scholarly journals The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco)

2020 ◽  
Vol 48 (1) ◽  
Author(s):  
Ayoub Lazaar ◽  
◽  
Kamal El Hammouti ◽  
Zakariae Naiji ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Soil Texture ◽  
Near Infrared ◽  
Prediction Models ◽  
Root Zone ◽  
Clay Fraction ◽  
Coefficient Of Determination ◽  
Good Prediction ◽  
Sand Fraction ◽  
Silt Fraction ◽  
North East

The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R2 = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R2=0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R2 = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference.

scholarly journals Geophysical mapping of soil texture variability in the root zone to improve modelling of the water and nutrient flow.

2020 ◽  
Author(s):  
Kim Schwartz Madsen ◽  
Bo Vangsø Iversen ◽  
Christen Duus Børgesen
Keyword(s):  
Soil Texture ◽  
Hydraulic Properties ◽  
Root Zone ◽  
Clay Fraction ◽  
Clay Content ◽  
Auxiliary Variable ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Sampling Density ◽  
Soil Hydraulic

<p>Modelling is often used to acquire information on water and nutrient fluxes within and out of the root zone. The models require detailed information on the spatial variability of soil hydraulic properties derived from soil texture and other soil characteristics using pedotransfer functions (PTFs). Soil texture can vary considerably within a field and is cumbersome and expensive to map in details using traditionally measurements in the laboratory. The electrical conductivity (EC) of the soil have shown to correlate with its textural composition.</p><p>This study investigates the ability of electromagnetic induction (EMI) methods to predict clay content in three soil layers of the root zone. As the clay fraction often is a main predictor in PTFs predicting soil hydraulic properties this parameter is of high interest. EMI and soil textural surveys on four Danish agricultural fields with varying textural composition were used. Sampling density varied between 0.5 and 38 points per hectare. The EMI data was gathered with a Dualem21 instrument with a sampling density 200-3000 points per hectare. The EC values were used together with the measured values of the clay content creating a statistical relationship between the two variables. Co-kriging of the clay content from the textural sampling points with the EC as auxiliary variable produces clay content maps of the fields. Unused (80%) texture points were used for validation. EMI-predicted clay content maps and clay content maps based on the survey were compared. The two sets of soil texture maps are used as predictors for PTF models to predict soil hydraulic properties as input in field-scale root zone modelling.</p><p>The comparisons between EC and clay content show some degree of correlation with an R<sup>2</sup> in the range of 0.55 to 0.80 for the four fields. The field with the highest average clay content showed the best relationship between the two parameters. Co-kriging with EC decreased mean error by 0.016 to 0.52 and RMSE by 0.04 to 1.80 between observed and predicted clay maps.</p>

scholarly journals Characterization and Classification of Soils of Jalingo Metropolis, North-east, Nigeria

2018 ◽  
pp. 72-80
Author(s):  
Osujieke D.N ◽  
Obasi N.S. ◽  
Imadojemu P.E ◽  
Ekawa M. ◽  
Angyu M.D.
Keyword(s):  
Soil Depth ◽  
Clay Fraction ◽  
Exchange Capacity ◽  
Descriptive Statistics ◽  
Soil Taxonomy ◽  
Sand Fraction ◽  
Reference Base ◽  
North East ◽  
World Reference Base

The study was aimed at the characterizing and the classifying of soils of Jalingo metropo- lis in Taraba State, North-East Nigeria. Profile pit was dug on each of the three different sites of the study area as identified using free survey. The profile pits were described and sampled bases on horizon differentiation for laboratory analyses. A total of 10 samples were collected. Data generated were analyzed using descriptive statistics to determine their coefficient of variation. The result indicated that the horizons were mostly reddish when moist at different contrasting level. The textural classes were mostly loamy sand while the sub-angular blocky structure was observed in the entire subsurface horizons. The horizons of the pedons were well drained. Sand fraction had means of 826.80 g/kg, 816.80 g/kg and 766.8 g/kg for pedons 1, 2, and 3 respectively. Clay fraction increased in an in- creasing soil depth which formed an argillic horizon. Sand fraction, bulk density and parti- cle density recorded low variation (≥0 % ≤5.22 %) in among the pedons. Soil pH(H2O) had a mean of 6.40 in pedon 1, 6.43 in pedon 2 and 6.41 in pedon 3. Organic carbon ranged from ≥2.0 g/kg ≤0.43 g/kg while cation exchange capacity ranged from ≥4.58 cmol/kg ≤5.01 cmol/kg among the pedons. The percent base saturation had a mean of 66.6 %, 65.1 % and 66 % in pedon 1, 2 and 3. Hence, pedons 1 and 2 were classified as Grossarenic Kandiustalfs (Arenic Lixisols), while pedon 3 was classified as Arenic Kandi- ustalfs (Loamic Lixisols) according to USDA soil taxonomy and correlated with world reference base.

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.

Improvement of Prediction Models for Nondestructive Detection of TVB-N Using Dual-Band Vis/NIR Spectroscopic Technique

2017 ◽  
Vol 60 (4) ◽  
pp. 1075-1082 ◽  
Author(s):  
Wenxiu Wang ◽  
Yankun Peng
Keyword(s):  
Light Source ◽  
Spectral Region ◽  
Near Infrared ◽  
Prediction Models ◽  
Great Influence ◽  
Nir Spectroscopy ◽  
Dual Band ◽  
Coefficient Of Determination ◽  
Band Selection ◽  
Nondestructive Detection

Abstract. This article discusses the influence of light source and band selection on prediction model performance. Two spectra acquisition systems for visible (Vis) and near-infrared (NIR) spectroscopy with a ring light source and a point light source were set up and compared based on the coefficient of variation (CV), signal-to-noise ratio (SNR), spectrum area change rate (ACR), and model results. Reflectance spectra of 61 pork samples were collected, and anomalous samples were eliminated by Monte Carlo method based on model cluster analysis. Partial least squares (PLS) models for total volatile basic nitrogen (TVB-N) based on a single spectral region (350-1100 nm or 1000-2500 nm) and a dual spectral region (350-2500 nm) were built to compare the influence of band choice. Based on the optimal chosen band, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS), and a new PLS model was established. The results showed that spectra acquired with the ring light source had better stability and achieved optimal prediction models. The dual spectral region, which contained more comprehensive information on TVB-N, yielded better results than any single spectral region. Based on the dual-band spectra, a simplified PLS model using feature variables achieved a coefficient of determination in the prediction set (Rp2) of 0.8767 and standard error of prediction (SEP) of 2.8354 mg per 100 g. The results demonstrated that the choice of light source and modeling band had great influence on prediction results, and improvement of models would promote the application of Vis/NIR spectroscopy in on-line or portable detection. Keywords: Band selection, Light source, Nondestructive detection, Pork, TVB-N, Vis/NIR spectroscopy.

scholarly journals Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1975
Author(s):  
Yanlei Li ◽  
Xiaochun Zheng ◽  
Dequan Zhang ◽  
Xin Li ◽  
Fei Fang ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
Shear Force ◽  
Near Infrared ◽  
Prediction Models ◽  
Simultaneous Detection ◽  
Nir Spectroscopy ◽  
Cooking Loss ◽  
Coefficient Of Determination ◽  
Sheep Meat ◽  
Different Types

The visible and near-infrared spectroscopy (Vis/NIRS) models for sheep meat quality evaluation using only one type of meat cut are not suitable for other types. In this study, a novel portable Vis/NIRS system was used to simultaneously detect physicochemical properties (pH, color L*, a*, b*, cooking loss, and shear force) for different types of sheep meat cut, including silverside, back strap, oyster, fillet, thick flank, and tenderloin cuts. The results show that the predictive abilities for all parameters could be effectively improved by spectral preprocessing. The coefficient of determination (Rp2) and residual predictive deviation (RPD) of the optimal prediction models for pH, L*, a*, b*, cooking loss, and shear force were 0.79 and 3.50, 0.78 and 2.28, 0.68 and 2.46, 0.75 and 2.62, 0.77 and 2.19, and 0.83 and 2.81, respectively. The findings demonstrate that Vis/NIR spectroscopy is a useful tool for predicting the physicochemical properties of different types of meat cut.

scholarly journals NIR SPECTROSCOPIC MODELS FOR PHENOTYPING WOOD TRAITS IN BREEDING PROGRAMS OF Eucalyptus benthamii

CERNE ◽  
2017 ◽  
Vol 23 (3) ◽  
pp. 367-375 ◽  
Author(s):  
Regiane Abjaud Estopa ◽  
Flaviana Reis Milagres ◽  
Ricardo Augusto Oliveira ◽  
Paulo Ricardo Gherardi Hein
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Genetic Selection ◽  
Basic Density ◽  
Chemical Properties ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Breeding Programs ◽  
Total Carbohydrates ◽  
Wood Traits

ABSTRACT Wood characterization must be done in huge populations of Eucalyptus breeding programs in order to efficiently select potential trees. In this study, Eucalyptus benthamii wood was non-destructively characterized and the performance of near infrared (NIR) spectroscopy in estimating the wood basic density, lignin, extractive, glucose, xylan contents and total carbohydrates was evaluated. NIR models for wood traits were performed from 481 trees from E. benthamii progeny test (4-year-old) managed for pulp cultivated in Santa Catarina state, Southern Brazil. Increment cores were sampled for chemical and physical characterization in laboratory, as well as for NIR spectroscopy analyses. Three 350 samples were selected from PCA for model calibrations whereas 131 were reserved for independent test validation. The E. benthamii wood presented the standards required for Kraft pulp processing. The predictive NIR models showed satisfactory ability for estimating the chemical properties of wood. The prediction models for total lignin, extractive and xylan contents and total carbohydrates showed coefficients of determination of 0.53, 0.65; 0.36 and 0.53, with RPD values for these traits ranging from 1.3 to 2.3. The predictive model for basic density of wood and glucose presented low coefficient of determination (0.13 and 0.10). However, isn’t possible to use these models for ranking in genetic selection because there was no correlation. Therefore, NIR spectroscopy can potentially be applied in breeding programs, as it enables an early, non-destructive selection of trees with adequate physical and chemical properties for pulp production process.

scholarly journals Application of Visible/Near Infrared Spectrometers to Quickly Detect the Nitrogen, Phosphorus, and Potassium Content of Chemical Fertilizers

2021 ◽  
Vol 11 (11) ◽  
pp. 5103
Author(s):  
Jiangang Shen ◽  
Weiming Qiao ◽  
Huizhe Chen ◽  
Jun Zhou ◽  
Fei Liu
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Rapid Determination ◽  
Coefficient Of Determination ◽  
Chemical Fertilizers ◽  
Infrared Band ◽  
Full Spectrum ◽  
Characteristic Wavelength ◽  
Phosphorus And Potassium ◽  
Nitrogen Phosphorus

The rapid determination of nitrogen, phosphorus, potassium and other major nutrient elements is an important technical guarantee in the quality control of chemical fertilizers. In this study, a small visible spectrometer and a small near-infrared spectrometer were used to collect spectrum information of 33 different common chemical fertilizers including compound fertilizers, blended fertilizers and controlled-release fertilizers. The 550~950 nm and 1050~1640 nm spectra with stable signals were intercepted as the analysis spectrum, and the competitive adaptive reweighted sampling algorithm (CARS) was used to select 161, 229, and 161 spectral characteristic wavelengths for the three nutrient contents of N, P2O5, and K2O respectively. The partial least squares (PLS) and extreme learning machine (ELM) models of N, P2O5, and K2O were established based on the 550~950 nm waveband, 1050~1640 nm waveband, full spectrum, and characteristic wavelength, respectively. The coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD) were used to evaluate the effect of the model. With the optimal prediction models, the values of Rp2 for N, P2O5, and K2O were 0.989, 0.963, 0.981, and for RPD were 9.71, 5.09, 7.29, respectively. The research results show that Vis/NIR spectroscopy can predict the content of nitrogen, phosphorus, and potassium nutrients in fertilizers, and the near-infrared band from 1050 nm to 1640 nm has a better prediction effect. The characteristic wavelength selection reduces the spectral variables by 9/10, and the performance of the model based on the characteristic wavelength is close to that of the full-spectrum model.

scholarly journals Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

Animals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 640 ◽  
Author(s):  
Goi ◽  
Manuelian ◽  
Currò ◽  
Marchi
Keyword(s):  
Mineral Content ◽  
Near Infrared ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Emission Spectrometry ◽  
Near Infrared Reflectance ◽  
Dog Food

The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. This study assessed the feasibility of near-infrared spectroscopy to predict mineral content in extruded dry dog food. Mineral content in commercial dry dog food samples (n = 119) was quantified by inductively coupled plasma optical emission spectrometry and reflectance spectra (850–2500 nm) captured with FOSS NIRS DS2500 spectrometer. Calibration models were built using modified partial least square regression and leave-one-out cross-validation. The best prediction models were obtained for S (coefficient of determination; R2 = 0.89), K (R2 = 0.85), and Li (R2 = 0.74), followed by P, B, and Sr (R2 = 0.72 each). Only prediction models for S and K were adequate for screening purposes. This study supports that minerals are difficult to determine with NIRS if they are not associated with organic molecules.

scholarly journals Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Haiqing Yang ◽  
Kuichuan Sheng
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calorific Value ◽  
Volatile Matter ◽  
Coefficient Of Determination ◽  
Single Type ◽  
Good Prediction ◽  
Cotton Stalk ◽  
Fixed Carbon

Rapid characterization of biochar for energy and ecological purpose utilization is fundamental. In this work, visible and near-infrared (vis-NIR) spectroscopy was used to measure ash, volatile matter, fixed carbon contents, and calorific value of three types of biochar produced from pine wood, cedar wood, and cotton stalk, respectively. The vis-NIR spectroscopy was also used to discriminate biochar feedstock types and pyrolysis temperature. Prediction result shows that partial least squares (PLS) regression calibrating the spectra to the values of biochar properties achieved very good or excellent performance with coefficient of determination (R2) of 0.86~0.91 and residual prediction deviation (RPD) of 2.58~3.32 for ash, volatile matter, and fixed carbon, and good prediction with R2 of 0.81 and RPD of 2.30 for calorific value. Linear discrimination analysis (LDA) of the principal components (PCs) produced from PCA of wavelength matrix shows that three types of biochar can be successfully discriminated with 95.2% accuracy. The classification of biochar with different pyrolysis temperatures can be conducted with 69% accuracy for all three types and 100% accuracy for single type of cotton stalk. This experiment suggests that the vis-NIR spectroscopy is promising as an alternative of traditionally quantitative and qualitative analysis of biochar properties.

Near-Infrared Spectroscopic Investigation of the Hydrothermal Degradation Mechanism of Wood as an Analogue of Archaeological Objects. Part I: Softwood

2008 ◽  
Vol 62 (11) ◽  
pp. 1209-1215 ◽  
Author(s):  
Tetsuya Inagaki ◽  
Katsuya Mitsui ◽  
Satoru Tsuchikawa
Keyword(s):  
Young’S Modulus ◽  
Near Infrared ◽  
Prediction Models ◽  
Young's Modulus ◽  
Degradation Mechanism ◽  
Good Prediction ◽  
Cellulose Crystallinity ◽  
Independent Variables ◽  
Hydrothermal Degradation ◽  
Archaeological Objects

The degradation mechanism of softwood due to the variation of strength was analyzed in conjunction with spectroscopy and chemometrics, where the sample was thermally treated with a steam atmosphere. Near-infrared (NIR) spectra, chemical composition, oven-dried density, equilibrium moisture content, compressive Young's modulus parallel to the grain, and cellulose crystallinity of artificially degraded hinoki cypresses as an analogue of archaeological objects were systematically measured. Partial least squares (PLS) regression analysis was employed to predict compressive Young's modulus using NIR spectra and some kinds of wood properties as independent variables. Good prediction models were obtained for both independent variables. The scores and the loading plots derived from PLS analysis were applied to consistently explain the mechanism of hydrothermal degradation. It was suggested that the variation of compressive Young's modulus with hydrothermal treatment was governed by two main components, that is, depolymerization of polysaccharides and variation of cellulose crystallinity.

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