To identify a novel optimized strategy for preventing fraudulent substitutions of squid species and origins, forty European squids (Loligo vulgaris) and forty flying squids (Todarodes sagittatus) from the Mediterranean Sea and Atlantic Ocean were analyzed for δ13C, δ15N, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Yb, and Lu using isotope ratio mass spectrometry and inductively coupled plasma-mass spectrometry. While δ13C and δ15N variations were mainly species-related, they alone could not reliably distinguish samples. To address this issue, decision rules were developed using Classification and Regression Tree analysis. Threshold values for δ13C (-19.91‰), δ15N (14.87‰), and Pr (0.49 μg kg-1) enabled successful discrimination among Mediterranean European squids, Atlantic European squids, Mediterranean flying squids, and Atlantic flying squids, achieving over 90% accuracy, 81% precision, 80% sensitivity, and 93% specificity. This method holds promise for enhancing traceability and safety in the seafood industry, ensuring product integrity and consumer trust.

Traditional coding methods based on graphics and digital or magnetic labels have gradually decreased their anti-counterfeiting because of market popularity. This paper presents a new magnetic anti-counterfeiting coding method. This method uses a high-performance coding material, which, along with small changes to the material itself and the particle size of the superparamagnetic nanomaterials, results in a large difference in the nonlinear magnetization response. This method, which adopts 12-site coding and establishes a screening model by measuring the voltage amplitude of 12-site variables, can code different kinds of products, establishing long-term stable coding and decoding means. Through the anti-counterfeiting experiment of wine, the experiment results show that the authenticity of the coded products can be verified using the self-developed magnetic encoding and decoding system. The new coding technology can verify the anti-counterfeiting of 9000 products, with a single detection accuracy of 97% and a detection time of less than one minute. Moreover, this coding method completely depends on the production batch of the superparamagnetic nanomaterials, which is difficult to imitate, and it provides a new coding anti-counterfeiting technology for related industries with a wide range of potential applications.

The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.

To trace the origin of the gushing water in the riverine area of the Beijing section of The Middle Route of South-to-North Water Diversion Project, a dataset was established comprising water chemistry, three-dimensional fluorescence spectra, and stable isotopes for different water bodies. Results indicated significant differences in Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Ca2+ concentration among the gushing water, river water, and the water from the Middle Route of South-to-North Water Diversion Project (MRSD). Analysis using parallel factor analysis (PARAFAC) and fluorescence index revealed that dissolved organic matter (DOM) in the MRSD mainly originated from endogenous sources, while the river water and gushing water showed influences from both endogenous and exogenous sources. Nitrate sources varied among the water bodies, with distinct contributions from domestic sewage and fertilizer sources. The evaporation lines of river water and gushing water exhibited similar intercepts and slopes, but their intercepts and slopes are much smaller than those of the MRSD, suggesting stronger kinetic evaporative fractionation. In conclusion, the gushing water in the riverine area of the MRSD was determined to originate from the river, providing a fast and efficient method for gushing water source identification.

The feasibility of Near Infrared Spectroscopy was assessed for aging traceability of steaks of Angus beef (Biceps femoris) individually vacuum-packaged, as well as for the prediction of the refrigeration storage time (0, 7, and 14 days). For this purpose, a total of 288 steaks homogeneously distributed among the sampling times were used. The model developed by Partial Least Squares-Discriminant Analysis offered high discrimination ability between aged beef vs. non-aged. The accuracy after external validation exceeded 90%. Regarding the predictive capacity of the storage time, it was greater on the set of aged samples, in which the accuracy achieved values higher than 96%, while the accuracy decreased to 75% for the non-aged samples. Results obtained support the ability of NIRS technology to be considered in any digital transformation strategy for traceability across the meat supply chain.

UNASSIGNED: Halal restaurants participating in online food delivery services do not require halal certification. The Halal status of products through the Halal logo provides the consumer with information on the basis of which he decides to buy. Online transactions involve potential risks related to online processes, payment methods, and product quality. The aim of this study was to determine whether a declared Halal label is in accordance with the business processes implemented.
UNASSIGNED: Halal authentication of Gofood\'s meatball partner products in Yogyakarta and Solo Raya determined the incompatibility of meatball ingredients. Sixty meatball samples were collected from Yogyakarta and 30 samples from Solo Raya. Halal certification test was carried out using the thermal cycle polymerase chain reaction method at Universitas Gadjah Mada Animal Husbandry Laboratory and the results were used to identify pork contamination in meatballs. The addition of pork or pork meatballs was used as a control.
UNASSIGNED: Eight meatball restaurants in the Solo Raya and Yogyakarta areas were found to be contaminated with pig DNA. The results of the tracing materials and processes, i.e., the grinding stage, are critical because all samples were supposed to be made from beef. It is known from interviews that contamination with pig DNA at the milling stage was accidental.
UNASSIGNED: Restaurants that sell meatballs are committed to adhering to product labels that are 91.1% safe from pork contamination. The Halal and original beef labels were in accordance with their statements. This study highlights the concept of Halal authentication with traceability to overcome pork contamination in meat products.

The Digital Product Passport (DPP) as a product-specific data set is a powerful tool that provides information on the origin or composition of products and increases transparency and traceability. This recycling case study accompanies the production of 2192 frisbees, which originated from collected beverage bottle caps. In total, 486.7 kg of feedstock was collected and transformed into 363.2 kg of final product with verified traceability through all process steps via a DPP, provided by the R-Cycle initiative and based on the GS1 standard. This demanded a generally agreed dataset, the availability of technical infrastructure, and additional effort in the processing steps to collect and process the data. R-Cycle offers a one-layer DPP where the data structure is lean and information is visible to everyone. This is beneficial to a variety of stakeholders in terms of transparency. However, it does not allow the sharing of sensitive information. On the one hand, the DPP has a high potential to be an enabler for customer engagement, origin verification, or as a starting point for more efficient and advanced recycling of plastics. On the other hand, the DPP involves a certain effort in data generation and handling, which must be justified by the benefits. For small, simple packaging items, the DPP may not be the perfect solution for all problems. However, with a broader societal mindset and legislative push, the DPP can become a widely used and trusted declaration tool. This can support the plastics industry in its journey towards a circular economy.

It is generally recognized that the use of physical and digital information-based solutions for tracking plastic materials along a value chain can favour the transition to a circular economy and help to overcome obstacles. In the near future, traceability and information exchange between all actors in the value chain of the plastics industry will be crucial to establishing more effective recycling systems. Recycling plastics is a complex process that is particularly complicated in the case of acrylonitrile butadiene styrene (ABS) plastic because of its versatility and use in many applications. This literature study is part of a larger EU-funded project with the acronym ABSolEU (Paving the way for an ABS recycling revolution in the EU). One of its goals is to propose a suitable traceability system for ABS products through physical marking with a digital connection to a suitable data-management system to facilitate the circular use of ABS. The aim of this paper is therefore to review and assess the current and future techniques for traceability with a particular focus on their use for ABS plastics as a basis for this proposal. The scientific literature and initiatives are discussed within three technological areas, viz., labelling and traceability systems currently in use, digital data sharing systems and physical marking. The first section includes some examples of systems used commonly today. For data sharing, three digital technologies are discussed, viz., Digital Product Passports, blockchain solutions and certification systems, which identify a product through information that is attached to it and store, share and analyse data throughout the product\'s life cycle. Finally, several different methods for physical marking are described and evaluated, including different labels on a product\'s surface and the addition of a specific material to a polymer matrix that can be identified at any point in time with the use of a special light source or device. The conclusion from this study is that the most promising data management technology for the near future is blockchain technology, which could be shared by all ABS products. Regarding physical marking, producers must evaluate different options for individual products, using the most appropriate and economical technology for each specific product. It is also important to evaluate what information should be attached to a specific product to meet the needs of all actors in the value chain.

To optimize the application of plant growth-promoting rhizobacteria (PGPR) in field trials, tracking methods are needed to assess their shelf life and to determine the elements affecting their effectiveness and their interactions with plants and native soil microbiota. This work developed a real-time PCR (qtPCR) method which traces and quantifies bacteria when added as microbial consortia, including five PGPR species: Burkholderia ambifaria, Bacillus amyloliquefaciens, Azotobacter chroococcum, Pseudomonas fluorescens, and Rahnella aquatilis. Through a literature search and in silico sequence analyses, a set of primer pairs which selectively tag three bacterial species (B. ambifaria, B. amyloliquefaciens and R. aquatilis) was retrieved. The primers were used to trace these microbial species in a field trial in which the consortium was tested as a biostimulant on two wheat varieties, in combination with biochar and the mycorrhizal fungus Rhizophagus intraradices. The qtPCR assay demonstrated that the targeted bacteria had colonized and grown into the soil, reaching a maximum of growth between 15 and 20 days after inoculum. The results also showed biochar had a positive effect on PGPR growth. In conclusion, qtPCR was once more an effective method to trace the fate of supplied bacterial species in the consortium when used as a cargo system for their delivery.

This study presents a comprehensive analysis of the elemental profiles of tea leaves coming from plants grown in several European gardens, with a focus on the bioaccumulation of essential and potentially toxic trace elements in relation to processing and location of tea garden. Samples were collected from various gardens across Europe, including Portugal, the Azores, Germany, the Netherlands, and Switzerland. Elemental analysis was conducted on fresh tea leaves, dried leaves, and leaves processed for the production of green and black tea, along with soil samples from the root zones of tea plants. The results reveal no significant differences in elemental content based on the processing of tea leaves. However, distinct elemental profiles were observed among tea leaves of plants grown in gardens from different European regions. Utilizing chemometric and machine learning tools, the study highlights the potential of these elemental profiles for enhancing the traceability of tea products.