Support Quality Control Research Articles

Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study

Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar and bacterial concentration in real-time, ensuring kombucha quality during production. The feature fingerprints of residual sugar and bacterial concentration were extracted by four variable selection algorithms and then reconstructed using serial and parallel processing methods. Based on these reconstructed features, Partial Least Squares (PLS) and Convolutional Neural Networks (1DCNN and 2DCNN) models were developed and compared. The experimental results showed that the 2DCNN model based on reconstruction features achieved superior performance. The RPDs of the residual sugar and bacterial concentrations models were 4.49 and 6.88, while the MAEs were 0.42 mg/mL and 0.04 (Abs), respectively. These results suggest that the proposed modeling strategy effectively supports quality control during kombucha production and provides a new perspective for spectral analysis.

Pharmacological mechanisms involved in the diuretic activity of the ethanol-soluble fraction of Baccharis milleflora (Less.) DC. - An ethnopharmacological investigation

Ethnopharmacological relevanceBaccharis milleflora (Less.) DC. is a plant native to Brazil that is frequently used in traditional medicine as a diuretic and antihypertensive. However, even though it is traditionally used for these purposes, its diuretic and hypotensive effects have not been fully elucidated. AimInvestigate the cardiorenal effects of the ethanol-soluble fraction (ESBM) of Baccharis milleflora in normotensive rats. Materials and methodsCladodes of B. milleflora were analyzed using light and scanning electron microscopy to provide anatomical data to support quality control. Subsequently, the ESBM was obtained and analyzed using LC-DAD-MS, and its components were annotated. The acute toxicity of ESBM was assessed in female Wistar rats. The acute and prolonged diuretic and hypotensive effects were then studied in Wistar rats. Finally, we assessed the mechanisms responsible for the diuretic effects of ESBM, including the activity of renal Na+/K+/ATPase, angiotensin-converting enzyme, and erythrocyte carbonic anhydrase. Additionally, we also investigated the involvement of bradykinin, prostaglandins, and nitric oxide. ResultsFrom LC-DAD-MS data, thirty-three metabolites were identified from ESBM, including chlorogenic acids, glycosylated phenolic derivatives, C-glycosylated flavones, and O-glycosylated flavonols. No signs of acute toxicity were observed in female rats. The findings showed that ESBM had significant diuretic and natriuretic effects, as well as a potassium-sparing effect. The treatment with ESBM was able to significantly decrease serum levels of creatinine and malondialdehyde, and also significantly increase levels of nitrite, an indirect marker of nitric oxide bioavailability. Furthermore, pre-treatment with L-NAME abolished all diuretic effects induced by ESBM. ConclusionThis study presented important morpho-anatomical and phytochemical data that support the quality control of Baccharis milleflora. The ESBM exhibited a significant diuretic and natriuretic effect following acute and seven-days repeated treatment in Wistar rats, without affecting renal potassium elimination. These effects appear to be dependent on the activation of the nitric oxide-cyclic GMP pathway. This study suggests the potential use of B. milleflora preparations in clinical situations where a diuretic effect is needed.

Neuroprotective role of vitamin B12 in streptozotocin-induced type 1 diabetic rats

Chronic hyperglycemia-induced neuropathological changes include neuronal apoptosis, astrogliosis, decrease in neurotrophic support, impaired synaptic plasticity, and impaired protein quality control (PQC) system. Vitamin B12 is indispensable for neuronal development and brain function. Several studies reported the neuroprotective effect of B12 supplementation in diabetic patients. However, the underlying molecular basis for the neuroprotective effect of B12 supplementation in diabetes needs to be thoroughly investigated. Two-month-old Sprague-Dawley rats were randomly assigned into three groups: Control (CN), diabetes (D; induced with streptozotocin; STZ), and diabetic rats supplemented with vitamin B12 (DBS; vitamin B12; 50 μg/kg) for four months. At the end of 4 months of experimentation, the brain was dissected to collect the cerebral cortex (CC). The morphology of CC was investigated with H&E and Nissl body staining. Neuronal apoptosis was determined with TUNEL assay. The components of neurotrophic support, astrogliosis, synaptic plasticity, and PQC processes were investigated by immunoblotting and immunostaining methods. H& E, Nissl body, and TUNEL staining revealed that diabetes-induced neuronal apoptosis and degeneration. However, B12 supplementation ameliorated the diabetes-induced neuronal apoptosis. Further, B12 supplementation restored the markers of neurotrophic support (BDNF, NGF, and GDNF), and synaptic plasticity (SYP, and PSD-95) in diabetic rats. Interestingly, B12 supplementation also attenuated astrogliosis, ER stress, and ameliorated autophagy-related proteins in diabetic rats. Overall, these findings suggest that B12 acts as a neuroprotective agent by inhibiting the neuropathological changes in STZ-induced type 1 diabetes. Thus, B12 supplementation could produce beneficial outcomes including neuroprotective effects in diabetic patients.

Detection of doping control sample substitutions via single nucleotide polymorphism-based ID typing.

The authenticity of a doping control sample is a key element of sports drug testing programmes. Doping control sample manipulation by providing another individual's urine or blood (instead of the tested athlete's sample) has been observed in the past and is an unequivocal violation of the World Anti-Doping Agency anti-doping rules. To determine attempts of manipulations by sample swapping, the utility of a single nucleotide polymorphism (SNP)-based sample authentication with a multi-target SNP panel was assessed. The panel comprises detection assays for 44 different SNPs, 3 gender markers and 5 quality control markers for DNA-profile determination. Sample analysis is based on a multiplex polymerase chain reaction step followed by a multiplex single base extension (SBE) reaction and subsequent SBE-product detection by MALDI-TOF MS. Panel performance was evaluated for urine and dried blood spot (DBS) samples. Urine (8ml) and DBS (20 μl) test samples were reliably typed and matched to whole blood reference samples, while efficient typing of urine samples correlated with sample quality and input amounts. Robust profiling of urine doping control specimens was confirmed with an assay input of 12 ml. Samples can be processed in a high-throughput format with an overall assay turnaround time of approximately 11 h. SNP-based DNA typing via MALDI-TOF MS thus represents a high throughput-capable possibility for doping control sample authentication. SNP profiling of samples could offer the opportunity to complement existing steroid profile analytics to substantiate sample manipulations and to support quality control processes in high throughput routine settings.

ANALYSIS OF CASE MANAGER FUNCTIONS IN IMPROVING QUALITY CONTROL AND COST CONTROL OF INPATIENT (CASE STUDY) AT GRANDMED HOSPITAL LUBUK PAKAM

Abstract
 The hospital has a process for implementing continuity of care in the hospital and integration between care-giving professionals (PPA) assisted by a patient service manager (MPP)/case manager. The current challenge that must be faced by hospitals in implementing the JKN program is implementing good quality control and cost control. The existence of MPP in the hospital case management system is expected to be able to support quality control and cost control. The purpose of this study was to analyze the functions of the case manager in improving quality control and cost control for inpatients (case study). This research was conducted at the Grandmed Lubuk Pakam Hospital, Deli Serdang Regency from September 2022 to May 2023. The type of research in this study was qualitative. The sample in this study were case managers at Grandmed Lubuk Pakam Hospital, Deli Serdang Regency, totaling 3 people and 1 director of medical services and 3 heads of inpatient rooms. The results showed that the implementation of case managers in improving quality control and cost control for inpatients was quite good. The case manager utility assessment has not gone well because there is no clear format for the initial screening of patients. Patient care planning by case managers has not been maximized. Not all case managers do patient care planning. The case manager's facilitation and advocacy has not been maximized. There are 2 case managers who do not coordinate with other fields, only coordinate with DPJP and nurses. The case manager evaluation has not gone well because there is no instrument format for case manager tasks so evaluation cannot be carried out. Post-discharge planning follow-up by the case manager has not gone well because the case manager has never followed up post-discharge planning. Case managers should carry out case manager functions for patients who are being treated in case management in accordance with the standard criteria set by STARKES 2022 accompanied by proper and accurate documentation to improve service quality and patient satisfaction.
 
 Keywords : Functions, Case Manager, Quality Control, Cost Control, Hospitalization, Case Study

Determination of Dencichine in Panax notoginseng in the Forest and Field Using High Performance Liquid Chromatography.

Dencichine is a nonprotein amino acid, an effective ingredient in Panax notoginseng with hemostatic and anti-inflammatory effects. There are few studies on the effects of regions and cultivation models on the accumulation of dencichine. In the current study, the content of dencichine in P. notoginseng collected from its global cultivation and trading center Yunnan, China, (>640 samples) was determined using an optimized high-performance liquid chromatography method coupled with a diode array detector but without derivatization. The recovery rate of this method was 80-110%, the relative standard deviation was <10%, and the limits of detection and quantification were 0.003% (w/w) and 0.01% (w/w), respectively. The content of dencichine in each part of P. notoginseng was as follows: rootlets (39.59%) > main roots (29.91%) > leaves (16.21%) > stems (14.29%). For leaves, P. notoginseng in the forest (5.52 ± 2.26 mg/g) was significantly higher than that in the field (3.93 ± 1.72 mg/g) but opposite for main roots. The origins and altitudes made different contributions to the accumulation of dencichine in P. notoginseng. This study provides an effective analytical method to determine dencichines in various parts of P. notoginseng from different origins and altitudes and supports quality control and product development of P. notoginseng.

Genome skimming as an efficient tool for authenticating commercial products of the pharmaceutically important Paris yunnanensis (Melanthiaceae)

BackgroundParis yunnanensis (Melanthiaceae) is a traditional Chinese medicinal plant of significant pharmaceutical importance. Due to previous taxonomic confusion, a congeneric species, Paris liiana, has been mistaken for P. yunnanensis and cultivated on a large scale, leading to the mixing of commercial products (i.e., seedlings and processed rhizomes) of P. yunnanensis with those of P. liiana. This may have adverse effects on quality control in the standardization of P. yunnanensis productions. As the lack of PCR amplifiable genomic DNA within processed rhizomes is an intractable obstacle to the authentication of P. yunnanensis products using PCR-based diagnostic tools, this study aimed to develop a PCR-free method to authenticate commercial P. yunnanensis products, by applying genome skimming to generate complete plastomes and nrDNA arrays for use as the molecular tags.ResultsBased on a dense intraspecies sampling of P. liiana and P. yunnanensis, the robustness of the proposed authentication systems was evaluated by phylogenetic inferences and experimental authentication of commercial seedling and processed rhizome samples. The results indicate that the genetic criteria of both complete plastomes and nrDNA arrays were consistent with the species boundaries to achieve accurate discrimination of P. yunnanensis and P. liinna. Owing to its desirable accuracy and sensitivity, genome skimming can serve as an effective and sensitive tool for monitoring and controlling the trade of P. yunnanensis products.ConclusionThis study provides a new way to solve the long-standing problem of the molecular authentication of processed plant products due to the lack of PCR amplifiable genomic DNA. The proposed authentication system will support quality control in the standardization of P. yunnanensis products in cultivation and drug production. This study also provides molecular evidence to clarify the long-standing taxonomic confusion regarding the species delimitation of P. yunnanensis, which will contribute to the rational exploration and conservation of the species.

Six Sigma Implementation on Wash Motor Twin Tub Washing Machine: A Case Study

The competition of electronics and household industry in Indonesia is very tight, thus encouraging the industries to be more creative and innovative to attract customers. PT XYZ as a company that produces washing machines, especially twin tub washing machine, often experiences problems related to the quality of its constituent parts. Part refers to products manufactured by the supplier. One of them is wash motor, which still many of line drops in production line. Based on the calculation of the average DPMO (Defects per Million Opportunities) and the average sigma value in January 2023 were 251.69 and 4.99. This shows that the sigma level value of the company is between levels 4 and 5 which is in the position of the US industry average. The aim of this study is to reduce the number of defects in twin tub washing machine production using Six Sigma method through the Define, Measure, Analyze, Improve, and Control (DMAIC) phases. The results show that there are four dominant types of defects, namely rivet loose, rusty, noise, and no function. Five-M checklist determines suggested improvements based on man, machine, method, material, and environment factors. The Six Sigma approach has the opportunity to be used to support quality control activities to overcome defects that appear on wash motor and to improve product quality in washing machine factories by providing alternative approaches to suppliers.

An environmental monitoring and control system for the ATLAS ITk Outer Barrel quality control and integration

This paper describes the development of a system based on Programmable Logic Controllers (PLC) for safety interlocking and environmental monitoring during ATLAS ITk Outer Barrel (OB) loaded local support quality control (QC) and later integration. The system has been developed at CERN with a focus on scalability, maintainability and reliability, and is expected to be deployed at the different ITk OB loading and integration sites.

Predicting Spacing Factor in Hardened Concrete Using the Fresh Concrete Measurements

Damage induced by freeze–thaw action in Portland cement concrete pavements can be minimized by ensuring the presence of a well-entrained air-void distribution within its cementitious paste. Several methods exist for assessing the air-void system in fresh concrete, principally ASTM C231-17a, ASTM C173-16, and AASHTO T 395. These methods accurately measure the fresh air content, but this alone is insufficient to assess air-void distribution parameters. ASTM C457-16 enables such an assessment but can only be performed on hardened concrete and consequently does not provide real-time assessment. Hence it was highly desirable to develop a system to accurately predict the air-void distribution properties, principally the spacing factor, of a concrete mixture, in real time, to support quality control/-assurance activities. A set of 271 observations was used to develop statistical models for predicting the spacing factor obtained by the RapidAir-based ASTM C457-16 method. The models were based on paste content and fresh concrete test measurements (predictors), for which least absolute shrinkage and selection operator regression analysis were used to select/reject variable(s). The predicted spacing factors (PSFs) obtained using these models in fresh concrete were evaluated on their ability to differentiate between a well-entrained (i.e., spacing factor &lt;0.2 mm) and coarsely entrained (i.e., spacing factor &gt;0.2 mm) air-void system. The PSFs from the base and final models exhibited 88.5% and 81.1% accuracy, respectively—much higher than that of the SAM number alone (48.4%). A larger fraction of the data set consisted of well-entrained spacing factor observations, which limited validation for coarsely entrained air-void systems.

Co-learning of Extrusion Deposition Quality for Supporting Interconnected Additive Manufacturing Systems

Additive manufacturing systems are being deployed on a cloud platform for providing networked manufacturing services. This paper explores the value of interconnected printing systems that share process data on the cloud in improving quality control. We employed an example of quality learning for cloud printers by understanding how printing conditions impact printing errors. Traditionally, extensive experiments are necessary to collect data and estimate the relationship between printing conditions vs. quality. This research establishes a multi-printer co-learning methodology to obtain the relationship between the printing conditions and quality using limited data from each printer. Based on multiple interconnected extrusion-based printing systems, the methodology is demonstrated by learning the printing line variations and resultant infill defects induced by extruder kinematics. The method leverages the common covariance structures among printers for the co-learning of kinematics-quality models. This paper further proposes a sampling-refined hybrid metaheuristic to reduce the search space for solutions. The results showed significant improvements in quality prediction by leveraging data from data-limited printers, an advantage over traditional transfer learning that transfers knowledge from a data-rich source to a data-limited target. The research establishes algorithms to support quality control for reconfigurable additive manufacturing systems on the cloud.

The Quality Analysis and Deterioration Mechanism of Liquid Egg White during Storage

The quality of liquid egg white (LEW) during storage is critical for the development of the egg industry. In order to effectively control its storage quality, the effects of packaging materials and storage conditions on the quality of LEW were investigated. High-throughput sequencing (HTS) was applied to explore changes in bacterial population proportions and microflora in the spoilage of LEW. The shelf life of LEW packaged with glass (LEW-PG), plastic (LEW-PP), and tinplate (LEW-PT) was preliminarily determined to be 8, 5, and 7 days, respectively. LEW-PG possessed superior sensory scores (65) and L values (87.5), and a lower growth rate of total volatile basic nitrogen (TVB-N) content among the three samples on the last day of shelf life, and was chosen for further study. During 24 days of storage, the sensory scores of the LEW-PG in 10 °C and 4 °C groups decreased by 32.7% and 25.7%, respectively. There was no significant difference in foaming properties of LEW-PG between the 10 °C and 4 °C groups (p &gt; 0.05). HTS analysis showed that the abundance of species composition in the 10 °C samples was higher than that in the 4 °C samples, though the latter possessed a higher community diversity. At the genus level, the dominant bacteria in the 10 °C group were Pseudomonas (21.79%), others (19.21%), and Escherichia (11.21%), while others (37.5%), Escherichia (30.40%), and Bifidobacterium (17.72%) were highly abundant in the 4 °C samples. It is hoped that this study could provide theoretical support for quality control of LEW during storage.

A hybrid AHPSort II and multi-objective portfolio selection method to support quality control in the automotive industry

There are several manufacturing errors of different criticality, severity, and occurrence. Correcting all of them is difficult and costly. In this paper, we propose a new hybrid approach to classifying manufacturing errors in an automotive plant and, consequently, to selecting the portfolio of processes that require more attention from the management of the company. Our approach consists of two steps. Firstly, we classify the errors in terms of priority, based on a set of criteria agreed with the management, using the multi-criteria decision-aiding method AHPSort II. The errors belonging to the highest priority class are the most urgent and critical for the company. Secondly, we define a multi-objective portfolio problem, selecting a set of the most critical processes based on the number of errors in each of these processes and on their priority. We adopted an interactive method for finding the most preferred portfolio of processes according to the stakeholder preferences. We applied this approach to a case study of the largest automotive company in Italy, Fiat Chrysler Automobiles, showing how classical quality control tools can benefit from the integration of multi-criteria decision-aiding techniques.

A protocol of using PTMiner for quality control and localization of protein modifications identified by open or closed search of tandem mass spectra.

In recent years, an open search of tandem mass spectra has greatly promoted the detection of post-translational modifications (PTMs) in shotgun proteomics. However, post-processing of the results from open searches remains an unsatisfactorily resolved problem, which hinders the open search mode from wide practical use. PTMiner is a software tool based on dedicated statistical algorithms for reliable filtering, localization and annotation of the modifications (mass shifts) detected by open search. Furthermore, PTMiner also supports quality control and re-localization of modifications identified by the traditional closed search. In this protocol, we describe how to use PTMiner for the two search modes. Currently, the search engines supported by PTMiner include pFind, MSFragger, MaxQuant, Comet, MS-GF + and SEQUEST.

Обеспечение процессов контроля качества на всех этапах жизненного цикла объектов капиталь-ного строительства в рамках концепции «Строительство 4.0»

Abstract. In this work, we identify the key challenges hampering the implementation of the Construction 4.0 concept when performing the quality control of construction projects. The research methodology involved a comprehensive study of the Construction 4.0 concept, which is applied in do-mestic and foreign practice for supporting quality control processes in construction. An integrated ap-proach to the analysis and accounting of all factors affecting construction quality management was ap-plied. Construction industries in different countries are undergoing massive transformations, shifting from traditional design and construction methods towards innovative ways of managing construction projects based on the use of digital technologies. The need for the introduction of these technologies is recognized by many subjects of the Russian construction industry, recognized at the state level and supported by a large number of national projects and development strategies. Such problematic as-pects, as the distortion of the information necessary for the implementation of a construction project, the increase in the time and cost of this process, the lack of proper quality control of industrial and civil facilities, have necessitated a transition towards digital technologies for the design of buildings and structures. The trend of digitalizing the construction industry, including the provision of quality control processes, predetermines the need to develop a scientific and methodological basis for the theory of managing the introduction of advanced technologies. The paper describes the implementation of the Construction 4.0 concept in the context of organizing quality control processes in construction. An analysis of practical examples of digital ecosystems allowed the authors to develop methodological foundations for the implementation of an integrated quality management system based on information modelling technologies, which could facilitate the digital transformation of the industry.

Business Process Re-Engineering to Digitalise Quality Control Checks for Reducing Physical Waste and Resource Use in a Food Company

Quality control is an essential element of manufacturing operations that reduces product defects and provides excellent products of the right specifications to the end consumer. Industry 4.0 solutions, such as digitalisation, along with lean manufacturing tools, may support quality control operations. This paper presents a case study of a food company wherein quality control checks were optimised using business process re-engineering to reduce physical waste and resource usage. Following close analysis of the company’s pack-house operations, it was proposed to adopt elements of Industry 4.0 by digitalising the quality control process. Implementing such a solution led to a reduction in the time needed to complete recorded checks, an increase in the time the pack-house quality control team spends with packers on the production lines, and the facilitation of defects identification. It also ensured that the product met the customers’ specifications and reduced the likelihood of rejection at the customers’ depot. The new system also enabled monitoring of each line in real-time and gathering of additional information faster and more accurately. This article proves how employing lean principles in combination with Industry 4.0 technologies can lead to savings in resources and a reduction in waste, which leads to improvements in operational efficiency.

Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops

It has been recognized for decades that low and inconsistent spray coverages of pesticide applications represent a major challenge to successful and sustainable crop protection. Deployment of water-sensitive spray cards combined with image analysis can provide valuable and quantitative insight into spray coverage. Herein we provide description of a novel and freely available smartphone app, “Smart Spray”, for both iOS and Android smart devices (iOS and Google app stores). More specifically, we provide a theoretical description of spray coverage, and we describe how Smart Spray and similar image-processing software packages can be used as decision support tools and quality control for pesticide spray applications. Performance assessment of the underlying pixel classification algorithm is presented, and we detail practical recommendations on how to use Smart Spray to maximize accuracy and consistency of spray coverage predictions. Smart Spray was developed as part of ongoing efforts to: (1) maximize the performance of pesticide sprays, (2) minimize pest-induced yield loss and to potentially reduce the amount of pesticide used, (2) reduce the risk of target pests developing pesticide resistance, (3) reduce the risk of spray drift, and (4) optimize spray application costs by introducing a quality control.

Comparative proteomic analysis of hybrid maize MR2008 and its parental lines

AbstractMaize is one of the three staple foods in the world. The white variety represents 60% of the maize importation with a world consumption of 1125 million tons in 2019/2020. Currently, new technologies could contribute to the analysis of this seed, supporting quality control and improvement. This study aims to carry out the morphological and proteomic comparison between the hybrid MR2008 and its parental lines LUG03 and CML491 through mass spectrometry and bioinformatics analysis. Herein, we identified that 34.8% of the hybrid proteome differs from the parental proteome. Also, ontological and morphological analyses determined that the hybrid exhibits more characteristics related to CML491 than LUG03, for example, metabolic pathways and enzymes, such as anthocyanidin 3-O-glucosyltransferase (UniProt P16166). This analysis allowed the identification of dominant characters, metabolic pathways and confirms the utility of this methodology in agricultural practices, mainly in processes of selection and quality control of a crop.

Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat.

This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.