Process Control Procedures Research Articles

Detecting mean changes in experience sampling data in real time: A comparison of univariate and multivariate statistical process control methods.

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T², EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T² procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design multivariate statistical process control procedure in the case of Ethio cement

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

Single-use Albarran module: A removable elevator system to enhance safety in biliary endoscopy?

Bacterial infections are a major burden in biliary endoscopy. Besides incomplete manual cleaning of endoscopes, hard-to-clean components of duodenoscopes are responsible for a substantial rate of bacterial contamination. A large meta-analysis showed that 15 % of duodenoscopes harbored microorganisms of gastrointestinal or oral origin, independent from the colony forming unit (CFU) count. This indicates that the current reprocessing and process control procedures, even with ethylene oxide (ETO), could be insufficient [1]. Therefore, US Food and Drug Administration (FDA) warnings led companies to redesign duodenoscopes with the aim of enhancing safety. Disposable caps or caps + elevator are available options on the market [2] [3]. Moreover, two disposable duodenoscopes models have recently been approved by the FDA: the Boston Scientifc EXALT Model D and the Ambu A/S Ambu aScope Duodeno. Studies on disposable duodenoscope feasibility and possible economic impact on the market yielded interesting results, although the data are still preliminary [4] [5] [6] [7] [8].

Efficacy of statistical process control procedures to identify deviations in continuously measured physiological and behavioral variables in beef heifers resulting from an experimentally combined viral-bacterial challenge.

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with remote continuous data collection could accurately differentiate between animals experimentally inoculated with a viral-bacterial (VB) challenge or phosphate buffer solution (PBS). Crossbred heifers (N = 38; BW = 230 ± 16.4 kg) were randomly assigned to treatments by initial weight, average daily gain (ADG), bovine herpes virus 1, and Mannheimia haemolytica serum titers. Feeding behavior, dry matter intake (DMI), animal activity, and rumen temperature were continuously monitored remotely prior to and following VB challenge. VB-challenged heifers exhibited decreased (P < 0.01) ADG and DMI, as well as increased (P < 0.01) neutrophils and rumen temperature consistent with a bovine respiratory disease (BRD) infection. However, none of the heifers displayed overt clinical signs of disease. Shewhart and cumulative summation (CUSUM) charts were evaluated, with sensitivity and specificity computed on the VB-challenged heifers (n = 19) and PBS-challenged heifers (n = 19), respectively, and the accuracy was determined as the average of sensitivity and specificity. To address the diurnal nature of rumen temperature responses, summary statistics (mean, minimum, and maximum) were computed for daily quartiles (6-h intervals), and these quartile temperature models were evaluated separately. In the Shewhart analysis, DMI was the most accurate (95%) at deciphering between PBS- and VB-challenged heifers, followed by rumen temperature (94%) collected in the 2nd and 3rd quartiles. Rest was most the accurate accelerometer-based traits (89%), and meal duration (87%) and bunk visit (BV) frequency (82%) were the most accurate feeding behavior traits. Rumen temperature collected in the 3rd quartile signaled the earliest (2.5 d) of all the variables monitored with the Shewhart, followed by BV frequency (2.8 d), meal duration (2.8 d), DMI (3.0 d), and rest (4.0 d). Rumen temperature and DMI remained the most accurate variables in the CUSUM at 80% and 79%, respectively. Meal duration (58%), BV frequency (71%), and rest (74%) were less accurate when monitored with the CUSUM analysis. Furthermore, signal day was greater for DMI, rumen temperature, and meal duration (4.4, 5.0, and 3.7 d, respectively) in the CUSUM compared to Shewhart analysis. These results indicate that Shewhart and CUSUM charts can effectively identify deviations in feeding behavior, activity, and rumen temperature patterns for the purpose of detecting sub-clinical BRD in beef cattle.

Quality-Oriented Statistical Process Control Utilizing Bayesian Modeling

Quality control is an important issue in semiconductor manufacturing. Statistical process control (SPC) is known as a powerful method for accomplishing process stability and reducing variability. In this paper, we adopt the quality-oriented statistical process control (QOSPC) method. In QOSPC, product quality test data, such as electrical performance and product reliability, are incorporated into the process control procedure. QOSPC has two major challenges: extracting process variables that affect product quality, and determining quality control limits (QCLs) for each variable. In this work, we fully exploit a Bayesian approach to resolve both of these challenges simultaneously. We introduced a linear bathtub model that contains parameters corresponding to QCLs as obvious change points and fit the model to the observed data by Bayesian inference (BI). In our experiments with artificial datasets, we demonstrated that the values of QCLs and their confidence, by which we can judge whether the measured process variable is related to product quality, are estimated successfully by BI. We verified the robustness of our method by testing it repeatedly. The proposed method reduced the human labor cost for extracting quality-related process variables and determining QCLs by 93%.

Selection of within-run quality control rules for laboratory biomarkers.

In order to release correct biomarker results of a laboratory test, it is a regulatory requirement to apply quality control standards for controlling analytical errors. Releasing an incorrect test result might lead to wrong diagnosis or treatment of a patient in medical decision-making. In laboratory medicine, one of the means to control analytical errors is statistical process control procedures proposed by James O.Westgard and his coworkers nowadays known as "Westgard rules." To judge their performance for discriminating in-control from out-of-control processes, power curves are used. In this article, we describe functions for the power curves of the within-run Westgard rules. Based on these power curves, we use a benchmark approach for selecting a quality control procedure out of the set of Westgard rules. It is shown that two graphical procedures proposed by Westgard and his coworkers can be reduced to this benchmark approach. Besides, a commonly used measure in laboratory medicine for describing out-of-control processes is critically examined revealing the threat of selecting too optimistic quality control rules.

Discovery of potential pharmacodynamic ingredients of Dang-Gui-Si-Ni decoction based on absorbed ingredients and molecular docking

Ethnopharmacological relevanceThe Dang-Gui-Si-Ni (DGSN) decoction as a classic prescription has been widely used for thousands of years in the clinical practice of traditional Chinese medicine (TCM). Especially in recent years, the potential efficacy of TCM for the treatment of Raynaud's syndrome has attracted great attention as there are still no specific remedies for this disease. However, the active constituents and underlying mechanisms responsible for the therapeutic benefits are not well understood, which makes it difficult to ensure quality control or to design research and drug development strategies. To identify the potential pharmacodynamic ingredients (PPIs) of TCM will help to achieve suitable process control procedures for industrial production and large-scale manufacturing. Aim of the studyIn the present study, we propose a multi-dimensional qualitative analysis method combining water-decoction spectra, in-vitro intestinal absorption spectra, in-vivo plasma spectra, and molecular docking of components to quickly identify the PPIs for the DGSN decoction of TCM. Materials and methodsWater-based decoctions of DGSN were prepared in accordance with the clinical use registered in ancient books. Ultra-high-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q/TOF-MS) coupled with computerized modelling activity screening was used to quickly identify the PPIs of the DGSN decoction. Bioactive compounds absorbed in vitro were identified using the everted intestinal sac model from rats and compounds absorbed in vivo were confirmed in portal vein blood samples obtained following oral administration in rats. Molecular docking validation experiments were adopted to predict the binding activity to coagulation factors I, II, VII, X, and IX. The active components were further confirmed by pharmacodynamics analysis. The anticoagulant activity of the DGSN decoction was verified using rat models. ResultsThirty-one compounds were identified in the DGSN decoction. According to the in vivo experiments, 22 compounds that could be absorbed in vivo were detected by the everted intestinal sac model in rats. This model greatly reduces the scope of PPIs and is easy to perform. Ten compounds were detected in the portal vein blood in rats. The compounds detected in plasma provide stronger evidence supporting the PPIs. Molecular docking in vitro experiments indicated that 7 compounds exhibited better binding activity with coagulation factors I, II, VII, X, and IX. The animal experiments confirmed that the DGSN decoction could improve the microcirculation, providing indirect proof of anticoagulant activity suggested by the molecular docking studies. Finally, based on the multi-dimensional methods, 9 potential compounds present in the DGSN decoction were identified as PPIs (i.e., ferulic acid, paeoniflorin, albiflorin, chlorogenic acid, cryptochlorogenic acid, liquiritin, liquiritin apioside, cinnamaldehyde and glycyrrhizic acid). ConclusionOverall, this study combined the water-decoction spectra, intestinal absorption spectra in vitro, plasma spectra in vivo, and molecular docking studies to establish a multi-dimensional qualitative analysis method of the DGSN decoction. Meanwhile, 9 compounds in DGSN decoction were identified as PPIs using this method, and are proposed for application as quality standards for complex TCM prescriptions.

Efficacy of statistical process control procedures to monitor deviations in physical behavior for preclinical detection of bovine respiratory disease in feedlot cattle

Opportunities exist to improve the efficacy of antimicrobial treatment and animal welfare standards through use of remote sensor technologies for early detection of bovine respiratory disease (BRD). A post-hoc analysis using statistical process control (SPC) procedures was performed on continuously-recorded physical activity data collected from BRD-diagnosed and healthy calves from Pillen et al. (2016). We hypothesized that SPC models that monitor physical activity traits (step count, motion index, standing time, lying bouts) would yield higher diagnostic accuracies compared to methods based on visual observation of clinical signs of illness for detecting the onset of BRD. Crossbred steers and bulls (n = 266; initial BW = 180 kg) at high risk for BRD were fitted with leg-attached accelerometers (IceQube, IceRobotics, Ltd.) upon arrival at a commercial feedlot and evaluated for 56 d Overall, calves experienced 48% morbidity, with the average day of first treatment occurring 16 d post-feedlot arrival. Shewhart charts were used to evaluate daily changes in each trait as univariate models and combined traits in principal component analysis (PCA)–constructed multivariate models, relative to the day of BRD diagnosis. Diagnostic test sensitivity and specificity were calculated for each model and compared. The univariate models had relatively low sensitivities (< 40%), with specificities ranging from 23 to 81%, and chart signaling occurred up to 2 d prior to visual diagnosis of BRD. The univariate model for step count had the highest accuracy (52.3%), while lying time had the lowest accuracy (32%). The multivariate models that included all 4 physical activity traits or only lying bout behavior had moderate sensitivities (44 to 57%), but low specificities (< 37%), and chart signaling occurred 2 d prior to visual diagnosis. The relatively low diagnostic test accuracies of the SPC models reported in this study may have been due to substantial within-animal daily variation in the physical activity traits and(or) the limited time for adequate model training prior to the onset of BRD cases. Future research should investigate SPC analysis of multifactorial algorithms using physical activity and feeding behavior traits to improve the accuracy of preclinical detection of BRD.

New Rule to Incorporate Inspection Errors in Process Control Using Markov Chains

A number of authors have considered the procedure of online process control in which every [Formula: see text]th item produced is inspected. The process is judged to be in control and continues if the inspected item is judged to conform to specifications. If the inspected item is judged to be nonconforming, then the process is judged to be out of control and stopped, examined, and adjusted as necessary. This inspection process is subject to misclassification and different schemes have been suggested for dealing with this. In this paper, we propose a new rule in which the final judgment that an item is conforming, and thus the process is in control, if and only if [Formula: see text] consecutive classifications as conforming occur before a total of [Formula: see text] classifications as nonconforming. We will examine various aspects of this protocol and provide advice on how to determine values of [Formula: see text] and [Formula: see text] for this rule.

Investigation of risks for Critical Infrastructures due to the exposure of SCADA systems and industrial controls on the Internet based on the search engine Shodan

Industrial Control Systems occur in automation processes and process control procedures within Critical Infrastructures (CI) - these are institutions with important significance for the common good of the state and thus for the maintenance of a society. Failures or disturbances in industrial plants can have serious physical consequences, such as power outages or interruptions in production. Energy suppliers, in particular, are an attractive target for cyber attacks due to their interdependencies with other infrastructures. A large number of SCADA systems and Industrial Control Systems are directly connected to the Internet and inadequately secured from an information technology perspective, this represents a considerable risk for IT security and, consequently, for the availability of Critical Infrastructures. The Shodan search engine reveals a worrying extent of exposed industrial control equipment on the Internet. The collected information and metadata about Industrial Control Systems from this search are freely available online. They can serve as a basis for potential attacks. Without authentication mechanisms, anyone can connect to open ports using industrial and remote maintenance protocols. The resulting risks and consequences for the companies, operators as well as for the society due the exposure of industrial plants and Critical Infrastructures are examined based on the Shodan search engine within the scope of this work.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica.

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with the remote continuous collection of feeding behavior patterns, accelerometer-based behaviors, and rumen temperature can accurately differentiate between animals experimentally inoculated with Mannheimia haemolytica (MH) or PBS. Thirty-six crossbred steers (BW = 352 ± 23 kg) seronegative for MH were randomly assigned to bronchoselective endoscopic inoculation with MH (n = 18) or PBS (n = 18). Electronic feed bunks were used to measure DMI and feeding behavior traits, accelerometer-based neck collars measured feeding- and activity-behavior traits, and ruminal thermo-boluses measured rumen temperature. Data were collected for 28 d prior to and following inoculation. Steers inoculated with MH exhibited elevated (P < 0.02) levels of neutrophils and rumen temperature indicating that MH challenge effectively stimulated immunologic responses. However, only nine of the MH steers exhibited increased serum haptoglobin concentrations indicative of an acute-phase protein response and one displayed clinical signs of disease. Shewhart charts (SPC procedure) were used for two analyses, and sensitivity was computed using all MH-challenged steers (n = 18), and a subset that included only MH-challenged haptoglobin-responsive steers (n = 9). Specificity was calculated using all PBS steers in both analyses. In the haptoglobin-responsive only analysis, DMI and bunk visit (BV) duration had the greatest accuracy (89%), with accuracies for head-down (HD) duration, BV frequency, time to bunk, and eating rate being less (83%, 69%, 53%, and 61%, respectively). To address the diurnal nature of rumen temperature, data were averaged over 6-h intervals, and quarterly temperature models were evaluated separately. Accuracy for the fourth quarter rumen temperature was higher (78%) than the other quarterly temperature periods (first = 56%, second = 50%, and third = 67%). In general, the accelerometer-based behavior traits were highly specific ranging from 82% for ingestion to 100% for rest, rumination, and standing. However, the sensitivity of these traits was low (0% to 50%), such that the accuracies were moderate compared with feeding behavior and rumen temperature response variables. These results indicate that Shewhart procedures can effectively identify deviations in feeding behavior and rumen temperature patterns to enable subclinical detection of BRD in beef cattle.

Constructing a Control Chart Using Functional Data

This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; heating, ventilation and air conditioning (HVAC); installation and control; and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0.

Analisis Tingkat Kecacatan Produk Lever Assy Parking Brake Menggunakan Metode Statistical Quality Control (SQC)

The company always tries to meet customer demands, so it must be able to provide good product quality. Quality control is always carried out by manufacturing companies for the creation of zero defects. Lever Assy Parking Brake is part of the cars spare parts produced by manufacturing company X and must have a zero defect product quality level. The purpose of this study was to determine the level of product defect using the Statistical Quality Control (SQC) method. Data was carried out with process control procedures. This study showed that the product defect rate of Lever Assy Parking Brake with Statistical Quality Control (SQC) method in March 2018 was very large. As seen from the 22 data production processes, 13 production process data (59 percent) were out-of-control but still fell within the specification limit of 9 ± 0.5 mm. Meanwhile, only 9 data production processes (41 percent) were in control. So there is a need to analyze causes and corrective actions. R control chart shows the process that has been stable or under control has a UCL (Upper Control Limit) value of 0.21, the LCL (Lower Control Limit) value of 0 and a value of CL (central line) of 0.10. Whereas 𝑥̅ chart shows the control limit with UCL value of 9.39, LCL value is 9.27 and CL value is 9.33.

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) “fit-for-purpose” approach, using dynamic multiple reaction monitoring (dMRM), which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC. SignificanceWe developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC ‘fit for purpose approach’ [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population.

Robust bootstrap control charts for percentiles based on model selection approaches

Abstract This paper presents two model selection approaches, namely the random data-driven approach and the weighted modeling approach, to construct robust bootstrap control charts for process monitoring of percentiles of the shape-scale class of distributions under model uncertainty. The generalized exponential, lognormal and Weibull distributions are considered as candidate distributions to establish the proposed process control procedures. Monte Carlo simulations are conducted with various combinations of the percentiles, false-alarm rates and sample sizes to evaluate the performance of the proposed robust bootstrap control charts in terms of the average run lengths. Simulation results exhibit that the two proposed robust model selection approaches perform well when the underlying distribution of the quality characteristic is unknown. Finally, the proposed process monitoring procedures are applied to two data sets for illustration.

Enhanced Event-Based Identification Procedure for Process Control

An enhanced event-based identification procedure for process control has been developed based on the information obtained from the oscillations that an event-based sampler introduces in the feedback loop. The describing function analysis is used to explain the basis of the method because the event-based sampler behaves as a static nonlinearity. Features of the method are (a) the event-based procedure does not require a priori process information, (b) noniterative algorithms are sufficient to derive the process parameters, (c) only one test is needed, and (d) it allows identifying the process at a user-specified phase angle in the third quadrant. The method is presented for estimation of most common transfer functions found in chemical and process industry: first and second order, as well as integrating processes with non-minimum-phase dynamics. Furthermore, the procedure can be extended to estimate models of any structure with five or more parameters.

High prevalence rate of digestive tract bacteria in duodenoscopes: a nationwide study

ObjectiveIncreasing numbers of outbreaks caused by contaminated duodenoscopes used for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures have been reported, some with fatal outcomes. We conducted a nationwide cross-sectional study to determine...

Statistical process control procedures for functional data with systematic local variations

ABSTRACTMany engineering studies for manufacturing processes, such as for quality monitoring and fault detection, consist of complicated functional data with sharp changes. That is, the data curves in these studies exhibit large local variations. This article proposes a wavelet-based local random-effect model that characterizes the variations within multiple curves in certain local regions. An integrated mean and variance thresholding procedure is developed to address the large number of parameters in both the mean and variance models and keep the model simple and fit the data curves well. Guidelines are provided to select the regularization parameters in the penalized wavelet-likelihood method used for the parameter estimations. The proposed mean and variance thresholding procedure is used to develop new statistical procedures for process monitoring with complicated functional data. A real-life case study shows that the proposed procedure is much more effective in detecting local variations than existing techniques extended from methods based on a single data curve.

036 Evaluation of statistical process control procedures to monitor feeding behavior and ruminal temperature changes associated with experimental inoculation of Mannheimia haemolytica

036 Evaluation of statistical process control procedures to monitor feeding behavior and ruminal temperature changes associated with experimental inoculation of Mannheimia haemolytica

Аналіз мікробної контамінації туш свиней в процесі забою та первинної обробки

The article presents the results of studies on the еxploring of microbial contamination of pigs' carcasses in the pork slaughter and primary processing with microorganisms such as general mesophilic bacteria, Enterobacteriaceae coliforms and E.coli. The carcass surfaces were examined in six technological operations: after bleeding (1) after scalding and removing bristles and hair (2) after singeing and polishing (3); аfter the nutration (4); аt the final point after a veterinary examination (5); аfter cooling down to a temperature of 4–5 °C (6). Sampling swabs was carried out during 2015–2016 years, 530 samples were selected from 260 carcasses accordance with ISO Standard 17604 Microbiology of food and animal feeding stuffs-Carcass sampling for microbiological analysis. Swabs sampling from carcass sites taken from thigh and outer and inner surfaces of the chest and abdominal wall. It was found that the high level of contamination of surfaces carcasses with general mesophilic bacteria, Enterobacteriaceae. They were exceeded after bleeding the microbiological criteria an average of 2.2–2.4 Log CFU/cm2 and 2.5-2.7 Log CFU/cm2, respectively. Each subsequent technological operation reduced the level of contamination of carcass surfaces: the amount of general mesophilic bacteria, Enterobacteriaceae after scalding, removing bristles and hair on the carcass surface, significantly decreased compared to levels that were after bleeding, but were above the standard values by an average of 0.7 Log CFU/Cm2 and by 0.35 Log CFU/cm2, respectively. The number of coliform forms and E. coli on the carcass surface after singeing and polishing was less than after bleeding by an average of 1.8 Log CFU/cm2 and 1.23 Log CFU/cm2, respectively. The intestinal tract is also the an important pathway for contamination of pigs' carcasses. Enterobacteriaceae and E. coli testing demonstrate the effectiveness of slaughter process control procedures and is the indicator for fecal contamination. After cooling of the carcasses, on their surfaces the amount of microorganisms studied was within the permissible levels.