Quality Control Protocols Research Articles

Short- and Mid-Term Loose Mix Conditioning Protocols for Asphalt Overlay Balanced Mix Design and Quality Control and Quality Acceptance

Many aspects of balanced mix design (BMD) and performance-related quality control and quality acceptance (QC/QA) testing depend heavily on how the asphalt mixes are conditioned. There is, therefore, a critical need for practical loose mix conditioning protocols. The main objective of this study was to establish practical loose mix condition protocols for both the BMD and QC/QA phases. To develop these protocols, this paper reviewed the field performance and pavement lives of more than 200 test sections around the United States and found most of these test sections were 50-mm thick asphalt overlays where cracking is evident in 1 to 4 years. Based on this finding, the paper developed two conditioning protocols for asphalt overlays: short-term and mid-term. The short-term conditioning is intended for volumetric design, BMD rutting performance test, and QC/QA testing at asphalt plants, while the mid-term conditioning is mainly for BMD cracking performance test. A series of IDEAL cracking tests were then conducted with 13 mixes and the test results confirmed the validity of the mid-term conditioning protocol. It was also found that conditioning time affected mix cracking resistance significantly. The longer the conditioning time, the poorer the cracking resistance. When conditioned too long, the normalized cracking resistance difference among various mixes diminished. For asphalt mix composition, binder source (or quality) and asphalt absorption are two significant factors affecting the normalized cracking resistance of asphalt mixes; in contrast, mix type, asphalt binder content, and the use of rejuvenator had insignificant influence.

An evaluation of errors in the mitochondrial COI sequences of Hydrachnidia (Acari, Parasitengona) in public databases.

Public molecular databases are fundamental tools for modern taxonomic studies whose usefulness rely on the soundness of the data within them. Here, we study potential errors that can arise along the data pipeline from sampling, specimen identification and molecular processing (digestion, amplification and sequencing) to the submission of sequences to these databases by using the DNA sequences of Hydrachnidia (Acari, Parasitengona) as a case study. Our results indicate that molecular information is available for only about 3% of the Hydrachnidia species known to date; yet, within this small percentage, errors are present in almost 5% of the species analyzed (0.5% of the sequences and almost 11% of the genera). This study underscores the scarcity of genetic data available for Hydrachnidia, but also that the proportion of errors in DNA sequences isrelatively small. Even so, it highlights the danger associated with using DNA sequences from public databases, particularly for species identification, and reinforces the need for greater quality control measures and/or protocols to avoid an intensification of errors in the (post) genomics era. Finally, our study emphasizes that potential errors may also reveal cryptic diversity within a species.

Validation of supercritical water oxidation to destroy perfluoroalkyl acids

AbstractSome of the same unique physical and chemical properties that make per‐ and polyfluoroalkyl substances (PFAS) desirable for a wide range of commercial applications render them recalcitrant to many liquid treatment technologies. As developments in PFAS‐related toxicological studies increasingly suggest potential adverse human health effects, our industry has made great progress in the past several years on concentrating PFAS into small volume waste streams via adsorption and separation mechanisms. Coupled with residual PFAS‐containing commercial products that are being phased out, management of these concentrated waste streams presents an urgent need for the development and validation of destructive treatment technologies. Here, we field‐validate supercritical water oxidation to treat a concentrated waste stream of 12 perfluoroalkyl acids (PFAAs) with liquid and gaseous analysis, adhering to the recent Other Test Method 45 for stack emission sampling from the United States Environmental Protection Agency (USEPA) and USEPA Method 537.1, with quality control and quality assurance protocols from the Department of Defense/Department of Energy Quality Systems Manual 5.3. Results generated suggest greater than 99.999% destruction and removal efficiency of these 12 PFAAs after two ∼120‐min continuous flow trials, with an overall defluorination percentage of approximately 62.6%.

Quality Control of Cell Lines Using DNA as Target

Cell lines are a widely used pre-clinical models for biomedical research. The accessibility and the relative simplicity of facilities necessary for the use of cell lines, along with the large number of potential applications, encourage many researchers to choose this model. However, the access to cell lines from a non-confident source or through the interlaboratory exchange results in uncontrollable cell lines of uncertain quality. Furthermore, the possibility of using cell lines as an endless resource through multiple passages can contribute to this uncontrolled scenario, the main consequence of which is the lack of reproducibility between the research results. Different initiatives have emerged to promote the best practices regarding the use of cell lines and minimize the effect on the scientific results reported, including comprehensive quality control in the frame of Good Cell Culture Practice (GCCP). Cell Banks, research infrastructures for the professional distribution of biological material of high and known quality and origin, are committed with these initiatives. Many of the quality controls used to test different attributes of cell lines are based on DNA. This review describes quality control protocols of cell lines whose target molecule is DNA, and details the scope or purpose and their corresponding functionality.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets.

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

The New Approach to Creation of Quality Control for Radiotherapy Equipment

This article is devoted to problems of technical maintenance radiotherapy equipment in different clinics. The quality control of using equipment is main part of quality guarantee radiotherapy program in every clinic. A lot of international recommendations and different national standards are demand to use quality control protocols in radiological practice of every countries. In the beginning of this century in some countries were created national protocols common for all accelerators together. But now the number of medical accelerators types quickly increase and one even good national protocol can’t to use for every of them. The analogous situation can see in other equipment for radiotherapy. The new approach to creation of quality control protocols for radiotherapy equipment is proposed. This approach propose to refuse from creation national universal protocols one for country. Clinics must work out protocols separately for every unit. It will speed up the process of inclusion unit in medical exploitation. Especially it treats for new model equipment. The details of technology of this work are discussed in the article.

A Nationwide Evaluation of the Prevalence of Human Papillomavirus in Brazil (POP-Brazil Study): Protocol for Data Quality Assurance and Control.

BackgroundThe credibility of a study and its internal and external validity depend crucially on the quality of the data produced. An in-depth knowledge of quality control processes is essential as large and integrative epidemiological studies are increasingly prioritized.ObjectiveThis study aimed to describe the stages of quality control in the POP-Brazil study and to present an analysis of the quality indicators.MethodsQuality assurance and control were initiated with the planning of this nationwide, multicentric study and continued through the development of the project. All quality control protocol strategies, such as training, protocol implementation, audits, and inspection, were discussed one by one. We highlight the importance of conducting a pilot study that provides the researcher the opportunity to refine or modify the research methodology and validating the results through double data entry, test-retest, and analysis of nonresponse rates.ResultsThis cross-sectional, nationwide, multicentric study recruited 8628 sexually active young adults (16-25 years old) in 119 public health units between September 2016 and November 2017. The Human Research Ethics Committee of the Moinhos de Vento Hospital approved this project.ConclusionsQuality control processes are a continuum, not restricted to a single event, and are fundamental to the success of data integrity and the minimization of bias in epidemiological studies. The quality control steps described can be used as a guide to implement evidence-based, valid, reliable, and useful procedures in most observational studies to ensure data integrity.International Registered Report Identifier (IRRID)RR1-10.2196/31365

Sensors and Methods for Measuring Greenhouse Gas Emissions from Different Components of Livestock Production Facilities

Greenhouse gas monitoring on a broader scale is necessary to ensure that a cap-and-trade system is effective, reduces measurement uncertainty, and detects fraudulent or illegal activities. The recent strict air quality regulation in livestock production facilities has accelerated the need for accurate on-farm determination of greenhouse gas (GHG) emission rates (ERs) from livestock operations in the United States under a wide range of production, management, and climate conditions. The estimation of GHG emissions from different ground-level sources or at a property line is a very complicated process, and such measurements require multidirectional expertise including engineering, micrometeorology, agronomy, applied physics, and chemistry. Accurate measurement of gaseous concentration from an emitting source is a prerequisite and of paramount importance for estimating emissions rates (ERs) using any micro-meteorological and sampling device-based method. This paper provides an overview of the state-of-the-art sensors and analyzers used to measure GHG concentrations. Sensor and analyzer selection and their performance in the laboratory and field were discussed. In addition, protocols for data quality control (QC) and quality assurance (QA) when deploying sensors in the area for long-term use were also discussed. In addition, the preparation of measurement systems, coupling of air samplers with sensing systems for measuring gaseous concentrations, and uncertainties inherent to such measurement methods as a whole to estimate ERs were discussed in this paper.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse.

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Evaluation of Identification and Estimation protocols for Berberine chloride in Berberis aristata stem and Coded Ayurvedic trial preparation using HPTLC and HPLC techniques

Ayurveda is an ancient science of India and helps the human body to keep fit while providing cures from indigenous plants, animal and mineral origin products for various ailments. In Ayurveda literature, Berberis aristata has been described as a major plant possess antidiarrheal, antimicrobial, antiprotozoal and antitrachoma activity and used in for several remedies including diarrhea, chronic rheumatism, jaundice, skin diseases, syphilis and urinary disorders. Berberis aristata contains photoactive constituents such as berberine, oxyberberine, berbamine, aromoline, a protoberberine alkaloid karachine, palmatine, oxycanthine and taxilamine and tannins, sugar, starch. The present study has been designed to develop a quality control protocol for the coded Ayurvedic drug by analyzing the reference standard of bioactive compound present in the formulation and respective ingredient. The Rf value of berberine chloride reference standard 0.05 was visible in test solution of Berberis aristata stem as well as in coded Ayurvedic drug extract and found comparable under UV 254 nm and 366 nm, respectively. The quantitative evaluation revealed the presence of berberine chloride in the coded Ayurvedic drug and Berberis aristata DC stem extract was 0.1261% and 5.3936%, respectively.

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes.

Group A Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the application of genotype-phenotype association studies in the era of whole-genome sequencing. We have catalogued the distribution and diversity of the transcription regulators of GAS, and employed phylogenetics, concordance metrics and machine learning (ML) to test for associations. In this review, we communicate the lessons learnt in the context of the recent bacteria genotype-phenotype association studies of others that have utilised both genome-wide association studies (GWAS) and ML. We envisage a promising future for the application GWAS in bacteria genotype-phenotype association studies and foresee the increasing use of ML. However, progress in this field is hindered by several outstanding bottlenecks. These include the shortcomings that are observed when GWAS techniques that have been fine-tuned on human genomes, are applied to bacterial genomes. Furthermore, there is a deficit of easy-to-use end-to-end workflows, and a lag in the collection of detailed phenotype and clinical genomic metadata. We propose a novel quality control protocol for the collection of high-quality GAS virulence phenotype coupled to clinical outcome data. Finally, we incorporate this protocol into a workflow for testing genotype-phenotype associations using ML and ‘linked’ patient-microbe genome sets that better represent the infection event.

A proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

AbstractBackgroundThe noradrenergic locus coeruleus (LC) in the brainstem shows earliest signs of protein pathologies in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Its small size (about 3‐4mm in width and 15 mm in length) makes it less suitable for Positron Emission Tomography (PET) investigations, so the past decade has seen a steep rise in structural and functional Magnetic Resonance (MR) studies aiming to characterise the LC's changes in ageing and neurodegeneration. However, given its position in the brainstem and small volume, great care has to be taken to yield methodologically reliable MR results as imprecisions in spatial transformations of functional data in the range of 2‐3mm might make LC activations at the group level effectively disappear (Figure 1B). Here, we suggest a post‐processing pipeline and set of quality control protocols which will allow LC researchers to reach the spatial precision necessary for investigating this small but potentially impactful brain structure.MethodUsing a combination of available toolboxes (SPM, ANTs, FSL, FreeSurfer), individual 3T scans for structural and functional LC characterisation are transformed into MNI space via a study‐specific template (Figure 1A). Concatenation of independently optimised transformation steps minimises interpolation applied to the data. Following this, spatial misalignment in individual MNI‐transformed are assessed using Euclidean distance (ED) measures using slice‐specific centres of structural LC masks and distance to landmarks defined based on salient anatomical features of mean functional LC images, respectively.ResultMedian Euclidean Distance of all landmarks on the transformed functional as well as structural LC imaging data were below 2 mm, thereby falling below the typical LC width of 2.5 mm shown in post‐mortem data (Fernandes et al., 2012), or half of the maximum width of the standard‐space LC mask of 4 mm (Keren et al. 2009).ConclusionWith the set of spatial post‐processing steps outlined in the poster and available for download soon, we hope to have taken a first step towards establishing reporting standards of LC imaging data and to give readers interested in LC imaging a starting point for more reliable analyses of structural and functional MR data of the LC.

Use of Uncertainty of Measurement for Traceability of Test Results and Setting up of own Quality Goal for Methods having Lower Stability- A Tertiary Care Hospital study.

Uncertainty of measurement (UM) provides a quantitative estimate for traceability of test results. The Nordtest guide was applied for calculating UM of 26 analytes. For this, internal and external quality control data from July 2019 to April 2020 was used. UM of test results were compared to %TEa values of CLIA '2019, RiliBÄK, and Ricos. It was observed that UM for all analytes were below %TEa values of RiliBÄK. UM value of Albumin, Calcium and Sodium could not meet CLIA '2019 and Ricos guidelines. For results of Albumin, Calcium and Sodium to be traceable, more frequent quality control protocols resulted in decrease in bias. Quality goals were set for these three parameters. This helped in reduction of quality control cycles and optimum utilization of resources.

Intercomparison and performance assessment of radionuclide calibrators used in nuclear medicine departments in Serbia

The purpose of this work is to assess accuracy and compare the performance of radionuclide calibrators (RNCs) used in nuclear medicine departments in Serbia.Testing of the RNCs included verification of measurement accuracy, as well as analysis of routinely used quality control protocols, by using the certified radioactivity standards (57Co, 137Cs). RNCs performances were assessed with 99mTc through comparison of reference value for radionuclide activity and RNC measurements.Results of the intercomparison revealed that RNCs, 15 in total, are accurate within 10% in vial geometry and within 15% in syringe geometry. Most of them showed similar performance. The results revealed that container geometry is an important influencing parameter in the accuracy of activity measurement.Obtained results indicate a need for regular calibration and implementation of Quality Control program in order to achieve and maintain the accuracy of activity measurements in nuclear medicine.

An Effective Preprocessing Method for High-Quality Pan-Genome Analysis of Bacillus subtilis and Escherichia coli.

Bacillus subtilis and Escherichia coli, as widely used microbial species, are of great significance in studying microbial community relationships, adaptive evolution in various niches, engineering cell factories that produce specific products, and designing genome reduction. The pan-genome analysis is an effective method for studying the characteristics and functions of genes among and within species. Many research directions and conclusions usually depend on accurate gene identification and reliable pan-genome results. However, there currently lack enough studies showing how to achieve high-quality pan-genome results between or within certain species. This chapter will take Bacillus subtilis as an example to introduce a stepwise manner for improving the quality of the pan-genome by gradually removing confounding strains step-by-step, and ultimately obtaining a reliable high-quality pan-genome landscape of Bacillus subtilis, which could be used as a quality control protocol in pan-genome analysis pipeline. Finally, we suggest furtherimprovingthe pan-genome analysis results of Escherichia coli to prove the feasibility and credibility of the quality control protocol for obtaining high-quality pan-genome landscape.

Development of the optimal procedure for increasing HbA1c concentration in control materials for external quality assessment

The research aimed to increase certain HbA1c concentrations at medical decision levels for external quality control samples from healthy donor blood. The in vitro study was performed from October 2019 to January 2021 at Quality Control Center for Medical Laboratory at University of Medicine and Pharmacy at Ho Chi Minh City. The study observed on the conditions including the optimal buffer solutions (BAGPM, BPS, Ringer, Saline), temperature (2°C - 8°C, 22°C - 24°C, 37°C), and glucose concentration (100 mM, 305 mM, 500 mM) affecting the HbA1c concentration in vitro to make the external quality control samples fell in normal, prediabetes, and diabetes range. At every condition, the HbA1c concentration was measured by Tina Quant method to look for the optimal procedure to increase HbA1c concentration required of the external quality control protocol. The highest HbA1c concentration (11.57±0.2%) was found in BAGPM solution with 100 mM glucose after 15 days with the baseline HbA1c 5.43±0.13%; the HbA1c level increase dramatically at 37°C in BAGPM 500 mM glucose solution in fifteen days (40.03±1.05%). The appropriate conditions were identified to prepare HbA1c standards for prediabetic and diabetic levels. The standards for HbA1c concentrations were recommended to prepare by incubating RBCs from non-diabetic donor blood in BAGPM solution containing glucose at 37°C for 24 hours. Glucose concentrations should be 100 mM and 500 mM, respectively, for prediabetic level (HbA1c ~ 6.0 ± 0.12%) and diabetic level (HbA1c ~ 9.6 ± 0.17%).

Regression-Adjusted Real-Time Quality Control.

Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.

Prioritizing Suitable Quality Assurance and Control Standards to Reduce Laboratory Airborne Microfibre Contamination in Sediment Samples

The ubiquity and distribution of microplastics, particularly microfibres, in outdoor and indoor environments makes it challenging when assessing and controlling background contamination, as atmospheric particles can be unintentionally introduced into a sample during laboratory analysis. As such, an intra-laboratory examination and literature review was completed to quantify background contamination in sediment samples, in addition to comparing reported quality assurance and control (QA/QC) protocols in 50 studies examining microplastics in sediment from 2010 to 2021. The intra-lab analysis prioritizes negative controls, placing procedural blanks in various working labs designed to prepare, process, and microscopically analyse microplastics in sediment. All four labs are subject to microfibre contamination; however, following the addition of alternative clean-air devices (microscope enclosure and HEPA air purifiers), contamination decreased by 66% in laboratory B, and 70% in laboratory C. A review of microplastic studies suggests that 82% are not including or reporting alternative clean-air devices in their QA/QC approaches. These studies are found to be at greater risk of secondary contamination, as 72% of them ranked as medium to high contamination risk. It is imperative that laboratories incorporate matrix-specific QA/QC approaches to minimize false positives and improve transparency and harmonization across studies.

Cortical surface abnormalities are different depending on the stage of schizophrenia: A cross-sectional vertexwise mega-analysis of thickness, area and gyrification

BackgroundBrain magnetic resonance imaging studies have not investigated the cortical surface comprehensively in schizophrenia subjects by assessing thickness, surface area and gyrification separately during the first-episode of psychosis (FEP) or chronic schizophrenia (ChSch). MethodsWe investigated cortical surface abnormalities in 137 FEP patients and 240 ChSch subjects compared to 297 Healthy Controls (HC) contributed by five cohorts. Maps showing results of vertexwise between-group comparisons of cortical thickness, area, and gyrification were produced using T1-weighted datasets processed using FreeSurfer 5.3, followed by validated quality control protocols. ResultsFEP subjects showed large clusters of increased area and gyrification relative to HC in prefrontal and insuli cortices (Cohen's d: 0.049 to 0.28). These between-group differences occurred partially beyond the effect of sample. ChSch subjects displayed reduced cortical thickness relative to HC in smaller fronto-temporal foci (d: −0.73 to −0.35), but not beyond the effect of sample. Differences between FEP and HC subjects were associated with male gender, younger age, and earlier illness onset, while differences between ChSch and HC were associated with treatment-resistance and first-generation antipsychotic (FGA) intake independently of sample effect. ConclusionsSeparate assessments of FEP and ChSch revealed abnormalities that differed in regional distribution, phenotypes affected and effect size. In FEP, associations of greater cortical area and gyrification abnormalities with earlier age of onset suggest an origin on anomalous neurodevelopment, while thickness reductions in ChSch are at least partially explained by treatment-resistance and FGA intake. Associations of between-group differences with clinical variables retained statistical significance beyond the effect of sample.

Identification and estimation of bioactive constituents Negundoside, Berberine chloride, and Marmelosin by HPLC and HPTLC for development of quality control protocols for Ayurvedic medicated oil formulation

BackgroundAnu Taila is an ancient medicated oil Ayurvedic preparation that is commonly used for nasya karma. It contains more than 25 herbs and goat milk as per the Ayurvedic Formulary of India (AFI). It strengthens the neck, shoulder, and chest muscles and improves the capacity of sense organs. It delays the aging process and reduces hair fall. Recent studies showed that it is also useful in COVID-19. In the current study, an attempt to develop quality control protocols and evaluate the standardization parameters like refractive index, iodine value, saponification value, peroxide value, acid value, rancidity, HPTLC fingerprint profile along with major bioactive compound and quantification of Berberine chloride, Negundoside, and Marmelosin by HPLC. Establishing quality protocol and standard parameters like physicochemical parameters and estimation of bioactive compounds of this preparation is significant for quality control.ResultsIn this study, HPTLC identifies bioactive chemical compounds like Berberine chloride, Marmelosin, Negundoside, glycyrrhizin, and para hydroxybenzoic acid (PHBA), Lupeol, Embelin, and Solasodine, which were present in the Anu Taila formulation. HPLC was used to estimate the bioactive marker compounds Negundoside, Berberine chloride, and Marmelosin were present in the Anu Taila formulation. The quantitative evaluation of Berberine chloride (0.0013%), Marmelosin (0.0366%), Negundoside (0.0086%) is present in Anu Taila formulation.ConclusionThe study reveals that sufficient quality control parameters were followed during the preparation of the formulation. Physicochemical analysis was carried out as per the guidelines of Ayurvedic Pharmacopeia of India. HPTLC and HPLC profiles generated in this particular study can be considered as a preliminary tool ascertaining the authenticity of Anu Taila.