  
Surface Properties ◽  
Bias Correction ◽  
Global Scale ◽  
Total Carbon ◽  
Dry Air ◽  
Earth’S Atmosphere ◽  
Aerosol Scattering ◽  
Earth's Atmosphere

Abstract. NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry air mole fractions, XCO2, in the Earth’s atmosphere for almost two years. In this paper, we describe the comparisons between the OCO-2 version 7Br retrievals and XCO2 estimates from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after bias correction, agree well globally with the TCCON for nadir, glint, and target observations, with median differences less than 0.5 ppm and RMS differences typically below 1.5 ppm. Target observations over TCCON stations correlate best with the TCCON data (R2 = 0.83) on a global scale. At local scales, the target comparisons reveal residual biases likely related to surface properties and aerosol scattering. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

scholarly journals Assimilation of atmospheric methane products in the MACC-II system: from SCIAMACHY to TANSO and IASI

2014 ◽  
Vol 14 (2) ◽  
pp. 2553-2599
Author(s):  
S. Massart ◽  
A. Agusti-Panareda ◽  
I. Aben ◽  
A. Butz ◽  
F. Chevallier ◽  
...  
Keyword(s):  
Near Infrared ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Winter Period ◽  
Tropical Region ◽  
Atmospheric Methane ◽  
Atmospheric Sounding ◽  
Scanning Imaging ◽  
Global Bias ◽  
The Impact

Abstract. The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board Envisat. Recently the \\textit{Laboratoire de Météorologie Dynamique} (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system has to now rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. This corresponds to a period during which the performance of SCIAMACHY was deteriorating. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and the methane column by 23 ppb compared the Total Carbon Column Observing Network (TCCON) data, when the global bias of the free run against the same HIPPO and TCCON data is respectively −5 ppb and 4 ppb. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and the column by 11 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 10 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, the resulting analysis is slightly less biased in the troposphere than the TANSO analysis when compared to the HIPPO data. In the meantime, this IASI plus TANSO analysis presents the worst correlation with the HIPPO data. In terms of column, the addition of the IASI data to the version v.2.0 of the TANSO data has a significant impact, mainly over extratropical regions, reducing the global bias to 6 ppb. The analysis based on the assimilation of the combination of TANSO and IASI products as well as the SCIAMACHY analysis should nevertheless be used with caution when looking at the seasonal cycle or inter-hemispheric gradient because of the unavailability of the measurements over large regions during the local winter period.

scholarly journals XCO<sub>2</sub> retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

2021 ◽  
Vol 14 (5) ◽  
pp. 3837-3869
Author(s):  
Stefan Noël ◽  
Maximilian Reuter ◽  
Michael Buchwitz ◽  
Jakob Borchardt ◽  
Michael Hilker ◽  
...  
Keyword(s):  
Water Vapour ◽  
Bias Correction ◽  
Near Infrared ◽  
Temporal Variations ◽  
Total Carbon ◽  
Time Interval ◽  
Data Sets ◽  
Cloud Detection ◽  
First Results ◽  
Shortwave Infrared

Abstract. Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT's wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON.

scholarly journals An eleven year record of XCO<sub>2</sub> estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

2021 ◽  
Author(s):  
Thomas E. Taylor ◽  
Christopher W. O'Dell ◽  
David Crisp ◽  
Akhiko Kuze ◽  
Hannakaisa Lindqvist ◽  
...  
Keyword(s):  
Bias Correction ◽  
Near Infrared ◽  
Earth Science ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Science Data ◽  
Modeling Systems ◽  
Quality Filtering ◽  
Carbon Dioxide Co2 ◽  
Earth Science Data

Abstract. The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (< 1M out of 24M). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (one sigma) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Similarly, global mean biases are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of order 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data. This dataset should be especially useful for studies of carbon cycle phenomena that span a full decade or more, and may serve as a useful complement to the shorter OCO-2 v10 dataset, which begins in September 2014.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

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