Use of Nanotechnology to Mitigate Biofouling in Stainless Steel Devices Used in Food Processing, Healthcare, and Marine Environments

In humid environments, the formation of biofilms and microfouling are known to be the detrimental processes that first occur on stainless steel surfaces. This is known as biofouling. Subsequently, the conditions created by metabolites and the activity of organisms trigger corrosion of the metal and accelerate corrosion locally, causing a deterioration in, and alterations to, the performance of devices made of stainless steel. The microorganisms which thus affect stainless steel are mainly algae and bacteria. Within the macroorganisms that then damage the steel, mollusks and crustaceans are the most commonly observed. The aim of this review was to identify the mechanisms involved in biofouling on stainless steel and to evaluate the research done on preventing or mitigating this problem using nanotechnology in humid environments in three areas of human activity: food manufacturing, the implantation of medical devices, and infrastructure in marine settings. Of these protective processes that modify the steel surfaces, three approaches were examined: the use of inorganic nanoparticles; the use of polymeric coatings; and, finally, the generation of nanotextures.

The paradoxes of just-in-time system: an abductive analysis of a public food manufacturing and exporting company in Thailand

Purpose study aims to investigate the implementation and impact of a just-in-time (JIT) system in a food manufacturing and exporting company in Thailand. Design/methodology/approach At the company, the authors used an anomaly case study. The authors performed content analysis on the data collected through semi-structured interviews and direct observations to determine operational flows through customer order, production and delivery. The authors constructed a framework that helped in mapping current operations and subsequently assessing JIT’s impacts; the authors reported the best practices to the company’s owner. Based on the follow-up after a year, the authors used an abductive approach to refine the JIT theory using data from case organizations and relevant studies. Findings The company encountered errors and delays in international delivery owing to inadequate inputs resulting from uncertain agricultural production, delayed contact with freight forwarders, improper documentation and insufficient staffing. Besides the highly centralized system, the limitations of the JIT philosophy contributed to the issues, thereby increasing mental and physical health problems and turnover rate. Owing to these paradoxical effects, the authors extended the JIT theory. Of the study’s several recommendations, the company observed only the following: contacting the freight forwarder after the purchase order confirmation, not production completion. The authors observed increased customer satisfaction, despite the additional cost of booking containers early. Originality/value This research presents a balanced JIT that can minimize JIT’s impacts and resource shortage, owing to demand-supply uncertainties and sustain competitiveness.

Molecular subtyping for source tracking of Escherichia coli using core genome multilocus sequence typing at a food manufacturing plant

When harmful bacteria are detected in the final product at a food manufacturing plant, it is necessary to identify and eliminate the source of contamination so that it does not occur again. In the current study, the source of contamination was tracked using core genome multilocus sequence typing (cgMLST) analysis in cases where Escherichia coli was detected in the final product at a food manufacturing plant. cgMLST analysis was performed on 40 strains of E. coli collected from the environment [floor (26 strains), drainage ditch (5 strains), container (4 strains), post-heating production line (1 strain)] and products [final product (3 strains) and intermediate product (1 strain)]. In total, 40 E. coli isolates were classified into 17 genogroups by cgMLST analysis. The 4 E. coli strains isolated from the intermediate and final products were classified into two genogroups (I and II). Certain isolates collected from the environment also belonged to those genogroups, it was possible to estimate the transmission of E. coli in the manufacturing plant. Thus, the dynamics of E. coli in the food manufacturing location were clarified by using cgMLST analysis. In conclusion, our results indicate that cgMLST analysis can be effectively used for hygiene management at food manufacturing locations.

U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

How Organizational Climate Mediates Employee Innovative Work Behavior among Food Manufacturing Industries in COVID-19 Pandemic: Implications to Business Economics and Management

In this context, the study explored the relationship between organizational climate and employee innovative work behaviour among food manufacturing industries in Malaysia. The study is a descriptive correlational survey research design where data is sourced out from a total of randomly sampled 260 employees. Results revealed that a favourable organizational climate on innovation, proactivity, and risk-taking is prevailing among the companies. A very high level of innovative work behaviour is emanating among the employees on idea exploration, generation, championing, and implementation. Test of differences showed that employee gender, position, unit, and years of service spelt significant differences in the perception of the employees on organizational climate and innovative work behaviour. A meaningful relationship surfaced between organizational climate and employee innovative work behaviour, suggesting that for food manufacturing industries to sustain innovative and competitive advantages, there is a need to promote a nurturing and encouraging entrepreneurial organizational climate. Finally, a congruency among the domains of organizational climate and employee innovative work behaviour emerged. It suggests that when higher positive organizational climate surfaces, the more likely the employee's manifest innovation work behaviour. This study addressed the gap by providing organizational climate and employee innovative work behaviour among food manufacturing industries in Malaysia.

Proposing a framework for specifying the appropriateness of pursuing a career regarding Employees’ healthiness and safeguard: A Case study of food manufacturing industry

BackgroundEmployee health is an essential issue for Human Resource Management (HRM). The employees' health level is undeniably correlated to the job position in which they work since it may harm their well-being, and they may not be capable of performing their duties properly. Prompt diagnosis and resolution of employees' physical complications are highly critical.MethodsMachine learning (ML) is the state-of-the-art method potentially utilized to make early predictions to safeguard employees' healthiness. The technical laborers within the food manufacturing company are included in this Research. The functional classification models, namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree, are exploited to predict the employees' wellness for their vocation. K-fold Cross-Validation (KCV) and Confusion Matrix were applied in this study, the former for estimating the model's functionality and the latter for forecasting accuracy.ResultsAfter implementing four models on the 231 employees, the accuracy was extracted out, SVM with 78%, KNN with 78%, Decision Tree with 80%, and the highest for LR algorithm with 84%.ConclusionsIn this Research, the LR algorithm was opted to paving the way for Human Resources Managers in order to utilize a functional system to predict the Suitability of factory workers concerning their healthiness. The Hearing condition was picked out as a leading factor in selecting employees for their job position. Consequently, it is significant to planning a hearing conservation program for employees, especially those exposed to excessive noise.Trial Registration: Retrospectively registered.

Establishing enhanced Listeria control: Coupling environmental monitoring with risk-based product testing

Prevention and control of Listeria monocytogenes remains a challenge in food manufacturing facilities and methods adopted vary across different production systems and food categories. Regulatory policies also vary from region to region, although there is a convergence across the world towards risk-based approaches. Given these inconsistencies, the objective of this commentary is to reiterate two fundamentally critical components of Listeria control and prevention, and the potential benefits of actively coupling food contact surface testing and risk-based product testing programs.

Biofilms in the food industry – impact on human health

Biofilms are complex microbial communities formed by one and more species embedded in an extracellular polymeric matrix of different compositions depending on the attached microbial species and the type of food manufacturing. Attachment of bacteria to food contact surfaces and the subsequent formation of biofilms can cause equipment damage, food spoilage and even human diseases. Foodborne diseases associated with biofilms in the food industry can be intoxications or infections and can have great impact on human health. Foodborne pathogens that express capacity for biofilm formation under different conditions in the food industry, and that are in the scope of our investigations, are Salmonella (which, on contaminating a food pipeline biofilm, could induce massive outbreaks and even death in children and elderly) and Listeria monocytogenes (a ubiquitous bacterium that can cause abortion in pregnant women and other serious complications in children and the elderly).