Abstract:External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against noncorrected (NC) spectral models developed with partial least squares regressio… Show more
“…4). The overall picture shows that the wavelengths between 2000 and 2450 nm followed by the visible range between 400 and 700 nm were most important for prediction of the investigated properties, which is in agreement with the literature (Munnaf and Mouazen, 2022;Soriano-Disla et al, 2014). Nevertheless, each local model has distinct and site-specific features that could not be at-tributed to specific soil characteristics while being important for the model development.…”
Section: Comparison Of General Models With Local Modelssupporting
Abstract. Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.
“…4). The overall picture shows that the wavelengths between 2000 and 2450 nm followed by the visible range between 400 and 700 nm were most important for prediction of the investigated properties, which is in agreement with the literature (Munnaf and Mouazen, 2022;Soriano-Disla et al, 2014). Nevertheless, each local model has distinct and site-specific features that could not be at-tributed to specific soil characteristics while being important for the model development.…”
Section: Comparison Of General Models With Local Modelssupporting
Abstract. Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.
“…4). The overall picture shows that the wavelengths between 2000 and 2450 nm followed by the visible range between 400 and 700 nm were most important for prediction of the investigated properties, which is in agreement with the literature (Munnaf and Mouazen, 2022;Soriano-Disla et al, 2014). Nevertheless, each local model has distinct and site-specific features that could not be attributed to specific soil characteristics but obviously were important for the model development.…”
Section: Comparison Of General Models With Local Modelssupporting
Abstract. The application of visual and near infrared soil spectroscopy (vis–NIR) is an easy and cost-efficient way to gain a wide variety of soil information to cover high spatial and temporal resolution in large-scale soil surveys and in local field-scale studies. However, unlike for conventional methods, the prediction accuracy of vis–NIR spectral models cannot yet be estimated before the data collection, which hampers its application at the local scale where often a high precision is required (e.g., field experiments). In this study we used soil data from six agricultural fields in Eastern Switzerland and calibrated i) field-specific (local) models and ii) general models (combining all fields) for organic carbon, total carbon, total nitrogen, permanganate oxidizable carbon and pH using partial least squares regression. 24 out of 30 local models showed an accurate or even excellent performance (ratio of performance to deviation (RPD) > 2) and the root mean square errors (RMSE) of prediction were, except for pH, maximum five times higher than the lab measurement error. The variability of a specific soil property and the mean carbonate concentration in the dataset were the two factors influencing the performance of the local models. We found a significant relationship between the coefficient of variation in the dataset and the metrics for model performance (R2, percental RMSE and RPD). Starting from a tolerable prediction error for the spectral measurements, the regressions can be used to develop a sampling design that matches the corresponding target variability. The five inaccurately performing local models with RPD < 2 were on the two fields with highest carbonate content raising the question if local vis–NIR models are suitable for soils with high carbonate concentration. General models combining the datasets from all six fields showed an accurate overall performance but the RMSE on the field level were higher compared to the local models.
“…Basically, 2 or 3 is the optimal hyperparameter for the EPO algorithm. 17 Figure 7.The image of Wilk’s ∧ varies with the external parameter orthogonalization hyperparameter. A higher Wilk’s ∧ indicates that different fabric samples have a higher degree of separation in spectral space relative to the same sample with different moisture levels.Figure 8.Cross-validation results.…”
Near-Infrared (NIR) spectroscopic analyses can be applied in waste textile recycling as a rapid and non-invasive method to provide both qualitative and quantitative results. However, it has been a challenge to enhance the accuracy rate of NIR-based waste textile sorting due to the major influences from water contexts in the samples. Orthogonalization of External Parameters (EPO) has been introduced to reduce the interference from water absorption in NIR spectral signals for better accuracy and reliability in modeling. Here we explore the feasibility of applying EPO strategy with varieties of algorithms, including partial least squares regression (PLS), artificial neural network (ANN), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), extreme random tree (Extra-tree), decision tree model based on AdaBoost algorithm (AdaBoost-tree), support Vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), and one-dimensional convolutional neural network with improved Inception structure (1D-Inception-CNN). 216 waste textiles samples from Xinjiang, China, were studied with different moisture levels. Among them, 80 samples were used to develop the EPO algorithm, 112 were used to establish the prediction models, and 24 were used as test datasets. Then, the samples were scanned using a near-infrared spectrometer at different moisture regain rates. Our results showed that the moisture content of waste textiles had strong absorption peaks near 1150 and 1450 nm, leading to a decrease in the near-infrared reflectance of waste textiles. To verify the effectiveness of the EPO algorithm, the decision coefficients (R2 score) and other indicators of the model without the EPO process and the model with EPO process are systematically compared. Our results show that the EPO algorithm preprocessing improves the accuracy of the NIR model (The average decision coefficient (R2 score) of the models was increased by 0.83), especially when the moisture interference level is significant. Therefore, the EPO integrated modeling method is a reliable approach for better accuracy in NIR-based waste textile sorting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
