Cold plasma for the disinfection of industrial food‐contact surfaces: An overview of current status and opportunities

In-package cold plasma treatment to extend the shelf life of food

Conventional food preservation methods such as heat treatment, irradiation, chemical treatment, refrigeration, and coating have various disadvantages, like loss of food quality, nutrition, and cost-effectiveness. Accordingly, cold plasma is one of the new technologies for food processing and has played an important role in preventing food spoilage. Specifically, in-package cold plasma has become a modern trend to decontaminate, process, and package food simultaneously. This strategy has proven successful in processing various fresh food ingredients, including spinach, fruits, vegetables, and meat. In particular, cold plasma treatment within the package reduces the risk of post-processing contamination. Cryoplasm decontamination within packaging has been reported to reduce significantly the microbial load of many foods' spoilage-causing pathogens. However, studies are needed to focus more on the effects of in-package treatments on endogenous enzyme activity, pest control, and removal of toxic pesticide residues. In this review, we comprehensively evaluated the efficacy of in-package low-temperature plasma treatment to extend the shelf life of various foods. The mechanisms by which cold plasma interacts with food were investigated, emphasizing its effects on pathogen reduction, spoilage mitigation, and surface modification. The review also critically assessed the effects of the treatments on food quality, regulatory considerations, and their potential as viable technologies to improve food safety and packaging life. In-package cold plasma treatment could revolutionize food storage when combined with other sophisticated technologies such as nanotechnology.

Intervention mechanisms of cold plasma pretreatment on the quality, antioxidants and reactive oxygen metabolism of fresh wolfberries during storage

Fresh wolfberries, a nutritious "super fruit", face limited marketing potential due to storage difficulties. This study aimed to enhance their storage stability using dielectric barrier discharge plasma (DBD) pretreatment and investigate the intervention mechanism. The results indicated that the optimal condition of DBD pretreatment for fresh wolfberries was 13.64 kV, 70 s and 2.7 kHz, which extended their shelf from 2 to 5 d at room temperature. This pretreatment reduced decay, weight loss, and firmness reduction by inactivating microorganisms and inhibiting respiration. Additionally, the decline of phenols, flavonoids, ascorbic acid, and antioxidant activity was inhibited, while maintaining high content of polysaccharides, titratable acid, and carotenoids. Interestingly, moderate DBD treatment produced reactive oxygen species (ROS) that triggered the defense response of wolfberries' ROS metabolism system and promoted the biosynthesis of flavonoids, thereby enhancing resistance to decay. The findings offer new insight into plasma effects on fruits and vegetables.

Alternative methods for RuBisCO extraction from sugar beet waste: A comparative approach of ultrasound and high voltage electrical discharge

Ultrasound (US) and high voltage electric discharge (HVED) with water as a green solvent represent promising novel non-thermal techniques for protein extraction from sugar beet (Beta vulgaris subsp. vulgaris var. altissima) leaves. Compared to HVED, US proved to be a better alternative method for total soluble protein extraction with the aim of obtaining high yield of ribulose-1,5-bisphosphate carboxylase-oxygenase enzyme (RuBisCO). Regardless of the solvent temperature, the highest protein yields were observed at 100% amplitude and 9 min treatment time (84.60 ± 3.98 mg/gd.m. with cold and 96.75 ± 4.30 mg/gd.m. with room temperature deionized water). US treatments at 75% amplitude and 9 min treatment time showed the highest abundance of RuBisCO obtained by immunoblotting assay. The highest protein yields recorded among HVED-treated samples were observed at a voltage of 20 kV and a treatment time of 3 min, disregarding the used gas (33.33 ± 1.06 mg/gd.m. with argon and 34.89 ± 1.59 mg/gd.m. with nitrogen as injected gas), while the highest abundance of the RuBisCO among HVED-treated samples was noticed at 25 kV voltage and 3 min treatment time. By optimizing the US and HVED parameters, it is possible to affect the solubility and improve the isolation of RuBisCO, which could then be purified and implemented into new or already existing functional products.

Practice and Progress: Updates on Outbreaks, Advances in Research, and Processing Technologies for Low-moisture Food Safety

Large, renowned outbreaks associated with low-moisture foods (LMFs) bring to light some of the potential, inherent risks that accompany foods with long shelf lives if pathogen contamination occurs. Subsequently, in 2013, Beuchat et al. (2013) noted the increased concern regarding these foods, specifically noting examples of persistence and resistance of pathogens in low-water activity foods (LWAFs), prevalence of pathogens in LWAF processing environments, and sources of and preventive measures for contamination of LWAFs. For the last decade, the body of knowledge related to LMF safety has exponentially expanded. This growing field and interest in LMF safety have led researchers to delve into survival and persistence studies, revealing that some foodborne pathogens can survive in LWAFs for months to years. Research has also uncovered many complications of working with foodborne pathogens in desiccated states, such as inoculation methods and molecular mechanisms that can impact pathogen survival and persistence. Moreover, outbreaks, recalls, and developments in LMF safety research have created a cascading feedback loop of pushing the field forward, which has also led to increased attention on how industry can improve LMF safety and raise safety standards. Scientists across academia, government agencies, and industry have partnered to develop and evaluate innovate thermal and nonthermal technologies to use on LMFs, which are described in the presented review. The objective of this review was to describe aspects of the extensive progress made by researchers and industry members in LMF safety, including lessons-learned about outbreaks and recalls, expansion of knowledge base about pathogens that contaminate LMFs, and mitigation strategies currently employed or in development to reduce food safety risks associated with LMFs.

Production of third generation bio-fuel through thermal cracking process by utilizing Covid-19 plastic wastes

During this pandemic, it has become customary to wear a face waste mask to guard against coronavirus illness (COVID-19). However, huge production of face waste masks, PPE kit and gloves pose environmental risks, since existing disposal methods such as incineration and reclamation which are emitting hazardous substances. In the present study covid-19 medical waste material like waste face waste masks; gloves and PPE kit (personal protective equipment) are considered as the feedstock for the thermal degradation process. Mainly nylon, polyethylene and polypropylene compounds are present in the Covid-19 medical waste compounds, further feedstock material is subjected to physical characterization process like proximate, ultimate and thermo gravimetric analysis (TGA), to determine the moisture, ash, volatile matter and decomposition temperature respectively. The waste waste mask has lower ash content of 9.7 %, whereas gloves and other PPEs has 11.8 and 11.2 % of ash respectively. Similarly volatile matter is also higher for waste waste mask than other feed stocks. Pyrolysis process is carried out between a temperature range of 100 °C to 700 °C and the products of the pyrolysis process are pyrolytic liquid, gas and residue. The maximum pyrolytic oil is produced from waste masks, gloves and other PPE kit at 300, 350 and 320 °C respectively. The calorific value of the pyrolytic oil from waste mask, gloves and other PPE kit possess 40.85,40.11,40.31 MJ/kg respectively, which indicates that all the pyrolytic oil has closer to the diesel fuel. Therefore pyroltic oil obtained from the Covid-19 medical waste can be used as an alternative fuel for CI engine.

Treatment of Fresh Meat, Fish and Products Thereof with Cold Atmospheric Plasma to Inactivate Microbial Pathogens and Extend Shelf Life

Assuring the safety of muscle foods and seafood is based on prerequisites and specific measures targeted against defined hazards. This concept is augmented by 'interventions', which are chemical or physical treatments, not genuinely part of the production process, but rather implemented in the framework of a safety assurance system. The present paper focuses on 'Cold Atmospheric pressure Plasma' (CAP) as an emerging non-thermal intervention for microbial decontamination. Over the past decade, a vast number of studies have explored the antimicrobial potential of different CAP systems against a plethora of different foodborne microorganisms. This contribution aims at providing a comprehensive reference and appraisal of the latest literature in the area, with a specific focus on the use of CAP for the treatment of fresh meat, fish and associated products to inactivate microbial pathogens and extend shelf life. Aspects such as changes to organoleptic and nutritional value alongside other matrix effects are considered, so as to provide the reader with a clear insight into the advantages and disadvantages of CAP-based decontamination strategies.

Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.