Principal Component Analysis Research Articles

Geographical variation in lung function: Results from the multicentric cross-sectional BOLD study.

Spirometry is used to determine what is "unusual" lung function compared with what is "usual" for healthy non-smokers. This study aimed to investigate regional variation in the forced vital capacity (FVC) and in the forced expiratory volume in one second to FVC ratio (FEV1/FVC) using cross-sectional data from all 41 sites of the multinational Burden of Obstructive Lung Disease study. Participants (5,368 men; 9,649 women), aged ≥40 years, had performed spirometry, had never smoked and reported no respiratory symptoms or diagnoses. To identify regions with similar FVC, we conducted a principal component analysis (PCA) on FVC with age, age2 and height2, separately for men and women. We regressed FVC against age, age2 and height2, and FEV1/FVC against age and height2, for each sex and site, stratified by region. Mean age was 54 years (both sexes), and mean height was 1.69 m (men) and 1.61 m (women). The PCA suggested four regions: 1) Europe and richer countries; 2) the Near East; 3) Africa; and 4) the Far East. For the FVC, there was little variation in the coefficients for age, or age2, but considerable variation in the constant (men: 2.97 L in the Far East to 4.08 L in Europe; women: 2.44 L in the Far East to 3.24 L in Europe) and the coefficient for height2. Regional differences in the constant and coefficients for FEV1/FVC were minimal (<1%). The relation of FVC with age, sex and height varies across and within regions. The same is not true for the FEV1/FVC ratio.

The Characteristics of Methylsiloxane Distribution in the Marine Surface Sediment, Masan Bay, Korea, in August 2018 and Suggesting Directions for Further Studies

The present study determined the distribution of fifteen methylsiloxane compounds (total methylsiloxanes, TMSs), including four cyclic (CMSs) and eleven linear (LMSs), in the marine surface sediment of Masan Bay, Korea, in 2018. The levels ranged from 76.6 to 2320 (median 264 ± 680), 5.00 to 3570 (median 359 ± 1310), and 81.6 to 5890 (median 607 ± 1940) ng/g-dw., in ΣCMSs, ΣLMSs, and ΣTMSs, respectively. The CMSs: LMSs ratio was 34:66, and the most dominant compound was D5 (27%), followed by L11 (22%) and L12 (20%). Principal component and cluster analyses categorized the ten sampling points into four groups based on the point sources: Group A (MS1) representing the industrial wastewater from the Changwon National Industrial complex; Group B (MS2) representing the ship movements; and Groups C (MS3, MS5, MS6, and MS7) and D (MS4, MS8, MS9, and MS10) representing the organic carbons. The present study hypothesized that the missing link between the methylsiloxane levels in 2013 and 2021 was due to the considerable industry recession in the bay. This was verified by positive correlations between the median ΣCMSs and the monthly production, the median ΣLMSs and ΣTMSs and the monthly employment in the complex. However, the hypothesis should be supplemented with the following perspectives: (1) selecting the sampling areas representing only the industrial wastewater, (2) considering the seasonal fluctuations, (3) estimating half-lives, and (4) investigating time series. This study is the first to link methylsiloxane levels and social issues in the bay, providing insights into illuminating the distribution characteristics of various pollutants.

Power forecast of hydro–wind–photovoltaic hybrid system dual-driven by physics and data

Short-term power forecast is an important way to guide operation of renewable energy stations and hybrid energy system (HES). The current studies focus on power forecast of single renewable energy station. However, the universality and applicability of power forecast model for HES is not clear. This study proposes a physics and data dual-driven day-ahead power forecast model for hydro–wind–photovoltaic HES. The WRF model and Xinanjiang model are used to drive meteorological and hydrological forecasts respectively. The hybrid variational mode decomposition - principal component analysis method is applied to further extract the features hidden in the meteorology or hydrology factors. The long short-term memory network is used to drive power forecast. China’s Guandi hydro–wind–PV HES is considered as a case study. Results show that the forecast root mean square error of dual-driven model decreases by 4.2% ~ 12.0% compared to single-driven model.

Residual Effects of Vinasse and Poultry Manure Application on Soil Quality and Spinach (Spinacia oleracea L.) Yield Grown in Calcareous Soil

Reuse poultry manure (PM) and vinasse (V) is a promising technology for the sustainability of various agricultural systems without environmental impact, but its effects on soil fertility index (SFI), hydraulic conductivity (Ks), porosity (TP), available water (AW), and spinach (Spinacia oleracea L.) growth in calcareous soils are poorly understood. Therefore, we studied the residual effect of combining PM either individually or mixed with V at different rates in pots. This study found that adding PM alone or in conjunction with V at various application rates resulted in an important improvement in SFI, Ks, TP, AW, and spinach yield. Vinasse contributed to an increase in more than 20% of the organic matter when adding it as 4.2 g V and 6.3 g PM kg−1 soil, and gave the best results in the organic matter fresh weight of spinach plant, SFI, and AW by 43.8%, 26.33%, 41.24%, and 63.63% compared to control. Spinach leaf uptake of NPK rose considerably when the soil was treated with PM alone or in combination with V. The SFI, cation exchange capacity (CEC), available N (AN), available P (AP), Ks, and AW were shown to be positively associated with spinach fresh weight utilizing principal component analysis, but soil electrical conductivity (EC), exchange sodium percentage (ESP), pH, and bulk density (BD) were found to be negatively correlated. Additionally, it was estimated by the K-Nearest Neighbors technique that under certain soil conditions, including AP 14.89 mg kg−1, AN 38.25 mg kg−1, microbial biomass carbon (MBC) 2.47 mg kg−1, CEC 11.66 cmol kg−1, exchangeable sodium percentage (ESP) 11.65%, and SFI 26.55%, spinach fresh weight increases to 13.8 t ha−1. These findings show how adding PM and V can improve soil quality and enhance nutrient uptake, all of which can contribute to a higher productivity of spinach and sustainability in calcareous soils.

Hydrogeochemical characteristics, source distribution, and health risk of high-fluoride groundwater in Swabi, Pakistan.

This paper evaluated fluoride (F-) pollution in drinking groundwater sources, which causes severe fluorosis and other human health concerns in Swabi, Pakistan. We determined F- concentration, prevalence, enrichment, distribution, and health hazards from water ingestion in Swabi, Pakistan. Therefore, 126 groundwater and 18 surface water samples were collected to analyze F- and other geochemical tracers to understand groundwater enrichment and F- mobilization in aquatic systems. The range and mean values of F- in groundwater were 0.02 to 14.2 and 4.0mg/L, and those in surface water were 1.12-0.8 and 1.4mg/L. Most residents used groundwater for drinking purposes. Thus, groundwater results showed that 72.2% of samples had surpassed the WHO guidelines of F- 1.5mg/L. The fluoride pollution index (FPI) declared that 48.73% of samples showed a higher risk, 41.95% medium risk, and 9.32% lower risk. Mineral phases using PHREEQC interactive software determined mineral saturation revealing the dissolution of host rock minerals and unsaturation showed precipitation of minerals within the aquifer. The principal component analysis multilinear regression (PCA-MLR) model showed a five-factor solution: (a) geogenic processes, (b) mixed geogenic and anthropogenic inputs, (c) geochemical processes, (d) agriculture pollution, and (e) industrial effluents which would release F- in the aquifer. The health hazard due to higher F- revealed that children showed high-risk levels compared to adults in endemic areas. The spatial distribution of F- in drinking groundwater increases towards the northern side and decreases in the south to the southeastern side. Therefore, effective water management techniques would be required to safeguard groundwater resources and secure human health from dental and skeletal fluorosis and other associated problems caused by high F- groundwater with varying fluoride concentrations. This study will help the water management authority to safeguard depleted groundwater for drinking demands.

Spatial analysis of femtosecond laser generated plasma using principal component analysis

The appearance of plasma generated during femtosecond laser machining depends strongly on the features present on the sample before machining occurs. However, the complexity of femtosecond light-matter interaction means that development of a theoretical understanding of plasma generation is challenging. In this work, principal component analysis is applied to experimental images of plasma generated during femtosecond laser machining of silicon to calculate the orthogonal spatial patterns of the plasma variance (plasma modes), and to identify which sample variance (sample modes) are associated with these plasma modes. The results demonstrate the potential of principal component analysis for data-driven scientific discovery in the field of femtosecond light-matter interactions.

Risk contagion of COVID-19 to oil prices: A Markov switching GARCH and PCA approach.

The COVID-19 pandemic and its impact on crude oil prices created additional risks throughout the financial industry. To contribute to the ongoing debates, this paper empirically examined the risk contagion of COVID-19 to oil prices by incorporating a Markov-Switching GARCH (MS-GARCH) framework and the multivariate GARCH time series model, BEKK-GARCH model. The study examines data collected between 27 January 2020 and 31 December 2020. Further, we used principal component analysis (PCA) to find principal factors explaining the overall variability of the global economic indicators that contribute to the risk. Finally, to support the risk transmission effects between COVID-19 and oil prices, we conducted regression analysis, while controlling for the factors extracted from the PCA method.

Application of MATLAB and SAS Viya AI models towards the elucidation of potential microplastics in the Neuse River Basin

Microplastics (MPLs) are ubiquitous particles derived from degradation from plastic refuse or directly from anthropogenic sources. These particles are present in aquatic environments with potential toxicology across the biosphere. In order to characterize their presence, the Neuse River basin was selected for sampling. However, like many MPLs, spectroscopic characterization can be sullied by weathering processes that obscure the native spectra. Therefore, to enhance MPL identification, Principal Component Analysis (PCA), K-means Clustering (KMC), MATLAB, and SAS Viya’s machine learning (ML) modules were implemented on the Neuse River basin's samples. 18 unknown samples were recorded with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy against the controls for PCA and KMC: high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and nylon-6 (PA6). Later on, these unknowns were tested against a new set of 900 commercial control samples partitioned by 9 classes for MATLAB and SAS Viya: cellulose acetate (CA), HDPE, LDPE, PP, PS, PVC, PET, polymethyl(methacrylate) (PMMA), and PA6 using µ-FTIR (micro-FTIR) for single-particle discrepancy. Application of MATLAB’s Classification Learner and SAS Viya for Learners software helped aggregate multiple machine learning (ML) models to test for corroboration of predictions with datasets derived from Version 1 (V1), Version 2 (V2), and Version 3 (V3) feature extraction algorithms. Novel feature extractions, based on regressions of discrete data from spectral intensities, in V2 and unique probing of the relationships informing the regressions in V3 showed moderate to moderately high predictor strength, contributing to accuracy increase in V3 according to various predictor strength algorithms in MATLAB. In the test scenario of the strongest version, V3, 63.2% (+ 5.3% from V1) of the models performed very strongly (90% cutoff in accuracy), and 89.5% (+ 0% from V1) of the models performed moderately strongly (80% cutoff in accuracy). The models, coupled with the unsupervised PCA and KMC, indicated microparticle (MP) from Stockinghead Creek in Duplin County, NC; SHR-1b(2), as LDPE. However, the change in corroboration across model types was not significant, according to Kruskal-Wallis (H = 0.555, p = .7577). An ANOVA was performed to see if LDPE incidence was similar across all unknowns, indicating a similar ratio of predictions, excluding NRCP-1 (F = 31.479, p < .0001). While it is unclear if this particle is truly LDPE, the results may suggest that LDPE could be of high presence in the river basin (specifically, PE) when taking into account the predictor set across all versions. Lastly, misclassification was further elucidated for samples “CF-3” and “NRCP-1(2)” which are thought to be extensively-degraded PVC MPLs.

Stomatocyte-Discocyte-Echinocyte Transformations of Erythrocyte Modulated by Membrane-Cytoskeleton Mechanical Properties.

Stomatocyte-discocyte-echinocyte (SDE) transformations in human red blood cells (RBCs) have significant influences on blood dynamics and related disorders. The mechanical properties of the RBC membrane, such as shear modulus and bending elasticity, play crucial roles in determining RBC shapes. Recent biophysical findings reveal that building a comprehensive model capable of describing SDE shape transformations is a challenging problem. Based on dissipative particle dynamics, this study develops a two-component RBC model considering the detachment between the lipid bilayer and cytoskeleton, as well as the cytoskeletal reorganization during echinocyte formation. This model is validated by comparing RBCs' geometric shape and the apparent membrane tension with previous experimental measurements. Results indicate that a complete SDE sequence represented by six typical shapes can be obtained by modulating the model's mechanical and geometric parameters. Furthermore, a phase diagram based on reduced variables is obtained using principal component analysis, demonstrating the phase transformations among SDE shapes. Our result suggests that the transformation from discocyte to stomatocyte is primarily influenced by dimensionless bending rigidity, whereas, during echinocyte formation, three key variables, i.e., dimensionless bending rigidity, targeting cytoskeleton shrinkage ratio, and connecting pattern, have joint impacts on the formation of spicules or bumps and the development of the cytoskeletal framework. The present two-component RBC model and the associated findings provide a perspective for a deeper understanding of the SDE transformation mechanism. This framework offers new insights into biological science and potential applications in the field of biomedical engineering.

Carbon meets credit: enhancing corporate ratings using principal component analysis and Gaussian mixture models

This study integrates environmental factors, such as carbon emissions and climate-related risks, with traditional financial metrics to enhance corporate credit rating predictions for approximately 2,000 publicly listed Chinese firms. Utilizing Principal Component Analysis (PCA) for dimensionality reduction and Gaussian Mixture Models (GMM) for probabilistic clustering, the approach effectively addresses multicollinearity and captures nuanced financial-environmental relationships. The PCA-GMM framework not only improves predictive accuracy over traditional methods but also maintains high interpretability, making it suitable for complex risk profiles. Empirical results demonstrate that firms with lower carbon emissions and robust financial health receive higher credit ratings, providing valuable insights for investors and policymakers. This model supports sustainable economic growth in alignment with China’s dual carbon goals by highlighting the critical interplay between financial performance and environmental responsibility.

Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models

In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R2: 0.76) and Morgan fingerprints (MAPE: 24%, R2: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science.

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine

Background and objectivePositron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large B-cell lymphoma (DLBCL) staging. However, many studies have neglected the role of patients' prognostic factors with respect to imaging PET/CT of quantitative features. In this paper, a multi-view learning (MVL) model is established to make full use of both clinical and imaging data to predict the prognosis of DLBCL patients and thereby assist doctors in decision-making.MethodsFeature engineering, including feature extraction, feature screening by recursive feature elimination, and dimensionality reduction by principal component analysis, are successively performed on the clinical data and imaging data of the research subjects to obtain the study data. After dividing the data into training and test sets, an instance weighting method is applied to the training data. Subsequently, kernel mapping is performed on the imaging features and clinical features separately, and this kernel mapping is processed in the new kernel feature space using kernel canonical correlation analysis (KCCA). Lastly, model training is performed on the obtained common kernel subspace using a support vector machine (SVM). The final overall model, named SVM-2view-KCCA (SVM-2 K), was compared with three other multi-view models (Ensemble-SVM, Multi-view maximum entropy discrimination, and canonical correlation analysis). The performance of the model was evaluated on the test data with respect to several dichotomous metrics: accuracy, sensitivity, F1 score, the area under the curve (AUC), and G-mean.ResultsThe SVM model improved AUC by 10.5%, sensitivity by 11.9%, accuracy by 9.8%, F1 score by 9.2%, and G-mean by 7.8% for the DLBCL test data after feature engineering based on dimensionality reduction and instance weighting. In the performance comparison of single-view learning models, the SVM-based integration of clinical and imaging features achieved the best overall performance (AUC = 86.3%, accuracy = 91.6%, sensitivity = 83.2%, F1 = 85.7%, and G-mean = 86.1%). In the comparison of MVL models, SVM-2 K achieved the best overall performance (AUC = 92.1%, accuracy = 96.9%, sensitivity = 90.9%, F1 = 92.8%, and G-mean = 91.4%), and the performance of each MVL model was better than that of the best single-view learning model.ConclusionsMVL models outperformed single-view learning models. Of the MVL models, the proposed SVM-2 K achieved the best overall performance and could accurately predict patient prognosis.

Intensive monocropping of bananas decreases the soil acid buffering capacity via ammonia-oxidizing bacteria

Ammonia-oxidizing microorganisms (AOM) are vital for soil nitrogen cycling, nutrient availability, and soil health during sustainable agriculture. Long-term continuous cultivation of bananas and improper chemical fertilization affect the adaptability of AOM; however, the underlying basis for this phenomenon is unclear. This study utilized 16S rRNA gene and metagenomic sequencing techniques to examine soil from banana plantations that were continuously cultivated for 2, 3, 7, 10, 12, and 13 years (Y2, Y3, Y7, Y10, Y12, and Y13, respectively). The results indicated a significant decrease in soil acidity buffering capacity (pHBC) with increasing years of continuous cropping. Furthermore, compared with forest soil (Y0), Y7, Y10, Y12, and Y13 soils exhibited a significantly increased potential nitrification rate (PNR) as well as an abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB), with no significant difference in complete ammonia oxidizers (comammox). Principal component analysis (PCA) further demonstrated marked differences in chemical properties and ammonia-oxidizing microbial community structures between the soils under long-term (Y7, Y10, Y12, Y13) and short-term (Y2, Y3) banana cultivation. In addition, metagenome analysis results indicated that the relative abundance of Nitrososphaera-AOA and Ca. Nitrosocosmicus-AOA as well as Nitrosospira-AOB, Nitrosovibrio-AOB, Nitrosomonas-AOB, and comammox Nitrospira jacus was significantly higher in Y7 and Y13 soils than in Y0 controls. Redundancy analysis (RDA) identified pHBC, CEC, and NH4+ as the primary chemical factor responsible for the differences in AOM microbial communities, whereas random forest analysis revealed that Nitrosospira-AOB significantly contributed to PNR. In summary, long-term continuous banana cultivation primarily stimulates AOB promote soil ammonia oxidation, leading to soil acidification.Graphical

Toward a circular economy in Italian agri-food: upstream partners in insect biorefineries

The insect biorefinery (IB) is central to recycling nutrients from food losses and waste (FLW) in an emerging circular bioeconomy approach. The sustainable leadership of the European insect industry and its integration into the bioeconomy depend on implementing symbiotic production models through site selection and partnerships. Only 8 studies, including 3 in the EU, have examined stakeholder acceptance. This pilot study is the first to focus on potential upstream symbiosis partners (USPs) in the EU, examining the factors that influence the willingness of potential USPs in suitable Italian locations to adopt IB. Preliminary analyses identified a final sample of 31 respondents who completed an online-survey divided into four sections. After data elaboration, a principal component analysis was conducted, considering 27 questions and their correlations with the first two principal components. The analysis revealed that the presence of internal research and development within the company, along with the company’s role in the supply chain and the seasonality of production, had a minor influence on explaining the variance of the data. Interestingly, company size was negatively correlated with the willingness to become part of the insect-rearing supply chain or receive information about this technology. On the contrary, interest in innovative production and self-management of by-products were strongly related. Finally, funding opportunities could positively influence both the Italian and EU contexts, encouraging innovative practices among small and medium-sized enterprises. In contrast, barriers to implementing insect farming include misinformation, anticipated negative media impacts on branding, and resistance to changing by-product management practices. This pilot study serves as a foundational exploration, providing insights into the acceptability of insect farming among potential USPs and offers valuable insights for future studies, thereby shaping the discourse on the acceptability and integration of insect farming within the circular bioeconomy.

Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis.

In studying the association between clinical measurements and time-to-event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time-to-event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi-likelihood method which builds pseudo normal distribution for the binary data and use the E-M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer.

Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security

This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&amp;CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p &lt; 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.

Application of multivariate statistical techniques in the assessment of long-term surface water quality in Dong Thap Province, Vietnam.

This study assessed the surface water quality in Dong Thap province during 2013-2023 using multivariate statistical techniques. Water quality index (WQI), Pearson correlation, principal component analysis (PCA), and cluster analysis (CA) were determined based on monitoring data for parameters. The findings showed that only some water samples had pH values below the permissible standard, while 95.5% of samples exceeded the standard of Escherichia coli in surface water. In some years, 100% samples having BOD5, TSS, NH4+, and E. coli exceeded the permissible limits. The PCA identified four, five, and seven major potential sources during 2013-2015, 2016-2020, and 2021-2023, respectively, explaining 75.9%, 63.0%, and 63.0% of variation in the corresponding duration times. For heavy metals, the number of water samples containing As, Cu, and Pb exceeding the water permissible limits were 15.5%, 21.4%, and 2.8% samples in 2021, respectively, but no heavy metal was above standard in 2022 and 2023. The evaluation of average water quality based on WQI showed that 79.31% and 76.19% of the sample sites were classified to be average during 2016-2020 and 2021-2023, respectively, and others were bad water quality. However, the water quality showed an improving trend with 70% of monitoring positions considered as good water quality in 2023. The results showed that anthropogenic activities and hydrological regime were the main source of pollution. This study provides guidance to policymakers in developing water management strategies to have proper actions to prevent water pollution.

Determination and Comparison of Volatile Organic Compounds (VOCs) in Celery (Apium graveolens L.) Seeds by Headspace-Solid-Phase Microextraction (HS-SPME) Gas Chromatography-Mass Spectrometry (GC-MS) and Multivariate Analysis

The utilization of celery seeds as seasonings and in herbal teas is widespread due to their significant nutritional and medicinal value. The determination of volatile organic compounds (VOCs) is a valuable tool for species identification and classification. Currently, there is an absence of information investigating the volatile constituents present in the cultivars of celery seeds. To analyze the varietal differences of celery seeds, VOCs were isolated using headspace solid-phase microextraction with by determination by gas chromatography-mass spectrometry. The samples underwent further comparison using multivariate statistical analysis to determine the changes in VOCs. Cluster analysis and principal component analysis were used to conduct correlation measurements on the samples. An orthogonal partial least squares discriminant analysis (OPLS-DA) model was developed to discriminate between samples and identify differential compounds. The distribution of these key compounds in these seeds was analyzed using a Sankey diagram. The samples contained 30 volatile components, mainly terpenes and esters. Correlation analysis revealed distinct variations in the VOCs. Furthermore, the utilization of the OPLS-DA model facilitated the identification of 11 differential compounds. The Sankey diagram illustrated significant disparities in the distribution patterns of these key compounds in the celery seeds.

Fertilization for Growth or Feeding the Weeds? A Deep Dive into Nitrogen’s Role in Rice Dynamics in Ecuador

Rice (Oryza sativa L.) is a crucial crop for employment and agricultural output and heavily reliant on family labor. This study evaluated the effects of nitrogen levels (80, 120, and 160 kg·ha−1) on weed incidence and key agronomic variables, including vegetative growth, yield, and related traits, in Ecuador’s primary rice-growing regions, Guayas and Los Ríos. A split-plot randomized complete block design was implemented using two rice varieties (INIAP-FL-Elite and SFL-11) and three planting densities (20 × 30, 25 × 30, and 30 × 30 cm). Weed incidence was higher in Los Ríos, dominated by grasses (55.28%), while Cyperaceae (46.27%) prevailed in Guayas. The data analysis included non-parametric tests to identify significant treatment effects, debiased sparse partial correlations (DSPCs) to reveal key agronomic interactions, and principal component analysis (PCA) to identify influential traits, ensuring robust and normalized interpretations. Analysis of variance indicated significant effects for all variables, with vegetative growth (VG) most affected (p &lt; 0.001). The number of panicles (NP) and days to flowering (DF) showed significant though less pronounced effects, while the panicle length (LP) and 1000-seed weight (TSB) exhibited moderate responses. The DSPCs highlighted the grains per panicle (GP) and total biomass (SB) as critical variables, with significant correlations between the days to flowering and the tiller count at 55 days (r = 0.750, p &lt; 0.001) and between the vegetative growth and the total biomass (r = 0.678, p &lt; 0.001). PCA explained 58.8% of the total variance, emphasizing the days to flowering, plant height, total biomass, and yield as the most influential traits. These findings underline the importance of integrated nutrient and weed management strategies tailored to Ecuador’s agroecological conditions.

Hydroxycinnamic Acid Extraction from Multiple Lignocellulosic Sources: Correlations with Substrate Composition and Taxonomy for Flavoring and Antioxidant Applications.

The extraction of hydroxycinnamic acids (HCADs) is a strategy for lignocellulosic biomass valorization due to their high value-added nature and the possibility of application as flavoring and antioxidants. This study proposes correlations between the composition and taxonomy of 28 globally available agro-industrial feedstocks with the production of HCADs using chemometric tools. Principal component analysis indicated strong correlations between ferulic acid release and hemicellulose type and content, especially in grass biomasses. Conversely, p-coumaric acid release was mainly correlated with cellulose content across diverse taxonomic origins. Among the evaluated agro-industrial feedstocks, corn-based biomasses were identified as prime sources of ferulic acid after mild alkaline treatment, releasing up to 10.5 g kg-1 and producing hydrolysates with an antioxidant capacity up to 3.3 mmol Trolox equivalents g-1. Notably, sugar cane bagasse was the best source of p-coumaric acid, yielding 4.8 g kg-1. Corn hydrolysates were successfully converted into 4-vinylguaiacol using a genetically modified Saccharomyces cerevisiae strain, achieving high yields of 0.75 g L-1. This work enhances our understanding of HCAD sources and biomass valorization strategies, demonstrating potential applications in the food and cosmetics sectors.