Facilitate Quality Control Research Articles

Effect of thermal pretreatment on the reactivity of red mud valorized as aluminosilicate precursor for geopolymer production

Thermal pretreatment has proven to be an effective method to improve the reactivity of red mud to be valorized as an aluminosilicate precursor for geopolymer production. This approach offers the dual advantage of recovering caustic residue in red mud and encapsulating heavy metal, thus, achieving the transformation of red mud into a cleaner and value-added construction material. However, the inconsistent properties of red mud, including variations in chemical composition, particle size, and amorphous content pose challenges and can lead to instability in geopolymer as well as the effectiveness of thermal pretreatment, limiting the scalability of red mud valorization. Here in this study, thermal pretreatment was employed on five types of red mud from three refineries by calcining each red mud at 800 ○C for 2 h and its effect on the reactivity of each red mud was evaluated by comparing the performance of the resultant geopolymer. The results demonstrated a significant enhancing effect from thermal pretreatment, particularly noticeable at NaOH concentrations of 4 M and 8 M. This again highlighted the potential of thermal pretreatment in the conversion of red mud into construction material. However, amplified disparities between each thermal pretreated-red mud were also observed. To further address the challenges associated with thermal pretreatment variability, the dissolubility of reactive content from each thermal pretreated-red mud under alkali environments was quantified through alkali leaching tests and correlated to the mechanical properties of its resultant geopolymer. A strong negative correlation was observed between the concentration of Si, Al, and Fe from thermal pretreated-red mud and the mechanical properties of its resultant geopolymer. This correlation has the potential to facilitate quality control measures for thermal pretreated-red mud and its derived geopolymer, enabling better process optimization and consistency in geopolymer production.

Full-cycle study on developing a novel structured micromixer and evaluating the nanoparticle products as mRNA delivery carriers

Achieving precise control of nanoparticle size while maintaining consistency and high uniformity is of paramount importance for improving the efficacy of nanoparticle-based therapies and minimizing potential side effects. Although microfluidic technologies are widely used for reliable nanoparticle synthesis, they face challenges in meeting critical homogeneity requirements, mainly due to imperfect mixing efficiency. Furthermore, channel clogging during continuous operation presents a significant obstacle in terms of quality control, as it progressively impedes the mixing behavior necessary for consistent nanoparticle production for therapeutic delivery and complicates the scaling-up process. This study entailed the development of a 3D-printed novel micromixer embedded with hemispherical baffle microstructures, a dual vortex mixer (DVM), which integrates Dean vortices to generate two symmetrical counter-rotating intensified secondary flows. The DVM with a relatively large mixer volume showed rapid mixing characteristics even at a flow rate of several mL min−1 and produced highly uniform lipids, liposomes, and polymer nanoparticles in a size range (50–130 nm) and polydispersity index (PDI) values below 0.15. For the evaluation of products, SARS-CoV-2 Spike mRNA-loaded lipid nanoparticles were examined to verify protein expression in vitro and in vivo using firefly luciferase (FLuc) mRNA. This showed that the performance of the system is comparable to that of a commercial toroidal mixer. Moreover, the vigorous in-situ dispersion of nanoparticles by harnessing the power of vortex physically minimizes the occurrence of aggregation, ensuring consistent production performance without internal clogging of a half-day operation and facilitating quality control of the nanoparticles at desired scales.

Few-shot anomaly detection with adaptive feature transformation and descriptor construction

Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.

Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect Detection.

Roll-to-roll (R2R) manufacturing depends on a system's capability to deposit high-quality coatings with precise thickness, width, and uniformity. Therefore, consistent maintenance requires the immediate and accurate detection of coating defects. This study proposes a primary color selection (PCS) method to detect edge defects in R2R systems. This method addresses challenges associated with training data demands, complexity, and defect adaptability through a vision data-centric approach, ensuring precise edge coating defect detection. Using color information, high accuracy was achieved while minimizing data capacity requirements and processing time. Precise edge detection was facilitated by accurately distinguishing coated and noncoated regions by selecting the primary color channel based on color variability. The PCS method achieved superior accuracy (95.8%), outperforming the traditional weighted sum method (78.3%). This method is suitable for real-time detection in manufacturing systems and mitigates edge coating defects, thus facilitating quality control and production optimization.

Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms

This study introduces a novel methodology for enhancing the compressive strength of self-compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a sophisticated and interpretable machine learning algorithm. It presents a data-driven model that aims to accurately predict the strength of SCC by considering the intricate interactions among its various elements. Additionally, the model provides insights into the variables that influence SCC's compressive strength. By using EBM in conjunction with XGBoost and CatBoost algorithms, this study conducts a comparative examination of predictive abilities using datasets related to composition and rheology. The findings reveal that CatBoost has greater predictive performance using rheology dataset, as shown by an R2 value of 0.977. Conversely, XGBoost exhibits a higher predictive capability using the composition dataset, as indicated by an R2 value of 0.947. The EBM can provide comprehensive explanations at both global and local levels. It effectively identifies the key factors that have a significant influence on compressive strength. These factors include the coarse aggregate content, cement content, water content, viscosity, and V-funnel flow time. The study findings provide more evidence to support the notion that including rheological data into the model leads to a notable improvement in its accuracy. This outcome further confirms the existence of a direct correlation between rheological properties and compressive strength. The explanatory insights provided by EBM give practical instructions for customising SCC mixes to attain desired strengths. This facilitates quality control and enables personalised concrete design in the field of construction. This study highlights the potential of interpretable machine learning algorithms in improving the predictive modelling of SCC features. This advancement may lead to the development of more durable, efficient, and customised building materials.

Abstract 6604: Spatial transcriptomics delineates tumor heterogeneity in NACT triple-negative breast cancer

Abstract BACKGROUND: Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer and only ~40% of TNBC patients achieve a pathological complete response (pCR) following neoadjuvant chemotherapy (NACT). Since molecular heterogeneity of TNBC may contribute to variable responses to NACT, we aimed to deconvolute TNBC’s histological and spatial transcriptomics (ST) patterns. METHODS: In total, 90 pre-treatment biopsies were collected from NACT-treated TNBC patients from archival samples and the ongoing FORCE (NCT03238144) clinical trial at Guy’s Hospital, London, UK. Of these, 38 achieved pCR (Responder), 46 had residual disease (Non-Responder) whilst 6 are currently undergoing NACT; 46 post-treatment surgical resections were collected from Non-Responders.Each sample was H&E stained, imaged, and annotated by a pathologist. A 6-digit classifier was designed to capture the histology of each region of interest (ROI), characterizing the tumour region, epithelial type and immune cell localization, population, distribution, and abundance.H&Es were used to guide ST profiling of serial sections which was performed on the GeoMx Digital Spatial Profiler. Tissues were stained with fluorescent antibodies against PanCK and CD45 to visualize the tissue, select ROIs and illuminate spatially resolved tumor and immune-enriched compartments, defined as PanCK+/CD45- and PanCK-/CD45+, respectively.Data analysis was performed in R 4.3.1 using the limma, dplyr, EdgeR, GeoMx Tools, GeoMx Workflows, RUV4, Spatial Experiment, SpatialDecon and GSVA packages, facilitating quality control, batch correction, differential and topographical gene expression, immune cell deconvolution and gene set enrichment analysis (GSEA), respectively. RESULTS: To date, we have performed ST profiling of 120 TNBC samples from 82 patients (46 Non-Responders; 36 Responders), totaling 3917 tumor or immune-cell enriched regions. Preliminary analyses revealed KEGG pathways involved in tissue adhesion are upregulated in tumor compartments of Non-Responders compared to Responders, whilst those involved in genomic integrity are suppressed. Unsupervised clustering of PanCK+CD45- regions in Non-Responders identified tumor clusters present in both pre- and post-NACT samples, suggesting potential chemo-resistant properties. Moreover, immune cell deconvolution of the PanCK-/CD45+ regions revealed spatially distinct patters of immune cell composition mapped to histological annotations identified by our ROI classifier. CONCLUSION: (S)Large scale ST of TNBC before and after NACT reveals novel patterns associated with response to treatment. We have designed a novel ROI classifier to spatially resolved transcriptomes of histological features, as such provide an in-depth characterization of the histo-genomics of TNBC. Citation Format: Isobelle Wall, Jelmar Quist, Sarah Pinder, KCL Cancer Biobank, Sheeba Irshad, Cheryl Gillet, Victoria Seewaldt, Kavitha Mukund, David Frankhouser, Shankar Subramaniam, Maddy Parsons, Anita Grigoriadis. Spatial transcriptomics delineates tumor heterogeneity in NACT triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6604.

ISCOPEM2D V1.0: An In Situ Method to Characterize and Compare Chemical Vapor Deposition Graphene Films Using Quality Matrix Approaches

Single‐layer chemical vapor deposition (CVD) graphene films are commonly transferred from their native growth substrates during characterization. However, the transfer process influences their intrinsic material properties and growth substrate‐related material information is lost. Herein, an in situ method is presented to suspend CVD graphene films directly on their growth substrate. The approach makes the sample compatible for characterization across the widely used optical and electron microscopy techniques such as Raman spectroscopy and transmission electron microscopy. In addition, a quality matrix framework “ISCOPEM2D V1.0” is provided to support the quantification, comparison, and validation of various material properties of single‐layer graphene. The approach provides a holistic overview of various properties and facilitates quality control and reproducibility of single‐layer graphene‐based application development. Similar approaches can be useful to develop quality matrix‐based for other 2D materials.

Death of a reviewer or death of peer review integrity? the challenges of using AI tools in peer reviewing and the need to go beyond publishing policies

Peer review facilitates quality control and integrity of scientific research. Although publishing policies have adapted to include the use of Artificial Intelligence (AI) tools, such as Chat Generative Pre-trained Transformer (ChatGPT), in the preparation of manuscripts by authors, there is a lack of guidelines or policies on whether peer reviewers can use such tools. The present article highlights the lack of policies on the use of AI tools in the peer review process (PRP) and argues that we need to go beyond policies by creating transparent procedures that will enable journals to investigate allegations of non-compliance and take decisions that will protect the integrity of the peer review system. Reviewers found to violate relevant policies must be excluded from the process to safeguard the integrity of the peer review system.

Cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BackgroundHighly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.ResultsHere, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.ConclusionsThe cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis.It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html. The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer.

Trends in authentication of edible oils using vibrational spectroscopic techniques.

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Advancements in Manufacturing Technology for the Biotechnology Industry: The Role of Artificial Intelligence and Emerging Trends

The biotechnology industry is evolving rapidly in terms of production processes. In this study, we will evaluate the manufacturing technology in bioprocessing, automation, and data integration in biotechnological engineering. It will also examine how artificial intelligence (AI) enhances industrial operations, Predictive maintenance through AI, and Process optimization using AI; it facilitates quality control and supply chain management performance with the implementation of artificial intelligence. The evaluation result significantly incorporates artificial intelligence into different stages of biomanufacturing processes, unveiling current trends within this sector and their implications for future growth. Artificial intelligence is a productive and novel result in biotechnologies

UV Spectrophotometric Method for Estimation of Voriconazole in Bulk Form

This study presents a validated UV spectrophotometric method for the precise estimation of voriconazole in bulk formulations. Voriconazole, an essential antifungal agent, demands accurate quantification for its pharmaceutical applications. The proposed method leverages the sensitivity of UV spectroscopy to determine voriconazole concentrations effectively.
 The method's validation adheres to regulatory guidelines, ensuring reliability and accuracy. Spectrophotometric analysis was performed within a specific wavelength range, demonstrating linearity, precision, accuracy, and robustness across different concentrations of voriconazole.
 Parameters such as specificity, sensitivity, and stability were evaluated to affirm the method's suitability for routine analysis.
 The results indicate the method's efficacy in quantifying voriconazole in bulk and semi-solid forms with high precision and sensitivity. This validated UV spectrophotometric method presents a valuable tool for pharmaceutical analysis, facilitating quality control and assurance in voriconazole formulations.
 Keywords: Voriconazole, UV spectrophotometry, Anti-fungal, semi-solid formulation, Beer’s Lambert’s law.

A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics.

Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.

Reviewing manuscripts for behavior-analytic journals: A primer.

The peer-review component of the editorial process is designed to facilitate quality control, legitimize scientific research, and self-regulate scientific communities. Even though serving as a reviewer undoubtedly has direct and indirect benefits, the peer-review system and the methods of teaching scholars to conduct reviews are nascent and relatively underdeveloped. This article describes the peer-review process and provides recommendations for writing reviews for scientific journals. The recommendations were developed based on the expertise and preferences of editors in chief and associate editors for behavior-analytic journals (Cengher & LeBlanc, 2024), and they include honoring your responsibility, knowing your audience, being constructive and kind, and carefully evaluating the merits of the study or review. These guidelines may serve as a primer for scholars who want to conduct reviews for scientific journals in behavior analysis.

Potential of fluorescence fingerprints for fish meat authentication: Differences in freshness evaluation in white and dark meat at frozen state.

As dark meat has a faster deterioration rate and its unintentional mixing occurs during processing, it is crucial to know the status and freshness indicators of dark meat to ensure fishery product quality. In this method, fluorescence fingerprints (FFs) was applied as a rapid and noninvasive quality authentication method to determine differences between white and dark meat in the evaluation of freshness indicators at frozen state. Spotted mackerel (Scomber australasicus) fish chunks with different postmortem conditions (0-40h ice stored) were obtained and frozen. A new generation of fluorescence spectrophotometer (F-7100) was used to acquire FFs of the frozen fish chunks (containing white and dark meat). Adenosine triphosphate metabolites and pH were determined in both white and dark meat using their relevant biochemical methods. Higher K-values in dark meat might be attributed to a higher accumulation rate of inosine (HxR) in dark meat than in white meat. The pH decrease rate in white meat was higher than that in dark meat during postmortem ice storage periods of fish. Principal component analysis of FFs spectra demonstrated clear discrimination (PC1+PC2=91.7%) between white and dark meat of frozen fish due to the influence of freshness parameters based on the fluorescence features of fish meat. Furthermore, partial least squares regression validation models revealed that freshness indicators of white meat could be predicted more accurately at the frozen state than those of dark meat. This method could be applied during the processing of fishery products, thereby facilitating quality control activities and making it a promising authentication tool for the fisheries industries.

MassWateR: Improving quality control, analysis, and sharing of water quality data.

The long-term protection and restoration of aquatic resources depends on robust monitoring data; data that require systematic quality control and analysis tools. The MassWateR R package facilitates quality control, analysis, and data sharing for discrete surface water quality data collected by monitoring programs of various size and technical capacity. The tools were developed to address regional needs for programs in Massachusetts, USA, but the principles and outputs can be applicable to monitoring data collected anywhere. Users can create quality control reports, perform outlier analyses, and assess trends by season, date, and site for more than 40 parameters. Users can also prepare data for submission to the United States Environmental Protection Agency Water Quality Exchange, thus sharing data to the largest water quality database in the United States. The automated and reproducible workflow offered by MassWateR is expected to increase the quantity and quality of publicly available data to support the management of aquatic resources.

The Spirit of Cachaça Production: An Umbrella Review of Processes, Flavour, Contaminants and Quality Improvement.

This review provides a comprehensive analysis of the production, classification, and quality control of cachaça, a traditional Brazilian sugarcane spirit with significant cultural importance. It explores the fermentation and distillation of sugarcane juice, the ageing process in wooden containers, and the regulatory aspects of cachaça labelling. It emphasises the role of quality control in maintaining the spirit's integrity, focusing on monitoring copper levels in distillation stills. Ethyl carbamate (EC), a potential carcinogen found in cachaça, is investigated, with the study illuminating factors influencing its formation and prevalence and the importance of its vigilant monitoring for ensuring safety and quality. It also underscores the control of multiple parameters in producing high-quality cachaça, including raw material selection, yeast strains, acidity, and contaminants. Further, the impact of ageing, wood cask type, and yeast strains on cachaça quality is examined, along with potential uses of vinasse, a cachaça by-product, in yeast cell biomass production and fertigation. A deeper understanding of the (bio)chemical and microbiological reactions involved in cachaça production is essential to facilitate quality control and standardisation of sensory descriptors, promoting global acceptance of cachaça. Continued research will address safety concerns, improve quality, and support the long-term sustainability and success of the cachaça industry.

Application of project management system in construction machinery enterprises

The application of project management systems in construction machinery enterprises can improve the efficiency of project execution and accurately control project costs, thereby promoting the development of the enterprise. The system improves the efficiency and collaboration of teams through task allocation and tracking, progress management, resource management and communication and collaboration. Through budget management, resource optimisation and risk management, the project management system is able to accurately control project costs and reduce the impact of wasted resources and risk on costs. In addition, the system can facilitate quality control and improvement, helping companies to improve product quality and customer satisfaction.

Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data.

Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review.

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.