Search Results (777)

Soil erosion is a serious problem threatening regional ecological security, particularly in the Loess Plateau of China. This study focuses on the Wuding River Basin on the Loess Plateau. Based on multi-source data from 2001 to 2024, the RUSLE model was used to estimate the soil erosion modulus. We used comprehensive methods, such as trend analysis, multiple regression, scenario simulation, partial least squares structural equation modeling (PLS-SEM), hot spot analysis, and Hurst exponent, to systematically analyze the spatiotemporal evolution characteristics of soil erosion, the contributions of driving factors, and the sustainability of trends. The results showed that over the 24-year period, the soil erosion modulus in the basin generally showed a decreasing trend, suggesting an improvement in soil erosion conditions. The area of mild and above erosion grades continued to shrink. Among the RUSLE factors, the vegetation cover factor (C) showed a significant downward trend (R² = 0.7721), with the decreasing area accounting for 95.8%; the rainfall erosivity factor (R) showed a slight upward trend, with the increasing area accounting for 92.7%; and the erosion control practice factor (P) remained stable in most areas (96.8%). Relative contribution analysis indicated that the R-factor dominated the largest area (46.85%), while absolute contribution analysis showed that the C-factor contributed most significantly to erosion reduction. PLS-SEM demonstrated that the influence pathways of natural factors and human activities on soil erosion differed significantly across spatial and temporal scales. On the temporal scale, the R-factor had the strongest direct positive effect on erosion; on the spatial scale, the topography factor (LS) had the strongest positive effect on erosion. Furthermore, we found that the disturbance of vegetation by human activities is being weakened with the continuous implementation of soil and water conservation projects. The cold and hot spots of erosion trends were concentrated in the southeastern part of the basin. Based on trend sustainability, the basin was divided into successfully treated areas (57.6%), potential rebound risk areas (29.4%), emergency treatment areas (11.2%), and monitoring priority areas (1.8%). Overall, this study advances the understanding of soil erosion evolution under long-term ecological restoration and provides a scientific basis for optimizing sustainable soil and water conservation management in the Wuding River Basin. Full article

To achieve early perception and accurate prediction of flatness quality, a partial least squares–particle swarm optimization–multi-output support vector regression (PLS-PSO-MSVR) is proposed. Firstly, we parameterized the flatness and used it as an evaluation indicator for flatness. Then, the prediction model was constructed using multi-output support vector regression (MSVR). In the modeling process, particle swarm optimization is used to optimize the parameters. To overcome the problem of information redundancy, reduce data dimensions to reduce computational time, and improve the prediction performance of the algorithm, this paper combines partial least squares and PSO-MSVR to achieve accurate prediction of the flatness features. Finally, the actual industrial process data from the hot rolling 1580 production line was used for validation, and the predicted performance was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). MAE decreased to 0.15, MSE decreased to 0.038, and RMSE decreased to 0.195. The R² approaches 1, indicating excellent model fit. This study achieves accurate prediction of the flatness characteristic coefficient, which not only enhances the diagnostic efficiency of steel flatness quality but also helps avoid unnecessary economic losses. Moreover, the prediction model provides a reliable basis for flatness control, offering operators a user-friendly reference tool. This approach compensates for the time lag inherent in the original system and contributes to improved accuracy in flatness control. Full article

Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic secondary metabolites such as alternariol (AOH), whose early detection remains analytically challenging. The aim of this study was to evaluate the potential of near-infrared transmittance (NIT) spectroscopy as a rapid, non-destructive pre-screening tool for the early identification of Alternaria-contaminated durum wheat lots and associated AOH risk. Samples from three durum wheat cultivars were artificially inoculated with Alternaria spp. and monitored over time. NIT spectra (570–1100 nm) were acquired in transmittance mode and analyzed using partial least squares (PLS) regression, focusing on the 870–1100 nm spectral region. Clear and time-dependent spectral differences were observed between inoculated and control samples, with the strongest discriminative features at 834 and 966 nm. Classification performance was high, with area under the curve (AUC) values between 0.96 and 0.97. ELISA analysis confirmed progressive AOH accumulation in inoculated kernels, consistent with the observed spectral changes, while control experiments excluded autoclaving and visual grain damage as confounding factors. From an applied perspective, the results indicate that NIT spectroscopy can support post-harvest decision-making as a rapid pre-screening approach, enabling the prioritization of suspect wheat lots for confirmatory analytical testing. Multivariate analysis further confirmed the consistency of spectral differences across datasets. Full article

Accurate and rapid determination of moisture content in waste sludge is essential for optimizing dewatering processes, reducing disposal costs, and minimizing environmental impact. This study investigates the use of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with Partial Least Squares Regression (PLS-R) for predicting the moisture content of dewatered sludge. A total of 96 sludge samples, with dry matter contents ranging from 12.4% to 24.6%, were collected from two treatment plants. FT-NIR spectra were acquired over the 800–2500 nm range, and chemometric models were developed to correlate spectral information with gravimetrically determined moisture content. The optimized PLS-R model demonstrated strong predictive performance, achieving a cross-validated coefficient of determination (R²_CV) of 0.87, a root mean square error of cross-validation (RMSECV) of 0.92%, and a residual predictive deviation (RPD) of 2.73. Independent test set validation confirmed the robustness of the model (R²_Test = 0.88, RMSEP = 0.88%, RPD = 2.92), supported by strong calibration results (R²_CT = 0.95, RMSEE = 0.60%, RPD = 4.46). Principal component analysis indicated that spectral variability observed in sludge samples was primarily associated with wastewater treatment plant (WWTP)-specific characteristics, reflecting moisture–organic matter interactions. These results demonstrate that FT-NIR spectroscopy is a promising tool for sludge moisture prediction. Full article

Near-infrared hyperspectral imaging (NIR-HSI) is widely used as a non-destructive technique for evaluating internal fruit quality; however, reliable pixel-wise visualization remains challenging due to geometry-induced spectral distortions and the lack of statistically interpretable validation criteria. This study proposes an integrated framework for three-dimensional visualization of soluble solids content (SSC) across the entire surface of strawberries using NIR-HSI combined with shape-aware spectral correction and pixel-level reliability assessment. Two complementary imaging systems—a line-scan system and a rotation-scan system—were used to acquire hyperspectral and 3D shape data. Fruit height and surface orientation were incorporated into spectral preprocessing to reduce illumination and curvature effects. Partial least squares regression (PLSR) models were developed using region-of-interest-averaged spectra and applied to pixel-wise SSC mapping. To assess the statistical validity of pixel-level predictions, an imaging reliability index based on the Mahalanobis distance in the PLS score space was introduced. The results show that models with high sample-level accuracy do not necessarily produce reliable SSC maps, whereas reliability-based model selection improves image interpretability. This framework enables consistent three-dimensional SSC visualization and is applicable to hyperspectral imaging of internal fruit attributes. Full article

Ciprofloxacin residues in chicken blood pose a potential food safety risk; however, rapid detection methods for complex chicken blood matrices are lacking. This study aimed to establish a surface-enhanced Raman spectroscopy (SERS) method for the rapid detection of ciprofloxacin in chicken blood using gold colloid as the SERS substrate. Gold colloid was synthesized via the Frens method with slight modification, and key SERS detection conditions were systematically optimized to maximize SERS intensities at 1265 cm⁻¹, including the amount of trisodium citrate solution, the electrolyte type, the amount of gold colloid, the amount of NaCl solution, and the adsorption time. Raw SERS spectra were pretreated with adaptive iteratively reweighted penalized least squares (air-PLS) combined with Savitzky–Golay (SG) smoothing. A genetic algorithm (GA) was used to extract characteristic Raman shifts, and a GA-SVR prediction model with radial basis function (RBF) as the kernel was constructed, with its performance compared with multivariate linear regression (MLR) and partial least squares regression (PLSR) models. The GA-SVR model exhibited the best performance, with a coefficient of determination for the calibration set ($R_{\mathrm{c}}^2$) value of 0.9893 and for the prediction set ($R_{\mathrm{p}}^2$) value of 0.9874. The root mean square error of calibration (RMSEC) and prediction (RMSEP) were 1.2953 and 1.8617, respectively, outperforming the MLR and PLSR models. These results demonstrate that the SERS method combined with GA-SVR enables rapid quantitative detection of ciprofloxacin residues in chicken blood, providing a technical reference for monitoring veterinary drug residues in livestock and poultry products. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

Fermentation is a promising sustainable and ecofriendly alternative for producing high-added-value chemicals such as 2,3-butanediol (2,3-BDO). The emergence of process analytical technology (PAT) tools, combined with advances in chemometrics, enables real-time process monitoring of product attributes, thereby ensuring quality. The aim of this study is to transfer near-infrared (NIR) partial least squares (PLS) models under two scenarios for the monitoring of 2,3-BDO production. PLS regression models initially developed under specific conditions were transferred across domains using dynamic orthogonal projection (DOP) and domain invariant (di)-PLS standard-free calibration transfer (CT) methods. For the 1st scenario involving model transfer from “mock samples” to “flask atline,” di-PLS was able to enhance NIR PLS model performance with improvements in RMSEC and RMSEP of 18 and 25% (2 g/L absolute error), respectively. In the 2nd scenario, however, DOP successfully transferred the model from the “flask atline” domain to the “500 mL bioreactor online” domain, achieving RMSEC and RMSEP values of 12 and 14 g/L, respectively. The feasibility of multivariate model transfer for PAT applications in complex fermentation systems from atline to online configurations using standard-free CT methods is demonstrated. This enhances model adaptability under varying conditions, fostering process scale-up and real-time monitoring. Full article

This study aims to evaluate the controlling factors of tight sandstone reservoir sensitivity in the third member of the Yanchang Formation, Xunyi–Yijun area, southern Ordos Basin. Based on core samples from 12 wells, we established a partial least squares regression (PLS) model through thin section observation, SEM, XRD, high-pressure mercury injection, and sensitivity flow experiments, to quantitatively analyze the relationship between reservoir sensitivity and its controlling factors. The results show that the study area reservoirs are dominated by feldspathic sandstone with moderate compaction, characterized by low porosity (4.4–17.8%, avg. 10.93%), low permeability (0.104–2.33 mD, avg. 0.82 mD), and heterogeneous distribution of clay minerals (mainly chlorite, illite, kaolinite, and illite/smectite mixed layer). The reservoirs generally show weak to moderately weak sensitivity. The PLS model reveals that reservoir sensitivity is controlled by the coupled effects of multiple factors, with no single absolute dominant factor for any sensitivity type. Porosity is the most influential variable for overall reservoir sensitivity, followed by feldspar, illite, and illite/smectite mixed layer, and porosity exerts the strongest control on most sensitivity types via VIP score analysis. This study provides a theoretical basis for reservoir damage prevention in the study area and a technical reference for quantitative sensitivity evaluation of similar tight sandstone reservoirs. Full article

Ground clove bud adulteration with cheaper materials, such as clove stem and soil, poses a significant threat to spice quality and consumer trust. This study introduces a novel, alternative analytical method for the authentication and detection of adulteration in ground clove bud samples. The approach combines voltammetric fingerprinting using a multi-walled carbon nanotube-modified electrode with robust chemometric analysis. Cyclic voltammetry of clove bud samples revealed anodic peaks above +0.5 V and a smaller cathodic peak between +0.5 and −0.3 V vs. Ag/AgCl, suggesting the presence of electroactive compounds. Voltammograms were obtained for authentic clove bud samples sourced from three major Indonesian production regions (South Sulawesi, North Maluku, and East Java), showing varying redox peak intensities. Chemometric analysis, specifically Partial Least Squares Discriminant Analysis (PLS-DA), was successfully employed to differentiate clove bud samples by geographical origin, and Principal Component Analysis (PCA) was used to discriminate authentic clove bud samples from adulterants. Furthermore, Partial Least Squares Regression (PLSR) was utilized to quantify adulteration levels, predicting adulterant concentration (10–100% w/w) using electrochemical signal intensities. The PLSR method exhibited strong linearity between observed and predicted values, confirming its robustness. This proposed method offers a simple, portable, and practical approach for the quality control of ground clove bud. The combination of rapid voltammetric measurement and chemometric modelling provides a valuable and practical tool to prevent fraud and ensure the integrity of the spice trade. Full article

Effective post-harvest management of avocados is essential for reducing supply chain losses. This requires an accessible, cost-effective method for accurately predicting ripeness under real-world conditions. This study developed a non-destructive framework for predicting avocado ripeness using portable visible–near-infrared (Vis-NIR) spectrometers and analyzed the storage temperature dependencies. A 10-point sensory-based ripeness index was correlated with second-derivative reflectance spectra using partial least squares (PLS) regression. To ensure model robustness, we employed repeated 10-fold cross-validation. The broadband PLS model achieved a residual predictive deviation (RPD) of 1.36, while a simplified model using six specific wavelengths (570, 977, 1120, 1161, 1398, and 1655 nm) demonstrated an RPD of 1.43, confirming its feasibility as a preliminary screening tool. Key wavelengths identified were associated with chlorophyll degradation and lipid accumulation. Furthermore, a significant logarithmic relationship (r = 0.9965) was observed between storage temperature (15–35 °C) and the daily ripening rate. Our results suggest that ripening progression is significantly suppressed at temperatures of approximately 12 °C or below. These findings provide quantitative guidelines for distributors to optimize logistics and shelf-life management using portable technology, contributing to the digitalization of consumer-aligned ripeness assessment. Full article

PM_2.5 is a major air pollutant characterized by complex sources and strong spatiotemporal heterogeneity. However, accurately quantifying the relative contributions of different factors remains difficult due to the lack of long-term datasets and the strong correlations between meteorological factors and emissions. To address this problem, the study utilizes the China long-term particulate matter (CLPM) dataset developed in previous research to investigate the dominant drivers and regional disparities of PM_2.5 concentration variations from 1980 to 2022. The analysis employs Gaussian Convolution (GC) to model pollutant diffusion, Partial Least Squares (PLS) regression to address multicollinearity, and the Lindeman-Merenda-Gold (LMG) method to quantify the relative contributions of each driver. The results reveal that as the convolution scale increased from 0.25° to 10°, dominant PM_2.5 sources shifted from local anthropogenic emissions to regional biomass burning and large-scale dust transport, highlighting the scale-dependent transition of pollution drivers. Furthermore, PM_2.5 concentrations are predominantly explained by emissions, which account for over 60% of the total variance and exceed 80% in eastern China, while meteorological factors are associated with 12–26%. Among these, total precipitation and downward surface solar radiation have the strongest influences on pollutants. It is important to note that these results reflect the statistical explanatory power of emissions and meteorological variables within the regression model. Overall, this research provides a method for separating the statistical influences of emissions and meteorological factors, offering methods for multi-scale explanatory power of PM_2.5 and other atmospheric pollutants. Full article

Electronic nose systems are advanced technological tools that enable the objective evaluation of odors through sensor arrays mimicking the human olfactory mechanism and sophisticated data processing algorithms. These systems facilitate rapid, reproducible, and standardized measurement of chemical components in applications such as food safety, environmental monitoring, medical diagnostics, and industrial quality control. In this study, measurements obtained from electronic nose sensors were compared with olfactometry panelist assessments using n-butanol as a reference substance in accordance with the TS EN 13725 standard. Furthermore, machine learning algorithms, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were applied to model the sensor data and evaluate their predictive accuracy. The results demonstrated the reliability and applicability of the electronic nose system, achieving training mean absolute percentage error (MAPE) values of 6.53% for PLS, 10.89% for SVR, and 0.15% for GPR. This study presents an innovative approach that systematically assesses the performance of electronic nose technology using a standardized reference odor and highlights the effectiveness of the modeling approach. Full article

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified. Full article