Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China.

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region.

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Magnetic susceptibility as a proxy for detection of total petroleum hydrocarbons in contaminated wetlands.

Soil petroleum hydrocarbon contamination in the wetlands could cause ecological risk, especially through leakage into water reservoirs. So, the detection of the spatial variability of total petroleum hydrocarbons (TPH) in these soils is very crucial. The variability of TPH and its associations with magnetic susceptibility (χlf) in contaminated soils around the Shadegan pond in southern Iran was investigated. TPH varied from 2.1 to 18.1% (w/w), by the variation of χlf from 14.08 to 713.93 × 10-8 m3 kg-1. The highest variability (coefficient of variation, CV = 107.12%) was obtained for χlf indicating significant impacts of magnetic minerals induced by crude oil contamination. High positive correlations were detected among TPH, χlf, and different forms of iron (Fed: extracted by CBD, Feo: extracted by oxalate, and Fet: total iron). The results of mineralogy by powdery XRD and scanning electron microscopy (SEM), also revealed the formation of ferrimagnetic minerals (magnetite, maghemite) during the biodegradation of petroleum hydrocarbons. The stepwise multiple regression analysis showed that χlf and Fed made a great contribution and could explain about 74% of TPH variability in the studied sites. For the extension of this cost-effective and rapid technique, further work is needed to assay saturation isothermal remnant magnetization and isothermal remanet magnetization in contaminated sites.

Prediction and variability mapping of some physicochemical characteristics of calcareous topsoil in an arid region using Vis-SWNIR and NIR spectroscopy.

Site-specific management of soils needs continuous measurements of soil physicochemical characteristics. In this study, Vis-NIR spectroscopy with two spectroscopic instruments, including charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers, was adopted to estimate some physicochemical characteristics of a calcareous topsoil in an arid climate. Partial least squares (PLS) as linear and artificial neural networks (ANN) as nonlinear multivariate techniques were utilized to enhance the accuracy of prediction. The best predictive models were then used to extract the variability maps of physicochemical characteristics. Diffuse reflectance spectra of 151 samples, collected from the calcareous topsoil, were acquired in the visible and short-wavelength near-infrared (Vis-SWNIR) (400-1100 nm) and near-infrared (NIR) (950-1650 nm) spectral ranges using CCD and InGaAs spectrometers, respectively. The results showed that NIR spectral data of the InGaAs spectrometer was necessary to reach the best predictions for all selected soil properties. The best predictive models based on the optimum spectral range could allow us the excellent predictions of sand (RPD = 2.63) and silt (RPD = 2.52), and very good estimations of clay (RPD = 2.35) and electrical conductivity (EC) (RPD = 2.224) by ANN and very good prediction of calcium carbonate equivalent (CCE) (RPD = 2.01) by PLS. The CCD device, however, resulted in acceptable predictions of sand (RPD = 2.13, very good) and clay (RPD = 1.66, fair) by ANN, and silt (RPD = 1.78, good), EC (RPD = 1.84, good) and CCE (RPD = 1.67, fair) by PLS. Similar variability was attained between pairs of predicted maps by best models and reference-measured maps for all studied soil properties. For clay, sand, silt, and CCE, the Vis/SWNIR-predicted and equivalent reference-measured maps had acceptable similarities, indicating the potential application of low-cost CCD spectrometers for prediction and the variability mapping of these parameters.

Broadband acoustic resonance dissolution spectroscopy as a rapid tool for the compositional analysis of food powders: A case study of edible salts.

Broadband acoustic resonance dissolution spectroscopy (BARDS) is a novel method that can be used for the analysis of food-based powders, which are mainly characterized by their composition and particle morphology. This study aimed to evaluate BARDS for the compositional analysis of food powders. The changes in the BARDS spectra due to the changes in composition and particle morphology of fifteen salt mixtures (constituting of NaCl, KCl, and MgCl2) in five particle size ranges were comprehensively studied. Moreover, different regression methods were utilized to estimate each mixture component content. The results revealed that the average time-frequency spectra of each mixture in a certain particle size class were highly distinct and allowed discrimination from others. The unique spectra of each salt mixture originated from the specific dissolution rate and degassing effect of each constitutive compound. Finally, the accurate prediction of each mixture component content confirmed the consistency and efficiency of the method.

Microwave spectroscopy based on the waveguide technique for the nondestructive freshness evaluation of egg.

A rectangular waveguide equipped with a network analyzer was used to assess the quality indices of shell egg. The scattering parameters of the eggs were acquired in the range of 0.9-1.7 GHz and they were then used to calculate microwave spectra of the samples. PLS and ANN regression methods were implemented to predict the egg quality indices and SIMCA and ANN classification methods were applied to classify the eggs based on their storage time. The best predictive models, however, obtained from ANN analysis where the yolk coefficient, air cell height, thick albumen height, Haugh unit, and albumen pH could be predicted with the residual predictive deviation (RPD) values of 3.500, 3.000, 2.411, 2.033, and 1.829, respectively. To classify the eggs according to their storage time, both SIMCA and ANN analyses resulted in the total accuracy of 100% when return loss spectra were used as the input.

Mass transfer characteristics during convective, microwave and combined microwave-convective drying of lemon slices.

BACKGROUND: The investigation of drying kinetics and mass transfer phenomena is important for selecting optimum operating conditions, and obtaining a high quality dried product. Two analytical models, conventional solution of the diffusion equation and the Dincer and Dost model, were used to investigate mass transfer characteristics during combined microwave-convective drying of lemon slices. Air temperatures of 50, 55 and 60 °C, and specific microwave powers of 0.97 and 2.04 W g(-1) were the process variables. RESULTS: Kinetics curves for drying indicated one constant rate period followed by one falling rate period in convective and microwave drying methods, and only one falling rate period with the exception of a very short accelerating period at the beginning of microwave-convective treatments. Applying the conventional method, the effective moisture diffusivity varied from 2.4 × 10(-11) to 1.2 × 10(-9) m(2) s(-1). The Biot number, the moisture transfer coefficient, and the moisture diffusivity, respectively in the ranges of 0.2 to 3.0 (indicating simultaneous internal and external mass transfer control), 3.7 × 10(-8) to 4.3 × 10(-6) m s(-1), and 2.2 × 10(-10) to 4.2 × 10(-9) m(2) s(-1) were also determined using the Dincer and Dost model. CONCLUSIONS: The higher degree of prediction accuracy was achieved by using the Dincer and Dost model for all treatments. Therefore, this model could be applied as an effective tool for predicting mass transfer characteristics during the drying of lemon slices.