Spectral characteristics of organic soil matter: A comprehensive review

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.

Nanoextraction from a flow of a highly diluted solution for much-improved sensitivity in offline chemical detection and quantification

Preconcentration of the target compound is a critical step that ensures the accuracy of the subsequent chemical analysis. In this work, we present a straightforward yet effective liquid-liquid extraction approach based on surface nanodroplets (i.e., nanoextraction) for offline analysis of highly diluted sample solutions. The extraction and sample collection were streamlined in a 3-m microcapillary tube. The concentration of the target analyte in surface nanodroplets was significantly increased compared to the concentration in the sample solution, reaching several orders of magnitude. A limit of detection (LOD) was decreased by a factor of ∼10³ for an organic model compound in Fourier-transform infrared spectroscopy (FTIR) measurements and ∼10⁵ for a model fluorescent dye in fluorescence detection. The quantitative analysis of the organic compound was also achieved in a wide concentration region from 10^-3 M to 10^-4 M. The total volume of surface nanodroplets can be manipulated to further enhance extraction efficiency, according to the principle that governs droplet formation by solvent exchange. Additionally, our method exhibited significantly improved sensitivity compared to traditional dispersive liquid-liquid microextraction (DLLME). The LOD of the fluorescent dye and the organic model compound obtained with DLLME was 3 orders of magnitude and 20 times higher than the LOD achieved through nanoextraction approach. The nanoextraction developed in this work can be applied to preconcentrate multi-compounds from river water samples, without clear interference from each other. This can further extend its applicability for the detection and quantification of target analytes in complex aqueous samples by common analytical instruments.

Interactions between organic matter and Fe oxides at soil micro-interfaces: Quantification, associations, and influencing factors

Iron (Fe) oxides are widely recognized to prevent the degradation of organic matter (OM) in environments, thereby promoting the persistence of organic carbon (OC) in soils. Thus, discerning the association mechanisms of Fe oxides and OC interactions is key to effectively influencing the dynamics and extent of organic C cycling in soils. Previous studies have focused on i) quantifying Fe oxide-bound organic carbon (Fe-OC) in individual environments, ii) investigating the distribution and adsorption capacity of Fe-OC, and iii) assessing the redox cycling and transformation of Fe-OC. Furthermore, the widespread application of high-tech instrumentation and methods has greatly contributed to a better understanding of the mechanism of organic mineral assemblages in the past few decades. However, few literature reviews have comprehensively summarized Fe-OC distributions, associations, and characteristics in soil-plant systems. Here, studies investigating the Fe-OC contents among different environments are reviewed. In addition, the mechanisms and processes related to OM transformation dynamics occurring at mineral-organic interfaces are also described. Recent studies have highlighted that diverse interactions occur between Fe oxides and OC, with organic compounds adhering to Fe oxides due to their huge specific surfaces area and active reaction sites. Moreover, we also review methods for understanding Fe-OC interactions at micro-interfaces. Lastly, developmental prospects for understanding coupled Fe-OC geochemical processes in soil environments at molecular- and nano-scales are outlined. The summary suggests that combined advanced techniques and methods should be used in future research to explore micro-interfaces and in situ descriptions of organic mineral assemblages. This review also suggests that future studies need to consider the functional and spatial complexity that is typical of soil/sediment environments where Fe-OC interactions occur.

Sediment organic carbon dominates the heteroaggregation of suspended sediment and nanoplastics in natural and surfactant-polluted aquatic environments

The aggregation of nanoplastics (NPs) and suspended sediment (SPS) is the key to the transport and environmental fate of NPs. However, the influence of SPS composition and environmental conditions on this process and its mechanisms are still unclear. In this study, the heteroaggregation of NPs and SPS of different compositions is systematically explored under natural and surfactant-polluted aquatic environments (NaCl, humic acid, cetyltrimethylammonium bromide (CTAB)). The results showed that sediment organic carbon (SOC) dominates the aggregation and that different kinds of SOC (comprised of both amorphous organic carbon (AOC) and black carbon (BC)) contribute vary under distinct conditions. In natural freshwater, AOC represents a larger contribution to aggregation because of its weaker electrostatic repulsion compared to that of BC. However, BC represents a larger contribution in natural seawater resulting from decreased electrostatic repulsion and more hydrogen bonding. Conversely, in surfactant-polluted aquatic environments, both AOC and BC have a high contribution owing to the bridge effect plus hydrogen bonding. Notably, minerals' contribution in aggregates remains low under all conditions. Furthermore, CTAB typically inhibits aggregation except under special conditions. The findings of this study contribute notably to a better understanding of the migration of nanoplastics in complex aquatic environments.

Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network

Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R². Through comparison, the R² of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R² = 0.4035), and Random Forest (RF) (RMSE = 2.577, R² = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R² reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved.

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.