Search Results (29)

This study develops a pH-responsive label by incorporating anthocyanin from Clitoria ternatea into a bacterial nanocellulose (BNC) film derived from acid whey fermentation. The labels were designed to display two distinct colors—pink and purple—by adjusting the pH of anthocyanin and were integrated into beef mince packaging to monitor quality changes over a 15-day storage period at 4 °C. Color variations were assessed using a chroma meter and visual inspection, with both label types exhibiting a shift to blue in response to a deterioration in freshness. Significant differences (p < 0.05) in total color difference (∆E) were observed across data collection days. The pink label showed an ∆E of 14.19 between day 0 and day 8, increasing to 27.39 by day 15. The purple label exhibited an ∆E of 12.94 by day 8 and 27.86 by day 15. A Total Volatile Basic Nitrogen (TVBN) analysis and microbial evaluations confirmed a degradation in the quality of the beef mince, with strong correlations between ∆E and ∆TVBN (r = 0.956 for pink, r = 0.993 for purple). Additionally, good correlations were recorded between label total color differences and coliform counts (r = 0.933 for pink, r = 0.875 for purple), as well as Total Plate Counts (TPCs) (r = 0.982 for pink, r = 0.950 for purple). These results highlight the potential of acid whey-derived nanocellulose films as real-time quality indicators for intelligent food packaging systems. Full article

The integration of biosensors and the Internet of Things (IoT) in food packaging is gaining significant interest in rapidly enhancing food safety and traceability worldwide. Currently, the IoT is one of the most intriguing topics in the digital and virtual world. Biosensors can be integrated into food packaging to monitor, sense, and identify early signs of food spoilage or freshness. When coupled with the IoT, these biosensors can contribute to data transmission via IoT networks, providing real-time insights into food storage and transportation conditions for stakeholders across each stage of the food supply chain, facilitating proactive decision-making practices. The technologies of combining biosensors with IoT could leverage artificial intelligence (AI) to enhance food safety, quality, and security in food industries, compared to conventional existing food inspection technologies, which are limited to assessing weight, volume, color, and physical appearance. This review focused on highlighting the latest and existing advancements, identifying the knowledge gaps in the applications of biosensors and the IoT, and exploring their opportunities to shape future food packaging, particularly in the context of 21st-century food safety. The review also aims to investigate the role of the IoT in creating smart food ecosystems and examines how data transmitted from biosensors to IoT systems can be stored in cloud-based platforms, in addition to addressing upcoming research challenges. Concerns of data privacy, security, and regulatory compliance in implementing the IoT and biosensors for food packaging are also addressed, along with potential solutions to overcome these barriers. Full article

The application of Industry 4.0 technologies in the halal meat supply chain (HMSC) is an emerging but underexplored area. While technologies like 3D printing, AI, AR, metaverse, digital twins, robotics, and big data analytics are widely discussed in food production, their specific use in HMSC lacks comprehensive analysis. These technologies can address challenges such as cross-contamination, fraud, and traceability issues, but few studies have examined their practical implementation, highlighting the need for further empirical research. This review explores how Industry 4.0 technologies enhance efficiency, traceability, and transparency in HMSC and highlights the potential use of AR for real-time product verification, metaverse for virtual inspections, AI and robotics for improving efficiency, compliance, and hygiene, and digital twins for training and product quality monitoring. The review also identified research gaps, particularly the lack of focus on intelligent packaging, in vitro meat, and the 3D printing of halal meat products. The findings stress the need for greater exploration of these technologies in the HMSC, emphasizing their transformative potential in creating a transparent and efficient halal food system. Further research on emerging technologies like 3D printing and in vitro meat is essential, especially regarding their impact on halal standards and sustainability, ensuring future growth. Full article

Among the most concerning threats impacting global forest ecosystems is the pinewood nematode (Bursaphelenchus xylophilus (Steiner and Buhrer, 1934) Nickle, 1970), the causal agent of pine wilt disease. In Europe, effective management of this pest requires comprehensive regulatory and monitoring strategies, including the annual collection of thousands of wood samples from symptomatic trees and their surroundings, inspection of wood packaging materials like pallets, and the trapping of the insect vector, Monochamus spp., through national networks. Insects and wood samples are sent to official laboratories, where the latter are sometimes incubated at 25 °C for 15 days, aiming to maximize the probability of the detection of the nematode. This study expected to elucidate the effect of the wood incubation process on the detection of B. xylophilus by analyzing wood samples from pallets and green wood obtained from pine stands, both harbouring nematodes in adult and juvenile stages. Additionally, the investigation sought to assess how the presence of fungi, which serve as a food source for the nematodes, enables B. xylophilus to persist in treated pallet wood that is colonized by these fungi. The results indicated that the incubation period is unnecessary for detecting B. xylophilus in pallets, except when the wood is heavily colonized by fungi providing suitable nutrition for the nematodes, although such occurrences are expected to be rare. Furthermore, this study found no significant differences in population growth between the two stages of the nematode’s life cycle. This suggests that second-stage juveniles present in wood samples, despite not undergoing sexual differentiation, do not hinder the reproductive capacity of B. xylophilus. The risk of a potential infestation in treated pallet wood is unlikely if the treatment has been performed correctly, and the incubation does not contribute to increasing the probability of detecting the PWN. Conversely, for samples obtained from trees, the incubation period significantly enhances nematode detection. Full article

The manufacturing industry environment is rapidly evolving into smart manufacturing. It prioritizes digital innovations such as AI and digital transformation (DX) to increase productivity and create value through automation and intelligence. Vision systems for defect detection and quality control are being implemented across industries, including electronics, semiconductors, printing, metal, food, and packaging. Small and medium-sized manufacturing companies are increasingly demanding smart factory solutions for quality control to create added value and enhance competitiveness. In this paper, we design and develop a high-speed defect detection system based on a line-scan camera using deep learning. The camera is positioned for side-view imaging, allowing for detailed inspection of the component mounting and soldering quality on PCBs. To detect defects on PCBs, the system gathers extensive images of both flawless and defective products to train a deep learning model. An AI engine generated through this deep learning process is then applied to conduct defect inspections. The developed high-speed defect detection system was evaluated to have an accuracy of 99.5% in the experiment. This will be highly beneficial for precision quality management in small- and medium-sized enterprises Full article

Electromagnetic systems, in particular microwave/terahertz sensing technologies, are the newest among nondestructive sensing technologies. Currently, increased attention is pointed towards their use in various applications. Among these, food inspection stands out as a primary area due to its potential risk to human safety. As a result, substantial efforts are currently focused on utilizing microwave/terahertz imaging as a tool to enhance the efficacy of food quality assessments. This paper deals with the exploitation of microwave/terahertz imaging technology for food quality control and assessment. In particular, the work aims at reviewing the latest developments regarding the detection of internal quality parameters, such as foreign bodies, i.e., plastic, glass, and wood substances/fragments, as well as checking the completeness of the packaged food under consideration. Emphasis is placed on the (inline) inspection of wrapped/packaged food, such as chocolates, cookies, pastries, cakes, and similar confectionery products, moving along production conveyor belts. Moreover, the paper gives a recent overview of system prototypes and industrial products and highlights emerging research topics and future application directions in this area. Full article

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries. Full article

Single-use packaging is one of the biggest contributors to plastic waste, and reuse has been identified as a key strategy to reduce such waste. However, reusable containers typically become worn, which may influence how consumers think and feel about reuse. The present research explored whether and how evaluations of a takeaway food service changed depending on the appearance of a reusable container. Two studies were conducted (using opportunity sampling) to (i) investigate the effects that signs of use have on people’s perceptions of reusable packaging systems using quantitative methods (Study 1) and (ii) understand the rationale underpinning these evaluations using qualitative methods (Study 2). Study 1 involved an online questionnaire where participants (n = 292) were shown images of reusable bowls for takeaway food with various levels of staining and asked to evaluate the container and the restaurant serving the food using rating scales. Study 2 involved in-person interviews where participants (n = 22) were given the opportunity to inspect either a clean bowl or a stained bowl and then were asked questions about the bowls. Signs of previous use seemed to undermine people’s willingness to reuse containers in the future and were associated with more negative evaluations of the packaging, product, and restaurant serving the food. These findings provide insights into the factors that affect people’s willingness to engage with reusable packaging systems, and we use these findings to suggest behavioural and design interventions that might mitigate negative evaluations and encourage reuse. Full article

This research aims to examine the feasibility of adopting a corporate social responsibility strategy that prioritises environmental protection within the food distribution and retail sectors. The environmental strategy involves ecofriendly packaging, streamlined logistics, and conservative energy use. The company Axfood serves as a case study. The study involved public records observation, store visits, and discussions with the managers and head of CSR. The study employed a case study approach, utilising data collected from various sources and analysing it for depth and breadth of understanding to uncover systemic causes of environmental concern at Axfood. Three outcomes were derived from the practical experience gathered from observation, repeated store inspection, interviewing customers and store managers, and five conversations with top management. Recycling and cutting costs through energy efficiency allow businesses to compete based on low prices and high-quality products. It is not a long-term fix to have the market pressure businesses to prioritise products above social audits and unclear reports. Finally, the answer for future business is to learn from competitors and reach parity by having what others have while being distinctive in some respects, such as having a superior environmental conscience. Full article

Chicken is the most popular meat in the United States, and consumers may be exposed to multidrug resistant Salmonella and Campylobacter through consumption of retail chicken breasts. This study aimed to (i) determine the percentage of raw, packaged, retail chicken breasts from 27 metro areas that tested positive for Salmonella and Campylobacter; (ii) investigate the antibiotic susceptibility profiles of a subset of the isolates; and (iii) compare the Salmonella prevalence data to establishment level Salmonella categorization data published by the U.S. Department of Agriculture (USDA). USDA Food Safety and Inspection Service (FSIS) Microbiology Laboratory Guidebook (MLG) methodology was used to isolate and identify Salmonella (n = 672), Campylobacter (n = 499) from 400 g samples. National Antimicrobial Resistance Monitoring System (NARMS) methodology was followed for antimicrobial susceptibility testing of Salmonella (n = 52) and Campylobacter (n = 16) isolates. Salmonella was found in 8.6% of samples and Campylobacter in 4.2%. Having a 3 rating in USDA’s Salmonella Categorization of Individual Establishments for chicken parts was predictive of having a higher Salmonella percent positive in our data set (p ≤ 0.05). A total of 73.1% of Salmonella isolates, and 62.5% of Campylobacter isolates were resistant to ≥one class of antibiotics, with 48.1% of Salmonella isolates resistant to ≥three classes. Current results support interventions that take a ‘farm-to-fork’ approach with distinction by poultry types and parts as well as serovars, to lower antibiotic resistant Salmonella infections in humans due to poultry. Highlights: Salmonella was found in 8.6% and Campylobacter in 4.2% of chicken breasts tested; A 3 rating by USDA was predictive of a higher Salmonella percent positive; 48.1% of Salmonella isolates were resistant to 3 or more classes of antibiotics. Full article

The increase in plastic recycling is an essential pre-requisite for the transition to a circular economy. Polystyrene (PS) is a low diffusive polymer and therefore a promising candidate for recycling back into food contact similar to polyethylene terephthalate (PET). However, such a recycling of PS cups has been not established to date on a commercial scale. Even if recycling back into food contact is desired, the health of the consumer must not be at risk. As a consequence, recycling processes must go through a conservative assessment by relevant authorities. For PS, however, evaluation criteria are not published, which is a drawback for process developers. Within the study, post-consumer PS recyclates were evaluated in a similar way to existing evaluation criteria for PET and HDPE. For the recycling of post-consumer PS back into packages with direct contact with food, there are still some points open which cannot be answered conclusively today. Upon closer inspection, there appears to be enough information available to give a first indication as to whether recycling of post-consumer PS packaging materials back into direct food contact can be considered safe. The knowledge gaps in PS recycling were determined and discussed. Full article

Economically motivated adulteration (EMA) and/or cross-contamination are the two major factors resulting in the substandard quality of premium edible oil like extra virgin olive oil (EVOO) produced in food and beverage (F&B) fast-moving consumer goods (FMCG) industries. Current quality assurance methods (e.g., spectroscopy and chromatography) in FMCG involve intrusive sample extraction and ex situ analysis in a laboratory using expensive bulky instrumentation, which is neither integrable inline nor scalable to match the production throughput. Such techniques do not meet the industrial requirements of in situ testing, non-intrusive analysis, and high throughput inspection (100% product verification) leading to food loss and package waste from unwanted batch rejects. Herein, a low-cost electrical approach based on capacitance is proposed to show the proof of concept for screening EVOO-filled containers non-invasively for adulteration without any sample extraction by capturing the differences in the dielectric properties of mixed oils. The sensor system displayed a fast response (100 ms) and low detection limits for different adulterants (olive oil (32.8%), canola oil (19.4%), soy oil (10.3%) and castor oil (1.7%)), which is suitable for high-throughput (>60 sample/min) screening. Furthermore, a low-cost automated system prototype was realized to showcase the possibility of translating the proof of concept for possible scaling up and inline integration. Full article

Microwave imaging has been recently proposed as alternative technology for in-line inspection of packaged products in the food industry, thanks to its non-invasiveness and the low-cost of the equipment. In this framework, simple and effective detection/imaging strategies, able to reveal the presence of foreign bodies that may have contaminated the product during the packaging stage, are needed to allow real-time and reliable detection, thus avoiding delays along the production line and limiting occurrence of false detections (either negative or positive). In this work, a novel detection/imaging approach meeting these requirements is presented. The approach performs the detection/imaging of the contaminant by exploiting the symmetries usually characterizing the food items. Such symmetries are broken by the presence of foreign bodies, thereby determining a differential signal that can be processed to reveal their presence. In so doing, the approach does not require the prior measurement of a reference, defect-free, item. With respect to the quite common case of homogeneous food packaged in circular plastic/glass jars, numerical analyses are provided to show the effectiveness of the proposed approach. Full article

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste. Full article

Alternative feed ingredients in farm animal diets are a sustainable option from several perspectives. Former food products (FFPs) provide an interesting case study, as they represent a way of converting food industry losses into ingredients for the feed industry. A key concern regarding FFPs is the possible packaging residues that can become part of the product, leading to potential contamination of the feed. Although the level of contamination has been reported as negligible, to ensure a good risk evaluation and assessment of the presence of packaging remnants in FFPs, several techniques have been proposed or are currently being studied, of which the main ones are summarized in this review. Accordingly visual inspections, computer vision (CV), multivariate image analysis (MIA), and electric nose (e-nose) are discussed. All the proposed methods work mainly by providing qualitative results, while further research is needed to quantify FFP-derived packaging remnants in feed and to evaluate feed safety as required by the food industries. Full article