Proposed Quality Control Research Articles

An overview of Synlab SDN Biobank’s quality control system

Biobanks are valuable service units that ensure the usage of high-quality biological samples. They contribute to translational research, and their support may improve future therapeutic approaches. They store biological samples that can be used to examine circulation biomarkers, immune cells, and immunohistochemistry aspects of illnesses and further in-depth examinations using NGS techniques. The IRCCS Synlab SDN Biobank has about 70,000 well-preserved cryopreserved human samples from various diseases, primarily oncological but also neurological and cardiovascular. These biospecimens were taken from 25,000 participants underwent imaging with a contrast agent. The goal is to propose quality control assays that meet the requirements of the international standard ISO 9001:2015 and ISO 20387:2020 accreditation. PBMCs viability was determined, and immune subset cells were analyzed by flow cytometry. Furthermore, the expression of ubiquitous miRNAs was used to assess plasma sample integrity. The quality controls demonstrated that the biological samples were correctly cryopreserved; the preservation of human biological samples did not affect the quality of the biological samples tested. Indeed, the cryopreserved PBMCs had a vitality of more than 80%, and the lymphocyte subsets could be selected for future immune cell investigations. Furthermore, miRNA expression was highest in thawed plasma samples compared to the positive and negative controls. We evaluated the quality of our randomly selected biobank-thawed human samples. Both PBMCs and plasma samples fulfill the high-quality standards needed for biomedical research, assuring their long-term preservation. However, further research is needed in the biobanking field to establish globally accepted procedures to confirm the quality of biological samples.

A proposed quality control process for front-end planning to minimize risk of field change orders in underground electrical construction

Controlling capital costs and cost overruns due to construction field change orders (FCOs) is essential for the electric power industry to provide affordable energy services. Conversion from overhead to underground systems due to security and climate change factors will increase the risk of FCOs due to site conditions. The failure in collaboration by front-end planning (FEP) teams can increase risk of FCOs due to missing scopes, errors in design, lack of existing field condition evaluation, constraints on the project schedule, or unexpected field conditions, among other causes. This study involved development of a quality control process that enables members of the FEP team to vote their confidence levels about risk control of FCOs before proceeding to final design. The proposed process utilized Analytical Hierarchy Process (AHP) methodologies to distribute the weights of stakeholder votes based on responsibilities for each category of FCOs to obtain an integrated metric of FEP team confidence. Data from an operational electric power utility was used to provide a case scenario approach and to illustrate the method. Three actual projects were analyzed to assess how well the process would have worked for them. The novelty of the proposed model is to enhance the effectiveness of collaborative working relationships across teams during the FEP process and to provide a quality control metric to capture risk of FCOs in the early phase to minimize cost overruns in the project execution phase.

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively.

Proposed Quality Control Method for Greig Fabric Production on RRC Shuttel Weaving Machine Using FTA-FMEA to Reduce Fabric Defects at PT XYZ

Proposed Quality Control Method for Greig Fabric Production on RRC Shuttel Weaving Machine Using FTA-FMEA to Reduce Fabric Defects at PT XYZ

Defect detection in laser-based powder bed fusion process using machine learning classification methods

The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design- and shape- agnostic.

Comments of the Auditing Standards Committee of the Auditing Section of the American Accounting Association on the PCAOB’s A Firm’s System of Quality Control and Other Proposed Amendments to PCAOB Standards, Rules, and Forms

SUMMARY On November 18, 2022, the Public Company Accounting Oversight Board (the Board or PCAOB) issued a request for comment on its proposed quality control standard, A Firm’s System of Quality Control and Other Proposed Amendments to PCAOB Standards, Rules, and Forms (Public Company Accounting Oversight Board (PCAOB) 2022). This commentary summarizes the participating committee members’ views on (1) the overall standard and selected questions and (2) recent research that we encourage the PCAOB to consider.

Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation.

precision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straight-forward time-saving strategy involves manual editing of AI-generated labels, which we call AI-collaborative labeling (AICL). Factors affecting the efficacy and utility of such an approach are unknown. Reduction in time effort is not well documented. Further, edited AI labels may be prone to automation bias. In this pilot, using a cohort of CTs with intracavitary hemorrhage, we evaluate both time savings and AICL label quality and propose criteria that must be met for using AICL annotations as a high-throughput, high-quality ground truth. 57 CT scans of patients with traumatic intracavitary hemorrhage were included. No participant recruited for this study had previously interpreted the scans. nnU-net models trained on small existing datasets for each feature (hemothorax/hemoperitoneum/pelvic hematoma; n = 77-253) were used in inference. Two common scenarios served as baseline comparison- de novo expert manual labeling, and expert edits of trained staff labels. Parameters included time effort and image quality graded by a blinded independent expert using a 9-point scale. The observer also attempted to discriminate AICL and expert labels in a random subset (n = 18). Data were compared with ANOVA and post-hoc paired signed rank tests with Bonferroni correction. AICL reduced time effort 2.8-fold compared to staff label editing, and 8.7-fold compared to expert labeling (corrected p < 0.0006). Mean Likert grades for AICL (8.4, SD:0.6) were significantly higher than for expert labels (7.8, SD:0.9) and edited staff labels (7.7, SD:0.8) (corrected p < 0.0006). The independent observer failed to correctly discriminate AI and human labels. For our use case and annotators, AICL facilitates rapid large-scale curation of high-quality ground truth. The proposed quality control regime can be employed by other investigators prior to embarking on AICL for segmentation tasks in large datasets.

Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach

Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

A Technique for Approximate Communication in Network-on-Chips for Image Classification

Approximation is an emerging design methodology for reducing power consumption and latency of on-chip communication in many computing applications. However, existing approximation techniques either achieve modest improvements in these metrics or require retraining after approximation. Since classifying many images introduces intensive on-chip communication, reductions in both network latency and power consumption are highly desired. In this paper, we propose an approximate communication technique (ACT) to improve the efficiency of on-chip communications for image classification applications. The proposed technique exploits the error-tolerance of the image classification process to reduce power consumption and latency of on-chip communications, resulting in better overall performance for image classification. This is achieved by incorporating novel quality control and data approximation mechanisms that reduce the packet size. In particular, the proposed quality control mechanisms identify the error-resilient variables and automatically adjust the error thresholds of the variables based on the image classification accuracy. The proposed data approximation mechanisms significantly reduce packet size when the variables are transmitted. The proposed technique reduces the number of flits in each data packet as well as the on-chip communication while maintaining an excellent image classification accuracy. Cycle-accurate simulation results show that ACT achieves 23% in network latency reduction and 24% in dynamic power reduction as compared to existing approximate communication techniques with less than 0.99% classification accuracy loss.

Gaseous Detectors for Field Applications: Quality Control, Thermal and Mechanical Stability

A cosmic muon imaging system is essentially a particle tracking detector as known from experimental High Energy Physics. The Multiwire Proportional Chamber (MWPC) once revolutionized this field of science, and as such it is a viable choice as the core element of an imaging system. Long term construction and operation experience was gathered from a Japanese–Hungarian collaboration that gave rise to the MWPC-based Muon Observatory System (MMOS), and is being used in Japan at the Sakurajima volcano. The present paper attempts to draw conclusions on the thermal and mechanical limits of the system, based on controlled measurements and detailed simulations. High temperature behavior and effects of thermal cycling and conditioning are presented, which appear to consistently allow one to propose quality control criteria. Regarding mechanical stability, the relation between gluing quality (tensile strength) and expected stress from vibration (during transportation) determines the safety factor to avoid damages. Both of these are presented and quantified in the paper using a conservative and austere approach, with mechanical simulations validated with experimental modal testing data. One can conclude that mechanical stress during industrial standard air freight shipping conditions is nearly a factor of three below the calculated maximum stress.

A quality control method based on improved IQR for estimating multi-GNSS real-time satellite clock offset

Real-time satellite clock offset plays a prominent role in the Global Navigation Satellite System (GNSS) community. However, the GNSS raw observations are impacted by outliers, resulting in the quality of observations becomes poorer, which will dramatically affect the performance of estimated multi-GNSS satellite clock offset in real-time. In this contribution, an improved Inter Quartile Range (IQR) method is proposed to improve the quality of multi-GNSS satellite clock offset estimation in real-time, in which the improvements include two aspects. Firstly, the empirical threshold C1 is determined to adapt multi-GNSS satellite clock offset estimation, if one residual is not between (Q1-C1∗IQR,Q3+C1∗IQR), the observation is deleted. More importantly, to guarantee the clock offset of each satellite can be estimated at each epoch, if one satellite corresponding to all observation of stations is deleted, the IQR is conducted again at the current epoch for these observations. Three schemes are designed: 50 stations are used to perform multi-GNSS satellite clock offset estimation in real-time, which are without quality control (S1), with the proposed quality control method in this contribution(S2), and with quality control method using 8.5 medians of residuals (S3). Compared to S1, the satellite clock offset accuracy of GPS, Galileo, BDS-3, and GLONASS for S2 can be significantly improved from 0.082 to 0.055 ns, 0.106 to 0.072 ns, 0.178 to 0.083 ns, and 0.146 to 0.100 ns, respectively. The improvement is 32.93%, 32.07%, 53.37%, and 31.51%, respectively. The clock offset performance of S2 is better than that of S3 in terms of stability and reliability for GPS, BDS-3, and GLONASS. Another 52 stations are applied to perform multi-GNSS kinematic real-time precise point positioning (RT-PPP). For the convergence time and positioning accuracy, the RT-PPP employing the satellite clock offset generated from S2 shows the best performance. The experiment shows that the proposed quality control method can obtain real-time, stable, high-precision, and reliable multi-GNSS real-time satellite clock offset.

Automated Quality and Process Control for Additive Manufacturing using Deep Convolutional Neural Networks

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.

Quality control for the geophone reorientation of ocean bottom seismic data using k‐means clustering

ABSTRACTDuring ocean bottom seismic acquisition, seafloor multicomponent geophones located in rugose and sloping water bottom can be affected by skewed energy distribution, such as leaked shear energy on the vertical geophones and leaked compressional energy on the horizontal geophones. To correct for the tilted energy distribution, which is one of most effective preprocessing steps, a geophone reorientation step is applied. This is a simple and straightforward process that applies a 3‐dimensional rotation matrix with respect to the orientation angles. Since the reorientation process highly affects the outcome of the entire data processing workflow, it has to be accompanied by a careful quality control process to verify its validity for the whole survey area. In this study, we propose a quality control workflow for the geophone reorientation by using unsupervised machine learning. A correlation analysis is employed to compare numerical versus analytical solutions of both the azimuth and the incidence angles for the direct arrivals. A comparison of both solutions aims to generate correlation coefficients that are indicative of the accuracy of geophone orientation. The correlation coefficients are subsequently investigated by the k‐means clustering algorithm to differentiate and identify normally and abnormally deployed/reoriented geophones. Numerical experiments on a field ocean bottom seismic data set confirm that the proposed workflow effectively provides reliable labels for normally and abnormally deployed/reoriented geophones. The labelling assigned by the proposed quality control workflow is a suitable indicator for abnormalities in the geophone reorientation step and will be helpful for further investigation, such as re‐correction or removal of abnormally reoriented geophones.

Development of an optimized and scalable method for isolation of umbilical cord blood-derived small extracellular vesicles for future clinical use.

Extracellular vesicles (EV) are a promising therapeutic tool in regenerative medicine. These particles were shown to accelerate wound healing, through delivery of regenerative mediators, such as microRNAs. Herein we describe an optimized and upscalable process for the isolation of EV smaller than 200 nm (sEV), secreted by umbilical cord blood mononuclear cells (UCB‐MNC) under ischemic conditions and propose quality control thresholds for the isolated vesicles, based on the thorough characterization of their protein, lipid and RNA content. Ultrafiltration and size exclusion chromatography (UF/SEC) optimized methodology proved superior to traditional ultracentrifugation (UC), regarding production time, standardization, scalability, and vesicle yield. Using UF/SEC, we were able to recover approximately 400 times more sEV per mL of media than with UC, and upscaling this process further increases EV yield by about 3‐fold. UF/SEC‐isolated sEV display many of the sEV/exosomes classical markers and are enriched in molecules with anti‐inflammatory and regenerative capacity, such as hemopexin and miR‐150. Accordingly, treatment with sEV promotes angiogenesis and extracellular matrix remodeling, in vitro. In vivo, UCB‐MNC‐sEV significantly accelerate skin regeneration in a mouse model of delayed wound healing. The proposed isolation protocol constitutes a significant improvement compared to UC, the gold‐standard in the field. Isolated sEV maintain their regenerative properties, whereas downstream contaminants are minimized. The use of UF/SEC allows for the standardization and upscalability required for mass production of sEV to be used in a clinical setting.

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

Assessing coral sperm motility

The declining reproductive viability of corals threatens their ability to adapt to changing ocean conditions. It is vital that we monitor this viability quantitatively and comparatively. Computer-assisted sperm analysis (CASA) systems offer in-depth analysis used regularly for domestic and wildlife species, but not yet for coral. This study proposes quality control procedures and CASA settings that are effective for coral sperm analysis. To resolve disparities between CASA measurements and evaluations by eye, two negative effects on motility had to be resolved, slide adhesion (procedural) and sperm dilution (biological). We showed that the addition of bovine serum albumin, or caffeine, or both to fresh sperm reduced adhesion in the CASA cassettes, improved motility and motile sperm concentration (P < 0.0001), yet these additions did not affect measurements of total sperm concentration. Diluting coral sperm reduced sperm motility (P = 0.039), especially from heat-stressed corals. We found CASA concentration counts comparable to haemocytometer and flow cytometer measures (P = 0.54). We also found that motile sperm per egg is a useful predictor of fertilisation success, using cryopreserved sperm. Standard measurements of coral reproductive characteristics inform our understanding of the impacts of climate change on reef populations; this study provides a benchmark to begin this comparative work.

Quality improvement of monolithic steel fiber concrete floor slabs with void formers

The features of the technology for the monolithic floors construction using void formers have been studied, its disadvantages have been identified and the ways to eliminate them have been proposed. At the first stage of this work, the reasons for the low quality of monolithic floors with void formers were studied by the method of visual and measuring control, and by comparing with known scientific and industrial data. At the second stage, by the method of systematization the main defects and damages arising in the construction, repair and usage of these floor slabs were classified and ways to eliminate them were proposed. At the third stage, the features of the quality control system, carried out directly during the construction of monolithic floor slabs using void formers, were studied, the composition of operations and control tools were proposed. According to the research results, it was found that the use of steel-fiber concrete in floors with non-removable void formers allows to reduce the percentage of reinforcement by 9.82%, compared to other known options, and also to reduce the consumption of concrete in comparison with a solid monolithic slab almost by half. The proposed quality control system will improve the efficiency of monolithic floor slabs using void formers.

Вероятностный подход к контролю качества и оценке начальной надежности сборных железобетонных конструкций

The study aims to improve methods for assessing the serviceability of prefabricated reinforced con-crete structures based on an integral estimation of their reliability following the operational monitoring of individual parameters. The serviceability criteria of the investigated structures are set on a probabil-istic basis, addressing the main methodology for their field tests according to GOST 8829-94. The proposed non-destructive testing of output is computerised and is carried out every shift together with the versatility of technological factors. The calculation results on the reliability assessment program are a basis for the acceptance of a batch according to the required strength, stiffness, cracking resistance. To develop a probabilistic algorithm for assessing the reliability of works, an optimal calculation model accurately describing stress-strain behaviour (SSB) under loading is necessary. It is demonstrated that a calculation based on design standards and a model with account non-linear nature of deformation occurring in reinforced concrete as elastoplastic material can be used. The reliability and adequacy of the selected models were confirmed by comparing the theoretical data with the results of work field tests performed at the Bratskzhelezobeton plant. As an example, a calculation model for the SSB as-sessment in load-bearing wall panels with applied combined stress (biaxial bending) was substantiated. A methodology and regulations for automated every-shift quality control of precast reinforced con-structions were developed following the versatility of technological factors. Introducing the proposed quality control system to factories manufacturing reinforced concrete products allows the number of structure field tests to be reduced by 6-10 times and the cost of manufactured products by approxi-mately 2.5%.

Quality control of white goods parts using robotic technologies

This paper presents a robotized cell that has been developed to automate the quality control process. The combination of innovative software and hardware helps the operators to efficiently perform quality control on parts from the white goods industry. Automation tools, such as robots and mechatronic devices have been integrated on a backend platform which controls and manages the different operations of the resources. These tools perform the quality control on the part, allowing the backend platform to receive the respective signals and ensure its correct functionality. A demonstrator has been setup and described as a use case of the proposed quality control mechanism.

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and investigate how these degradations influence the performance of a PD detection system. Given that the specific degradation is known, we explore the effectiveness of a variety of enhancement algorithms in compensating this mismatch and improving the PD detection accuracy. Then, we propose two approaches to automatically control the quality of recordings by identifying the presence and type of short-term and long-term degradations and protocol violations in voice signals. Finally, we experiment with using the proposed quality control methods to inform the choice of enhancement algorithm. Experimental results using the voice recordings of the mPower mobile PD data set under different degradation conditions show the effectiveness of the quality control approaches in selecting an appropriate enhancement method and, consequently, in improving the PD detection accuracy. This study is a step towards the development of a remote PD detection system capable of operating in unseen acoustic environments.