Optimal Quality Control Research Articles

Integrating widely targeted and oxylipin-targeted lipidomics unravels lipid characteristic evolution and oxidation markers in walnuts during deterioration

The susceptibility of walnut lipids to deterioration constitutes challenges for industry development, and the oxylipins formed during this process remain to be explored. This study employed lipidomics to reveal the dynamic evolution of lipid characteristics and identify oxidation markers from oxylipins in walnuts during accelerated storage. Glycerophospholipid (GP) content continuously declined in the initial and severe deterioration stages. The accumulation of diglycerides and partial lysophospholipids characterized initial deterioration. Triglycerides were prone to direct oxidation, while GPs tended to be first hydrolyzed. GP metabolism especially phosphatidylethanolamine degradation triggered walnut deterioration. Moreover, ten oxylipins derived from linoleic acids were identified in walnuts. Trans-EKODE-(E)-Ib, 13-HODE, 9-HODE, and 9(S),12(S),13(S)-TriHOME were screened as oxidation markers. The cellular structure exhibited the cell membrane and oil body membrane rupture during deterioration. Potential mechanisms of lipid deterioration were proposed, providing a scientific basis and guidance in optimizing quality control strategies and assessing practical deterioration degrees of walnuts.

Simultaneous prediction of the API concentration and mass gain of film coated tablets using Near-Infrared and Raman spectroscopy and data fusion

This study investigates the simultaneous prediction of active pharmaceutical ingredient (API) concentration and mass gain in film-coated tablets using Partial Least Squares (PLS) regression combined with three data fusion (DF) techniques: Low-Level (LLDF), Mid-Level (MLDF), and High-Level (HLDF). Near-Infrared (NIR) and Raman spectroscopy were utilized in both reflection and transmission modes, providing four types of spectral data per tablet. Transmission models proved more effective for API prediction by capturing data from the entire tablet, while reflection models excelled in assessing mass gain by focusing on the surface layer. Among the DF strategies, MLDF with Principal Component Analysis (PCA) offered the most significant improvements in predictive accuracy by filtering out irrelevant information. Variable selection methods further enhanced model performance by reducing the number of latent variables required. Overall, the integration of multiple spectral datasets and DF techniques resulted in models that gave predictions for evaluation samples with lower errors, demonstrating their potential to optimize quality control in pharmaceutical manufacturing.

SWQC: Efficient sequencing data quality control on the next-generation sunway platform

Sequencing data quality control can significantly prevent low-quality data from impacting downstream applications in bioinformatics. The enormous growth of biological sequencing data in recent years introduces new challenges to the efficiency of quality control processes and motivates the need for fast implementations on modern compute systems. The powerful next-generation heterogeneous Sunway platform holds significant potential for addressing this challenge. However, there are currently no dedicated quality control applications that can fully utilize its computational power. To bridge this gap, we introduce SWQC, a novel quality control application specifically designed for the Sunway platform. We present an efficient distributed FASTQ I/O framework for Sunway-based workstations and supercomputers to take advantage of fast SSDs and the parallel file system. In order to support both process-level and thread-level (CPE-level) parallelism to leverage the computational power, we refactor and optimize all standard quality control modules for the heterogeneous Sunway architecture. When using a single node, SWQC achieves speedups between 2 and 40 over highly optimized quality control applications executed on a high-end 48-core AMD server. Additionally, when using 16 nodes, SWQC achieves parallel efficiencies of 70% (for reading and writing a single file) and 95% (for reading one file and writing split files) compared to a single node. Overall, SWQC is able to perform quality control operations for a 140GB FASTQ file within only 70 s using a single Sunway node. It is publicly available at https://github.com/RabbitBio/SWQC.

Chemometric approaches for polysaccharide viscosity profiling

Traditional viscosity measurements for carrageenan are laborious, present practical and environmental challenges, and fail to provide structure-property understanding for application and manufacturing development. We hypothesize that integrating Size Exclusion Chromatography (SEC) with Multi-Angle Light Scattering (MALS) and online viscometry, combined with chemometric techniques, can develop a more efficient and environmentally friendly method for determining the apparent viscosity of carrageenan solutions.To test this hypothesis, predictive chemometric models were developed using SEC-MALS data for carrageenan extracted from four different seaweed species. By integrating SEC-MALS with Partial Least Squares (PLS) regression, key molecular parameters such as hydrodynamic radius, intrinsic viscosity, and molecular mass were identified as significant influencers of viscosity. The model for carrageenan from Eucheuma denticulatum yielded the lowest prediction error (RMSEP 8.4), while those for carrageenan extracted from Kappaphycus alvarezii or from several species of the Chondrus genus showed higher errors due to κ-carrageenan sensitivity. For carrageenan extracted from seaweed of the Gigartina genus, incorporating the root mean square radius resulted in a low prediction error of 10.This study concludes that integrating SEC-MALS with PLS regression effectively identifies key molecular parameters influencing carrageenan viscosity, enhancing structure-property understanding and providing a reliable analytical method for optimizing quality control and application in various industries.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Prevention of Transition from Acute Kidney Injury to Chronic Kidney Disease Using Clinical-Grade Perinatal Stem Cells in Non-Clinical Study.

Acute kidney injury (AKI) is widely recognized as a precursor to the onset or rapid progression of chronic kidney disease (CKD). However, there is currently no effective treatment available for AKI, underscoring the urgent need for the development of new strategies to improve kidney function. Human placental mesenchymal stromal cells (hpMSCs) were isolated from donor placentas, cultured, and characterized with regard to yield, viability, flow cytometry, and potency. To mimic AKI and its progression to CKD in a rat model, a dedicated sensitive non-clinical bilateral kidney ischemia-reperfusion injury (IRI) model was utilized. The experimental group received 3 × 105 hpMSCs into each kidney, while the control group received IRI and saline and the untreated group received IRI only. Urine, serum, and kidney tissue samples were collected over a period of 28 days. The hpMSCs exhibited consistent yields, viability, and expression of mesenchymal lineage markers, and were also shown to suppress T cell proliferation in a dose-dependent manner. To ensure optimal donor selection, manufacturing optimization, and rigorous quality control, the rigorous Good Manufacturing Practice (GMP) conditions were utilized. The results indicated that hpMSCs increased rat survival rates and improved kidney function by decreasing serum creatinine, urea, potassium, and fractionated potassium levels. Furthermore, the study demonstrated that hpMSCs can prevent the initial stages of kidney structural fibrosis and improve kidney function in the early stages by mitigating late interstitial fibrosis and tubular atrophy. Additionally, a robust manufacturing process with consistent technical parameters was established.

Quality control for serological testing

ObjectivesThe quality control of serological assays remains controversial. The aim of this project was to describe the problems associated with a working model for controlling these assays and solutions, including using a source of well-defined targets and acceptable limits, a process to identify lot-to-lot reagent variation and an interpretation of the result that accounted for the clinical situation. False-negative results are problematic but can be reduced by identifying and comparing reagent lot variation with previous results. MethodsThe components of the Quality Assurance strategy are the following: Lot-to-lot reagent and calibrator variation assessment; dynamic, big-data approach to determine accurate targets and acceptable limits for manufacturer-provided QC material; negative QC monitoring process; use of commutable EQA with a sufficient method subgroup size to assess bias; clinical assessment of any statistically flagged error; and provision of support to the clinician for the interpretation of results. ResultsThe model described has been used for twelve months, and acceptable variation has been maintained. ConclusionsThe paper presents a solution that emphasizes the early detection of reagent lot variation and patient risk rather than instrument control.Reducing the risk of a false result to patients requires optimal assay quality control and an effective mechanism to support the clinician’s use of these results in diagnosis and monitoring. The problems of serological assays are well-known, but there remain few integrated solutions in the literature.

Industry 5.0 in Manufacturing: Enhancing Resilience and Responsibility through AI-Driven Predictive Maintenance, Quality Control, and Supply Chain Optimization

This integrative literature review investigates the transformative impact of artificial intelligence (AI) on manufacturing, focusing on AI-driven predictive maintenance, machine learning-based quality control, and AI-driven supply chain optimization. By examining current literature, the study highlights AI's potential to automate and revolutionize manufacturing operations, enhancing efficiency, resilience, and transparency. The study's conceptual framework is grounded in three primary pillars: AI-driven supply chain optimization, predictive analytics, and machine learning-based quality control, each contributing to the overall enhancement of manufacturing efficiency, resilience, and transparency. The methodology involves a comprehensive review of scholarly articles, reports, and academic publications, focusing on AI applications in predictive maintenance, quality control, and supply chain optimization. The analysis reveals significant improvements in operational efficiency and resilience due to AI, alongside concerns about biases, transparency, and implementation issues. The findings confirm AI's transformative potential in manufacturing but emphasize the necessity for ongoing supervision, regular audits, and the development of AI models capable of detecting and rectifying operational anomalies. The study proposes creating jobs such as AI Manufacturing Oversight Officer (AIMOO), AI Manufacturing Compliance Officer (AIMCO), and AI Manufacturing Quality Assurance Officer (AIMQAO) to ensure responsible AI utilization, maintaining the integrity and efficiency of manufacturing operations while addressing implementation challenges. The review concludes that AI is promising for transforming manufacturing; however, careful implementation is crucial to uphold operational integrity and resilience. Future research should prioritize longitudinal studies to evaluate AI's long-term impact, focus on addressing implementation concerns, and ensure fair and transparent integration of AI technologies. These findings have significant implications for practice and policy, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in manufacturing.

Real-time monitoring of middle cerebral artery blood flow using intraoperative transcranial doppler during trans-carotid artery revascularization

ObjectiveTranscarotid artery revascularization (TCAR) has emerged as a safe and effective method for carotid revascularization, offering several key advantages over carotid endarterectomy (CEA) and carotid artery stenting (CAS). Intraoperative transcranial Doppler (TCD) monitoring plays a pivotal role in assessing cerebral hemodynamics and detecting embolic signals during TCAR at our institution. MethodsThis review synthesizes the current literature and provides guidance for TCD monitoring throughout the various phases of TCAR, from preoperative assessment to postoperative management. Key considerations include probe placement, waveform evaluation, interpreting monitoring parameters such as mean flow velocity (MFV), pulsatility index (PI), and percentage change in the MFV (Δ%). Techniques for maintaining the insonation of the middle cerebral artery (MCA) M1 segment and optimal parameter settings for intraoperative TCD monitoring are detailed. ResultsTCAR phases are highlighted, including transcarotid access and vessel control, sheath insertion, the establishment of flow reversal, pre-dilation, stent placement, post-dilation, and closure, while emphasizing the importance of real-time feedback provided by TCD monitoring in identifying embolic signals and assessing changes in cerebral perfusion. The review discusses limitations of TCD monitoring, such as inadequate temporal windows, incorrect vessel identification and reliability issues with automatic emboli detection counters. Furthermore, practical advice is provided on how to navigate common pitfalls encountered during intraoperative TCD monitoring. ConclusionBy understanding the nuances of TCD monitoring and its application in TCAR, intraoperative TCD monitoring may aid to minimize the low but potential risk of intraprocedural embolic events, periprocedural hypoperfusion and postoperative hyperperfusion. Finally, we suggest opportunities for further research in embolization quantification and additional strategies to optimize quality control in TCAR.

Sigma metrics analysis of serology screening assays to enhance quality and efficiency in New Zealand blood services

Sigma metric analysis was conducted across two New Zealand Blood Services (NZBS) laboratories (Auckland and Christchurch) to optimize quality control (QC) procedures. We evaluated five assays (anti-HCV, HIV Ag/Ab combo, HTLV-I/II, HBsAg, and Syphilis) using internal quality control (IQC) and third-party daily QC data extracted from four Architect i2000SR instruments during Jan 2 -31st, 2023. Mean, standard deviation (SD), and coefficient of variation (CV%) were calculated, assuming zero bias. Sigma metrics were determined using the Total Allowable Error (TEa %) based on difference between positive control mean and signal-to-cutoff (s/co) cut-off. Most assays exhibited CV% values ≤10 % except for HBsAg IQC (18.5 %) and anti-HCV third-party QC (13.4 %) at Christchurch. TEa % ranged from 38 % to 90 %. Overall, the assays demonstrated Six Sigma performance (σ > 6), except for HBsAg IQC (3.97) and anti-HCV third-party QC (5.46) at Christchurch. These high-quality serology assays can benefit from simplified QC design without compromising blood safety.

Optimal harvest period and quality control markers of cultivated Flos Chrysanthemi Indici using untargeted/targeted metabolomics, chemometric analysis and in vivo study

Ethnopharmacological relevanceFlos Chrysanthemi Indici (FCI), the flower of Chrysanthemum Indicum L., is a popular traditional Chinese medicine (TCM) for treatment of inflammatory diseases in China. FCI is also a functional food, and is widely used as herbal tea for clearing heat and detoxicating. Aim of the studyTo explore quality control markers of FCI based on the optimal harvest period. Materials and methodsFirst, UPLC-Q-TOF/MS based untargeted metabolomics was applied to explore the chemical profiles of FCIs collected at bud stages (BS), initial stages (IS), full bloom stages (FS) and eventual stages (ES) from eight cultivated regions in China. Subsequently, lipopolysaccharide (LPS)-induced RAW264.7 cell inflammatory model and carrageenan-induced rat paw edema model were used to confirm the anti-inflammatory effect of FCIs collected at IS/FS. Then, UPLC-PDA targeted metabolomics was used to quantitatively analyze 9 constituents with anti-inflammatory activity (7 flavonoids and 2 phenolic acids) changed significantly (VIP > 4) during flowering stages. Finally, ROC curves combined with PCA analysis based on the variation of 9 active constituents in FCIs from different flowering stages were applied to screen the quality markers of FCI. ResultsFCIs at IS/FS had almost same chemical characteristics, but quite different from those at BS and ES. A total of 32 constituents in FCIs including flavonoids and phenolic acids were changed during flowering development. Most of the varied constituents had the highest or higher contents at IS/FS compared with those at ES, indicating that the optimal harvest period of FCI should be at IS/FS. FCI extract could effectively suppress nitric oxide (NO) production in LPS-induced RAW264.7 cells and regulate the abnormal levels of cytokines and PGE2 in carrageenan-induced paw edema model rat. The results of quantitatively analysis revealed that the variation trends of phenolic acids and flavonoids in FCIs were different during flowering development, but most of them had higher contents at IS/FS than those at ES in all FCIs collected from eight cultivated regions, except one sample from Anhui. Finally, linarin, luteolin, apigenin and 3,5-dicaffeoylquinic acid were selected as the Q-markers based on the contribution of their AUC values in ROC and clustering of PCA analysis. ConclusionsOur study demonstrates the optimal harvest period of FCI and specifies the multi-constituents Q-markers of FCI based on the influence of growth progression on the active constituents using untargeted/targeted metabolomics. The findings not only greatly increase the utilization rate of FCI resources and improve quality control of FCI products, but also offer new strategy to identify the Q-markers of FCI.

REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY

Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.

Simplified Modeling Method for Prefabricated Shear Walls Considering Sleeve Grouting Defects

The sleeve grouting connection is the most common form of vertical connection for prefabricated shear walls. However, during construction, this type of connection is prone to defects such as insufficient anchorage length of reinforcement, deviation of reinforcement, and insufficient amount of sleeve grouting, which significantly impact the integrity and seismic performance of the prefabricated shear wall structure. The finite element analysis of the prefabricated shear wall with sleeve grouting connection is still based on solid element modeling. This method has the disadvantages of complex models and low computational efficiency. In this paper, a simplified modeling method for prefabricated shear walls considering sleeve grouting defects was proposed to address this issue. Firstly, the equivalent constitutive relationship of the sleeve grouting defect connector was constructed based on the uniaxial tensile test of the existing sleeve grouting defect connector, and the T3D2 element was used to simulate the sleeve grouting connector. Then, the mechanical behavior of the horizontal joint between the shear wall and the foundation beam was simulated by the cohesive force model, and the prefabricated shear wall models with sleeve grouting defects were established. The accuracy of the simplified modeling method was verified by comparing the experimental results and numerical simulation results of the seismic performance of the prefabricated shear wall with sleeve grouting defects. The results showed that the hysteresis curve, skeleton curve, and failure mode of the numerical simulation were in good agreement with the test results. However, the stiffness of the concrete degenerated rapidly due to the apparent development of plastic-damaged concrete, which made the falling section of the hysteresis curve of the numerical simulation different from that of the test. The proposed simplified modeling method can be further applied to the performance study of prefabricated shear walls with sleeve grouting defects and can provide a reference for structural performance evaluation, design optimization, and construction quality control to a certain extent.

Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systems

The effectiveness of e-nose systems as high-throughput tools for volatile profiling in watermelon was investigated focusing on discerning subtle changes induced by the use of different rootstocks. Partial Least Square Discriminant Analysis (PLS-DA) models, both GC-MS and e-nose data, demonstrated moderate performance in classification due to nuanced differences among groups (the same F1 hybrid was used as scion). However, PLS-DA biplots revealed a clear correlation between GC-MS and e-nose data. This methodology enabled the e-nose system to identify the effects of specific root-scion combinations compared to non-grafted controls and detect combinations with more variable volatile profiles. Remarkably, the e-nose system identified samples with anomalous volatile profiles, mirroring the capabilities of GC-MS data. Additionally, PLS models were developed to provide reasonably accurate predictions of key compound contents like geranylacetone, (Z)-6-nonen-1-ol, or (Z)-6-nonenal, crucial for watermelon flavor and taste perception. Overall, this study highlights the potential of e-nose systems in discerning nuanced variations in watermelon volatile profiles affecting aroma. Incorporating volatile profile evaluation capabilities using such systems will significantly optimize quality control processes and plant breeding programs.

A dual-responsive sensor derived from carbonized polymer dots for the determination of cyanidin-3-O-glucoside

Accurate detection of cyanidin-3-O-glucoside (C3G) is crucial for ensuring food safety and optimizing quality control measures. Dual-mode sensing provides a robust approach to achieve enhanced accuracy in detection; however, the utilization of dual-mode detection for C3G remains largely unexplored. The present study introduced a novel, simple, and versatile nanomaterial carbonized polymer dots (CPDs), which harnessed the exceptional spectral properties of CPDs to achieve colorimetric-fluorescence dual-mode sensing of C3G. In the presence of C3G, the fluorescence of CPDs was quenched, resulting in a visible blue color to the naked eye. The utilization of distinct signal generation mechanisms enabled simultaneous acquisition of two independent signals, thereby providing self-verification functionality and enhancing reliability and accuracy. This dual-mode sensor exhibited high specificity, exceled in colorimetric and fluorescence detection, and was well-suited for precise identification of C3G in complex samples. It was poised to emerge as a highly sensitive and dependable tool for food safety analysis. This research project showcased the immense potential of CPDs as an efficient optical sensor while imparting invaluable insights for future C3G detection.

Evaluating optimal quality assurance and quality control conditions of activation measurements at the accelerator-based boron neutron capture therapy system employing a lithium target

Evaluating neutron output is important to ensure proper dose delivery for patients in boron neutron capture therapy (BNCT). It requires efficient quality assurance (QA) and quality control (QC) while maintaining measurement accuracy. This study investigated the optimal measurement conditions for QA/QC of activation measurements using a high-purity germanium (HP-Ge) detector in an accelerator-based boron neutron capture therapy (AB-BNCT) system employing a lithium target. The QA/QC uncertainty of the activation measurement was evaluated based on counts, reproducibility, and standard radiation source uncertainties. Measurements in a polymethyl methacrylate (PMMA) cylindrical phantom using aluminum-manganese (Al–Mn) foils and aluminum-gold (Al–Au) foils and measurements in a water phantom using gold wire with and without cadmium cover were performed to determine the optimal measurement conditions. The QA/QC uncertainties of the activation measurements were 4.5% for Au and 4.6% for Mn. The optimum irradiation proton charge and measurement time were determined to be 36 C and 900 s for measurements in a PMMA cylindrical phantom, 7.0 C and 900 s for gold wire measurements in a water phantom, and 54 C and 900 s at 0–2.2 cm depth and 3,600 s at deeper depths for gold wire measurements with cadmium cover. Our results serve as a reference for determining measurement conditions when performing QA/QC of activation measurements using HP-Ge detectors at an AB-BNCT employing a lithium target.

Optimizing Quality Control and Assurance of Design-Build Transportation Projects through Alignment of RFP Selection Criteria with Owners Expectations

Optimizing Quality Control and Assurance of Design-Build Transportation Projects through Alignment of RFP Selection Criteria with Owners Expectations

Out-of-Mold Sensor-Based Process Parameter Optimization and Adaptive Process Quality Control for Hot Runner Thin-Walled Injection-Molded Parts.

Injection molding is a highly nonlinear procedure that is easily influenced by various external factors, thereby affecting the stability of the product's quality. High-speed injection molding is required for production due to the rapid cooling characteristics of thin-walled parts, leading to increased manufacturing complexity. Consequently, establishing appropriate process parameters for maintaining quality stability in long-term production is challenging. This study selected a hot runner mold with a thin wall fitted with two external sensors, a nozzle pressure sensor and a tie-bar strain gauge, to collect data regarding the nozzle peak pressure, the timing of peak pressure, the viscosity index, and the clamping force difference value. The product weight was defined as the quality indicator, and a standardized parameter optimization process was constructed, including injection speed, V/P switchover point, packing, and clamping force. Finally, the optimized process parameters were applied to the adaptive process control experiments using the developed control system operated within the micro-controller unit (MCU). The results revealed that the control system effectively stabilized the product weight variation and standard deviation of 0.677% and 0.0178 g, respectively.

Six Sigma driven QC management in hepatitis C serology

The Westgard quality control (QC) rules are often applied in infectious diseases serology to validate the quality of results, but this requires a reasonable tradeoff between maximum sensitivity to errors and minimum false rejections. This article, in addition to illustrate the six sigma methodology in the QC management of the (anti-HCV Architect®) test, it discusses the main influencing factors on sigma value. Data from low positive and in-kit control materials spreading over 6 months and using four reagent kits, were used to calculate the precision of the test. The difference between the control material reactivity and the cut-off defined the error budget. Sigma values were >6, which indicates that the method produces four erroneous results per million tests. The application of the six sigma concept made it possible to argue the choice of the new QC strategy (use of 13S rule with one positive control) and to relax the existing QC rules. This work provides a framework for infectious diseases serology laboratories to evaluate tests performances against a quality requirement and design an optimal QC strategy.

Optimalisasi Pengendalian Mutu Guna Mengurangi Jumlah Kerusakan Produk Pada IKM Tempe 21 A Yosodadi Metro Timur

The research problem formulation is what are the factors that cause the amount of productdamage and how to optimize quality control to reduce the amount of product damage in IKMTempe 21A Yosodadi Metro Timur. how to optimize quality control to reduce the amount ofproduct damage in IKM Tempe 21A Yosodadi Metro Timur. The type of research isqualitative research, the data collection technique uses observation, and interviews. Thisstudy focuses on the implementation of Quality Control (quality control) in IKM Tempe 21AYosodadi Metro Timur. Based on the results of research and discussion, it can be concludedthat the factors that cause the amount of damage to tempe products in Mr. Khairunas' TempeIKM consist of lack or excess of yeast, duration of fermentation, changing room temperatureconditions, washing soybeans that are not clean, damaged machines. , and the quality ofsoybeans and how to reduce the amount of product damage, namely ensuring the size of theyeast with the appropriate amount of soybeans so that there is no shortage or excess,ensuring cleanliness in soybean washing, ensuring the fermentation process is good enoughwithout overlapping tempeh, and repairing the machine and seeing the quality of soybeans.become raw materials.