Robust Analytical Method Research Articles

A simple and robust method for the comprehensive analysis of VOCs in liquid household products

A simple and robust method for the comprehensive analysis of VOCs in liquid household products

Integrative greenness, blueness and whiteness determination of analytical RP-HPLC method for identifying Imipenem, Cilastatin and Relebactam in combined tablet form followed by forced degradation studies

Most clinically preferred, US FDA and EU approved anti-infective drug, a multi-component formulation of Imipenem (IPM), Cilastatin (CS) and Relebactum (REL). The combination is used in medicating the most complicated, gram-negative bacterial infections. Henceforth, it requires the development of selective, precise, accurate, robust analytical method. Hence the present work comprises RP-HPLC method development for determination of IPM, CS and REL. The detection is achieved by using new and apt conditions i.e., Discovery C18 (4.6 × 150 mm, 5 µm) column, 0.01 N Potassium Dihydrogen Phosphate buffer: Acetonitrile (60:40) as mobile phase. The detection was found at short RT i.e., 2.32, 3.08, and 3.62-min for IPM, CS and REL correspondingly. Good sensitivity was observed, where LOD of 0.11 µg/ml (IPM), 0.14 µg/ml (CS), and 0.06 µg/ml (REL) was obtained. While, LOQ was noticed at 0.33 µg/ml (IPM), 0.43 µg/ml (CS) and 0.18 µg/ml (REL). As a counterpart, other study of forced degradation was also performed for the selected drugs at different extreme conditions. A sustainability evaluation has been done on the proposed method. The creative tools such as AGREE, GAPI, ChlorTox scale in green analytical counterpart were performed providing good score. Another BAGI software was opted as blue principle-evaluator, that resulted in a good score of 85 %. Highlighting the use of the most recent sustainability determination software, RGBfast model in present work with a good score of 74 %. Moreover, white and green evaluation has been performed in the form of comparative mode, for better understanding of the significance of current method.

Robust RTSs-based esprit method for low frequency oscillations analysis

Robust RTSs-based esprit method for low frequency oscillations analysis

Forced stability studies and estimation of encapsulated ellagic acid in nano-formulations using UV-spectroscopy

Abstract There is a growing interest in dietary materials to explore their therapeutic activities. Ellagic acid is considered as a dietary supplement and is naturally present in fruits and other foods. It has anticancer, antimalarial, antiviral, antioxidant, and anti-inflammatory activities. These activities can be enhanced with nanoformulations, which can increase its oral bioavailability. However, there is a need for an economical, simple, sensitive, and robust analytical method for the estimation of entrapped ellagic acid in the nanoformulations. Therefore, the present study presents the development and validation of a UV–visible spectroscopy method for the estimation of EA in nanoformulations. The phosphate buffer (pH 7.2) and the detection wavelength of 253.5 nm were used for the method development, and its validation was performed according to the ICH Q2A (R1) guidelines. The coefficient of determination value of the developed method was found to be 0.9988 in the concentration range of 1 μg mL−1 to 6 μg mL−1. The method was found to be linear, precise, sensitive, and robust. This method can be used for the estimation of EA in nanoformulations, bulk dosage forms, and other pharmaceutical formulations.

Highly sensitive and robust LC-MS/MS method for determination of up to 15 small molecule nitrosamine impurities in pharmaceutical drug substances

Nitrosamine impurities have been classified as probable human carcinogens for decades. These impurities were reported in water, food, tobacco, pesticides, and plastics but received attention in mid-2018 when N-nitrosodimethylamine (NDMA) was reported in valsartan drug products. Subsequently, it was revealed that several small molecule and complex nitrosamine impurities can form in any active pharmaceutical ingredient (API) or drug product in which secondary or tertiary amines are present (as API or as impurities) along with a nitrosating agent. Consequently, regulators have provided several guidelines for the risk assessment of nitrosamine formation during manufacturing, storage, or from contaminated supply chains. This has led to a demand for validated analytical methods that quantify N-nitrosamine impurities in pharmaceutical products.In this study, a highly sensitive and robust analytical method was developed and validated for quantitatively determining up to 15 small nitrosamines at low levels (0.01 ppm) in sartan drug substances. The study also suggests that this method can be extended not only to corresponding sartan drug products but could also be used as a generic screening method to test a variety of drug substances, and drug products with the minimum required optimization of method conditions.

Human Biomonitoring of Serum Polybrominated Diphenyl Ethers by Supported Liquid Extraction and Gas Chromatography Coupled With Tandem Mass Spectrometry.

This study aims to develop and validate a robust analytical method for the quantification of polybrominated diphenyl ethers (PBDEs) in human serum using gas chromatography-tandem mass spectrometry. We compared procedural blanks, recoveries, and operational convenience of liquid-liquid extraction and supported liquid extraction for the determination of serum PBDEs. We evaluated different extraction solvents for their effect on PBDE recoveries. Supported liquid extraction was selected for method validation due to its operational convenience. The method demonstrated satisfactory linearity, sensitivity, and reproducibility, with the range of 0.10-5.00µg/L for most PBDE congeners and 0.20-10.0µg/L for PBDE-154 and PBDE-183, with limits of detection ranging from 2 to 48ng/L, and with matrix effects ranging from 94% to 113%. Quality control assessments indicated that recoveries ranged from 85% to 110% and relative standard deviations of less than 11%. The proposed method was applied to biomonitoring of 111 healthy adults, revealing detectable levels of PBDEs in over 90% of the samples. BDE-47 and BDE-183 were the most prevalent, with mean concentrations of 4.13 and 22.1ng/L, respectively. Detection frequencies ranged from 0.90% for BDE-17 and BDE-85 to 25.2% for BDE-47. Males had higher mean concentrations of BDE-183 than females.

Guidance Strategy and Fast Robustness Analysis for Space Debris Flyby

Envisioning the scenario of space debris inspection by orbital flyby, this paper investigates the guidance strategy and fast robustness analysis method considering the relative state constraints. First, the corrected state transition matrix is derived to predict and correct the perturbed relative state at the flyby moment. Based on the state transition matrix, a free-flight corridor model is established to characterize the maximum allowed magnitude of the deviation, which can quickly estimate the robustness of the trajectory under uncertainties and circumvent intensive numerical sampling. The corresponding optimization, with four norm-extreme constraints, can be solved by a proposed double-layer algorithm with proven convergence. Numerical simulations demonstrate that the guidance algorithm is effective, and the free-flight corridor can accurately and efficiently reflect the robustness of the nominal trajectory.

Simultaneous Analysis of 504 Pesticide Multiresidues in Crops Using UHPLC-QTOF at MS1 and MS2 Levels.

A robust analytical method was developed for the simultaneous detection of 504 pesticide multiresidues in various crops using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF). The method integrates both MS1 and MS2 levels through sequential window acquisition of all theoretical mass spectra (SWATH) analysis, allowing for accurate mass measurements and the construction of a spectral library to enhance pesticide residue identification. An evaluation of the method was carried out according to international standards, including the FAO guidelines and SANTE/11312/2021. Validation across five representative crops-potato, cabbage, mandarin, brown rice, and soybean-demonstrated exceptional sensitivity, with over 80% of the analytes detected at trace levels (≤2.5 μg/kg). Moreover, an impressive 96.8% to 98.8% of the compounds demonstrated LOQs of ≤10 μg/kg. Most compounds exhibited excellent linearity (r2 ≥ 0.980) and satisfactory recovery rates at spiking levels of 0.01 and 0.1 mg/kg. Among 42 crop samples analyzed, pesticides were detected in 1 cabbage, 3 mandarin, and 6 rice samples, with a mass accuracy within ±5 ppm and a Fit score ≥ 70.8, confirming the method's practical applicability and reliability. The detected residues ranged from 12.3 to 339.3 μg/kg, all below the established maximum residue limits (MRLs). This comprehensive approach offers an efficient, reliable, and scalable solution for pesticide multiresidue monitoring, supporting food safety programs and regulatory compliance.

An integrated paradigm between green analytical chemistry and quality-by-design for robust analysis of alcaftadine and ketorolac tromethamine in aqueous humor via sustainable chromatographic methods

The field of pharmaceutical industry has witnessed the launch of a new co-formulated eye drop, composed of alcaftadine and ketorolac tromethamine, for alleviating allergic conjunctivitis. Such new formulation evokes the need for a sensitive, robust and fast analytical method capable of their quantification in their dosage form, in aqueous humor for pharmaco-kinetics studies as well as in presence of plausible degradation products. In this work, two validated stability-indicating chromatographic methods were developed for alcaftadine and ketorolac assaying with good adherence to values of green analytical chemistry. Drugs stabilities were tested under various stress conditions where drugs showed liability for oxidative degradation only. For the first chromatographic method, an integrated merger between analytical quality-by-design and green analytical chemistry was implemented via a UPLC method coupled with photo-diode array detector. A short C18 analytical column of 100 mm was utilized with mobile phase of ethanol: aqueous solution pH 4.0 containing 0.1% triethylamine (40: 60, by volume). Second method was thin layer chromatographic one using HPTLC silica plates as a stationary phase and a developing system of dichloromethane: ethanol (5:4, by volume). Both methods achieved to selectively quantify the studied drugs with high accuracy and precision without any interference from degradation products or related excipients in eye drops. The methods were also successfully exploited for ALF and KTC assaying in rabbit aqueous humor, with no need for prior extraction procedures, for further pharmacokinetic studies. Finally, usage of green solvents gives us the advantage of applying various evaluation tools to conclude method greenness.

Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills.

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.

Mapping 18F-FDG PET uptake in the aortic wall and thrombus

Abstract Background Positron Emission Tomography (PET) uptake allows for the assessment of molecular activity, being 18F-FDG uptake indicative of glucose metabolism. PET uptake is commonly quantified manually by drawing regions of interest, which is subjective, poorly-reproducible and may miss relevant information. Purpose To develop and test an image analysis method to quantify and map 18F-FDG uptake on the whole aortic wall and thrombus. Methods PET-MRI was acquired in patients with thoracic or abdominal aortic aneurysms. The aorta was semi-automatically segmented in the MR angiography using 3DSlicer, generating a surface mesh that was expanded by 3 mm inward and outward. PET uptake was interpolated over this volume with a uniform spatial resolution of 1 mm. By means of 9 reference points, the aortic wall was discretized into 52 (thoracic) or 80 (thoraco-abdominal) standardized regions (i.e. wall patches). For each aortic patch and for the whole thrombus volume, the target to background Ratio (TBR) was calculated by dividing the uptake by a reference uptake in the left atrium. Two observers segmented the aorta and thrombus, located the anatomical reference points and quantified TBR. Inter-observer agreement for segmentation and aortic wall discretization was evaluated in terms of the 95% Hausdorff Distance (HD) and Dice Score Coefficient (DSC), while both median and 95% TBR in each region were tested via intra-class correlation coefficient (absolute value, single measure). Results Data from 20 patients with thoracic or abdominal aneurysms were analysed, 10 of them presented thrombus. The reproducibility of the segmentation of the aorta (median HD 3.16 IQR [2.42 - 3.94] mm, DSC 0.90 [0.89 - 0.92]) and of the thrombus (HD 4.87 [3.51 - 8.45] mm, DSC 0.85 [0.80 - 0.87]) were excellent. The inter-observer agreement in the localization of reference points was good (HD 5.10 [3.29 - 8.73] mm), resulting in excellent overlapping of the standardized aortic wall patches (HD 3.80 [2.44 - 5.08] mm). Inter-observer agreement of median and 95% TBR in each aortic patch were excellent (ICC of 0.90 and 0.93, respectively, Figure 1), with minimal bias (Figure 2). Similarly, excellent interobserver reproducibility was observed for median and 95% TBR in the thrombus (ICC of 0.97 and 0.96, respectively, Figure 2). Conclusions A robust and reproducible image analysis method to map PET uptake along the aortic wall was developed, extending the possibilities to study aortic diseases molecular activity and its association with local aortic characteristics.

A robust method for canonical correlation analysis

Canonical correlation analysis (CCA) explores the relationships between two sets of random variables. However, the conventional computation of this analysis relies on sample covariance matrices, which are highly influenced by outliers. In this study, we introduce a robust approach to perform canonical correlation analysis by employing the reweighted orthogonalized comedian estimator. To evaluate the effectiveness of the proposed method, a simulation study is conducted, comparing it with four well-established estimators across various sampling scenarios. The simulation results strongly advocate for the adoption of our method in situations where outliers are present in the data. Furthermore, we investigate the robustness of our method using breakdown plots, demonstrating its ability to withstand substantial levels of contamination. To validate the practical utility of our approach, we analyze a real-life dataset, observing consistent results with those obtained from the simulation study.

Raman-Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations.

Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell-free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real-time, on-line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae-based biopharmaceutical production.

Longitudinal Analysis of Natural History Progression of Rare and Ultra-Rare Cerebellar Ataxias Using Item Response Theory.

Degenerative cerebellar ataxias comprise a heterogeneous group of rare and ultra-rare genetic diseases. While disease-modifying treatments are now on the horizon for many ataxias, robust trial designs and analysis methods are lacking. To better inform trial designs, we applied item response theory (IRT) modeling to evaluate the natural history progression of several ataxias, assessed with the widely used scale for assessment and rating of ataxia (SARA). A longitudinal IRT model was built utilizing real-world data from the large autosomal recessive cerebellar ataxia (ARCA) registry. Disease progression was evaluated for the overall cohort as well as for the 10 most common ARCA genotypes. Sample sizes were calculated for simulated trials with autosomal recessive spastic ataxia Charlevoix-Saguenay (ARSACS) and polymerase gamma (POLG) ataxia, as showcased, across multiple design and analysis scenarios. Longitudinal IRT models were able to describe the changes in the latent variable underlying SARA as a function of time since ataxia onset for both the overall ARCA cohort and the common genotypes. The typical progression rates varied across genotypes between relatively high in POLG (~ 0.98 SARA points/year at SARA = 20) and very low in COQ8A ataxia (~ 0.003 SARA points/year at SARA = 20). Smaller trial sizes were required in case of faster progression, longer trials (~ 75-90% less with 5 years vs. 2 years), and larger drug effects (~ 70-80% less with 100% vs. 50% inhibition). Simulating under the developed IRT model, the longitudinal IRT model had the highest power, with a well-controlled type I error, compared to total score models or end-of-treatment analyses. The established longitudinal IRT framework allows efficient utilization of natural history data and ultimately facilitates the design and analysis of treatment trials in rare and ultra-rare genetic ataxias.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Fatigue life assessment and verification of turbine joints: current status and prospects

The research status of fatigue life assessment technology for turbine joint structures is introduced. The progress, shortcomings and development trends of existing research are discussed from the aspects of multi-field load analysis, crack initiation life assessment, crack propagation simulation and test technology. The evaluation methods of service life and damage tolerance of turbine joint structures are discussed in detail. The results show that the existing analysis and test methods can basically realize the fatigue life assessment of turbine joints, but due to various limitations, the engineering applicability needs to be improved urgently. Robust load reduction analysis methods, life assessment methods based on physical mechanisms and data-driven, load history-related crack propagation life assessment methods and test technologies under complex thermal environments are still needed to establish a fatigue life assessment and verification system for advanced aero-engine turbine joint structures.

Monitoring of 260 pesticides in extra virgin olive oil and risk assessment for consumers within the framework of the European multiannual control program

The aim of the present research was to develop and validate a robust analytical method for the monitoring of 260 pesticide residues in Extra Virgin Olive Oil (EVOO) expanding the 185 molecules requested by the multiannual control program. The analytical procedure included an ultrasound-assisted liquid-liquid microextraction followed by low-pressure gas chromatography (LP-GC) and ultra-high-performance liquid chromatography (UHPLC), both coupled to triple quadrupole mass spectrometry. Matrix-matched calibration curves showed good linearity with coefficients of determination greater than 0.999. Accuracy values ranged from 65.5 % to 122.3 % and from 61.1 % to 133.3 % for LP-GC and UHPLC, respectively. The recoveries ranged from 14.0 % to 131.3 %.Fifty commercial EVOO, from Italian and EU production, were analyzed to assess pesticide contamination during the 2021–2023 harvesting seasons. The research focused on evaluating consumer risk by assessing both chronic and acute dietary exposure, using the Pesticide Residue Intake Model developed by EFSA.

ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve. However, in the case of vaccine research for complicated bacteria such as Mycobacterium tuberculosis (Mtb), there is no prior information regarding the antigen against which high-affinity antibodies are generated and therefore plotting a standard curve is not feasible. Consequently, the analysis of ELISA data in this instance is based on a comparison between vaccinated and unvaccinated groups. However, to the best of our knowledge, no robust data analysis method exists for "non-standard curve" ELISA. In this paper, we provide a straightforward R-based ELISA data analysis method with open access that incorporates end-point titer determination and curve-fitting models. Our modified method allows for direct measurement data input from the instrument, cleaning and arranging the dataset in the required format, and preparing the final report with calculations while leaving the raw data file unchanged. As an illustration of our method, we provide an example from our published data in which we successfully used our method to compare anti-Mtb antibodies in vaccinated vs non-vaccinated mice.

الصيغ التحليلية للاهتزازات المحورية والانحناء والقص والالتواء المقترنة لعوارض تيموشينكو ذات الصفائح المركبة الغير متماثلة

This paper presents the exact closed-form solutions for the vibration analysis of extension-bending-shear-torsion coupled responses in asymmetric laminated Timoshenko beams. The study derives four governing differential equations and corresponding boundary conditions using Hamilton’s variational principle. The formulation, based on first-order shear deformation theory (FSDT), accounts for rotary inertia, Poisson’s ratio, and extensional-flexural-torsional-shear coupling effects arising from material anisotropy. The exact solutions are obtained for various harmonic bending and twisting excitations under different boundary conditions, capturing the fully coupled axial, bending, and torsional responses. This approach offers a robust analytical method for understanding the complex dynamic behavior of laminated composite beams subjected to dynamic loads, with potential applications in aerospace, mechanical, and civil engineering structures, such as aircraft components, turbine blades, and bridges. Keywords: Closed-form solution; extension-bending-shear-torsion coupled response; harmonic excitations; asymmetric laminated beam.

Robust analytical method for the enantiomeric separation of lurasidone hydrochloride: Integrating analytical quality by design and green chemistry principles

Schizophrenia, affecting approximately 1% of the global population, necessitates effective treatments like Lurasidone (LUR). However, the accurate separation of LUR from its enantiomeric impurities remains a significant challenge in pharmaceutical analysis. This study addresses this issue by developing and validating an optimized HPLC method for the simultaneous determination of LUR and its enantiomeric impurity using Analytical Quality by Design (AQbD) principles. The separation was achieved on a Chiralcel OD-H column with isocratic elution employing a hexane-ethanol mixture (92:8, v/v) at a flow rate of 0.9 mL/min, UV detection at 215 nm, and a column temperature of 32 °C. The method was validated using accuracy profiles. Environmental sustainability was assessed using various Green Analytical Chemistry (GAC) metrics, demonstrating favorable greenness attributes. This methodological framework not only enhances pharmaceutical quality control but also promotes sustainability in analytical practices, supporting its application in drug manufacturing and regulatory compliance.