Reliable Method Research Articles

Development and validation of a novel scoring system for acute ischemic stroke

BackgroundThis study aimed to assess the clinical application of the Artery Occlusion Image Score (AOIS), a new metric based on computed tomographic angiography (CTA) that reflects the severity of occlusive changes in the main intracranial arteries.Materials and MethodsPatients diagnosed with acute ischemic stroke (AIS) were divided into three groups: anterior circulation infarcts (ACI group), posterior circulation infarcts (PCI group), and both anterior and posterior circulation infarcts (ACI + PCI group). The sensitivity and specificity of AOIS were evaluated using the Basilar Artery on Computed Tomography Angiography (BATMAN) score, the Clot Burden Score (CBS), and the National Institutes of Health Stroke Scale (NIHSS) as comparators through receiver-operating characteristic (ROC) curve analysis.ResultsThe final analysis included 439 consecutive patients. In the ACI group, AOIS demonstrated high sensitivity (86.3%) and specificity (85.0%) and outperformed CBS in predicting patient prognosis. In the PCI group, AOIS also showed high sensitivity (88.9%) and specificity (90.0%) and outperformed BATMAN in predicting patient prognosis. In the ACI + PCI group, AOIS positively correlated with the NIHSS score (Spearman’s ρ = 0.602, P < .001). Additionally, the scoring time of AOIS did not significantly differ from CBS and BATMAN.ConclusionAOIS is a convenient and reliable method for guiding treatment and predicting outcomes in patients with ACI or/and PCI. Furthermore, AOIS is the first CTA-based scoring system that covers both the anterior and posterior circulation, providing a convenient and reliable evaluation for patients with concurrent acute ischemic stroke in both circulations.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted -norm to regularize the deformation field instead of the traditional -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical -test results also proved that these improvements of our method have statisticalsignificance. In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

A new application of Elasticnet regression based near-infrared spectroscopy model: Prediction and analysis of 2,3,5,4′-tetrahydroxy stilbene-2-O-β-D-glucoside and moisture in Polygonum multiflorum

This study constructed a near-infrared spectroscopy (NIRS) quantitative analysis model of 2,3,5,4′-tetrahydroxy stilbene-2-O-β-D-glucoside (TSG) and Polygonum multiflorum's water content based on Elasticnet regression (ER). NIRS data, TSG and the moisture content data sets of 117 samples of P. multiflorum were obtained by using NIR scanning and high-performance liquid chromatography analysis (HPLC) methods. MSE was used to determine the hyperparameters α and λ of Elasticnet. The optimal α values of TSG and water content were 0.83 and 0.01, and the optimal λ values were 0.03 and 1.13, respectively. By preprocessing the original spectral data, the NIRS model was established based on the source data set using ER, compared with the model established by PLS and its optimization algorithm. The results show that the ER model has good performance compared with other methods, with R2c value was 0.9879, RMSEC value was 0.09, R2p value was 0.9784, RMSEP value was 0.13 and RPD value was 6.8. In addition, according to the properties of Elasticnet's variable selection, we speculate that the final coefficients of spectral features may indirectly reflect the spectral features of different components. The results showed better prediction accuracy and superior performance. This is a much more reliable method used to forecast the content of effective components in plants.

Comparison of extraction methods in vitro Plasmodium falciparum: A 1H NMR and LC-MS joined approach

Malaria is a parasitic disease that remains a global concern and the subject of many studies. Metabolomics has emerged as an approach to better comprehend complex pathogens and discover possible drug targets, thus giving new insights that can aid in the development of antimalarial therapies. However, there is no standardized method to extract metabolites from in vitro Plasmodium falciparum intraerythrocytic parasites, the stage that causes malaria. Additionally, most methods are developed with either LC-MS or NMR analysis in mind, and have rarely been evaluated with both tools. In this work, three extraction methods frequently found in the literature were reproduced and samples were analyzed through both LC-MS and 1H NMR, and evaluated in order to reveal which is the most repeatable and consistent through an array of different tools, including chemometrics, peak detection and annotation. The most reliable method in this study proved to be a double extraction with methanol and methanol/water (80:20, v/v). Metabolomic studies in the field should move towards standardization of methodologies and the use of both LC-MS and 1H NMR in order to make data more comparable between studies and facilitate the achievement of biologically interpretable information.

Abundant soliton solutions of the modified KdV-KP equation

The precise solutions of the (2+1)-dimensional modified KdV-KP equation are examined in this study. Three reliable methods, namely, the modified auxiliary equation scheme, extended rational sine–cosine methodology, and extended rational sinh–cosh technique, are utilized for the first time to extract multiple soliton solutions for the governing model. These types of solutions have applications in plasma, solid state, neuronal, biological production, and diffusion processes. Graphical simulations of the obtained solutions are included in the form of 3 dimensional surface plots to reveal their physical structure and dynamical features.

Large Deflection Geometrically Nonlinear Bending of Porous Nanocomposite Cylindrical Panels on Elastic Foundation

Large deflection nonlinear bending of functionally graded (FG) porous cylindrical panels reinforced with graphene platelets (GPLs) on a Pasternak-type elastic foundation is examined by developing a reliable and effective 2D meshfree-based nonlinear numerical method. The large displacement field is express by the first-order shear deformation theory (FSDT) and the von Kármán nonlinearity, and approximated by 2D natural element method (NEM) in conjunction with the stabilized MITC3+ shell concept and the shell surface–rectangular grid geometry transformation. The nonlinear simultaneous equations are solved by a load incremental Newton–Raphson scheme. The developed nonlinear numerical method is justified from by comparing with the reference solutions, and the load–deflection and bending moment of FG-GPLRC porous cylindrical panels on elastic foundation are scrutinizingly examined. Four different symmetric GPL distribution patters (except for FG-Λ) and three different symmetric porosity distributions are considered and their combined effects on the nonlinear bending behavior are investigated, as well as the effects of foundation stiffness and GPL amount. Also, the results are compared with those of FG CNT-reinforced porous cylindrical panels.

Generic COVID-19 epidemic forecast for Estonia by Gaidai multivariate reliability method

This study aims at accurate spatiotemporal assessment of future epidemic outbreak risks, occurring within any given return period, and within any relevant administrative region of Estonia. Authors have recently developed novel statistical spatiotemporal methodology, that can be applied directly to multivariate raw clinical datasets. Novel Gaidai multivariate reliability methodology, described in this study, being particularly appropriate for multi-regional environmental, biological and public health systems. Advocated methodology has been sufficiently validated in the recent studies, yielding reliable long-term risk forecasts of the future epidemic outbreaks. COVID-19 (SARS-COV-2) daily recorded patient numbers were selected in all impacted Estonia national regions. This study results conclude that recommended spatiotemporal approach may effectively utilize even limited raw clinical datasets, the latter can be useful in a wide range of bioinformatics and public health applications. Confidence intervals have been estimated for predicted epidemiological levels.

Rapid and sensitive detection of large yellow croaker iridovirus by real-time RPA and RPA-LFD.

Large yellow croaker (Larimichthys crocea) is a vital marine-cultured species in China. Large yellow croaker iridovirus (LYCIV) can cause a high mortality rate in L. crocea. Rapid and convenient detection of LYCIV is an urgent demand for diagnosis. In this study, rapid and simple recombinase polymerase amplification (RPA), real-time RPA and RPA combined with lateral flow dipstick (RPA-LFD) methods were developed for the detection of LYCIV based on the conserved sequence of the LYCIV major capsid protein (MCP) gene. With these optimized RPA analyses, LYCIV detection could be completed within 20 min at 40°C. Both RPA and real-time RPA could detect viral DNA as low as 102 copies/μL, while the detection limit of RPA-LFD was 101 copies/μL, and there was no cross-reaction with other aquatic pathogens (KHV, CyHV-2, GCRV-JX01, SVCV, LCDV and LMBV). In practical evaluation of RPA, real-time RPA and RPA-LFD methods, the results showed consistency with the general PCR detection. In short, the developed RPA, real-time RPA and RPA-LFD analyses could be simple, rapid, sensitive and reliable methods for field diagnosis of LYCIV infection and have significant potential in the protection of LYCIV infection.

Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach—simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called “Chenpi”. That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Analyzing coastal erosion and sedimentation using Sentinel-1 SAR change detection: An application on the Volta Delta, Ghana

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning.

Identification and quantification of both isomers of hexahydrocannabinol, (9R)-hexahydrocannabinol and (9S)-hexahydrocannabinol, in three different matrices by mass spectrometry.

Hexahydrocannabinol (HHC), a compound derived from synthetic production using cannabidiol (CBD) or delta-9-tetrahydrocannabinol (Δ9 -THC), has gained recent attention due to its presence in seized materials across Europe. Sold legally in various forms, HHC poses potential health risks, particularly as a legal alternative to THC in some countries. Despite its historical description in the 1940s, limited toxicology data, pharmacological understanding, and analytical methods for HHC exist. This study proposes analytical techniques using mass spectrometry to detect, identify, and quantify (9R)-HHC and (9S)-HHC, concurrently with THC and CBD in various matrices, including oral fluid, whole blood, and seized material. Three distinct methods were employed for different matrices: GC/MS for seized material, GC/MS/MS for whole blood, and UHPLC/MS/MS for oral fluid. Methods were validated qualitatively for oral fluid with a FLOQSwab® device and quantitatively in whole blood and seized material according to Peters et al's recommendations and ICH guidelines. Validated methods were considered reliable in detecting and quantifying HHC isomers in terms of repeatability, reproducibility, and linearity with r2 systematically >0.992. These methods were applied to authentic cases, including seized materials and biological samples from traffic control (whole blood and oral fluid). In seized materials, (9R)-HHC levels ranged from 2.09% to 8.85% and (9R)-HHC/(9S)-HHC ratios varied from 1.36 to 2.68. In whole blood sample, (9R)-HHC and (9S)-HHC concentrations were, respectively, 2.38 and 1.39 ng/mL. For all analyzed samples, cannabinoids such as THC and CBD were also detected. This research contributes analytical insights into differentiating and simultaneously analyzing (9R)-HHC and (9S)-HHC, using widely applicable mass spectrometric methods. The study emphasizes the need for vigilance among toxicologists, as new semisynthetic cannabinoids continue to emerge in Europe, with potential health implications. The findings underscore the importance of reliable analytical methods for monitoring these compounds in forensic and clinical settings.

A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR)

The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor’s decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.

Strength-toughness matching mechanisms of a boride-reinforced Ti6Al4V compositionally graded material fabricated using spark plasma sintering

In this work, an in-situ boride-reinforced Ti6Al4V compositionally graded material was prepared successfully by spark plasma sintering (SPS), and exhibited a good potential for military application because of its stepwise transition properties from a high-toughness end to a high-strength end along the thin-thickness direction. Microstructure characterization shows the chemical gradient ranged from 0 to 30 wt% TiB2 along the thickness direction, which was correlated with increasing micro-hardness values ranging from 565±5HV to 1270±5HV, respectively. The strength-toughness matching mechanisms of the gradient composites with different compositional gradients were discussed based on the static compression experiments and the dynamic Hopkinson compression rod experiments. The primary phases of the gradient composites included the α-Ti phase, β-Ti phase, remained TiB2 and in-situ formed whisker-like TiB phases. The composites with a compositional gradient of G84 (0, 0, 15, 30 wt%) exhibited the best impact resistance which achieved a good balance of high toughness and strength, and exhibited a compressive strength of 2148±5 MPa and a good elongation of 20 ± 0.5%. The strength-toughness matching mechanisms can be explained by the dispersion strengthening effect due to the presence of the in-situ formed TiB phase, and the consumption of more fracture energy by dislocation defects, which can prevent the crack propagation. Moreover, the pull-out of the whisker-like TiB phases toughened the gradient composites furthermore. This research demonstrated a reliable method for preparing TiB2/Ti–6Al-4Vgradient composites with high-impact resistance.

Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method

Black tea (Camellia sinensis) is a widely consumed beverage and is subjected to adulteration. In this study, the combination of ion mobility spectrometry and machine learning techniques was employed to detect synthetic colorants in black tea. To accomplish our objective, six synthetic colorants (carmine, carmoisine, indigo carmine, brilliant blue, sunset yellow, and tartrazine) were added to pure tea at different concentrations. A qualitative model was built using partial least squares discriminant analysis (PLS-DA) for the collected data and exhibited 100% accuracy in identifying synthetic colorants in black tea. For quantitative analysis, a PLS regression model was employed. The R2 values obtained for the test set ranged from 0.986 to 0.997. The method developed in this study has proven to be reliable and effective in detecting synthetic colorants in black tea. Also, this method is a simple, rapid, and trustworthy tool for identifying adulteration in black tea.

Evaluation of transrectal ultrasound-guided tru-cut biopsy as a complementary method for predicting pathological complete response in rectal cancer after neoadjuvant treatment: a phase II prospective and diagnostic trial.

Patients with pathological complete response (pCR) of rectal cancer following neoadjuvant treatment had better oncological outcomes. However, reliable methods for accurately predicting pCR remain limited. To evaluate whether transrectal ultrasound-guided tru-cut biopsy (TRUS-TCB) adds diagnostic value to conventional modalities for predicting pathological complete response in patients with rectal cancer after neoadjuvant treatment. This study evaluated data of patients with rectal cancer who were treated with neoadjuvant treatment and reassessed using TRUS-TCB and conventional modalities before surgery. This study is registered with ClinicalTrials.gov. The primary outcome was accuracy, along with secondary outcomes including sensitivity, specificity, negative predictive value, and positive predictive value in predicting tumour residues. Final surgical pathology was used as reference standard. Between June 2021 and June 2022, a total of 74 patients were enroled, with 63 patients ultimately evaluated. Among them, 17 patients (28%) exhibited a complete pathological response. TRUS-TCB demonstrated an accuracy of 0.71 (95% CI, 0.58-0.82) in predicting tumour residues. The combined use of TRUS-TCB and conventional modalities significantly improved diagnostic accuracy compared to conventional modalities alone (0.75 vs. 0.59, P =0.02). Furthermore, TRUS-TCB correctly reclassified 52% of patients erroneously classified as having a complete clinical response by conventional methods. The occurrence of only one mild adverse event was observed. TRUS-TCB proves to be a safe and accessible tool for reevaluation with minimal complications. The incorporation of TRUS-TCB alongside conventional methods leads to enhanced diagnostic performance.

Incidence and characteristics of developmental dysplasia of the hip in a Saudi population: A comprehensive retrospective analysis.

Developmental dysplasia of the hip (DDH) is a common hip disorder in infants. Ultrasonographic screening is considered the most reliable method for early detection of DDH in newborn infants. This study aimed to determine the incidence of DDH at King Abdulaziz University Hospital. From January 2017 to December 2018, this retrospective study analyzed the data of 8031 infants who underwent an early clinical examination of the hips. At the initial evaluation, 212 infants referred to the ultrasound department had risk factors for DDH and/or clinical instability. Different well-known risk factors of DDH have been identified including breech position, skeletal deformities, positive family history, hip side, sex and clicky hip. The findings revealed an incidence rate of 93 cases of DDH among 8031 infants (11.58 per 1000) births. Among the infants diagnosed with DDH, 55 were females (59.14 %), and 38 were males (40.86%). The results of the current study revealed that 45% of infants with DDH exhibited bilateral involvement. Moreover, results showed that the IIa hip joint was more prevalent in female than in male newborns, but without any significant difference. The incidence rate of DDH in this study was 11.58 per 1000 live births, and it was more prevalent among female than male newborns. Ultrasonography (US) should be used as a complementary imaging modality for clinical examination of DDH. Infants with breech presentation, clicky hip, and skeletal deformity should be scanned by US. Further prospective studies are recommended.

Advancing Molecular Weight Determination of Lignin by Multi-Angle Light Scattering.

Due to the complexity and recalcitrance of lignin, its chemical characterization is a key factor preventing the valorization of this abundant material. Multi-angle light scattering (MALS) is becoming a sought-after technique for absolute molecular weight (MW) determination of polymers and proteins. Lignin is a suitable candidate for MW determination via MALS, yet further investigation is required to confirm its absolute MW values and molecular size. Studies aiming to break down lignin into a variety of renewable products will benefit greatly from a simple and reliable determination method like MALS. Recent pioneering studies, discussed in this review, addressed several key challenges in lignin's MW characterization. Nevertheless, some lignin-specific issues still need to be considered for in-depth characterization. This study explores how MALS instrumentation manages the complexities of determining lignin's MW, e.g., with simultaneous fractionation and fluorescence interference mitigation. Additionally, we rationalize the importance of a more detailed light scattering analysis for lignin characterization, including aspects like the second virial coefficient and radius of gyration.

Quantifying the Contribution of Soil Heavy Metals to Ecological and Health Risk Sources

Quantifying the risk of soil heavy metal sources can identify the main pollution sources. It can provide a scientific basis for reducing the ecological and human health risks of soil heavy metals. Taking the shallow soil in a Pb-Zn mine watershed in northern Guangxi as a research object， ecological and human health risk assessments were conducted using potential ecological risk assessment （RI） and human health risk assessment （HRA）， and the source apportionment of soil heavy metals was completed using the absolute principal component-multiple linear regression receptor （APCS-MLR） model and random forest （RF） model. Then， a combined risk assessment model， consisting of RI， HRA， and APCS-MLR， was used to quantify the risk of soil heavy metal sources. The results showed that the contents of Pb， Zn， Cu， and Cd exceeded the environmental screening values for agricultural land with mean values of 342.77， 693.34， 61.27， and 3.08 mg·kg-1， respectively， and there was a certain degree of contamination. Pb， Cr， and As were the main health risk impact factors， with higher health risks for children than for adults. Three sources were identified： mining activities （Source Ⅰ）， soil parent material sources and original formation （Source Ⅱ）， and unknown sources. Pb， Zn， Cu， and Cd were mainly derived from Source Ⅰ， and Cr and As were controlled by unknown sources and Source Ⅱ. The source risk assessment results of soil heavy metals indicated that the potential ecological risk and non-carcinogenic risk were mainly from Source Ⅰ and Source Ⅱ， and carcinogenic risk was mainly from unknown sources. The unknown sources had a high proportion in source apportionment and risk assessment， and should be further researched to provide scientific basis for soil heavy metal control. The combined risk assessment model based on source analysis， focusing on the risk characteristics of different sources， can accurately identify high-risk pollution sources. It is a more reasonable and reliable risk assessment method.

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees.