Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking 

2021 ◽  
Author(s):  
Dongxue Zhao ◽  
Maryem Arshad ◽  
Jie Wang ◽  
John Triantafilis
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
High Rate ◽  
Support Vector ◽  
Least Squares Regression ◽  
Spectral Library ◽  
Calibration Models ◽  
Development Data ◽  
Different Depths ◽  
Spectral Libraries

<p>Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of Australia is becoming depleted of exchangeable (exch.) cations. For long-term development, data on exch. calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) throughout the soil profile is required. However, traditional laboratory analysis is tedious. The visible-near-infrared (Vis-NIR) spectroscopy is an alternative; whereby, spectral libraries are built which couple soil data and Vis-NIR spectra using models. While various models have been used to predict exch. cations, their performance was seldom systematically compared. Moreover, most previous studies have focused on prediction of topsoil (0–0.3 m) exch. cations while the effects of depth on applicability of topsoil spectral libraries are rarely investigated. Our first aim was to determine which model (i.e. partial least squares regression (PLSR), Cubist, random forest (RF), or support vector machine regression (SVMR)) produces the best prediction of topsoil exch. Ca, Mg, K and Na. The second aim was to evaluate if the best topsoil model can be used to predict subsurface (0.3–0.6 m) and subsoil (0.9–1.2 m) cations. The third aim was to explore the effect of spiking on the prediction in subsurface and subsoil. The fourth aim was to see if combining all depths to build a profile spectral library improved prediction. Based on independent validation, PLSR was superior for topsoil exch. cations prediction, while Cubist outperformed PLSR in some cases when spiking was applied, and the profile spectral library was considered. Topsoil PLSR could be applied to predict exch. Ca and Mg in the subsurface and subsoil, while spiking improved prediction. Moreover, a profile spectral library achieved equivalent results with when topsoil samples coupled with spiking were considered. We, therefore, recommended to predict exch. Ca and Mg throughout the profile using topsoil spectral library coupled with spiking approach.</p>

scholarly journals Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

2021 ◽  
Vol 23 (1) ◽  
pp. 220
Author(s):  
Soo-In Sohn ◽  
Subramani Pandian ◽  
John-Lewis Zinia Zaukuu ◽  
Young-Ju Oh ◽  
Soo-Yun Park ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Near Infrared ◽  
Rapid Development ◽  
Nir Spectroscopy ◽  
Gm Crops ◽  
F1 Hybrids ◽  
Support Vector ◽  
Composition Analysis ◽  
Least Squares Regression ◽  
Brassica Napus L

In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

scholarly journals Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6729
Author(s):  
Shree R. S. Dangal ◽  
Jonathan Sanderman
Keyword(s):  
Soil Properties ◽  
Predictive Models ◽  
Soil Samples ◽  
Soil Survey ◽  
Least Squares Regression ◽  
Spectral Library ◽  
Calibration Transfer ◽  
Mid Infrared ◽  
Calibration Models ◽  
Spectral Libraries

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO3, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.

Application of Support Vector Regression for Simultaneous Modelling of near Infrared Spectra from Multiple Process Steps

2015 ◽  
Vol 23 (2) ◽  
pp. 75-84 ◽  
Author(s):  
Rikke Tange ◽  
Morten Arendt Rasmussen ◽  
Eizo Taira ◽  
Rasmus Bro
Keyword(s):  
Near Infrared ◽  
Quality Parameters ◽  
Support Vector ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Global Models ◽  
Task Support ◽  
Global Calibration ◽  
Calibration Models ◽  
The Individual

Near infrared (NIR) spectroscopy in combination with partial least-squares regression (PLS) is widely applied in process control for non-destructive measurement of quality parameters during production. PLS assumes an approximate linear relationship between the parameter to be estimated and the intensity of its absorption bands. Spectra, however, may contain non-linearities for various reasons such as differences in viscosity, temperature, pH, particle size and chemical composition of the sample matrix. In such cases, PLS might not predict the parameter of interest sufficiently well, and one must find other methods for the calibration task. Support vector machine regression (SVR) has been gaining interest within chemometrics in recent years. SVR is capable of modelling highly non-linear data, also when data are of very high dimensions. The aim of this study was to develop calibration models of NIR spectra from four different process steps in a raw-sugar factory. The models were intended for monitoring two quality parameters at the individual process steps. Our goal was to obtain global calibration models covering all four process steps in order to obtain simple calibration maintenance. SVR was used for the calibration task, since all of the above-mentioned sources of non-linearities were present. SVR results were benchmarked against PLS. SVR had a better prediction performance than PLS (1) for models built on individual process steps, (2) for global models covering all four process steps and (3) when the global models were evaluated on the individual process steps. Moreover, the majority of SVR models had prediction errors close to reference uncertainty and hence were close to being optimal. Finally, the global SVR models predicted the individual process steps better than the corresponding local PLS models. We conclude that the nonlinear modelling method SVR was able to model non-linearities caused by pooling NIR spectra from multiple different process steps. Implementation of the global SVR models would have several advantages over the local PLS models. First, they would allow simple calibration maintenance because only one model per quality parameter would have to be maintained. Second, they would allow more precise estimation of the quality parameters and therefore better process monitoring.

scholarly journals Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration

2011 ◽  
Vol 1 ◽  
pp. 92-96 ◽  
Author(s):  
Hai Qing Yang
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Harvest Time ◽  
Least Squares Regression ◽  
Calibration Models ◽  
Optimal Harvest ◽  
Leave One Out

In situ determination of optimal harvest time of tomatoes is of value for growers to optimize fruit picking schedule. This study evaluates the feasibility of using visible and near infrared (VIS-NIR) spectroscopy to make an intact estimation of harvest time of tomatoes. A mobile, fibre-type, AgroSpec VIS-NIR spectrophotometer (Tec5, Germany), with a spectral range of 350-2200 nm, was used for spectral acquisition of tomatoes in reflection mode. The harvest time of tomatoes was measured by the days before harvest. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least-squares regression (PLSR) with leave-one-out cross validation to establish calibration models. Validation of calibration models on the independent prediction set indicates that the best model can produce excellent prediction accuracy with coefficient of determination (R2) of 0.90, root-mean-square error of prediction (RMSEP) of 2.5 days and residual prediction deviation (RPD) of 3.01. It is concluded that VIS-NIR spectroscopy coupled with PLSR models can be adopted successfully for in situ determination of optimal harvest time of tomatoes, which allows for automatic fruit harvest by a horticultural robot.

scholarly journals Evaluation of Carbohydrate Concentrations in Phalaenopsis Using Near-infrared Spectroscopy

2018 ◽  
Vol 143 (6) ◽  
pp. 494-502
Author(s):  
Yung-Kun Chuang ◽  
I-Chang Yang ◽  
Chao-Yin Tsai ◽  
Jiunn-Yan Hou ◽  
Yung-Huei Chang ◽  
...  
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Dry Weight ◽  
Internal Quality ◽  
Least Squares Regression ◽  
First Derivative ◽  
Derivative Spectra ◽  
Calibration Models ◽  
Quantitative Analyses

Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. In this study, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of -0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc = 0.96; SEC = 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias = -0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias = -0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.

scholarly journals Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Da Wang ◽  
Wenwen Wei ◽  
Yanhua Lai ◽  
Xiangzheng Yang ◽  
Shaojia Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absolute Difference ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Mid Infrared ◽  
Mir Spectra

The quality of strawberry powder depends on the freshness of the fruit that produces the powder. Therefore, identifying whether the strawberry powder is made from freshly available, short-term stored, or long-term stored strawberries is important to provide consumers with quality-assured strawberry powder. Nevertheless, such identification is difficult by naked eyes, as the powder colours are very close. In this work, based on the measurement of near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectra of strawberry powered, good classification results of 100.00% correct rates to distinguish whether the strawberry powder was made from freshly available or stored fruit was obtained. Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. Optimal variables were selected by successive projections algorithm (SPA), uninformation variable elimination, and competitive adaptive reweighted sampling, respectively. The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. The results of this work confirmed the feasibility of using NIR and MIR spectroscopic techniques for rapid identification of strawberry powder made from freshly available and stored strawberry.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

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