Search Results (2,019)

Polygonatum sibiricum (P. sibiricum) is a significantly health-promoting plant unique in medicine and food. Currently, research on the bioactive components of P. sibiricum primarily focuses on polysaccharides. According to the Chinese Pharmacopoeia, the polysaccharide content in P. sibiricum must be at least 7.0%, which is the only criterion used for determining its content. In contrast, this study aims to thoroughly investigate and clarify the various components that contribute to the therapeutic and functional properties of P. sibiricum. We seek to broaden the focus beyond polysaccharides to identify other potentially significant bioactive substances. We established High-Performance Liquid Chromatograph (HPLC) fingerprints for wine-processed P. sibiricum from various regions and identified 17 common peaks. The antioxidant activities of these components were assessed using ABTS and DPPH methods. The spectrum–effect relationship was elucidated through partial least squares regression and grey relational analysis. The results revealed that the antioxidant active components in wine-processed P. sibiricum include 5-hydroxymethylfurfural, p-hydroxycinnamic acid, myricetin, caffeic acid, vanillic acid, and adenosine. This research not only clarifies the antioxidant material basis of wine-processed P. sibiricum but also establishes a scientific foundation for enhanced quality control in future applications. Full article

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIRS) technology to measure the absorbance of Korla fragrant pears. The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). The pre-processed spectral data were subjected to characteristic band extraction using the successive projections algorithm (SPA) and uninformative variable elimination (UVE) methods. Subsequently, detection models for the color indices L*, a*, and b* of Korla fragrant pears were established using the partial least squares regression (PLSR) with full-spectrum and characteristic extracted spectral data. The optimal detection models were determined. The results indicated that pre-processing and characteristic extraction improved the accuracy of the PLSR model. The optimal detection model for the color index L* was SGCD-UVE-PLSR (correlation coefficient (R) = 0.80, Root Mean Square Error (RMSE) = 1.19); for the color index a*, it was VN-SPA-PLSR (R = 0.84 and RMSE = 1.28), and for the color index b*, it was MSC-UVE-PLSR (R = 0.84 and RMSE = 1.25). This research provides a theoretical reference for developing color detection instruments for Korla fragrant pears. Full article

In this work, we simultaneously detected and predicted the concentration levels of serotonin (SE) and dopamine (DA) neurotransmitters (NTs) for in vitro mixtures, with measurements obtained using conventional glassy carbon electrodes (CGCEs) and differential pulse voltammetry (DPV). The NTs were estimated by deconvolving the multiplexed signals of both NTs using Principal Component Analysis with Gaussian Process Regression (PCA-GPR) and Partial Least Squares with Gaussian Process Regression (PLS-GPR), both with exponential–isotropic kernels. The average testing accuracies of estimation using PCA-GPR for DA alone, SE alone and their mixture (DA–SE) were 87.6%, 88.1%, and 96.7%, respectively. Using PLS-GPR, the testing accuracies of estimation for DA alone, SE alone, and their mixture (DA–SE) were 87.3%, 83.8%, and 95.1%, respectively. Furthermore, we explored methods of reducing the procedural complexity in estimating the NTs by finding reduced subsets of features for accurately detecting and predicting their concentrations. The reduced subsets of features found in the oxidation potential windows of the NTs improved the testing accuracy of the estimation of DA–SE to 97.4%. We thus believe that reducing complexity has the potential to increase the detection and prediction accuracies of NT measurements for practical clinical uses such as deep brain stimulation. Full article

The increasing popularity of short videos has presented sellers with fresh opportunities to craft video advertisements that incorporate diverse modal information, with each modality potentially having a different influence on consumer engagement. Understanding which information is most important in attracting consumers can provide theoretical support to researchers. However, the dimensionality of the multimodal features of short video advertisements is often higher than the available data, posing specific difficulties in data analysis. Therefore, designing a multimodal analysis framework is needed to comprehensively extract and reduce the dimensionality of the different modal features of short video advertisements, thus analyzing which modal features are more important for consumer engagement. In this study, we chose TikTok as the research subject, and employed deep learning and machine learning techniques to extract features from short video advertisements, encompassing visual, acoustic, title, and speech text features. Subsequently, we introduced a method based on mixed-regularization sparse representation to select variables. Ultimately, we utilized multiblock partial least squares regression to regress the selected variables alongside additional scalar variables to calculate the block importance. The empirical analysis results indicate that visual and speech text features are the key factors influencing consumer engagement, providing theoretical support for subsequent research and offering practical insights for marketers. Full article

Digital soil organic carbon (SOC) mapping is used for ecological protection and addressing global climate change. Sentinel-1 (S-1) microwave radar remote sensing data offer critical insights into SOC dynamics through tracking variations in soil moisture and vegetation characteristics. Despite extensive studies using S-1 data for SOC mapping, most focus on either single or multi-date periods without achieving satisfactory results. Few studies have investigated the potential of time-series S-1 data for high-accuracy SOC mapping. This study utilized S-1 data from 2017 to 2021 to analyze temporal variations in the correlation between SOC and time-series S-1 data in southern Xinjiang, China. The primary objective was to determine the optimal monitoring period for SOC. Within this period, optimal feature subsets were extracted using variable selection algorithms. The performance of the partial least squares regression, random forest, and convolutional neural network–long short-term memory (CNN-LSTM) models was evaluated using a 10-fold cross-validation approach. The findings revealed the following: (1) The correlation between time-series S-1 data and SOC exhibited both interannual and monthly variations, with the optimal monitoring period from July to October. The data volume was reduced by 73.27% relative to the initial time-series dataset when the optimal monitoring period was determined. (2) Introducing time-series S-1 data into SOC mapping significantly improved CNN-LSTM model performance (R² = 0.80, RPD = 2.24, RMSE = 1.11 g kg⁻¹). Compared to models using single-date (R² = 0.23) and multi-date (R² = 0.33) data, the R² increased by 0.57 and 0.47, respectively. (3) The newly developed vertical–horizontal maximum and mean annual cumulative indices made a significant contribution (17.93%) to mapping SOC. Therefore, integrating the optimal monitoring period, feature selection, and deep learning model offers significant potential for enhancing the accuracy of digital SOC mapping. Full article

This review discusses how the integration of machine learning (ML) tools enhances the analytical capabilities of the Rapid Visco Analyser (RVA), aiming to provide a deeper understanding of the starch gelatinization in different starchy food ingredients and products. The review also discusses some of the limitations of RVA as a tool for assessing the pasting and viscosity behavior of starch, emphasizing the potential of different ML tools such as principal component analysis (PCA) and partial least squares (PLS) regression to offer a better analytical approach. Examples of the utilization of ML combined with RVA to enhance the analysis of starch and non-starch ingredients are also provided. Furthermore, the importance of preprocessing techniques, such as derivatives, to improve the quality and interpretability of RVA profiles is discussed. The aim of this review is to provide examples of the utilization of RVA combined with ML tools in starchy food ingredients and products. Full article

Aging spirits in wooden casks is a traditional and mandatory process for the production of certain products, such as whisky. The physicochemical and sensory changes that occur during aging are shaped by the characteristics of the barrels and the aging method used. In this paper, we examined the behavior of the same malt spirit when aged using two different Sherry Casks^® methods. The first one was static aging, with the distillate remaining still in the cask, and the second one was a dynamic system, characterized by the regular racking of the spirit between casks at different aging stages (Criaderas and Solera). For 36 months, the aging spirits were sampled and analyzed to determine any changes in acidity, volatile, and phenolic compound content that might indicate changes in their chemical profile. The spirits were also subjected to sensory evaluations. The analysis revealed a significant evolution of the distillate in either system, although with different chemical profiles. Multiple Linear Regression Models (MLR and PLS) were successfully used to estimate the age of the distillates at a high level of confidence. Although, after the first racking operation, the distillates in the dynamic system had an average age greater than the theoretical one, these differences tended to fade away as the system gradually stabilized. Full article

The estimation of Above-Ground Biomass (AGB) in Amorphophallus konjac (Konjac) is essential for field management and yield prediction. While previous research has demonstrated the efficacy of Unmanned Aerial Vehicle (UAV) RGB imagery in estimating AGB for monoculture crops, the applicability of these methods to AGB estimation in Konjac remains uncertain due to its distinct morphological traits and prevalent intercropping practices with maize. Additionally, the Vegetation Indices (VIs) and Texture Features (TFs) obtained from UAV-based RGB imagery exhibit significant redundancy, raising concerns about whether the selected optimal variables can maintain estimation accuracy. Therefore, this study assessed the effectiveness of Variable Selection Using Random Forests (VSURF) and Principal Component Analysis (PCA) in variable selection and compared the performance of Stepwise Multiple Linear Regression (SMLR) with four Machine Learning (ML) regression techniques: Random Forest Regression (RFR), Extreme Gradient Boosting Regression (XGBR), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), as well as Deep Learning (DL), in estimating the AGB of Konjac based on the selected features. The results indicate that the integration (PCA_(PCA_VIs+PCA_TFs)) of PCA-based VIs and PCA-based TFs using PCA achieved the best prediction accuracy (R² = 0.96, RMSE = 0.08 t/hm², MAE = 0.06 t/hm²) with SVR. In contrast, the DL model derived from AlexNet, combined with RGB imagery, yielded moderate predictive accuracy (R² = 0.72, RMSE = 0.21 t/hm², MAE = 0.17 t/hm²) compared with the optimal ML model. Our findings suggest that ML regression techniques, combined with appropriate variable-selected approaches, outperformed DL techniques in estimating the AGB of Konjac. This study not only provides new insights into AGB estimation in Konjac but also offers valuable guidance for estimating AGB in other crops, thereby advancing the application of UAV technology in crop biomass estimation. Full article

(1) Background: Soybean storage quality is crucial for subsequent processing and consumption, making it essential to explore an objective, rapid, and non-destructive technology for assessing its quality. (2) Methods: crude fatty acid value is an important indicator for evaluating the storage quality of soybeans. In this study, three types of soybeans were subjected to accelerated aging to analyze trends in crude fatty acid values. The study focused on acquiring raw spectral information using hyperspectral imaging technology, preprocessing by the derivative method (1ST, 2ND), multiplicative scatter correction (MSC), and standard normal variate (SNV). The feature variables were extracted by a variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), and a successive projections algorithm (SPA). Partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models were developed to predict crude fatty acid values of soybeans. The optimal model was used to visualize the dynamic distribution of these values. (3) Results: the crude fatty acid values exhibited a positive correlation with storage time, functioning as a direct indicator of soybean quality. The 1ST-VISSA-SVM model was the optimal predictive model for crude fatty acid values, achieving a coefficient of determination (R²) of 0.9888 and a root mean square error (RMSE) of 0.1857 and enabling the visualization of related chemical information. (4) Conclusions: it has been confirmed that hyperspectral imaging technology possesses the capability for the non-destructive and rapid detection of soybean storage quality. Full article

Aboveground biomass (AGB) is crucial in forest ecosystems and is intricately linked to the carbon cycle and global climate change dynamics. This study investigates the efficacy of synthetic aperture radar (SAR) data from the X, C, and L bands, combined with Sentinel-2 optical imagery, vegetation indices, gray-level co-occurrence matrix (GLCM) texture metrics, and topographical variables in estimating AGB in the Küre Mountains National Park, Türkiye. Four machine-learning regression models were employed: partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), multivariate linear, and ridge regression. Among these, the PLS regression (PLSR) model demonstrated the highest accuracy in AGB estimation, achieving an R² of 0.74, a mean absolute error (MAE) of 28.22 t/ha, and a root mean square error (RMSE) of 30.77 t/ha. An analysis across twelve models revealed that integrating ALOS-2 PALSAR-2 and SAOCOM L-band satellite data, particularly the SAOCOM HV and ALOS-2 PALSAR-2 HH polarizations with optical imagery, significantly enhances the precision and reliability of AGB estimations. Full article

An attempt was made to quantify soil properties using hyperspectral remote-sensing techniques and machine-learning algorithms. In total, 100 soil samples representing various locations and soil-nutrient statuses were collected, and the samples were analyzed for soil pH, EC, soil organic carbon, available nitrogen (AN), available phosphorus (AP), and available potassium (AK) by following standard methods. Soil had a wide range of properties, i.e., pH varied from 5.62 to 8.49, EC varied from 0.08 to 1.78 dS/m, soil organic carbon varied from 0.23 to 0.94%, available nitrogen varied from 154 to 344 kg/ha, available phosphorus varied from 9.5 to 25.5 kg/ha, and available potassium varied from 131 to 747 kg/ha. The same set of soil samples were subjected to spectral reflectance measurement using SVC GER 1500 Spectroradiometer (spectral range: 350 to 1050 nm). The measured spectral signatures of various soils were organized for developing a spectral library and for deriving various spectral indices to correlate with soil properties to quantify the nutrients. The soil samples were partitioned into 60:40 ratios for training and validation, respectively. In order to select optimum bands (wavelength) from the soil spectra, we have employed metaheuristic algorithms i.e., Particle Swarm Optimization (PSO), Moth–Flame optimization (MFO), Flower Pollination Optimization (FPO), and Battle Royale Optimization (BRO) algorithm. Further partial least square regression (PLSR) was used to find the latent variable and to evaluate various algorithms for their performance in predicting soil properties. The results indicated that nutrients could be quantified from spectral reflectance measurement with fair to good accuracy through the Battle Royale Optimization technique with a $R^2$ value of 0.45, 0.32, 0.48, 0.21, 0.71, and 0.35 for pH, EC, soil organic carbon, available-N, available-P, and available-K, respectively. Full article

Near-infrared spectroscopy (NIRS) is an efficient, non-destructive method for evaluating the chemical composition of various compounds. This study aimed to assess both the proximate composition, fibres, and fatty acid (FA) content of Total Mixed Rations (TMRs) in dairy buffalo nutrition. A total of 240 TMR samples were collected from ten dairy buffalo farms across four seasons to develop predictive models using Partial Least Squares Regression (PLSR). Calibration models for dry matter (DM), crude protein (CP), ether extract (EE), and starch demonstrated good predictive accuracy, with coefficients of determination in cross-validation (R²cv) around 0.90 and Residual Predictive Deviation (RPDcv) values exceeding 3.0. Fatty acid models showed slightly lower R²cv values, ranging from 0.80 to 0.90. A good predictive performance was observed for linoleic acid (18:2 n-6) and α-linolenic acid (18:3 n-3), with RPDp values above 3.0, indicating reliable predictions. The inclusion of omega-3-rich compounds in the diet provides significant benefits for both animal health and product quality, highlighting the importance of ration monitoring. The findings confirm that while NIRS is effective for assessing chemical composition, further refinement is needed to improve FA prediction accuracy. These results support the use of NIRS as a practical tool for nutritional monitoring in lactating buffaloes. Full article

The conventional diagnostic techniques for ethylene cracker furnace tube coking rely on manual expertise, offline analysis and on-site inspection. However, these methods have inherent limitations, including prolonged inspection times, low accuracy and poor real-time performance. This makes it challenging to meet the requirements of chemical production. The necessity for high efficiency, high reliability and high safety, coupled with the inherent complexity of the production process, results in data that is characterized by multimodal, nonlinear, non-Gaussian and strong noise. This renders the traditional data processing and analysis methods ineffective. In order to address these issues, this paper puts forth a novel soft measurement approach, namely the ‘Mixed Student’s t-distribution regression soft measurement model based on Variational Inference (VI) and Markov Chain Monte Carlo (MCMC)’. The initial variational distribution is selected during the initialization step of VI. Subsequently, VI is employed to iteratively refine the distribution in order to more closely approximate the true posterior distribution. Subsequently, the outcomes of VI are employed to initiate the MCMC, which facilitates the placement of the iterative starting point of the MCMC in a region that more closely approximates the true posterior distribution. This approach allows the convergence process of MCMC to be accelerated, thereby enabling a more rapid approach to the true posterior distribution. The model integrates the efficiency of VI with the accuracy of the MCMC, thereby enhancing the precision of the posterior distribution approximation while preserving computational efficiency. The experimental results demonstrate that the model exhibits enhanced accuracy and robustness in the diagnosis of ethylene cracker tube coking compared to the conventional Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), Gaussian Mixture Regression (GMR), Bayesian Student’s T-Distribution Mixture Regression (STMR) and Semi-supervised Bayesian T-Distribution Mixture Regression (SsSMM). This method provides a scientific basis for optimizing and maintaining the ethylene cracker, enhancing its production efficiency and reliability, and effectively addressing the multimodal, non-Gaussian distribution and uncertainty of the coking data of the ethylene cracker furnace tube. Full article

Anthropogenic and climatic stresses threaten water security across the Yangtze River Basin (YZRB), which safeguards the population and economic development that is responsible for nearly half China’s GDP. Understanding trends and drivers of open surface water in the YZRB is crucial yet remains poorly investigated. This study proposes a new method to eliminate shadow impacts on water extraction, achieving 96% accuracy, and develops a long-term dataset from 1986 to 2022 using Landsat imagery on the Google Earth Engine platform. Trends in water area changes were analyzed for source region, typical river and lake dominated regions of YZRB, and partial least squares regression was used to attribute the major climatic and anthropogenic drivers of water change. The results show that water area generally increased by 39.88%, with divergent trends across regions. Source and river dominated regions both exhibited notable growths by 49.43% and 37.01%, respectively. Increases in the water area in the source region comes from both lakes and rivers, driven by increasing temperature and precipitation in permafrost regions, while increases in river dominated regions come from permanent water induced by construction of mega dams. Changes of the water body in lake dominated regions are mainly influenced by seasonal water and show varied trends. Poyang and Dongting lakes have decreasing water area, from 3354.24 to 2168.82 km² and 1504.79 to 850.47 km², respectively, which are both attributed to the impoundment of the Three Gorges Reservoir and alterations in precipitation patterns. While Tai Lake Basin experienced an increase from 1986 to 2003 due to expanded anthropogenic water bodies, it was followed by a decline after 2003 which was driven by urbanization. We therefore suggest systematically optimizing reservoir dispatching and land–water configurations to balance economic, societal, and environmental benefits. Full article