Lower Control Limit Research Articles

Penentuan Kapasitas Produksi Supplier pada Rantai Pasok

This research aims to determine the production capacity of a supplier in order to anticipate the demand from manufacturers. Production capacity is a reference in maintaining the continuity of the relationship between manufacturers and suppliers in a supply chain, where manufacturers can provide information on varying demand quantities. This condition causes uncertainty in the supplier's capacity, including unused facilities or excess load. Regarding this situation, it is important for the supplier to adjust its capacity to meet consumer needs, especially if the supplier is a small and medium enterprise (SME), which is generally controlled by the flow of demand from manufacturers. In this research, a control chart is utilized to determine the estimated production capacity limit. The variation in production capacity limits should be evaluated with a break-even point (BEP) approach to determine viable production capacity to meet the needs of manufacturers, which is influenced by volume and cost. The test results show that the control limit is able to anticipate variations in demand from manufacturers, with the upper and lower control limits that can be used as production references by SMEs to meet the needs of manufacturers. However, it is important to note that the selection of the control limit used as a production reference depends on the viable production area found in the BEP approach.

Analysis of Quality Defects in Plastic Seed Products Using Statistical Process Control (SPC) Method

Plastic seed products are products made from recycled plastic materials such as raw materials for plastic bags and some screen printing plastics. UD. ABC is a company that produces PP (Polypropylene) plastic pellets. The existing problems include the discovery of instability in the production of plastic pellets which is in a negative direction for the company, namely there are product defects in plastic pellet products that exceed the standard defect tolerance limit of the company by 2%. Based on the Pareto diagram, it can show that the type of protrusion defect is 69.56% more dominant than the type of connecting defect, which is 30.44%. Based on the control chart based on the p-chart, it shows that some dates are outside the limits of the control chart, namely the sample lot number 3 (March) of 0.0352, 4 (April) 0.0273, 7 (July) 0, 0134, 8 (August) 0.271. The data that comes out of the control chart limits exceeds the upper control limit (BKA/UCL) of 0.02649 and the lower control limit (BKB/LCL) of 0.02395. So what must be done to repair the engine factor is to carry out periodic briefings on how the machine works starting from setting the temperature and changing the filter. Meanwhile, the human factors section, namely directing the replacement of machine filter workers with temperature operators so that workers do not get tired easily, conducting periodic supervision of each worker for the leader and providing guidance on knowledge about machine work systems, mixing raw materials and operating machine temperature controls regularly.

A simple procedure to improve the performance of Klein's 2‐of‐2 run rule in the Shewhart X¯$\bar{X}$ control chart

AbstractSupplementary run‐rules have been used since the 1950s to improve the performance of the basic Shewhart control chart. A procedure known as Klein's 2‐of‐2, a simple and efficient supplementary rule, signals that the process is out‐of‐control only when two sequential sample averages are observed above the upper control limit (UCL) or below the lower control limit (LCL). The use of this rule results in a substantial reduction of the average run length (ARL) in detecting small process shifts when compared to the traditional control chart. However, it presents a poor performance of the out‐of‐control ARL for moderate to large process shifts (the converges to 2 for large shifts in the mean). In order to minimize this issue, some authors introduce additional control limits to delimit decision zones in relation to deciding whether the process is in control or out‐of‐control. Despite a better performance for moderate‐to‐large process shifts, the process complexity is also higher. In this paper, we include a simple procedure (with no additional control limits) in relation to the 2‐of‐2 rule by examining if all values of each unit of the sample are beyond the control limits whenever its average is beyond the control limits. The new procedure has a similar performance in terms of ARL for small shifts in the mean and a better performance for moderate‐to‐large shifts (the now converges to 1) when compared to the standard 2‐of‐2 rule (where the converges to 2).

Data-Driven Process Analysis for Iron Foundries with Automatic Sand Molding Process

This paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality.

Analisis Pengendalian Kualitas Paving Block Menggunakan Metode Statistical Quality Control (SQC)

The development of an increasingly competitive industrial world makes companies to be innovative and pay attention to the quality of the products they produce. CV. ABS is a company that produces construction complementary materials in building manufacture. The research aims to reduce the damage level of paving blocks using the Statistical Quality Control method which produces Central Line (CL) = 0.313, Upper Control Limit (UCL) = 0.409, and Lower Control Limit (LCL) = 0.217. The results of the P-Chart graphic data show that there is an average non-conformity or declared in an uncontrolled state at four points below the LCL (0.217) namely on the second day (0.13), seventh (0.18), ninth (0.43 ), and eleventh (0.50) One of the factors causing defective products is the lack of experience of workers, the use of low quality materials, the method of mixing materials is not according to standards and the printing equipment used is in old condition.

Pengendalian Kualitas Benang Plastik Dengan Pendekatan Metode Statistical Process Control

PK. Rosella Baru is a company operating in the plastic sack industry. In the production process of plastic sacks, there are several stages, including the production of plastic thread. The company still encounters a significant number of defects in the plastic thread, exceeding 2%, resulting in obstacles in the weaving process of the sacks and suboptimal production output. The research utilizes the Statistical Process Control method to identify solutions for quality control of the plastic thread products at PK. Rosella Baru. Based on the Pareto diagram, the most dominant defect is found in the unevenness of thread rolls, accounting for 43.3% of the defects. The control chart analysis shows that there are samples exceeding the upper control limit (UCL) in samples 1 and 9, and data exceeding the lower control limit (LCL) in samples 3 and 6. The cause and effect diagram identifies five factors: labor, machine, material, method, and environment. The most dominant cause is the lack of training for the workforce and insufficient maintenance of the machines, resulting in defective plastic thread. Therefore, the suggested solution is to perform regular machine maintenance, provide training to the workforce, and pay attention to machine settings and the quality of raw materials used.

The 2022 and 2023 Emergency Medicine Residency Match: A Cautionary Tale.

Introduction The emergency medicine (EM) match has undergone significant shifts in 2022 and 2023. While variation in specialty fill rates is expected over time, EM programs noted a significant increase in open positions starting in 2022. Utilizing National Resident Matching Program (NRMP) data over a 10-year period, we identified significant deviations in the emergency medicine match. Methods Shewhart control charts were used to plot the match results over time. A 10-year sample was used to establish the baseline value. From this value, the upper and lower control limits were established. Residency program expansion, decreasing applicant numbers, and changing applicant types were evaluated to detect any non-random changes to the process. Results While the number of EM PGY-1 positions added over time was within the expected range, both the number of unmatched positions and the change in the number of total US MD applicants were outside of this range and are considered to be "out of control." Conclusion It is not yet clear which contributing causes may underlie this sudden change. Several potential etiologies exist, including mismatches in supply and demand for positions, changes in perceptions of the specialty, the effects of COVID-19, and changing workforce needs. Historically similar experiences affecting other specialties, including anesthesia and radiation oncology, are analyzed. Potential solutions for returning to the necessary and usual success of the emergency medicine specialty match are explored.

Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance

PurposeTo assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes. Materials and methodsA total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20–1000 were used to estimate the lower control limit (LCL). Based on the iterative “Identify-Eliminate-Recalculate” and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL0) and false alarm rate (FAR0) were calculated to evaluate the performance of LCL. ResultsThe ground truth of the values of LCL, FAR0, and ARL0 obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL0 values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the “Identify-Eliminate-Recalculate” procedure, only the median LCL values obtained by the WSD method were closest to the ground truth. ConclusionsSampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative “Identify-Eliminate-Recalculate” procedure exhibited sufficient robustness and reliability.

Correlation between patient-specific quality assurance in volumetric modulated arc therapy and 2D dose image features

In radiotherapy, air-filled ion chamber detectors are ubiquitously used in routine dose measurements for treatment planning. However, its use has been restricted by intrinsic low spatial resolution barriers. We developed one procedure for patient-specific quality assurance (QA) in arc radiotherapy by coalescing two adjacent measurement images into a single image to improve spatial resolution and sampling frequency, and investigated how different spatial resolutions affect the QA results. PTW 729 and 1500 ion chamber detectors were used for dosimetric verification via coalescing two measurements with 5 mm-couch shift and the isocenter, and only isocenter measurement, which we call coalescence and standard acquisition (SA). Statistical process control (SPC), process capability analysis (PCA), and receiver operating characteristic (ROC) curve were used to compare the performance of the two procedures in determining tolerance levels and identifying clinically relevant errors. By analyzing 1256 γ values calculated on interpolated data points, our results indicated that detector 1500 showed higher averages in coalescence cohorts at different tolerance criteria and the dispersion degrees were spread out smaller. Detector 729 yielded a slightly lower process capability of 0.79, 0.76, 1.10, and 1.34, but detector 1500 exhibited somewhat different results of 0.94, 1.42, 1.19, and 1.60 in magnitude. The results of SPC individual control chart showed that cases in coalescence cohorts with γ values lowering its lower control limit (LCL) were greater than those in SA cohorts for detector 1500. A combination of the width of multi-leaf collimator (MLC) leaf, the cross-sectional area of the single detector, and the spacing between adjacent detectors might lead to discrepancies in percent γ values across diverse spatial resolution scenarios. The accuracy of reconstructed volume dose is mainly determined by the interpolation algorithm used in dosimetric systems. The magnitude of filling factor in the ion chamber detectors determined its ability to detect dose deviations. SPC and PCA results indicated that coalescence procedure could detect more potential failure QA results than SA while enhancing action thresholds.

Monitoring water quality using control charts at PDAM Surya Sembada Surabaya

Statistical quality control using control charts is an easy-to-implement method to improve quality. Improvements in the quality of products and services are continuously implemented to meet consumer needs. Products and services must maintain the desired quality with as few defects as possible. Variations in products and services are naturally created to meet needs. Unintentional variation, but the cause can be found. Control charts can be used to monitor production; particularly serving as an early warning index of processes that are potentially out of control. To keep production under control, different control charts are prepared for different cases, created by combining upper and lower control limits. Points plotted on a graph can reveal certain patterns, which in turn allow the user to get specific information. Information on water production is very important in PDAM because water is the main product that meets the needs for the survival of humans, animals, plants, and various other needs. The supply of clean water that meets the requirements of quality standards is always pursued by PDAM Surya Sembada Surabaya. The Statistical control chart training will increase productivity and improve water quality, not only in terms of chemical, physical and biological quality. Good water quality will add value to the trust and community of PDAM.

558. Increasing Safety Net Antibiotic Prescriptions (SNAP) for Acute Otitis Media Treatment in Pediatric Urgent Care Clinics

Abstract Background Acute otitis media (AOM) is the most common diagnosis for which antibiotics are prescribed in pediatrics. National guidelines recommend offering a safety net antibiotic prescription (SNAP) to select patients, to be filled if symptoms worsen or do not improve within 48-72 hours. However, clinicians continue to prescribe immediate antibiotics for most cases of AOM. Our goal was to increase the percentage of eligible patients diagnosed with AOM in our pediatric urgent care (PUC) clinics who are offered a SNAP from our baseline of 7.7% to 40% within nine months. Methods A multidisciplinary team of PUC clinicians, an Infectious Diseases physician, and a data analyst, used quality improvement (QI) methodology to identify barriers to offering SNAP (Figure 1). Patients without chronic ear conditions were considered eligible for SNAP if they had no or mild otalgia for < 48 hours, fever < 39oC, no otorrhea, and for patients ≤ 24 months old, if they had only unilateral AOM. We analyzed all encounters of patients ≥ 6 months old with a discharge diagnosis of AOM from three PUC clinics during baseline (October 2020 – September 2021) and study (October 2021 – March 2022) periods. Interventions included clinician education, standardized discharge instructions, electronic health record updates to improve documentation, and educational flyers for clinicians and families. Our outcome measure was the percentage of eligible encounters during which SNAP was offered. Our process measure was the percentage of AOM encounters in which SNAP eligibility was documented. Our balancing measure was the percentage of patients returning with AOM symptoms within 14 days. Results We reviewed 12,502 encounters from October 2020 through March 2022. At baseline, clinicians documented SNAP eligibility in 5.5% of AOM encounters and offered SNAP to 7.7% of eligible patients. By March 2022, clinicians documented SNAP eligibility in 58.9% of AOM encounters and offered SNAP to 40.7% of eligible patients (Figure 2). There was no change in our balancing measure. UCL: Upper control limit. LCL: Lower control limit. Conclusion Using QI methodology, we increased the percentage of eligible patients with AOM who were offered SNAP, reducing antibiotic exposure in PUC clinics. Efforts are ongoing to further improve this process. Disclosures Brian R. Lee, PhD, MPH, CDC: Grant/Research Support|Merck: Grant/Research Support.

Quality Analysis Of Low Quality Orthodox Black Tea At PT.Perkebunan Nusantara VIII Kebun Kertamanah Bandung, Jawa Barat

The purpose of this research are (1) Find out the control limits of the implementation of quality control on PT.Perkebunan Nusantara VIII Kebun Kertamanah. (2) to determine the production of low quality black tea products under controlled conditions in PT.Perkebunan Nusantara VIII Kebun Kertamanah. (3) to determine the factors that cause low quality black tea products to not meet the standards in PT.Perkebunan Nusantara VIII Kebun Kertamanah. (4) to find out the percentage value of off grade low quality black tea in PT.Perkebunan Nusantara VIII Kebun Kertamanah. The basic method is descriptive with the implementation method is the case study. Data were analyzed using (1) check sheet (2) p chart (3) fishbone diagram. The results showed that (1) control limits of low quality black tea which are in the upper and lower control limits. (2) production of low quality black tea products under controlled conditions (3) factors that cause low quality black tea products to not meet standards (4) value Percentage off grade low quality black tea in PT.Perkebunan Nusantara VIII Kebun Kertamanah.

Statistical process control for the analysis of quality control in urodynamics: A potential new approach for quality review of urodynamics.

To analyze quality control in urodynamic studies, using a proportion control chart (p-chart) for statistical process control. This single-center study was conducted at the Urodynamic Center of West China Hospital, Sichuan University. We randomly selected 15 samples from each month in 2020, and 180 urodynamic traces were finally enrolled. We used the p-chart of statistical process control for analysis. We calculated the proportion of the incidence of a selected set of artefacts in the monthly urodynamic study process, including non-standard zero setting, no cough test, incomplete records of all measurements by urodynamicists, catheter displacement, and baseline drift. Through the specific calculation formula of statistical process control, we obtained the values of the center line, lower control limit, and upper control limit. All data points of each artefact were within zone A. However, one outlier was found in the p-chart of all artefacts in October, which might have been caused by inexperienced operators. Statistical process control may play an important role in the process control of urodynamic studies and guide us in identifying the cause of poor quality in process management.

The 𝑛𝑝-Chart with 3-𝜎 Limits and the ARL-Unbiased 𝑛𝑝-Chart Revisited

Abstract In the statistical process control literature, counts of nonconforming items are frequently assumed to be independent and have a binomial distribution with parameters ( n , p ) (n,p) , where 𝑛 and 𝑝 represent the fixed sample size and the fraction nonconforming. In this paper, the traditional n ⁢ p np -chart with 3-𝜎 control limits is reexamined. We show that, even if its lower control limit is positive and we are dealing with a small target value p 0 p_{0} of the fraction nonconforming ( p ) (p) , this chart average run length (ARL) function achieves a maximum to the left of p 0 p_{0} . Moreover, the in-control ARL of this popular chart is also shown to vary considerably with the fixed sample size 𝑛. We also look closely at the ARL function of the ARL-unbiased n ⁢ p np -chart proposed by Morais [An ARL-unbiased n ⁢ p np -chart, Econ. Qual. Control 31 (2016), 1, 11–21], which attains a pre-specified maximum value in the in-control situation. This chart triggers a signal at sample 𝑡 with probability one if the observed number of nonconforming items, x t x_{t} , is beyond the lower and upper control limits (𝐿 and 𝑈), probability γ L \gamma_{L} (resp. γ U \gamma_{U} ) if x t x_{t} coincides with 𝐿 (resp. 𝑈). A graphical display for the ARL-unbiased n ⁢ p np -chart is proposed, taking advantage of the qcc package for the statistical software R. Furthermore, as far as we have investigated, its control limits can be obtained using three different search algorithms; their computation times are thoroughly compared.

Reduction of non-conforming through statistical process control charts in textile industry

The textile industry of Pakistan is a growing sector that contributes to the economy. Pakistan exports depend heavily upon textile goods. A minor defect in the finished good can cause a major loss of the export goods. Due to the involving the number of workers checking the product repeatedly can be very expensive therefore the quality engineering techniques of Statistical Control Charts are used in the textile industry. This research study aims at developing process control charts for the textile industry in Pakistan. For this purpose, the textile industry was taken into consideration. P-chart was developed to monitor the variation in the process with a Six Sigma standard deviation. The collection of data was for six months from various departments of the textile industry. The attribute data were collected for the analysis from 4 different units of the industry. The construction of the P-Chart includes the Control Limits (CL), Upper Control Limits (UCL), 3 sigma deviations from the mean Control Limit (CL), Lower Control Limits (LCL), –3 sigma deviation from the mean Control Limit (CL). The result showed that the processes of the production units were under control, however, the mean was not centred which was due to some common cause of the process which is acceptable. The P-chart can serve as a standard for the new process to be developed.

Segi Sanitasi pada Pendaratan Ikan Tuna di Pelabuhan Perikanan Samudera Bungus, Sumatra Barat

Clean and hygienic fish landings to meet sanitation requirements and guarantee the quality of the fish caught are an important part of the role of the fishing ports. However, it has not been done much at Bungus PPS, which was marked by the handling of fish quality that was not standardized, some unloading equipment and transportation facilities were rarely cleaned, and the environmental quality at the fish landing dock was not maintained. The study aimed to evaluate the quality control level of tuna fish, the performance of fish landing sanitation, and estimate the potential losses from neglected fish landing sanitation. The study used the scoring system, p control chart analysis, regression method, and profit/loss analysis. Organoleptically, the quality of tuna landed on Bungus PPS was at 7.50‒7.77 (fresh enough to fresh). The quality of tuna was still under control, with the value of upper control limit (UCL) = 0.220, central line (CL) = 0.07 and lower control limit (LCL) = 0.000. The sanitation performance of fish landings on PPS Bungus was around 3,45 (scale 1-5). Sanitation of fish handling (x) significantly affects the quality of tuna caught (y), which was indicated by the equation y = 0,1308x + 7,175. The sanitation conditions explained that 84.4% of the tuna caught were maintained. Potential losses from neglected landing sanitation were (a) the loss of the best-selling price of tuna and (b) the cost of nutrition consultation and disease management due to damage to the nutritional content of tuna.
 
 Keywords: bungus, tuna caught, fishing port, fish landing sanitation

STABILITY ANALYSIS OF THE TECHNOLOGICAL PROCESS OF MECHANICAL WOOD PROCESSING USING CONTROL CHARTS

This paper presents a quality control tool, that is, a quality control method, which in this case concernsthe mechanical processing of wood, but in general, it can be implemented in any form of production (mechanicalengineering, traffic, food technology...). The goal of the research is to show the way of applying the control chartmethod, that is, to determine the stability of the technological process of mechanical wood processing in order to beable to define appropriate corrective measures in order to improve that part of the production process. The controlchart that was created in this paper is a graph consisting of a horizontal (abscissa) and a vertical axis (ordinate). Theorder of observations is plotted on the abscissa, and the corresponding values on the ordinate. Apart from theprecise creation of control charts, their correct interpretation is also of great importance. It is easiest to interpretcontrol charts where the process is "outside statistical control", that is, the process where the values of individualmeasurements are outside the control limits. This means that some special cause of variation is present in the processand an adjustment needs to be made. However, placing all points within the control limits, that is, obtaining acontrol map on which the process is "within statistical control", does not necessarily mean that such a process isacceptable by statisticians. On the basis of the results, that is, on the basis of the graph that has the shape of a brokenline, we observe whether the control limits have been exceeded, and we draw certain conclusions about the stabilityof the process. If the graph is between the control limits, we can conclude that our process is stable and undercontrol, that is, that our quality variation is within normal limits. If there are any jumps, ie. if some value in someobservation is out of bounds (above the upper control limit or below the lower control limit), then we conclude thatour process is not stable, not under control and that our quality variation is not within normal limits. In that case,certain corrective measures were proposed, which should improve the technological process of mechanical woodprocessing and bring it under control.Control charts, as an engineering-mathematical tool, are a very suitable tool for achieving the goals of statisticalcontrol. Optimal application of control charts ensures constant monitoring of the process, direction towards thedesired flow and undertaking of possible corrective measures. Also, their application ensures the fulfillment of thedesigned or required quality characteristics, that is, the fulfillment of the requirements of a certain standard. Withtheir simplicity of application and reliability, they provide users with convenience in work and relevant processindicators, and provide process controllers with valid data about the process flow. As such, they represent a tool thatkeeps the entire process within the given limits.

Accessibility in People with Disabilities in Primary Healthcare Centers: A Dimension of the Quality of Care.

The purpose of this research is to evaluate universal accessibility in primary healthcare (PHC) centers in the Atacama region, Chile, through an analytical cross-sectional study with a quality approach, which uses the external audit model with the application of a dichotomous comparison guideline, evaluating levels of compliance with four dimensions of universal accessibility described in the literature: participation, information, accessibility chain and architectural aspects. This was carried out in 18 PHC, and set as Lower Control Limit (LCL) of 70% to compare levels of compliance, and a hierarchical model and k-mean analysis were applied. Results: Very low compliance averages were obtained, 37.7% participation, 4% information, 44.4% access chain, and 63.9% architectural aspects, indicating a critical situation. Moreover, the cluster comparison allowed to observe that a group of healthcare centers complies more than other groups, which requires more attention. Conclusions: The low level of accessibility for people with disabilities may be associated with various factors that require further monitoring and analysis. However, low levels of accessibility require changing the way of relating to this vulnerable group of the population, and considering including them in the design and reasonable adjustments made in PHC centers. The findings from this research open the possibility for future research that increases understanding of how to reduce barriers in a such wide variety of forms of disability.

PRODUCT QUALITY SUPERVISION MANAGEMENT AT DIKI CERAMICS (HOME INDUSTRY) WEDI DISTRICT, KLATEN REGENCY

Quality control is a series of effective and efficient integrated activities that develop, maintain and improve the quality of some products and services to make consumers satisfied. The current study aims to identify consumers opinions on product quality, quality management systems, and costs required for it. The research was conducted at the Diki Ceramic Home Industry. That Data analysis used control chart, cost analysis and qualitative descriptive. That The data used in this study are consumer perception data and the number of products produced, the number of defective products, and the costs required to support them control process for one period. The results of the study show that product quality is controlled as a proportional value of damage is between the upper and lower limits, indicating that the controlling limitis (p) = 14%, the upper control limit is (UCL) 16,68%, the lower control limit (LCL) is 11,31%. Total cost required to control process control (QCC) Rp 535,714,285 the cost needed to guarantee the quality is Rp. 10,500,000., and the cost of quality (TQS) of Rp. 546,214,285 means company can compress the product by 210 earthenware or 14% of the total production. Score optimum damage (q*) is 1500 of respondents indicated very positive perception, 50% indicate positive perception, and 6.67% indicate neutral perception.

Quantitative Bio-Mapping of Salmonella and Indicator Organisms at Different Stages in a Commercial Pork Processing Facility.

The purpose of this study was to develop a quantitative baseline of indicator organisms and Salmonella by bio-mapping throughout the processing chain from harvest to final product stages within a commercial conventional design pork processing establishment. Swab samples were taken on the harvest floor at different processing steps, gambrel table, after polisher, before final rinse, after the final rinse, post snap chill, and after peroxyacetic acid (PAA) application, while 2-pound product samples were collected for trim and ground samples. The samples were subjected to analysis for indicator microorganism enumeration, Aerobic Count (AC), Enterobacteriaceae (EB), and generic Escherichia coli (EC), with the BioMérieux TEMPO®. Salmonella prevalence and enumeration was evaluated using the BAX® System Real-Time Salmonella and the SalQuant™ methodology. Microbial counts were converted to Log Colony-forming units (CFU) on a per mL, per g or per sample basis, presented as LogCFU/mL, LogCFU/g and LogCFU/sample, prior to statistical analysis. All indicator microorganisms were significantly reduced at the harvest floor (p-value < 0.001), from gambrel table to after PAA cabinet location. The reduction at harvest was 2.27, 2.46 and 2.24 LogCFU/mL for AC, EB and EC, respectively. Trim sample values fluctuated based on cut, with the highest average AC count found at neck trim (2.83 LogCFU/g). Further process samples showed the highest AC count in sausage with a mean of 5.28 LogCFU/g. EB counts in sausage (3.19 LogCFU/g) showed an evident increase, compared to the reduction observed at the end of harvest and throughout trim processing. EC counts showed a similar trend to EB counts with the highest value found in sausage links (1.60 LogCFU/g). Statistical microbial process control (SPC) parameters were also developed for each of the indicator microorganisms, using the overall mean count (), the Lower control limit (LCL) and Upper control limit (UCL) at each sampling location. For Salmonella prevalence, a total of 125/650 samples were found positive (19%). From those positive samples, 47 samples (38%) were suitable for enumeration using the BAX® System SalQuant™, the majority detected at the gambrel table location. From those enumerable samples, 60% were estimated to be between 0.97 and 1.97 LogCFU/sample, while the rest (40%) were higher within the 2.00–4.02 LogCFU/sample range. This study provides evidence for the application of indicator and pathogen quantification methodologies for food safety management in commercial pork processing operations.