Parameter Extraction Research Articles

Neural Network Boundary Approximation for Uncertain Nonlinear Spatiotemporal Systems and Its Application of Tracking Control.

This brief addresses the neural network (NN) approximation problem for uncertain nonlinear systems with time-varying parameters (that is, unknown nonlinear spatiotemporal systems). Due to the fact that the unknown spatiotemporal functions cannot be directly approximated by NNs, a so-called time-varying parameter extraction is given to separate time-varying parameters from uncertain nonlinear spatiotemporal functions. By using the supremum of Euler norm of the extracted time-varying parameters, the nonlinear spatiotemporal function is mapped to an unknown state-based boundary function, which can be approximated by NNs. Based on the time-varying parameter extraction, an adaptive neural tracking control law is designed for uncertain strict-feedback nonlinear spatiotemporal systems, which guarantees the convergence of the tracking error with a trajectory performance. The effectiveness of the designed method is verified by simulations.

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

A large number of flammable hazardous materials are stored in chemical tank farms, where fire-induced domino accidents can be easily triggered. In this study, a novel real-time fire situation awareness (FSA) approach based on UAV is proposed to capture spatio-temporal evolution characteristics and predict development trends of fire accidents. Firstly, fire images are acquired by UAV, and the key parameters of fire are extracted in real time based on YOLOv8 network. Then, the thermal radiation and impact on surrounding equipment are predicted by combining LSTM network, solid flame model and improved probit model. The proposed method is verified by small-scale tank fire experiments, which demonstrate its superiority in terms of physical consistency and prediction accuracy. The results show that the mean absolute percentage error (MAPE) of fire parameter extraction is not higher than 5.43%, the MAPE of thermal radiation prediction is not higher than 25%, and the dynamic time to failure (dttf) for the model tank at different location is predicted. This work has the potential to provide a novel solution for real-time assessment of fire size and trend prediction to support firefighting, emergency rescue and decision making in fire accident scenarios.

Parameter extraction of tandem solar cell model including radiative coupling from measured data of photovoltaic system

Parameter extraction of tandem solar cell model including radiative coupling from measured data of photovoltaic system

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. RR2-10.1136/bmjopen-2021-050785.

Intelligent speech elderly rehabilitation learning assistance system based on deep learning and sensor networks

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

Ultrasound assisted extraction of phytochemicals from Piper betel L.

Piper betel contains phytochemicals with diverse pharmacological effects. The objective of this study was to enhance the extraction efficiency of phytochemicals and the chlorophyll content using ultrasonication. The Box-Behnken design was employed to optimize the time (10, 20, 30 min), temperature (20, 30, and 40 °C), and solid-solvent ratio (1:10, 1:20, 1:30) by utilizing response surface methods with three independent variables. Multiple parameters, including extract yield, total phenol, total flavonoid, antioxidant activity, and chlorophyll content were used to optimize the conditions. The linear relationship between power intensity and responses was determined to be statistically significant, with a p-value less than 0.01. The interaction effect of temperature, time, and ratio of solid solvent was shown to be statistically significant (p < 0.05) for all the obtained results. The optimal parameters for achieving the highest extract yield were as follows: a temperature of 40 °C, a sonication time of 30 min, and a solid solvent ratio of 1:10. These conditions result in an extract yield of 21.99 %, a total flavonoid content of 44.97 mg/GAE, a total phenolic content of 185.05 mg/GAE, a DPPH scavenging activity of 99.1 %, and a chlorophyll content of 49.95 mg/ml. This study highlights the significance of customized extraction methodologies for optimizing the bioactive capacity of phytochemicals derived from betel leaves. The elucidation of extraction parameters and the resultant phytochemical profiles serves as a fundamental framework for the advancement of innovative pharmaceuticals and nutraceuticals, capitalizing on the therapeutic attributes of this traditional medicinal botanical.

Green bean seed coagulant for turbidity removal: parameter optimization and modelling

ABSTRACT This study investigates the effectiveness of a natural coagulant derived from green bean (GB) seeds for treating synthetic turbid water. The properties of GB and green bean coagulant (GB-Co) were analysed using FTIR, SEM, and EDX. The study focuses on the interaction between extraction parameters, such as mixing time (MT), mix concentration (MC), and solvent concentration (SC), on turbidity removal and protein concentration. The optimal parameters were determined using response surface methodology (RSM) and desirability functions. The analysis of variance (ANOVA) showed good agreement between experimental data and the proposed model. The quadratic model showed that factors SC and MC significantly influence both responses. The optimum extraction conditions were MT, SC, and MC at 48 min, 1.38 M, and 10 g/L, respectively. The coagulant achieved a turbidity removal of 95.57% and a protein concentration of 1092.11 mg/L. The results suggest that GB-based coagulants can effectively remove turbidity from wastewater.

Preparation of a sustainable magnetic sorbent for the extraction and preconcentration of progestogens in natural water samples

A film composed of agarose and graphene (G) and magnetic nanoparticles (G-MNPs) is proposed as a sorbent for the extraction and determination of medroxyprogesterone (MED), levonorgestrel (LEV), norethisterone (NOR) and progesterone (PRO) in natural water samples. Both the preparation of the film and the extraction procedure were optimized. The optimal extraction parameters were as follows: isopropyl alcohol as activation solvent, sample pH value of 3.0, extraction time of 30 min, 1.00 mL of acetonitrile as eluent, elution time of 5 min and sample volume of 100.00 mL. HPLC with photodiode array detector was used for the separation and determination. The method presented a linear range between 2.50 and 75.0 μg L−1 for all analytes, and the LODs were between 1.40 and 1.80 μg L−1. The method was applied to natural water samples, obtaining satisfactory recovery values (75–111 %). In conclusion, for the immobilization of the G-MNPs, agarose was used, which is a non-toxic, renewable and biodegradable material. The G-MNPs-agarose film was reused up to 70 times, without losing its extraction capacity significantly and presenting excellent sorbent properties, which allow the extraction and preconcentration of the progestogens under study.

Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems

The characteristic parameters of renewable energy sources (RESs) are changes over time under changing operating conditions. Therefore, the accuracy of the model is important in order to establish appropriate control and operating plans for the stable operation of such systems. Determining the parameters of RESs and creating an accurate model of them is an inevitable reality and plays an important role in the success of system modeling. Many methods have been used to determine these parameters in the literature. Furthermore, optimization algorithms are now considered to be among the most significant approaches for resolving problems pertaining to system parameter extraction in many scientific areas. In the study, this parameter extraction is done by a new optimization method called stochastic fractal search algorithm (FDB-NSM-SFS-OBLs), which includes natural survivor updating (NSM) and fitness distance balance (FDB) guiding mechanisms, and opposite based learning (OBL) methods. The performance of the proposed FDB-NSM-SFS-OBL algorithm has been tested in two different experimental studies. In the first experimental study, the proposed approach is tested on CEC2020 benchmark test functions and the algorithm structure containing the best OBL approach is determined using nonparametric Wilcoxon and Friedman statistical analysis methods. The second experimental study is carried out with the proposed optimization algorithm to estimate the parameters of the photovoltaic cell used in the modeling, which is the building block of photovoltaic panels used in renewable energy systems, proton-exchange membrane fuel cell (PEMFC) that is another renewable energy source, and Li-Ion battery used as a backup power unit. The FDB-NSM-SFS-OBL algorithm, which is used both in the CEC2020 benchmark test functions and in determining the parameters of photovoltaic (PV) cells, PEMFC and Li-Ion batteries, performed better in searching and finding the global solution point during the optimization process.

Recent advances of ECG monitoring and webserver health monitoring applications: A review

In response to recent improvements in mobile technology and cloud computing-based real-time monitoring systems, numerous cloud-based healthcare services and devices have been developed. In the cloud system, it is feasible to collect and distribute medical data immediately from anywhere to medical specialists, and patients can receive network-based feedback. This paper reviews the most recent developments and trends in wearable technology and biomedical equipment (mostly ECG), as well as the development of a cloud-based solution for mobile device and web browser users. The system contains ECG enhancement, ECG quality grading, and ECG parameter extraction methods. A use case was utilized to showcase the system, in which a web server processed ECG data from a mobile phone in real-time. The system has been proved to be functional, accurate, and efficient. We review the primary principles underlying the operation of the ECG and its fundamental operation, as well as the recording and transmission paradigm for ECG signals. Detailed processes were also provided for the system components, their configuration, and the employed technologies. In conclusion, we examine the complete study and the project's future scope.

Nutritional Profiling of Underutilised Citrullus Lanatus Mucosospermus Seed Flour

The seed of Citrullus lanatus mucosospermus, known as egusi, is versatile and explored for its oil and flour functionality. Raw flour can be used as a raw material in a nutritional program due to its oil-rich, remarkably high protein content, and richness in omega-6 fatty acids. There is a need to explore eco-friendly defatting methods using the supercritical CO2 extraction method (SFECO2) to preserve this seed’s generic richness and to control the flour–oil ratio in processing formulations. The supercritical fluid extraction method uses temperature, pressure, and CO2 flow rate to determine the best yield and extraction parameters. Defatted egusi flour (DEF) was extracted using three runs. Firstly, at 60 °C, 30 g/h, and 450 bar (DEF1); secondly, at 55 °C, 30 g/h, and 600 bar (DEF2); and thirdly, extraction was performed at 75 °C, 30 g/h and 600 bar (DEF3). Trace and major elements were analysed using Agilent 7700 quadruple ICP-MS (Agilent Technologies Network, Palo Alto, CA, USA) and Thermo Cap 6200 ICP-AES (Thermo Scientific, Waltham, MA, USA), respectively. The sugar was separated on a gas chromatograph coupled to a Mass Selective Detector (MSD). The fundamental pasting property measurements were performed using a Rapid Visco Analyser RVA 4500 Perten instrument Sin 214 31208-45 Australia. Data analysis was conducted using IBM SPSS version 29 software (v. 2022). The protein content of defatted egusi flour ranged from 48.4 for DEF2 to 60.4% w/w for DEF1 and differed significantly, with a rich amino acid high in glutamine ranging from 9.8 to 12.9 g/100 g). DEF2 (512.0 cP) showed the highest peak viscosity and was the most viscous among the samples. Defatted flour with lower temperature and lower pressure (60 °C and 450 bar) offered the best nutritional properties, proffering defatted egusi flour from SFECO2, a novel flour for dietary programs.

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

Isolation and Identification of Indole Alkaloids from Aspergillus amstelodami BSX001 and Optimization of Ultrasound-Assisted Extraction of Neoechinulin A.

This study aimed to investigate the alkaloid secondary metabolites of Aspergillus amstelodami BSX001, a fungus isolated from Anhua dark tea, and to improve the extraction yield of the active ingredients by optimizing the extraction process. The structural characterization of the compounds was investigated using mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. The antioxidant activity of echinulin-related alkaloids was evaluated by determining the total reducing power and DPPH radical scavenging capacity. The extraction process of the compound with optimum activity was optimized by a single-factor test and response surface methodology (RSM) combined with Box-Behnken design (BBD). The optimized result was validated. Finally, a new alkaloid 8-hydroxyechinulin (1), and four known alkaloids, variecolorin G (2), echinulin (3), neoechinulin A (4), and eurocristatine (5), were isolated. Echinulin-related compounds 1, 3, and 4 possessed certain antioxidant activities, with IC50 values of 0.587 mg/mL, 1.628 mg/mL, and 0.219 mg/mL, respectively, against DPPH radicals. Their total reducing power at a concentration of 0.5 mg/mL was 0.29 mmol/L, 0.17 mmol/L, and 4.25 mmol/L. The extraction process of neoechinulin A was optimized with the optimum extraction parameters of 72.76% methanol volume fraction, 25 mL/g solid-liquid ratio, and 50.8 °C soaking temperature. Under these conditions, the extraction yield of neoechinulin A was up to 1.500 mg/g.

Optimization of toothtablets incorporating Albizia myriophylla Benth. stem extract obtained by eco-friendly microwave-assisted extraction

Albizia myriophylla Benth. stems are composed of the bioactive compound lupinifolin, which has exhibited anti-Streptococcus mutans activity. Extracting this plant for incorporation into oral healthcare products is challenging. This study aimed to optimize the eco-friendly microwave-assisted extraction of A. myriophylla stems. It also optimized toothtablets incorporating A. myriophylla stem extract. First, lupinifolin was isolated, and its structure was determined for use as a biomarker in this study. Next, three extraction parameters—the solvent-to-solid ratio, microwave power, and microwave time—were screened using a one-factor-at-a-time approach before being optimized using a Box–Behnken design. The results showed that the optimized conditions that maximized the extraction yield and lupinifolin content were a solvent-to-solid ratio of 23:1, microwave power of 800 W, and microwave time of 70 s. This extract contained 16.27 ± 0.43 % lupinifolin. The extract exhibited a minimum inhibitory concentration of 32 μg/mL and a minimum bactericidal concentration of 256 μg/mL. The half-maximal inhibitory concentration with human gingival fibroblast cells was 102.70 ± 1.02 μg/mL for the extract and 76.75 ± 1.03 μg/mL for lupinifolin. The extract was solidified using microcrystalline cellulose and colloidal silicon dioxide before being incorporated into the toothtablet formulation. Three factors in toothtablet production—compression force, microcrystalline cellulose content, and hydrated silica content—were also screened using a one-factor-at-a-time approach before being optimized using a Box–Behnken design. The results showed that the optimized conditions that approached the desired properties were a compression force of 2500 psi, microcrystalline cellulose content of 12 %, and hydrated silica content of 10 %. The developed toothtablets contained 35.64 ± 0.45 mg of lupinifolin per 650 mg toothtablet (or approximately 5.5 % lupinifolin per toothtablet) and retained their anti-S. mutans activity. They appeared safe for human gingival fibroblasts, even when treated for 2 min, with a maximum concentration of 2000 μg/mL. In conclusion, the developed toothtablets incorporating A. myriophylla stem extract can potentially serve as a high-quality, antibacterial, and safe alternative to traditional toothpaste.

Metal-Organic Framework (MOF-199) Coated by Molecularly Imprinted Poly MMA-EGDMA for Solid Phase Extraction of Carbofuran from Fruit Samples

A selective adsorbent was synthesized based on the coating of a molecularly imprinted poly (methyl methacrylate-ethylene glycol dimethacrylate) (poly (MMA-EGDMA)) on the surface of metal-organic frameworks (MOFs) to obtain MMA-EGDMA@MOF-199 and used for the separation and preconcentration of carbofuran. Subsequently, carbofuran was determined by high-performance liquid chromatography with a diode array detector (HPLC-DAD). X-ray diffraction, field-emission scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis were used to characterize the synthesized adsorbent. Both pH and extraction time were optimized as crucial extraction parameters. The adsorption capacity of the adsorbent was 20 mg g−1. The selectivity of the synthesized adsorbent for similar compounds was also investigated. Under the optimal conditions, the calibration curve was linear from 1.0 to 400 μg L−1. A high extraction efficiency (94%) was obtained for carbofuran with a limit of detection (LOD) of 0.4 µg L−1. The adsorbent was successfully applied to the SPE of carbofuran from fruit samples after its extraction, followed by high-performance liquid chromatography analysis.

Optimization of Pressurized Liquid Extraction (PLE) Parameters for Extraction of Bioactive Compounds from Moringa oleifera Leaves and Bioactivity Assessment.

Moringa oleifera leaves are rich sources of bioactive compounds with potential health benefits, including antioxidants and anti-inflammatory agents. Pressurized liquid extraction (PLE) stands out as a promising technique for effectively extracting valuable compounds from natural sources. In this study, we aimed to optimize PLE parameters, such as temperature, extraction duration, and pressure, to maximize bioactive compound (polyphenols, flavonoids, and ascorbic acid) yield from M. oleifera leaves and evaluate their antioxidant and anti-inflammatory activities. According to the outcomes of this research, the maximum achieved total polyphenol content was 24.10 mg gallic acid equivalents (GAE)/g of dry weight (dw), and the total flavonoid content was increased up to 19.89 mg rutin equivalents (RtE)/g dw. Moreover, after HPLC-DAD analysis, neochlorogenic and chlorogenic acids, catechin and epicatechin, rutin, and narirutin were identified and quantified. As far as the optimum ascorbic acid content is concerned, it was found to be 4.77 mg/g dw. The antioxidant activity was evaluated by three different methods: ferric reducing antioxidant power (FRAP), the DPPH method, and the anti-hydrogen peroxide activity (AHPA) method, resulting in 124.29 μmol ascorbic acid equivalent (AAE)/g dw, 131.28 μmol AAE/g dw, and 229.38 μmol AAE/g dw values, respectively. Lastly, the albumin denaturation inhibition was found to be 37.54%. These findings underscore the potential of PLE as an efficient extraction method for preparing extracts from M. oleifera leaves with the maximum content of bioactive compounds.

Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

Carboxyl-functionalized mesoporous silica-coated solid-phase microextraction Arrow for the determination of six biogenic amines in animal-derived meat

Mesoporous silica materials, SBA-15 and SBA-16, that functionalized with the carboxyl, vinyl and amino groups were synthesized in this study. The spectroscopic analysis evaluated the physicochemical characteristics of these materials. These materials were coated on the arrow-typed solid phase microextraction (SPME Arrow) to extract biogenic amines (BAs). The influences of these materials' functional groups and spatial structure on their extraction efficiency were studied. Carboxyl group modified SBA-15 (SBA-15-C) exhibited the highest extraction capacity to BAs because of the size exclusion effect and the electrostatic interaction. The extraction and desorption parameters of the SPME Arrow method were optimized. The SBA-15-C-SPME Arrow was coupled with a gas chromatography-mass spectrometry (GC-MS) system for the determination of BAs in animal-derived meat samples, which showed good repeatability (RSD<15%), satisfactory linear range (0.01–1 mg kg–1), low limit of detection (0.001–0.004 mg kg–1), and high recovery (84–129%). This method showed excellent applicability for the quantitative and qualitative detection of six BAs in animal-derived meat samples. This study provides new insight into the development of new sorbents with high extraction capacity to BAs for the selective and sensitive determination of BAs in complex meat samples.

Optimization of Extraction and Refining Parameters of Oil from Dotted Gizzard Shad (Konosirus punctatus).

To address the challenges associated with resource inefficiency, low extraction rates, environmental concerns, and high energy consumption in traditional fish oil production from dotted gizzard shad (Konosirus punctatus), a novel approach is needed. This study aimed to develop and evaluate two innovative methods for fish oil extraction and refinement, focusing on their effects on fish oil quality, fatty acid profile, and volatile compound composition throughout the respective processes. The findings of the study revealed that the ethanol-assisted enzymatic extraction method surpassed the conventional enzymatic approach in extraction efficiency, achieving an optimal extraction rate of 74.94% ± 0.45% under optimized process conditions. Moreover, the ethanol-NaOH one-step degumming and deacidification method proved effective in simultaneously removing phospholipids and free fatty acids. Under optimal conditions, a notable reduction in phospholipid content in dotted gizzard shad oil, from 6.80 ± 0.01 mg/g to 1.18 ± 0.01 mg/g, and a substantial decrease in acid value, from 3.31 mg/g to 0.31 mg/g, were observed. In summary, the study analyzed the physicochemical properties, fatty acid composition, and volatile components of fish oil before and after refinement. The refining process was found to preserve the fatty acid composition while efficiently eliminating hydroperoxides and reducing unpleasant odors in the crude oil.