Product Quality Research Articles

Effects of industrial slicing on the microbiological quality and safety of mozzarella cheese and ham

Slicing of animal origin food affects the final quality of products and potentially transfers pathogenic microorganisms as it occurs after microorganism control and elimination steps, such as cooking and pasteurization. Therefore, the food industry needs to ensure the safety of food products to reduce the incidence of foodborne diseases. This requires an understanding of the dangers involved in each stage of production, along with the microorganisms transferred during processing. In this study, we aimed to understand the impact of industrial slicing on mozzarella cheese and ham by investigating the quality indicator microorganisms, aerobic mesophiles, psychrotrophs, total coliforms, Staphylococcus aureus, and pathogens, such as Escherichia coli, Salmonella spp., and Listeria monocytogenes. Samples were collected from pieces (20) and slices (20) of mozzarella cheese and ham. The number of mesophilic aerobes differed significantly before and after slicing (P < 0.05) for mozzarella. However, no difference was observed in the psychrotrophic microorganism counts during slicing. The counts of total coliforms and S. aureus increased after slicing (P > 0.05). In total, 844 isolates were analyzed for pathogenic genes, and Salmonella spp. were identified in 40% of the piece and 20% of the sliced mozzarella cheese samples and in 30% of the piece and 50% of the sliced ham samples. Furthermore, S. aureus genes with the potential to produce enterotoxins C and D were identified in two sliced mozzarella cheese samples. Slicing of ready-to-eat food is an important step in food microbiology. Our results highlight the importance of maintaining hygienic practices during processing and demonstrate that the microbiological quality of the slicing sample affects the quality and safety of the sliced product.

Effect of pulsed electric field processing on microbial and enzyme inactivation in blended plant-based milk alternatives: A case study on a microbial challenge test for a non-presterilized oat-based beverage enriched with pea protein

Microbial and enzyme inactivation by pulsed electric field (PEF) treatment was assessed in a blended plant-based milk alternative (PBMA) containing oats and pea protein without the application of presterilization. This was conducted to investigate a novel approach that permits PEF process validation for PBMA containing heat-labile components (protein and starch). Continuous PEF processing at moderate field strengths (9–10 kV/cm) combined with mild preheating (30–45 °C) for 25 s was applied over a range of specific energy inputs (77–245 kJ/L). Selective plating permitted bacterial enumeration of inoculated surrogate organism, despite the presence of natural microbial contaminants in the non-presterilized product. A minimum 5-log reduction of inoculated surrogates Escherichia coli and Listeria innocua alongside native microbiota was identified after preheating to 35–45 °C followed by PEF at 192–215 kJ/L. α-amylase activity was reduced by up to 89% during a similar PEF treatment (205 kJ/L, 40 °C). Overall, continuous PEF combined with mild preheating temperature was shown to achieve microbial inactivation in the pea protein enriched PBMA in compliance with current food safety regulations. Industrial relevancePresterilization is typically applied prior to conducting microbial challenge testing on food products to validate novel pasteurization treatments according to current regulatory requirements that a minimum 5-log reduction of relevant inoculated food pathogens must be achievable. However, for a variety of blended PBMA containing heat-labile proteins, presterilization is not advisable as the occurrence of irreversible changes in the product quality linked to protein coagulation and phase separation will affect the inactivation efficacy during subsequent microbial challenge testing. Hence, an alternative approach is presented utilizing a non-presterilized PBMA and thorough microbiological analysis, combining non-selective and selective plating techniques. This allows an identification of PEF treatment parameter with a minimum 5-log reduction of inoculated surrogate organism alongside naturally present microbial contaminants. This may serve as a valuable guideline for stakeholders in the food industry when addressing the efficacy of novel preservation treatments such as PEF in PBMA products with similar heat-induced product stability challenges.

Optimizing gula apong production with an IoT-based temperature monitoring system

&lt;p&gt;Determining the quality of &lt;em&gt;gula apong&lt;/em&gt; is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The &lt;em&gt;gula apong&lt;/em&gt; industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of &lt;em&gt;gula apong&lt;/em&gt;. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality &lt;em&gt;gula apong&lt;/em&gt; is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of &lt;em&gt;gula apong&lt;/em&gt; to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of &lt;em&gt;gula apong&lt;/em&gt; but also reduce the cooking time and cost.&lt;/p&gt;

An Artificial Intelligence-based Tool to Predict “Unhealthy” Wine and Olive Oil

The application of Artificial Intelligence (AI) in the agri-food industry has witnessed significant advancements, particularly in healthy food, quality assessment, and geographical origin determination of agri-products such as wine and olive oils, whose international market is constantly growing. This paper explores the applicability of Machine Learning (ML) models for predicting “unhealthy” wines and olive oils. As for the healthy factor, two main characteristics of wines and olive oils were evaluated: their quality and their geographical origin. In the study, 3 ML algorithms were compared (Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) to predict just by observing their chemical characteristics: i) the quality of red and white wine and ii) the geographical origins of wine and olive oils. Real datasets were used for these case studies. The Synthetic Minority Over-sampling Technique (SMOTE) was used to manage imbalanced data, and well-adopted AI metrics were collected to evaluate the accuracy of the predictions made by the 3 ML algorithms. 3 datasets were analyzed: dataset #01 contains ∼7000 data entries related to 2 types of wine (red and white Portuguese wines) and 11 wine’s chemical characteristics; dataset #02 contains 178 data entries related to 3 different Italian vineyards and 13 wine’s chemical characteristics; dataset #03 contains 572 data entries related to 9 different Italian geographical regions for olive oil and 7 fatty acid characteristics. A total of ∼7700 data points, 5 wines and 9 olive oil types, and 31 chemical characteristics were analyzed. 12 quality metrics were collected per each dataset analysis. The designed ML models achieved good accuracy (>0.65) in predicting the wine quality, a very high accuracy (>0.99) in predicting its vineyard, and a very good accuracy (>0.97) in predicting the origin of olive oil. Moreover, an AI-based tool has been developed that stakeholders can use to predict the quality and geographical origin of wine and olive oil to avoid possible fraudulent and unhealthy product labels. The obtained prediction results showcase AI and ML's potential to help the agri-food industry, providing valuable insights and innovations for producers, distributors, and consumers alike. Integrating AI and ML in the agri-food industry may enhance product quality, consumer trust, and market transparency.

Brand Churn A Significant Percentage of Customers Leaving for Competitors

Abstract: Brand churn, characterized by a significant percentage of customers leaving for competitors, poses a substantial challenge for businesses across industries. To understand the underlying factors contributing to brand churn and devise effective retention strategies, a survey questionnaire method was employed. The questionnaire was designed to gather insights into customers' reasons for switching brands, their perceptions of competitor offerings, and their preferences for brand loyalty incentives. A diverse sample of respondents representing various demographic segments and purchasing behaviours was targeted to ensure comprehensive data collection. The survey findings revealed key drivers of brand churn, including dissatisfaction with product quality, pricing competitiveness, and customer service experiences. Additionally, insights into customers' perceptions of competitor brands and the effectiveness of loyalty programs in retaining customers were garnered. The results of this survey provide valuable insights for businesses seeking to mitigate brand churn by addressing customer needs and preferences effectively. By implementing targeted retention strategies informed by these findings, businesses can enhance customer loyalty, reduce churn rates, and maintain a competitive edge in the marketplace.

Optimizing MEP Design in Pharmaceutical Cleanroom with the Integration of 2D To 7D in BIM

Abstract: This paper examines the optimization of pharmaceutical cleanroom design using 2D to 7D Modelling in Building Information Modelling (BIM). The goal is to enhance the reliability, different methods, statistical data, and performance of pharmaceutical production plants by effectively allocating components within design constraints. Mathematical optimization techniques improve facility design, ensuring robustness and operational efficiency. The study proposes a reliable optimal design for air-conditioning systems in pharmaceutical cleanrooms, considering uncertainties in design parameters and operational strategies. Resilient cleanroom systems are developed, and adaptable to various conditions while maintaining high performance standards. Additionally, the research explores the application of experimental design methodologies in optimizing drug delivery systems, such as factorial and etc, to enhance the quality and efficiency of pharmaceutical products and processes.

Simulation Study of Sand Casting by Using Runner &amp; Gates Modification in Gating System

Abstract: Sand casting remains a prevalent method for manufacturing complex metal components due to its costeffectiveness and versatility. However, optimizing the gating system, particularly through runner riser modification, is crucial for enhancing casting quality and minimizing defects. This study employs computer simulation techniques to investigate the effects of various modifications in the gating system on the solidification behavior and quality of castings. Through numerical simulations using advanced software tools, different configurations of the gating system are analyzed and compared, focusing on parameters such as flow rate, velocity distribution, and solidification patterns. The results reveal insights into the effectiveness of runner riser modification strategies in reducing defects like shrinkage and porosity, thereby improving the overall casting quality. Additionally, the study explores the trade-offs between different modification approaches in terms of cost, complexity, and performance. These findings contribute to the optimization of sand-casting processes, offering practical guidance for engineers and manufacturers aiming to enhance product quality and efficiency.

Combined effects of low pressure superheated steam drying and vacuum drying on sugar reduction and quality attribute in mango (Mangifera indica L.) slices

Abstract Consumers paying more attention to physical health has led to an increasing market demand for low-sugar dried fruit products. The quality of products dried via low pressure superheated steam drying (LPSSD) is not only superior to those dried via conventional hot air or vacuum drying (VD), but also has the potential to reduce sugar content. In order to elucidate the mechanism of reducing the sugar of mango slices by LPSSD and obtain low-sugar dried mango slices, the combined effect of LPSSD–VD on mango slices was studied and an evaporation experiment of a sugar solution in a low pressure superheated steam environment was performed. This study revealed that the sugar reduction of mango slices was mainly due to the superheated steam carryover phenomenon in the second half of the constant-temperature stage and the occurrence of Maillard reaction during LPSSD. The quality attributes of mango slices dried using LPSSD–VD was improved compared with LPSSD and VD. As a result, LPSSD–VD could be used to regulate the sugar content in dried fruit and provide a theoretical basis for the production of low-sugar preserved fruits.

Influence of the rehydration period on yield quality and harvest performance in Manzanilla de Sevilla super high-density olive orchards

AbstractSuper-high density olive orchards may increase profitability for table olive producers. However, water needs and fruit damage could limit their viability. Deficit irrigation scheduling would reduce the amount of water required, but rehydration before harvest is extremely important. The aim of this work was to compare a typical deficit farm management model with a regulated deficit irrigation one based on the plant water status. The experiment was carried out during three seasons in a three years-old (4 × 1.5 m) commercial table olive orchard. In both irrigation scheduling plots, seasonal amount of applied water was similar. Irrigation treatments were: common farm management (CFM), an almost constant rate of irrigation, and Regulated deficit (RDI) based on midday shaded water potential. Vegetative measurements suggested that RDI increased the external surface of the hedge, and this was related with greater yield in this treatment. Significant differences were found in of fruit size at harvest in some seasons, with larger fruits in RDI compared to CFM. However, fruit damage occurred during mechanical harvest in both irrigation managements, despite the higher skin firmness in RDI, and shows the need to carry out postharvest treatments to improve the quality of the final product. Accurate control of the tree water status would allow optimizing the amount of water available for table olive orchards.

Exogenous melatonin prolongs raspberry postharvest life quality by increasing some antioxidant and enzyme activity and phytochemical contents

There is a growing trend towards enhancing the post-harvest shelf life and maintaining the nutritional quality of horticultural products using eco-friendly methods. Raspberries are valued for their diverse array of phenolic compounds, which are key contributors to their health-promoting properties. However, raspberries are prone to a relatively short post-harvest lifespan. The present study aimed to investigate the effect of exogenous melatonin (MEL; 0, 0.001, 0.01, and 0.1 mM) on decay control and shelf-life extension. The results demonstrated that MEL treatment significantly reduced the fruit decay rate (P ≤ 0.01). Based on the findings, MEL treatment significantly increased titratable acidity (TA), total phenolics content (TPC), total flavonoid content (TFC), and total anthocyanin content (TAC). Furthermore, the MEL-treated samples showed increased levels of rutin and quercetin content, as well as antioxidant activity as measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reduction activity potential (FRAP). Additionally, the samples exhibited higher levels of phenylalanine ammonia-lyase (PAL) and catalase (CAT) enzymes compared to the control samples. Moreover, the levels of pH, total soluble solids (TSS), and IC50 were decreased in the MEL-treated samples (P ≤ 0.01). The highest amount of TA (0.619 g/100 ml juice), rutin (16.722 µg/ml juice) and quercetin (1.467 µg/ml juice), and PAL activity (225.696 nm/g FW/min) was observed at 0.001 mM treatment, while, the highest amount of TAC (227.235 mg Cy-g/100 ml juice) at a concentration of 0.01 mM and CAT (0.696 u/g FW) and TAL activities (9.553 nm/100 g FW) at a concentration of 0.1 mM were obtained. Considering the lack of significant differences in the effects of melatonin concentrations and the low dose of 0.001 mM, this concentration is recommended for further research. The hierarchical cluster analysis (HCA) and principal component analysis (PCA) divided the treatments into three groups based on their characteristics. Based on the Pearson correlation between TPC, TFC, TAC, and TAA, a positive correlation was observed with antioxidant (DPPH and FRAP) and enzyme (PAL and CAT) activities. The results of this study have identified melatonin as an eco-friendly compound that enhances the shelf life of raspberry fruits by improving phenolic compounds, as well as antioxidant and enzyme activities.

Dynamic changes of tenderness, moisture and protein in marinated chicken: the effect of different steaming temperatures.

The steam processing characteristics of chicken are a key factor in the simplicity and versatility of steamed chicken dishes. The aim of this study was to investigate in depth the changes in tenderness and water retention of marinated chicken at different slow steaming endpoint temperatures, and to further explore the effect of the evolution of protein conformations on the water status. The results showed that chicken samples' shear force peaked at 80 °C and decreased rapidly at 90 °C. As the steaming endpoint temperature increased between 50 and 90 °C, T21, T22, moisture content and centrifugal loss decreased, but P21, P22 and myofibril water-holding capacity showed regular changes. The electrophoretic bands and protein conformation changes showed that protein in marinated chicken underwent different degrees of denaturation, degradation and aggregation. And at 70 °C, with an increase of hydrophobic groups and crosslinking of disulfide bonds as well as an increase in the number of denatured sarcoplasmic proteins, the intermolecular network was enhanced, thus affecting the water retention. Water status of chicken meat heated at different steaming temperatures is closely related to the evolution of protein conformations. The present study serves as a robust theoretical foundation for enhancing the quality of steamed chicken products at an industrial scale. © 2024 Society of Chemical Industry.

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

The Impact of Advanced Manufacturing Technology on Achieving Competitive Advantage: A Field Study at Foodstuff Manufactories in the Capital Municipality of Sana’a

The study aimed to investigate the impact of advanced manufacturing technology on comparative advantage of foodstuff manufactories in the Capital Municipality of Sana’a. Using an analytical approach, data were collected from a sample of 90 employees through a questionnaire. The data were analyzed using inferential statistical methods applied with SPSS software. The findings revealed a positive influence of advanced manufacturing technology on the competitive advantage of foodstuff manufacturers in the Capital Municipality of Sana’a. The study also revealed that information technology dimension was the most influential factor affecting the competitive advantage of foodstuff manufactories, while the design and engineering dimension had the least impact. Based on these findings, the study recommends further strengthening the application of advanced manufacturing technology in foodstuff manufactories in the Capital Municipality of Sana’a. Additionally, it emphasizes the importance of robust supervision of the implemented manufacturing systems to ensure product quality. The study underscores the need for higher focus on the quality of raw materials used in the manufacturing process.

Energy-conserving cold chain with ambient temperature, path flexibility, and hybrid fleet: formulation and heuristic approach

Cold chain logistics networks represent intricate systems that require harmonisation of their influence on the economy, environment, and society. However, simultaneously achieving these goals is hard. This paper defined a comprehensive model aiming to achieve a tradeoff of these goals related to cost efficiency, product quality, delivery timeliness, and environmental impacts. Meanwhile, the influences of ambient temperature, path flexibility, and hybrid fleet on the proposed dual-mode location-routing problem-based cold chain logistics (DMLRPCCL) are analyzed. A meticulously crafted hyper-heuristic framework employing Q-learning has been developed to address the complexity of cold chains to obtain high-quality solutions. The numerical study has shown that the proposed model can analyze various scenarios for perishable products and evaluate their impact on cost, emissions, and quality. The proposed algorithm is efficient and effective in achieving competitive results compared to three tailored algorithms. Extensive analyses are performed to empirically assess the effect of path flexibility, hybrid fleet, and ambient temperature on the DMLRPCCL planning. Several managerial insights are presented.

Exploration of the Application of Agricultural Intelligent Robots in the Qinghai-Tibet Plateau

In order to improve agricultural efficiency, reduce labor intensity, and improve the quality and yield of agricultural products on the Tibetan Plateau, this paper takes the application of traditional agricultural robots as an example and proposes the application of agricultural robots to agriculture on the Qinghai-Tibet Plateau based on the special breeding environment of the Qinghai-Tibet Plateau combined with the current situation of traditional agriculture. Through the application practice of robots in traditional agriculture, the possibility of the application of agricultural robots in the Qinghai-Tibet Plateau is pointed out, the working methods and advantages of robots in the Qinghai-Tibet Plateau are expounded, and the application of intelligent agricultural robots in the agriculture of the Qinghai-Tibet Plateau is discussed and analyzed. The results show that with the progress of science and technology, intelligent robots have gradually played an important role in various fields, and intelligent robots have broad application prospects in plateau agriculture. The application of agricultural robots in the Qinghai-Tibet Plateau should be aimed at the Qinghai-Tibet Plateau to make some adaptive research and development, intelligent upgrading and demonstration project promotion in order to promote the Qinghai-Tibet Plateau agricultural automation.

Analysis of Distribution and Marketing Efficiency of Pineapples from a Sharia Economic Perspective

The agricultural sector plays an important role in long-term economic development and rural economic recovery in Indonesia. The development of agricultural-based businesses and sustainable agricultural systems is needed to improve the quality of human resources and agricultural products. Horticultural commodities, such as fruit, have high economic potential and can be a source of income for farmers. However, there are several obstacles in the agricultural sector, such as competition with other horticultural products, low quality and quantity of local supplies, and a lack of transparent market information. In addition, distribution in Islam has principles that can be applied in the context of the distribution of wealth and economic resources in Islam, such as justice, equality, public benefits, prohibition of exploitation, and social solidarity. This research aims to analyze pineapple marketing distribution and pineapple marketing efficiency from a sharia economic perspective. The research method used is field research using primary and secondary data. The research results show that there are two marketing distribution channels that are used, namely marketing distribution channel I and marketing distribution channel II. Marketing distribution channel I involves farmers selling directly to the city without intermediaries, while marketing distribution channel II involves collectors buying from farmers and selling to the city. Keywords: Distribution, Efficiency, Marketing

Emergence of cheese packaging by edible coatings for enhancing its shelf-life

AbstractCheese is one of the most popular and extensively consumed dairy products. It comes in a variety of flavors, textures, shapes and is prone to infection and decomposition. Edible coatings as cheese packaging serve various key functions in the preservation and enhancement of cheese quality, including increasing shelf life, retaining texture, boosting sensory qualities, and lowering plastic waste and environmental impact. In recent decades, the development of edible coatings has witnessed tremendous growth, and it is projected to have a significant impact on the quality of cheese products in the next few years. Novel edible materials and processing methods are of great interest due to their intriguing potential as revolutionary cheese packaging systems. This review summarizes recent trends in adding edible coating to increase the quality and the shelf-life of cheese, plus the functional of edible coating and their application on cheese to reduce the plastic waste and environmental impact.

The Influence of Halal Labeling, Product Quality, and Price on Purchasing Decisions for Tteokbokki Products

The goal is to find out the influence of halal labeling, product quality, and price on purchasing decisions on tteokbokki products. This study used literature studies and questionnaires with data collection techniques using a Likert scale. The population of this study were students of Sharia Economics, University of Sultan Ageng Tirtayasa. This type of research uses correlational research, which is a type of research with a problem characteristic in the form of a correlation relationship between two or more variables. As a result of data processing with SPSS Version 26, the r square value at the coefficient of determination of .374 or 37.4% of the variables of halal labeling, product quality, and price affect the variables of purchase good decisions. The regression equation obtained KP = -3.293+.721 LH- .076 KPr+.679+e. The calculated F value is 7.379 and the resulting Ftable is 2.86. Fcount&gt;Ftable (7.379&gt;2.86), which means that Ho4 was rejected and Ha4 was accepted. It can be seen that simultaneously the variables of halal labeling, product quality, and price have a positive effect on purchasing decisions. But partially, product quality does not affect purchasing decisions. Keywords: halal labeling, product quality, price, purchase decision

High-Fiber Crackers Supplemented with Asparagus Hard-Stem: Impacts of Supplementation Ratios and Water Amounts in Cracker Recipe on the Product Quality

High-Fiber Crackers Supplemented with Asparagus Hard-Stem: Impacts of Supplementation Ratios and Water Amounts in Cracker Recipe on the Product Quality

Post-market surveillance of six COVID-19 point-of-care tests using pre-Omicron and Omicron SARS-CoV-2 variants.

Post-market surveillance of test performance is a critical function of public health agencies and clinical researchers that ensures tests maintaining diagnostic characteristics following their regulatory approval. Changes in product quality, manufacturing processes over time, or the evolution of new variants may impact product performance. During the COVID-19 pandemic, a plethora of point-of-care tests (POCTs) was released onto the Canadian market. This study evaluated the performance characteristics of several of the most widely distributed POCTs in Canada, including four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT). All tests were challenged with 149 SARS-CoV-2 clinical positives, including multiple variants up to and including Omicron XBB.1.5, as well as 29 clinical negatives. Results were stratified based on whether the isolate was Omicron or pre-Omicron as well as by reverse transcriptase quantitative PCR Ct value. The test performance of each POCT was consistent with the manufacturers' claims and showed no significant decline in clinical performance against any of the variants tested. These findings provide continued confidence in the results of these POCTs as they continue to be used to support decentralized COVID-19 testing. This work demonstrates the essential role of post-market surveillance in ensuring reliability in diagnostic tools.IMPORTANCEPost-market surveillance of diagnostic test performance is critical to ensure their reliability after regulatory approval. This is especially critical in the context of the COVID-19 pandemic as the use of point-of-care tests (POCTs) became widespread. Our study focused on four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT) that were widely distributed across Canada, assessing their performance using many SARS-CoV-2 variants, including up to Omicron subvariant XBB.1.5. Overall, we found no significant difference in performance against any variant, reinforcing confidence in their use. As concerns in test efficacy have been raised by news outlets, particularly regarding the BTNX Rapid Response, this work is even more timely and crucial. Our research offers insights into the performance of widely used COVID-19 POCTs but also highlights the necessity for post-market surveillance.