Browsing by Author "Nawar, Said"

Now showing 1 - 7 of 7
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    Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy
    (Elsevier, 2015-08-14) Nawar, Said; Buddenbaum, Henning; Hill, Joachim; Kozak, Jacek; Mouazen, Abdul M.
    The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content.
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    Evaluation of vis-NIR reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils
    (Cranfield University, 2018-01-18 08:49) Coulon, Frederic; Douglas, Reward; del carmen Alamar Gavidia, Maria; Mouazen, Abdul; Nawar, Said
    Underpinning data on1. hydrocarbons data analysis by GCMS - quantification and 2. vis-NIR spectra analysis and chemometrics
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    Modelling the influence of soil properties on crop yields using a non-linear NFIR model and laboratory data
    (MDPI, 2021-02-16) Whetton, Rebecca L.; Zhao, Yifan; Nawar, Said; Mouazen, Abdul M.
    This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model.
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    Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression em
    (Cranfield University, 2018-12-04 12:08) Coulon, Frederic; Cipullo, Sabrina; Campo Moreno, Pablo; Mouazen, Abdul; Nawar, Said
    Raw data for total and bioavailable concentrations of petroleum hydrocarbons compounds, heavy metals and metalloids in the five soils.Spectra raw data are also provided
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    Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques
    (Elsevier, 2016-12-22) Nawar, Said; Mouazen, Abdul Mounem
    The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties based on mobile measurements is essential for site specific soil management at fine sampling scale. The objective of the present study was to compare the mobile and laboratory prediction performance of vis-NIR spectroscopy for total nitrogen (TN), total carbon (TC) and soil moisture content (MC) of field soil samples based on single field (SFD), two-field dataset (TFD), UK national dataset (UND) and European continental dataset (ECD) calibration models developed with linear and nonlinear data mining techniques including spiking. Fresh soil samples collected from fields in the UK, Czech Republic, Germany, Denmark and the Netherlands were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration (75%) and validation (25%) sets, spectra in the calibration set were subjected to three multivariate calibration models, including the partial least squares regression (PLSR), multivariate adaptive regression splines (MARS) and support vector machines (SVM), with leave-one-out cross-validation to establish calibration models of TN, TC and MC. Results showed that the best model performance in cross-validation was obtained with MARS methods for the majority of dataset scales used, whereas the lowest model performance was obtained with the SFD. The effect of spiking was significant and the best model performance in general term was obtained when local samples collected from two target fields in the UK were spiked with the ECD, with coefficients of determination (R2) values of 0.96, 0.98 and 0.93, root mean square error (RMSE) of 0.01, 0.1 and 1.75, and ratio of performance to interquartile distance (RPIQ) of 7.46, 6.57 and 3.98, for TC, TN and MC, respectively. Therefore, these results suggest that ECD vis-NIR MARS calibration models can be successfully used to predict TN, TC and MC under both laboratory and mobile measurement conditions.
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    Rapid detection of alkanes and polycyclic aromatic hydrocarbons
    (Cranfield University, 2018-01-18T08:53:28Z) Coulon, Frederic; Mouazen, Abdul; Nawar, Said; del carmen Alamar Gavidia, Maria; Douglas, Reward
    Vis-NIR data spectra analysis and chemometric modelling
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    Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques
    (Elsevier, 2017-11-09) Douglas, Reward K.; Nawar, Said; Alamar, M Carmen; Mouazen, Abdul; Coulon, Frederic
    Petroleum hydrocarbons contamination in soil is a worldwide significant environmental issue which has raised serious concerns for the environment and human health (Brevik and Burgess, 2013). Petroleum hydrocarbons encompass a mixture of short and long-chain hydrocarbon compounds. However the difference between the term petroleum hydrocarbons (PHC) as such and the term total petroleum hydrocarbons (TPH) should be noted. PHC typically refer to the hydrogen and carbon containing compounds that originate from crude oil, while TPH refer to the measurable amount of petroleum-based hydrocarbons in an environmental matrix and thus to the actual results obtained by sampling and chemical analysis (Coulon and Wu, 2017). TPH is thus a method-defined term. Among a range of techniques, gas chromatography is preferred for the measurement of hydrocarbon contamination in environmental samples, since it allows to detect a broad range of hydrocarbons and can provide both sensitivity and selectivity depending on the detector and hyphenated configuration used (Brassington et al., 2010; Drozdova et al., 2013). However, GC-based techniques can be time consuming and expensive and do not allowed rapid and broad scale analysis of petroleum contamination on-site (Okparanma and Mouazen, 2013; Okparanma et al., 2014).