Quality Control Methodology Research Articles

Flow injection analysis-based methodology for automatic on-line monitoring and quality control for biodiesel production

An automated on-line approach based on determination of free and bound glycerol was here proposed to monitor biodiesel production. The method was based on liquid–liquid extraction of glycerol from the biodiesel to an aqueous ethanolic phase in which glycerol is oxidized to formaldehyde with meta periodate with subsequent reaction with acetylacetone. The reaction product was photometrically measured at 410 nm. Free and bound glycerol were differentiated by glycerides hydrolysis with potassium ethylate. The experimental set-up consisted of a flow-injection manifold for liquid–liquid extraction without phase separation and iterative change of the flow direction that enabled: (a) filling the flow manifold with a meta periodate–acetylacetone acceptor phase; (b) sampling of small amounts (μl) from the reactor; (c) determination of free glycerol by extraction from biodiesel to the aqueous phase with simultaneous oxidation-reaction with acetylacetone in the acceptor phase; (d) continuous monitoring of the aqueous phase by passage through a photometric detector; (e) filling the flow manifold with a potassium ethylate-meta periodate–acetylacetone new acceptor phase; (d) repetition of steps b-to-d to determine total glycerol after saponification of the bound glycerol by potassium ethylate; and (f) determination of bound glycerol by difference between the second and first analyses. The results showed that the proposed automated on-line method is a suitable option in routine analysis during biodiesel production.

A model-based methodology for on-line quality control

An on-line model development and quality control methodology is presented for manufacturers in the process industries with the goal of enabling automated quality assurance. Given appropriate process instrumentation, the methodology starts with the characterization of statistical variation for the process while operating in steady state. Significant process conditions are then perturbed by six standard deviations to bound the expected long-term process variation including lot-to-lot variability of feedstock materials. If the process is found to be robust, acquired process data is used to model the process behavior using principle components analysis (PCA). The PCA model is then used to accept and reject manufactured parts given real-time process data. This methodology is applied to an instrumented injection molding process that is subjected to 12 common process faults. The results indicate that the methodology was able to detect every one of 33 defective molding cycles caused by eight of the imposed faults as well as two additional faults that did not result in observable defective products. The quality controller did not detect the two remaining imposed process faults that did not produce defects and also rejected three molding cycles (2% of the total) that appeared to produce acceptable products.

Methodology of a real-time quality control for the multispectral digital colposcope (MDC)

Background. The diagnostic ability of algorithms developed for the Multispectral Digital Colposcope (MDC) is highly dependent on the quality of the image. The field of objective medical image quality analysis has great potential but has not been well exploited. Various researchers have reported different measures of image quality but with an existence of a reference image. The quality of an image can be attributed to several sources of errors, a few of which would be inclusion of presence of extraneous components, improper illumination, or an image out of focus. This can be due to motion artifact or the region of interest out of the focal plane. Methods. With spectroscopic measurements, assessment of data quality has been used by our group in the past to avoid hardware errors at the time of acquisition. We are currently developing algorithms that will help identify hardware and acquisition errors to the clinician in under a few seconds. Results. Minimizing these errors not only provides quality images for a diagnostic algorithm, but reduces the necessity for complex and time intensive post-processing software for enhancing the images. Conclusion. We propose a no reference image quality system specifically designed for MDC that can be modified to similar spectroscopic imaging applications.

The Real-Time Data Management System for Argo Profiling Float Observations

Abstract Argo is an internationally coordinated program directed at deploying and maintaining an array of 3000 temperature and salinity profiling floats on a global 3° latitude × 3° longitude grid. Argo floats are deployed from research vessels, merchant ships, and aircraft. After launch they sink to a prescribed pressure level (typically 1000–2000 dbar), where most floats remain for 10 days. The floats then return to the surface, collecting temperature and salinity profiles. At the surface they transmit the data to a satellite and sink again to repeat the cycle. As of 10 August 2006 there are 2489 floats reporting data. The International Argo Data Management Team oversees the development and implementation of the data management protocols of Argo. Two types of data systems are active—real time and delayed mode. The real-time system receives the transmissions from the Argo floats, extracts the data, checks their quality, and makes them available to the users. The objective of the real-time system is to provide Argo profiles to the operational and research community within 24 h of their measurement. This requirement makes it necessary to control the quality of the data automatically. The delayed-mode quality control is directed at a more detailed look at the profiles using statistical methods and scientific review of the data. In this paper, the real-time data processing and quality-control methodology is described in detail. Results of the application of these procedures to Argo profiles are described.

Assessment of Climate– Coccidioidomycosis Model

Understanding the predictive relationships between climate variability and coccidioidomycosis is of great importance for the development of an effective public health decision-support system. Preliminary regression-based climate modeling studies have shown that about 80% of the variance in seasonal coccidioidomycosis incidence for southern Arizona can be explained by precipitation and dust-related climate scenarios prior to and concurrent with outbreaks. In earlier studies, precipitation during the normally arid foresummer 1.5-2 years prior to the season of exposure was found to be the dominant predictor. Here, the sensitivity of the seasonal modeling approach is examined as it relates to data quality control (QC), data trends, and exposure adjustment methodologies. Sensitivity analysis is based on both the original period of record, 1992-2003, and updated coccidioidomycosis incidence and climate data extending the period of record through 2005. Results indicate that models using case-level data exposure adjustment do not suffer significantly if individual case report data are used "as is." Results also show that the overall increasing trend in incidence is beyond explanation through climate variability alone. However, results also confirm that climate accounts for much of the coccidioidomycosis incidence variability about the trend from 1992 to 2005. These strongly significant relationships between climate conditions and coccidioidomycosis incidence obtained through regression modeling further support the dual "grow and blow" hypothesis for climate-related coccidioidomycosis incidence risk.

A systematic methodology for quality control in the product development process

Quality control in the product development process is vital to the final quality of a product. Customer requirements are the foundation of quality control. Product development has to be linked with customers through customer demand and quality characteristics (QCs). Therefore, QCs are the key control factors in the whole product development process. Quality control is focused on how to translate customer requirements into Product-QCs, and decompose and transform Product-QCs into Component-QCs, Part-QCs, Geom-Feature-QCs and Tolerance-QCs correctly. The representation of QCs has been studied and their features in different stages of the product development process have been analysed. A systematic approach is presented that would support quality control through decomposing, transforming, optimizing, evaluating and monitoring QCs in product development. The corresponding model-oriented Quality Characteristics Processing Unit (QCPU) is proposed, which consists of describing and formulating customers’ requirements, and decomposing and transforming QCs, optimizing QCs or sub-QCs, and analysing and validating QC results. A process model combined with quality tools for decomposing and transforming of the QCs is put forward. The relationship between quality tools and each step of QC processing is depicted. On the basis of the proposed methods and approaches, a quality control software system, QQ-Dplatform, has been developed. The conceptual model, functional model and the browser/server-based system architecture of QQ-Dplatform are introduced and discussed. System platform and tools are studied and evaluated using a simple case.

Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and time consuming analytical techniques. There is a need for fast and simple analytical techniques for the objective of a quality control methodology. Virgin olive oils present characteristic NIR spectra. Chemometric treatment of NIR spectra was assessed for the quantification of fatty acids and triacylglycerols in virgin olive oil samples ( n = 125) and for their classification (PLS1-DA) into five very geographically closed registered designations of origin (RDOs) of French virgin olive oils (“Aix-en-Provence”, “Haute-Provence”, “Nice”, “Nyons” and “Vallée des Baux”). The spectroscopic interpretation of regression vectors showed that each RDO was correlated to one or two specific components of virgin olive oils according to their cultivar compositions. The results were quite satisfactory, in spite of the similarity of cultivar compositions between two denominations of origin (“Aix-en-Provence” and “Vallée des Baux”). Chemometric treatments of NIR spectra allow us to obtain similar results than those obtained by time consuming analytical techniques such as GC and HPLC, and constitute a help fast and robust for authentication of those French virgin olive oils.

INVESTIGATION OF ELECTROPHYSICAL PARAMETERS OF SILICON P-I-N PHOTODIODES

Electrophysical parameters of test structures formed on high-resistivity silicon during manufacturing of p-i-n photodiodes have been investigated. Using specially constructed MIS test structures the following important electrophysical parameters of Si-SiO2 and Si-SiO2 -Si3 N4 systems characterizing the quality of silicon and semiconductor-insulator interface have been analyzed: surface generation velocity Sg, bulk generation life-time of minority carriers τg , fixed charge Qss and mobile charge Qi in dielectric. The influence of different types of insulators on Sg values and effect of voltage applied to the insulator on minority carrier generation velocity in MIS structures were found. The corellation between Qi and reverse currents in p-i-n photodiodes was revealed. High effectiveness of used test structures and proposed methodology for quality control of technological processes in p-i-n photodiodes processing have been established

SPC Procedures for Monitoring Autocorrelated Processes

The inference about the statistical properties of quality control methodologies is based on the assumptions of normality and independence. In real industrial environments though process data is often correlated or exhibits some serial dependence affecting the efficiency of Statistical Process Control (SPC) methodologies. New technology gives managers the option of using more sophisticated SPC models which more accurately reflect the process being monitored, by relaxing some of the assumptions. The aim of this paper is to present, to apply and to evaluate control charts that are designed to account for autocorrelation.

Grey Predictive Process Control Charts

It has been stated by Pan (2002) that “modern manufacturing environments where the life cycle of products has decreased rapidly and customized short-run manufacturing process become quite common for achieving custom satisfactions”. In classical statistical control theory, it is assumed that for a stable production process if the product measurements were within control, the intrinsic variability poses no impacts to product quality. In this article, we will investigate a small-sample oriented grey predictive control scheme which is developed in terms of grey differential equation theory. This new quality control methodology does not impose the stability constraint to the production process and easy to be implemented for constant monitoring product quality. As illustrative examples, grey predictive Shewhart chart and CUSUM control chart are discussed. A grey fuzzy predictive control scheme is proposed and investigated as well. Finally, we discuss the usage of grey systematic equation system for process control predictive charts.

SU‐FF‐T‐100: Application of Statistical Process Control to IMRT Quality Assurance

Purpose: An investigation was undertaken to determine the efficacy of the application of statistical process control (SPC) methods to the quality assurance measurements for individual patients prior to the application of intensity‐modulated radiation therapy (IMRT). Method and Materials: SPC is a quality control methodology developed by industrial engineers. IMRT treatments were delivered using 6MV and 18MV x‐ray beams produced by a medical linear accelerator equipped with a 120‐leaf multi‐leaf collimator. Treatment plans for delivering IMRT were computed for fifteen patients using a commercial treatment planning system. A “phantom plan” was calculated for each patient. To compute the “phantom plan”, the patient's virtual simulation CT data set was replaced by a CT scan of an ellipsoidal water equivalent phantom. The dose distribution was recomputed using the machine parameters that had been computed for the patient. Measurements of dose were made using a calibrated 0.2 cc Farmer‐type ionization chamber in the water equivalent phantom. The dose was delivered using the same computer files that were to be used for treating the patient, and exactly the same medical linear accelerator parameters. The dose measured at points in the phantom was compared with the dose computed at the same points in the “phantom plan”. The dose differences were analyzed using the methods of SPC. For each patient a sample size of 2 was used. SPC Charts were constructed for the remaining ten patient cases employing the control limits derived from the first five cases. Additional studies of the performance of SPC were made using measurements with controlled errors introduced into the treatment delivery. Results: The patient cases remained in statistical process control. Addition measurement demonstrated that the SPC graphs detected intentionally introduced errors. Conclusion: Statistical Process Control is a practical technique for analyzing the patient specific quality assurance data for IMRT.

Cure-Window-Based Proactive Quality Control in Topcoat Curing

Automotive topcoat curing is critical to the quality of the final coating. In production, topcoat curing quality is commonly judged through the evaluation of sample panel temperature-time series data from the finished product directly against the cure window that is specified for the polymeric coating material used. This inspection-based post-process quality control (QC) is not only methodologically passive, but also is very likely to be erroneous in its conclusions on coating quality. It can hardly generate effective guidance for quality improvement. This paper introduces a novel cure-window-based proactive QC methodology. Using this methodology, dynamic process-product models are deployed to predict the key indicators of film curing quality, according to a given cure window. A dynamic optimization method is then used to search for the optimal operational settings for film curing. The methodology can be used for the development of on-line optimal QC strategies, and it is also applicable for improving traditional inspection-based reactive QC practice. The efficacy of this methodology is demonstrated with a case study on the curing of a topcoat.

Distribution Deviation of Large Aggregates from Uniformity in Waste Containment Concrete. II: Experimental Results and Quality Assurance

A quality control methodology was implemented for aggregate distribution in concrete that can be used in waste containment using the analytical method developed in Part I of this paper. This involves the comparison of three concrete placement methods: rodding, vibration, and placement with neither rodding nor vibration. The concrete mix consists of coarse (#4 stone and #67 stone) aggregate and fine aggregate, the latter of which is sand (making up 29.4% by weight). The experimental results have been used herein to develop quality control charts based on the deviation index, the range of the deviation index within each cylinder, and nonconformity of the aggregate to even distribution, respectively. The nonconformity quality control chart is found to be the most appropriate scheme for evaluating aggregate distribution deviation. The results obtained from the nonconformity quality control chart show that the upper control limit, when specified at three times the square root of the mean deviation index, has a value of 23.4. This is a relatively large value and the deviation indices per slice all fall below it, indicating that the quality of the concrete mix is satisfactory.

Origin of French Virgin Olive Oil Registered Designation of Origins Predicted by Chemometric Analysis of Synchronous Excitation−Emission Fluorescence Spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and very expensive analytical techniques, so there is a need for fast and inexpensive analytical techniques for use in a quality control methodology. Virgin olive oils present an intense fluorescence spectra. Synchronous excitation-emission fluorescence spectroscopy (SEEFS) was assessed for origin determination of virgin olive oil samples from five French registered designation of origins (RDOs) (Nyons, Vallée des Baux, Aix-en-Provence, Haute-Provence, and Nice). The spectra present bands between 600 and 700 nm in emission due to chlorophylls a and b and pheophytins a and b. The bands between 275 and 400 nm in emission were attributed to alpha-, beta-, and gamma-tocopherols and to phenolic compounds, which characterize the virgin olive oils compared to other edible oils. The chemometric treatment (PLS1) of synchronous excitation-emission fluorescence spectra allows one to determine the origin of the oils from five French RDOs (Baux, Aix, Haute-Provence, Nice, and Nyons). Results were quite satisfactory, despite the similarity between two denominations of origin (Baux and Aix) that are composed by some common cultivars (Aglandau and Salonenque). The interpretation of the regression coefficients shows that RDOs are correlated to chlorophylls, pheophytins, tocopherols, and phenols compounds, which are different for each origin. SEEFS is part of a global analytic methodology that associates spectroscopic and chromatographic techniques. This approach can be used for traceability and vindicates the RDOs.

Optimization of a multi-response problem in Taguchi's dynamic system

Taguchi method was known as an off-line quality control methodology to be used in many industries. Until now, most applications only focus on optimizing a single-response in a static system. Furthermore, due to the increasing complexity of the product design, more than one quality characteristic must be considered simultaneously to improve the production quality. Therefore, there are several studies to address the multi-response problem. In order to satisfy the requirements of the production's design, optimization of a dynamic system been mentioned by Taguchi has received more attentions in the recent years. Hence, optimizing a multi-response problem in a dynamic system becomes an important issue to address the quality improvement. This study proposes a procedure utilizing the statistic regression analysis and desirability function to optimize the multi-response problem with Taguchi's dynamic system consideration. Firstly, the regression analysis is employed to screen out the control factors significantly affecting the quality variation, and the adjustment factors significantly affecting the sensitivity of a Taguchi's dynamic system. Then, the desirability function will be applied to optimize such a multi-response problem. Finally, the effectiveness of the proposed procedure will be demonstrated by an example of a biological reduction of ethyl acetoacetate process experiment project at the Union Chemical Laboratories of the Industrial Technology Research Institute in Taiwan.

MASQOT: a method for cDNA microarray spot quality control.

BackgroundcDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more.ResultsA novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies.ConclusionThe proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process.

Application of a multivariate concentric method system for the location of Listeria monocytogenes in a poultry slaughterhouse

A methodology for food quality control, consisting of several consecutive concentric stages, is shown. It deals with a systematic and concentric study method, based on statistical results. The aim of this technique is to find the source of dissemination of a defined risk along the operations of a food process. It has been applied to the location of the presence of Listeria monocytogenes ( Lm) within the different stages in a poultry slaughterhouse. By means of application of statistical techniques like chi-square test, the classifying stage, where the carcasses are greatly handled, has been identified as the main source of the Lm in the studied poultry slaughterhouse. Besides, by means of the application of logistic regression the Lm psychrotropic behavior has been confirmed.

MO-D-I-618-03: A Web-Based Lead Apparel Management System for Medical Enterprises

Purpose: Accrediting bodies often require facilities to have a system for managing radiation protective apparel. In larger hospitals and medical enterprises, the standardization and monitoring of radiation protective apparel becomes increasingly difficult. The purpose of this work was to develop a web-based infrastructure for inventory control of radiation protective (lead) apparel across a large-scale hospital system. Method and Materials: A centralized database was developed and made accessible by a departmental website using Active Server Pages (ASP). Unique identifiers were assigned and entered into the database for the current apparel inventory. Quality control (QC) records — including lead patency checks were also integrated into the system. An automated email notification system was implemented for overdue QC tests. Web-based education modules were posted and an enterprise wide policy was formed to identify staff responsibilities and quality control methodology. Results: Responsible individuals were assigned to monitor local section inventory changes and ensure proper care of apparel. Quality checks, unique identification and centralized ordering of apparel were coordinated through the Medical Physics office. For the four major enterprise sections, (two hospitals — with inpatient and outpatient centers) over 1081 different pieces of lead apparel were uniquely identified, inventoried and inspected. The web-based system allowed staff and managers across the enterprise to access current inventory and latest quality control results for apparel in their section. Reminders of overdue checks are sent out monthly with no user interaction. Conclusion: The web-based information system is effective at dissemination of information throughout a medical enterprise. Centralized systems of inventory control and quality assurance testing reduce redundancy throughout departments. Assigning unique identifiers has the potential to reduce conflicts in inventory management and provides a system to return apparel that has been accidentally moved. Policy formation encourages this integration throughout the enterprise.

Study and analysis of residual stresses due to various secondary processes in AA 2219 annealed sheet using acoustic emission and hole drilling methods

This study highlights the methods of assessing the variations in residual stress of AA 2219 annealed sheet undergoing various secondary operations like blanking, forming, welding, etc. Attempt is made here to use acoustic emission (AE) as a tool to assess the residual stress and to compare it with the results obtained by Hole Drilling method. This paper discusses the experimental set up, methodology and observations. The observations are expected to provide enhanced quality control methodology for AA 2219 sheet metal components, especially where thermomechanical treatments are involved in the process of realization.

Integration of reconfigurable inspection with stream of variations methodology

This paper describes an advanced closed loop quality control methodology for reconfigurable manufacturing systems. The methodology enables rapid root-cause diagnostics for faster ramp-up of reconfigurable systems through integration of the Reconfigurable Inspection Machine (RIM) and the Stream of Variations (SoV) methodology. The RIM enables reconfigurable, rapid, and accurate inspection using non-contact sensors while the SoV methodology is used to quickly analyze the measurements and identify the root-cause of the errors produced during machining. The feasibility of the industrial concept was experimentally validated. A machining error was introduced during machining of an engine head. Measurement information collected by the RIM was processed and used to locate the root-cause of the error using the SoV methodology.