Sensitive Analytical Method Research Articles

Active Learning of Ensemble Polynomial Chaos Expansion Method for Global Sensitivity Analysis

Global sensitivity analysis (GSA) techniques, especially Sobol indices, have gained prominence in assessing the impact of uncertainty across all input variables on the variation of model response. However, GSA can incur intensive computational costs, particularly when dealing with time-consuming models. To address this issue, an ensemble polynomial chaos expansion (EPCE) surrogate model based on an active learning hybrid (ALH) criterion is proposed. The proposed method integrates a weighted combination of multiple sparse PCE surrogate models to formulate the EPCE method. To enhance the performance, the ALH criterion, which balances local exploitation and global exploration, is developed to sequentially select samples for updating the EPCE model. In terms of local exploitation, the model correlation and sensitivity pursuit criteria are derived to identify the local region displaying both the largest discrepancy among multiple sparse PCE methods and the most substantial contribution to the total variance. The ALH criterion also incorporates a minimum distance-based measure for global exploration. The performance of the proposed method is evaluated by four benchmark functions and an engineering application. The numerical studies demonstrate that the proposed method exhibits favorable performance for conducting GSA.

Determination of 13 potential anti-obesity agents in hair by dispersive liquid-liquid microextraction-assisted UHPLC-MS/MS

As the adulteration of dietary supplements with synthetic drugs remains a prevalent issue, the inclusion of anti-obesity agents may pose health risks, potentially leading to central nervous system or cardiovascular diseases. However, surveillance studies on the use of anti-obesity agents by the Chinese population are limited. This study aims to establish an efficient and rapid hair pretreatment method using dispersive liquid-liquid microextraction (DLLME) combined with high-speed grinding and develop a sensitive and accurate analytical method employing ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for detecting 13 potential anti-obesity agents in hair samples. Herein, hair samples were washed sequentially with 0.1% sodium dodecyl sulfate (SDS), water and acetone, and then ground at high speed using 1 mL of an extraction solution (internal standard solution-n-butanol-1.2 mol/L Na2HPO4, pH10.0, 100:400:500, v/v/v for procaterol; internal standard solution-ethyl acetate-1.2 mol/L Na2HPO4, pH8.0, 100:300:600, v/v/v for other 12 anti-obesity agents) while simultaneously performing DLLME. The developed method successfully detected 13 anti-obesity agents within 11 min, including bambuterol, clenbuterol, ractopamine, clorprenaline, formoterol, salbutamol, terbutaline, procaterol, phentermine, bupropion, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine, which improved the screening efficiency. The calibration curves exhibited good linearity of 0.025–5 ng/mg, achieving correlation coefficients of r ≥ 0.99. The lower limits of quantification (LLOQs) for the analytes were 0.025 ng/mg, demonstrating acceptable levels of accuracy and precision. Recovery rates ranged between 73.30% and 107.47% across the three concentrations of 0.075, 0.375, and 3.75 ng/mg. The validated method was successfully applied to 369 real cases and detected six analytes, including bambuterol, salbutamol, terbutaline, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine. This method offers several advantages, including simple pretreatment, high extraction efficiency, rapid extraction, solvent economy, and pollution mitigation, making it highly suitable for large-scale surveillance of usage of added anti-obesity agents.

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the “3060 dual-carbon” goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building’s energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines.

Selective and Sensitive MIP-Based Electrochemical Sensor for the Analytical Determination of Lanreotide

Lanreotide (LAN) is an analog of somatostatin used to treat acromegaly and neuroendocrine tumors. Considering drug toxicity and the lack of sensitive techniques in drug analysis, the development of sensitive analytical methods is of great importance. During this research, a molecular imprinted polymer (MIP) based film [P(HEMA-MAGA)@MIP/GCE] was prepared by photopolymerization technique for electrochemical determination of LAN for the first time. N-methacryloyl-L-glutamic acid (MAGA) and hydroxyethyl methacrylate (HEMA) were used as functional and basic monomers, respectively. The surface alterations were investigated using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) and the chemical composition of the MIP-based sensor. Theelectrochemical characterization of the modifiedelectrodes was assessed using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Using differential pulse voltammetry (DPV) in standard solution and spiked serum sample of LAN, the linear response range for LAN was obtained as 1.0x10 − 14 M – 1.0x10 − 13 M. The limit of detection (LOD) values were 1.76x10 − 15 M and 1.82x10 − 15 M in water and commercial human serum, respectively. The limit of quantitation(LOQ) values for these were 5.85x10 − 15 M and 6.07x10 − 15 M, respectively. The P(HEMA-MAGA)@MIP/GCE sensor also showed excellent recovery in pharmaceutical preparation and commercial human serum form with of 99.37 % and 99.27 %, respectively, with a good RSD value (0.51–0.73), confirming the accuracy and precision of the sensor. As a control, the non-imprinted polymer (NIP) was also prepared to serve in the same way but without the template. The selectivity experiments were conducted using somatostatin (SOM) and octreotide (OC) as potential competitors to demonstrate the selectivity of the proposed sensor against LAN molecules.

The isolation strategy and chemical analysis of oil cells from Asari Radix et Rhizoma.

Single-cell analysis, a rapidly evolving field, encounters significant challenges in detecting individual cells within complex plant tissues, particularly oil cells (OCs). The intricate process of single-cell isolation, coupled with the inherent chemical volatility of oil cells, necessitates a comprehensive methodology. This study presents a method for obtaining intact OC from Asari Radix et Rhizoma (ARR), a traditional herbal medicine. The developed approach facilitates both qualitative and quantitative analysis of diverse OCs. To determine the most reliable approach, four practical methods-laser capture microdissection, micromanipulation capturing, micromanipulation piping, and cell picking-were systematically compared and evaluated, unequivocally establishing cell picking as the most effective method for OC isolation and chemical analysis. Microscopic observations showed that OCs predominantly distribute in the cortex of adventitious and fibrous roots, as well as the pith and cortex of the rhizome, with distinct morphologies-oblong in roots and circular in rhizomes. Sixty-three volatile constituents were identified in OCs, with eighteen compounds exhibiting significant differences. Safrole, methyleugenol, and asaricin emerged as the most abundant constituents in OCs. Notably, cis-4-thujanol and tetramethylpyrazine were exclusive to rhizome OCs, while isoeugenol methyl ether was specific to fibrous root OCs based on the detections. ARR roots and rhizomes displayed marked disparities in OC distribution, morphology, and constituents. The study highlights the efficacy of cell picking coupled with HS-SPME-GC-MS as a flexible, reliable, and sensitive method for OC isolation and chemical analysis, providing a robust methodology for future endeavors in single-cell analyses.

APPLICATION OF MACHINE LEARNING MODELS AND GSA METHOD FOR DESIGNING STUD CONNECTORS

The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R2 of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength.

LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)

Abstract. The single-column model, with its advantages of low computational cost and fast execution speed, can assist users in gaining a more intuitive understanding of the impact of parameters on the simulated results of climate models. It plays a crucial role in the study of parameterization schemes, allowing for a more direct exploration of the influence of parameters on climate model simulations. In this paper, we employed various methods to conduct sensitivity analyses on the 11 parameters of the Single Column Atmospheric Model (SCAM). We explored their impact on output variables such as precipitation, temperature, humidity, and cloud cover, among others, across five test cases. To further expedite experimentation, we utilized machine learning methods to train surrogate models for the aforementioned cases. Additionally, three-parameter joint perturbation experiments were conducted based on these surrogate models to validate the combined parameter effects on the results. Subsequently, targeting the sensitive parameter combinations identified from the aforementioned experiments, we further conducted parameter tuning for the corresponding test cases to minimize the discrepancy between the results of SCAM and observational data. Our proposed method not only enhances model performance but also expedites parameter tuning speed, demonstrating good generality at the same time.

A developed and validated centrifugal ultrafiltration coupled with high performance liquid chromatography-tandem mass spectrometry method for rapid quantification of unbound lenvatinib in human plasma

In clinical practice, the determination of unbound drug concentration is very important for dose adjustment and toxicity prediction because only the unbound fraction can achieve a pharmacological effect. A fast, sensitive and accurate analytical method of centrifugal ultrafiltration coupled with high performance liquid chromatography-tandem mass spectrometry method was developed and applied to allow the quantification of unbound lenvatinib concentration. The application of linear regression analysis was used to examine the effects of centrifugal force, centrifugal time, and protein content on ultrafiltrate volume (Vu). The results indicated that the centrifugal force and centrifugal time have an influence on Vu that is significantly positive (P < 0.05). This developed method with good linearity (r2 = 0.9996), good accuracy (bias % ≤ 2.24 %), good precision (CV % ≤ 7.10 %), and good recovery (95.46 %−106.46 %) was suitable for routine clinical practice and studies. Particularly, the ultrafiltration membrane had no non-specific binding to lenvatinib. The unbound fractions can be separated in just 15 min. This method was applied to quantify clinical samples and to determine the plasma protein binding and unbound fraction of lenvatinib. This study provides a more effective and promising method for determination of unbound lenvatinib. It could be beneficial to measure the unbound concentration of lenvatinib in personalized medicine and therapeutic drug monitoring in routine clinical practice.

High-Performance Liquid Chromatographic Quantification of the Plant Hormone Abscisic Acid at ppb Levels in Plant Samples after a Single Immunoaffinity Column Cleanup.

High-performance liquid chromatography with ultraviolet detection (HPLC-UV) is a common analysis technique due to its high versatility and simple operation. In the present study, HPLC-UV detection was integrated with immunoaffinity cleanup (IAC) of the sample extracts. The matrix effect was greatly reduced, and the limit of detection was as low as 1 ng/g of free abscisic acid (ABA) in fresh plant tissues. A monoclonal antibody 3F1 (mAb 3F1) was developed to specifically recognize free ABA but not ABA analogues. The mAb 3F1-immobilized immunoaffinity column exhibited a capacity of 850 ng/mL and an elution efficiency of 88.8-105% for standards. The extraction recoveries of the column for ABA ranged from 80.4 to 108.9%. ABA content was detected in various plant samples with IAC-HPLC-UV. The results were verified with ultraperformance liquid chromatography-electrospray tandem mass spectrometry. IAC-HPLC-UV can be a sensitive and cost-efficient method for plant hormone analysis.

Optimization of excitation and emission wavelengths for the UHPLC fluorescence detector for priority polycyclic aromatic hydrocarbons (PAHs)

AbstractPolycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants (POPs) that are widely distributed in the environment and cause significant environmental damage. Furthermore, they endanger human health by polluting food from the natural environment and food processing. Therefore, it is necessary to accurately detect PAHs in various sample matrices, which requires precise, practical, and rapid detection methods. The purpose of this research is to develop a high sensitivity analysis method by analyzing the optimum excitation and emission wavelengths of EPA's 15 priority polyaromatic hydrocarbons in the UHPLC fluorescence detector (Acenaphthene, Anthracene, Benzo[a]anthracene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Benzo[ghi]perylene, Benzo[a]pyrene, Chrysene, Dibenzo[a,h]anthracene, Fluoranthene, Fluorene, Indeno[1,2,3-cd]pyrene, Naphthalene, Phenanthrene, and Pyrene). An average of 17–25 analyses were performed for each polyaromatic hydrocarbon, and optimized excitation and emission wavelengths were obtained. LOD levels between 2 and 90 ppt were obtained with the method created in this direction. It is worth mentioning that the limits achieved for some PAH parameters are lower than those reported in the literature after pre-concentration steps.

Sensitivity analysis to determine the importance of input variables in groundwater stress

This study aims to provide insights into groundwater stress and subsidence in the Varamin plains, located in central Iran. To achieve this, groundwater distributed modeling was simulated, and the groundwater stress index was evaluated accordingly. The area was divided into nine sections based on the intensity and spatial distribution of stresses and subsidence. The study conducted sensitivity analysis to reduce uncertainty, focusing on the importance of abstraction, recharge, and environmental flow at groundwater footprint and stress index equation. The impact of input variables on the groundwater stress index was evaluated using two methods of local and global sensitivity analysis, and the results were compared. A partial correlation criterion was also applied to measure the relationship between stress-dependent variables and three independent variables. The analysis revealed that the correlation analysis and global sensitivity analysis are consistent with each other and differ from the local sensitivity analysis. The abstraction based on global sensitivity analysis and correlation analysis has the greatest impact on stress, while a local sensitivity analysis showed that the reduction in recharge had the greatest effect. The study found that the impact of environmental flow requirements is negligible. Based on the results, the study presented different scenarios for reducing stresses in critical areas and proposed corresponding scenarios to reduce stress in the future management of the plain. A review of three different scenarios for the Varamin plain revealed that recharge is the most effective parameter that correlates with local sensitivity analysis. However, based on the results of the analysis of global sensitivity and correlation coefficient analysis, abstraction has the highest effectiveness.

From laboratory to industrial use: Understanding process variation during enzymatic protein hydrolysis with dry film fourier-transform infrared spectroscopy

Industries are continuously looking for dedicated sensor solutions for evaluating the chemical composition of products that can provide valuable insights into process control, process optimization, and product quality. A rapid and robust analytical method, Fourier-Transform Infrared (FTIR) spectroscopy, holds significant potential in this regard as it provides detailed compositional values of food nutrients. The main aim of the present study was to use dry film FTIR spectroscopy as an analytical tool to characterize products from an industrial enzymatic protein hydrolysis process and link this to understand industrial process variations. For this purpose, 463 protein hydrolysate samples were obtained from an industrial enzymatic protein hydrolysis plant. In the same period, systematic variations in process parameters such as raw material composition, enzyme type, and water addition, were performed. All samples were analyzed using dry film FTIR spectroscopy. Two subsets containing 200 and 68 hydrolysate samples were chosen for measuring average molecular weight (AMW) and collagen content respectively, providing reference data for constructing calibration models based on partial least squares regression (PLSR). The percentage of low molecular weight constituents was derived from the molecular weight distribution of size exclusion chromatograms of protein hydrolysates and also used in the modeling. Calibration models for the prediction of AMW, low molecular weight constituents, and collagen content were obtained with a good fit and lower estimation errors. Principal component analysis (PCA) of protein hydrolysates' FTIR spectra effectively differentiated process variations (enzyme types and raw materials) without extensive reference analysis. One-factor analysis of variance (ANOVA) tests was used to observe the impact of process variation on product quality. FTIR proved to be a sensitive method for liquid protein analysis and process control with a significant potential in process optimization approaches. To the authors’ knowledge, this is the first time dry film FTIR spectroscopy has been used to evaluate an industrial bioprocess with designed process variations.

Predicting the Mix Proportions of Concrete Containing Calcined Clay Using Hybridized CNN and XGB and Employing the Shapley Method for Sensitivity Analysis

Predicting the Mix Proportions of Concrete Containing Calcined Clay Using Hybridized CNN and XGB and Employing the Shapley Method for Sensitivity Analysis

Development of a highly sensitive method based on QuEChERS and GC-MS/MS for the determination of polycyclic aromatic hydrocarbons in infant foods.

Polycyclic aromatic hydrocarbons (PAHs) are environmental contaminants that can be found in various food products, including those intended for infants. Due to their potential health risks, it is crucial to develop sensitive analytical methods for the accurate determination of PAHs in infant foods. This study describes the development and validation of a highly sensitive method for the quantification of European PAH markers, namely benzo[a]pyrene, benzo[a]anthracene, chrysene, and benzo[b]fluoranthene, using gas chromatography-tandem mass spectrometry (GC-MS/MS), in baby food samples. The first step was the optimization of the sample preparation procedure, performed using different methods based on the QuEChERS approach, also testing different extraction solvents. Several factors such as extraction efficiency, selectivity, and recovery were evaluated to choose the most effective procedure for sample preparation. Furthermore, the GC-MS/MS method was optimized, evaluating parameters such as linearity, sensitivity, accuracy, and robustness using spiked infant food samples. The method demonstrated excellent linearities with a correlation coefficient higher than 0.999 over a wide concentration range, and limits of detection and limits of quantification in the range 0.019-0.036 μg/kg and 0.06-0.11 μg/kg, respectively. Extraction recoveries were between 73.1 and 110.7%, with relative standard deviations always lower than 8%. These findings are compliant with the indications of the European Commission (Reg. 836/2011). To assess the applicability of the method to official control activities, a survey was conducted on commercially available infant food products. Four markers were determined in commercial samples belonging to different food categories for infants and young children. The outcome of this monitoring showed that PAH contamination, in all samples, was below the quantification limits. In conclusion, the developed GC-MS/MS method provides a highly sensitive and reliable approach for the determination of PAHs in baby foods. The optimized sample preparation, instrumental parameters, and validation results ensure accurate quantification of 4 PAHs even at trace levels. This method could contribute to the assessment of PAH exposure in infants and it could support regulatory efforts to ensure the safety and quality of infant food products with regular monitoring.

A SENSITIVITY ANALYSIS METHOD COMBINING DEMPSTER-SHAFER THEORY AND MACHINE LEARNING FOR ENERGY-SAVING EVALUATION OF BUILDING OCCUPANT BEHAVIOR

ABSTRACT For a very long time, the research of the sensitivity analysis of occupant behavior to energy assessment has been in the spotlight. The key element of the research is determining the exact probability of occupant behavior uncertainty. However, due to the specificity of occupant behavior, data on occupant behavior from different independent sources of information can differ significantly. This paper explores the use of Dempster-Shafer theory to the sensitivity analysis of energy evaluation of occupant behavior in buildings. The Dempster-Shafer theory is an imprecise probability theory that allows the system to create assumed confidence intervals based on interval values probability combined with knowledge of uncertainty factors from many different sources of information. The findings show that the data processing approach based on Dempster-Shafer theory provides effective and reliable information for evaluating energy related to human behavior in buildings. To begin with, the sensitivity analysis process might be accelerated by applying machine learning to process the data. Then, in order to ensure the accuracy of the simulation results, multiple learning methods can be used. Finally, in this paper, model parameters were chosen based on the specific circumstances as soon as the model had been built in order to effectively reduce costs related to operation and increase model accuracy. To establish the final results, the model is evaluated using global sensitivity analysis methods.

Kernel-based Sensitivity Analysis for (Excursion) Sets

In this article, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robust optimization problem under constraints. While most sensitivity analysis methods deal with scalar outputs, this article introduces a novel approach to perform sensitivity analysis with set-valued outputs. Our innovative methodology is designed for excursion sets, but is versatile enough to be applied to set-valued simulators, including those found in viability fields, or when working with maps like pollutant concentration maps or flood zone maps. We propose to use the Hilbert-Schmidt Independence Criterion (HSIC) with a kernel designed for set-valued outputs. After setting a probabilistic framework for random sets, a first contribution is the proof that this kernel is characteristic, an essential property in a kernel-based sensitivity analysis context. To measure the contribution of each input, we then propose to use HSIC-ANOVA indices. With these indices, we can identify which inputs should be neglected (screening) and we can rank the others according to their influence (ranking). The estimation of these indices is also adapted to the set-valued outputs. Finally, we test the proposed method on three test cases of excursion sets.

Development of Noninvasive Method for the Automated Analysis of Nine Steroid Hormones in Human Saliva by Online Coupling of In-Tube Solid-Phase Microextraction with Liquid Chromatography–Tandem Mass Spectrometry

Accurate measurement of steroid hormones is crucial to elucidate new mechanisms of action and diagnose steroid metabolism-related diseases. This study presents a simple, sensitive, and automated analytical method for nine representative steroid hormones. The method involves on-line coupling of in-tube solid-phase microextraction (IT-SPME) with liquid chromatography–tandem mass spectrometry (LC–MS/MS). The steroid hormones were extracted and enriched on a Supel-Q PLOT capillary column using IT-SPME. Subsequently, they were separated and detected within 6 min using a Discovery HS F5-3 column and positive ion mode multiple reaction monitoring system via LC–MS/MS. Calibration curves of these compounds using each stable isotope-labeled internal standard (IS) showed linearity with correlation coefficients greater than 0.9990 in the range of 0.01–40 ng/mL, with limits of detection (S/N = 3) of 0.7–21 pg/mL. Moreover, intra- and inter-day variations were lower than 8.1 and 15% (n = 6), respectively. The recoveries of these compounds from saliva samples were in the range of 82–114%. The developed IT-SPME/LC–MS/MS method of steroid hormones is a highly sensitive, specific, and non-invasive analytical method that allows extraction and enrichment with no organic solvents, and enables direct automated online analysis by simply ultrafiltrating a small sample of saliva.

An Agile and Accurate Approach for N-Nitrosamines Detection and Quantification in Medicines by DART-MS.

N-nitrosamines (NAs) are prevalent mutagenic impurities in various consumer products. Their discovery in valsartan-containing medicines in 2018 prompted global regulatory agencies to set guidelines on their presence and permissible levels in pharmaceuticals. In order to determine the NAs content in medicines, efficient and sensitive analytical methods have been developed based on mass spectrometry techniques. Direct analysis in real time-mass spectrometry (DART-MS) has emerged as a prominent ambient ionization technique for pharmaceutical analysis due to its high-throughput capability, simplicity, and minimal sample preparation requirements. Thus, in this study DART-MS was evaluated for the screening and quantification of NAs in medicines. DART-MS analyses were conducted in positive ion mode, for both direct tablet analysis and solution analysis. The analytical performance was evaluated regarding linearity, precision, accuracy, limits of detection, and quantification. The DART-MS proved to be suitable for the determination of NAs in medicines, whether through direct tablet analysis or solution analysis. The analytical performance demonstrated linearity in the range from 1.00 to 200.00 ng mL-1, limits of quantification about 1.00 ng mL-1, precision and accuracy lower than 15%, and no significant matrix effect for six drug-related NAs. In conclusion, the DART-MS technique demonstrated to be an alternative method to determine NAs in medicines, aligning with the principles of green chemistry.

Efficient global sensitivity analysis method for dynamic models in high dimensions

AbstractDynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs.

Advances in SARMs anti-doping analysis.

Selective androgen receptor modulators (SARMs) are performance-enhancing drugs (PEDs) that stimulate anabolism, increase muscle mass and strength and promote recovery from exercise. The use of SARMs in sports is considered doping and is strictly prohibited by the World Anti-Doping Agency (WADA) and the International Federation of Horseracing Authorities (IFHA). To monitor the abuse of SARMs in sports, it is essential to develop advanced, selective and sensitive analytical methods that provide reliable results. This review evaluates the advances in this area, with a focus on the identification of target analytes related to SARMs, such as SARMs, their metabolites or markers. The aim is to identify targets that could extend the detection windows of SARMs, provide scientific support for results management and/or offer an indirect biomarker-based approach to doping control. This review also aims to evaluate the current liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) methods developed for the monitoring of SARMs in different biological matrices, including traditional matrices such as urine and serum/plasma samples, as well as alternative matrices such as dried blood spots, hair and nail samples.