Changes in source composition of wet nitrate deposition after air pollution control in a typical area of Southeast China.

In recent years, China has adopted numerous policies and regulations to control NOx emissions to further alleviate the adverse impacts of NO3--N deposition. However, the variation in wet NO3--N deposition under such policies is not clear. In this study, the southeastern area, with highly developed industries and traditional agriculture, was selected to explore the variation in NO3--N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO3--N in precipitation were 0.67 mg L-1 and 0.54 mg L-1 in 2014-2015 and 2021-2022, respectively. The average wet NO3--N depositions in 2014-2015 and 2021-2022 was 7.76 kg N ha-1 yr-1 and 5.03 kg N ha-1 yr-1, respectively, indicating a 35% decrease. The δ15N-NO3- and δ18O-NO3- values were lower in warm seasons and higher in cold seasons, and both showed a lower trend in 2021-2022 compared with 2014-2015. The Bayesian model results showed that the NOx emitted from coal-powered plants contributed 53.6% to wet NO3--N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014-2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021-2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants.

A high security coding and anti-counterfeiting method based on the nonlinear magnetization response of superparamagnetic nanomaterials.

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.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis.

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

Data fusion of near-infrared and mid-infrared spectroscopy for rapid origin identification and quality evaluation of Lonicerae japonicae flos.

A data fusion strategy based on near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were developed for rapid origin identification and quality evaluation of Lonicerae japonicae flos (LJF). A high-level data fusion for origin identification was formed using the soft voting method. This data fusion model achieved accuracy, log-loss value and Kappa value of 95.5%, 0.347 and 0.910 on the prediction set. The spectral data were converted to liquid chromatography data using a data fusion model constructed by the weighted average algorithm. The Euclidean distance and adjusted cosine similarity were used to evaluate the similarity between the converted and the real chromatographic data, with results of 247.990 and 0.996, respectively. The data fusion models all performed better than the models constructed using single data. This indicates that multispectral data fusion techniques have a wide range of application prospects and practical value in the quality control of natural products such as LJF.

Can stable carbon isotope fingerprints be competent for geographic traceability of rice?

This study aimed to improve the traceability of rice-producing areas to address the increasing demand for accurate methods to confirm food quality and safety. Compound-specific δ13C of fatty acids, δ13C of starch and bulk of rice were measured. PCA, PLS-DA and VIP value analysis of the obtained data were performed to track the source of rice from the six regions. The PLS-DA model established with bulk δ13C, starch δ13C, and fatty acid δ13C, which clearly separated the rice from six regions. The VIP graph showed the value of starch, C18:0 and C18:2 δ13C values (VIP > 1) were important to distinguish the origin of rice. Also, according to loading plots the contribution of starch δ13C was the largest. The findings indicate that the introduction of starch δ13C improves the precision of rice traceability and provides an effective method for identifying rice origin.

Effectiveness of handheld near infrared spectrometer for traceability of Angus steaks.

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.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Traceability of gushing water in the MiddleRoute of the South-to-North Water Diversion (Beijing section) through the river area.

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.

Stepping-up accurate quantification of chlorinated paraffins: Successful certification of the first matrix reference material.

BACKGROUND: Chlorinated paraffins (CPs) are industrial chemicals categorised as persistent organic pollutants because of their toxicity, persistency and tendency to long-range transport, bioaccumulation and biomagnification. Despite having been the subject of environmental attention for decades, analytical methods for CPs still struggle reaching a sufficient degree of accuracy. Among the issues negatively impacting the quantification of CPs, the unavailability of well-characterised standards, both as pure substances and as matrix (certified) reference materials (CRMs), has played a major role. The focus of this study was to provide a matrix CRM as quality control tool to improve the comparability of CPs measurement results. RESULTS: We present the process of certification of ERM®-CE100, the first fish reference material assigned with certified values for the mass fraction of short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs, respectively). The certification was performed in accordance with ISO 17034:2016 and ISO Guide 35:2017, with the value assignment step carried out via an intercomparison of laboratories of demonstrated competence in CPs analysis and applying procedures based on different analytical principles. After confirmation of the homogeneity and stability of the CRM, two certified values were assigned for SCCPs, depending on the calibrants used: 31 ± 9 µg kg-1 and 23 ± 7 µg kg-1. The MCCPs certified value was established as 44 ± 17 µg kg-1. All assigned values are relative to wet weight in the CRM that was produced as a fish paste to enhance similarity to routine biota samples. SIGNIFICANCE AND NOVELTY: The fish tissue ERM-CE100 is the first matrix CRM commercially available for the analysis of CPs, enabling analytical laboratories to improve the accuracy and the metrological traceability of their measurements. The certified CPs values are based on results obtained by both gas and liquid chromatography coupled with various mass spectrometric techniques, offering thus a broad validity to laboratories employing different analytical methods and equipment.

Secure traceability mechanism of green electricity based on smart contracts and provenance model.

Green and low-carbon development is an important part of global sustainable development. Green power trading provides strong support and assurance for promoting green and low-carbon development. Due to the long cycle of green power data chains and their susceptibility to malicious tampering, the integrity and traceability of data are difficult to guarantee. Therefore, this paper first proposes a security provenance model with enhanced relations based on the core structure of PROV and blockchain technology, which can securely capture provenance records, use the transfer time and number of transactions between various links in the traceability network as reasoning clues, realize the correlation tracing of the green electricity transfer process. Under the model, a traceability mechanism of green electricity is designed based on smart contracts. Trustworthy green electricity data collection is achieved through data filling and data verification techniques. Traceability query technique is adopted to achieve trustworthy traceability of green electricity. And the effectiveness of the proposed solution is demonstrated through simulation experiments.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products.

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

Traceability and control as levers to prevent leakage from legal circulation when legalizing medical cannabis.

OBJECTIVE: Aim: To reveal traceability and control as levers to prevent leakage from legal circulation when legalizing medical cannabis. PATIENTS AND METHODS: Materials and Methods: The methodological basis of this research work is based on a systematic approach. Methods of structural and logical analysis, bibliosemantic, abstraction and generalization were used in this article. RESULTS: Results: The analysis of the regulatory framework and regulatory initiatives in the field of circulation of narcotic drugs, in particular, cannabis (in total 56 documents) demonstrated repeated attempts to reform it in Ukraine in order to increase the availability and efficiency of medical and pharmaceutical services. Recently adopted law on the legalization of medical cannabis pays special attention to the traceability of the circulation of medical cannabis and cannabis-based medicines (CbMs) by digitalization and creation of the appropriate electronic information system. CONCLUSION: Conclusions: With the adoption of the law on the legalization of medical cannabis Ukraine became the 57th country in the world to legalize such cannabis. The study and analysis of the regulatory framework of Ukraine, taking into account the best world practices, showed that the legalization of medical cannabis will allow for providing more effective care to many patients including wounded defenders.

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning.

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.

A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays.

Food traceability and authentication systems play an important role in ensuring food quality and safety. Current techniques mainly rely on direct measurement by instrumental analysis, which is usually designed for one or a group of specific foods, not available for various food categories. To develop a general strategy for food identification and discrimination, a novel method based on fluorescence sensor arrays is proposed, composed of supramolecular assemblies regulated by non-covalent interactions as an information conversion system. The stimuli-responsiveness and tunability of supramolecular assemblies provided an excellent platform for interacting with various molecules in different foods. In this work, five sensor arrays constructed by supramolecular assemblies composed of pyrene derivatives and perylene derivatives are designed and prepared. Assembly behavior and sensing mechanisms are investigated systematically by spectroscopy techniques. The traceability and authentication effects on several kinds of food from different origins or grades are evaluated and verified by linear discriminant analysis (LDA). It is confirmed that the cross-reactive signals from different sensor units encompassing all molecular interactions can generate a unique fingerprint pattern for each food and can be used for traceability and authentication toward universal food categories with 100% accuracy.

Real-Time PCR (qtPCR) to Discover the Fate of Plant Growth-Promoting Rhizobacteria (PGPR) in Agricultural Soils.

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.

Review and Assessment of Existing and Future Techniques for Traceability with Particular Focus on Applicability to ABS Plastics.

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.

How the Digital Product Passport Can Lead the Plastics Industry towards a Circular Economy-A Case Study from Bottle Caps to Frisbees.

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.

Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems.

Verification of beef production systems and authentication of origin is becoming increasingly important as consumers base purchase decisions on a greater number of perceived values including the healthiness and environmental impact of products. Previously Raman spectroscopy has been explored as a tool to classify carcases from grass and grain fed cattle. Thus, the aim of the current study was to validate Partial Least Squares Discriminant Analysis (PLS-DA) models created using independent samples from carcases sampled from northern and southern Australian production systems in 2019, 2020 and 2021. Validation of the robustness of discrimination models was undertaken using spectral measures of fat from 585 carcases which were measured in 2022 using a Raman handheld device with a sample excised for fatty acid analysis. PLS-DA models were constructed and then employed to classify samples as either grass or grain fed in a two-class model. Overall, predictions were high with accuracies of up to 95.7% however, variation in the predictive ability was noted with models created for southern cattle yielding an accuracy of 73.2%. While some variation in fatty acids and therefore models can be attributed to differences in genetics, management and diet, the impact of duration of feeding is currently unknown and thus further work is warranted.

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds.

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

Source traceability of microplastics in road dust using organic/inorganic plastic additives as chemical indicators.

Microplastics (MPs) are environmental pollutants of great concern around the world. The source of MPs in road dust need to be identified to develop strategies to control and reduce MPs emissions by stormwater runoff, one of the main sources of MPs to the aquatic environment. However, little information on the sources of MPs in road dust is available due to lack of their suitable indicators. In this study organic/inorganic plastic additives were used as chemical indicators to understand the source of MPs in road dust. The polymers, organic additives, and heavy metals in 142 commercial plastic products suspected of being source of MPs in road dust were determined. As the results, 147 organic additives and 17 heavy metals were identified, and different additive profiles were found for different polymer types and use application of plastic products. Further, 17 road dust samples were collected from an urban area in Kumamoto City, Japan. and analyzed the MPs (1-5 mm diameter) and their additive chemicals. Polymethyl methacrylate (PMMA) was the dominant polymer accounting for 86 % in the samples, followed by ethylene vinyl acetate (EVA) and polyvinyl chloride (PVC). In total, 48 organic additives and 14 heavy metals were identified in the MPs samples. The organic/inorganic additive profiles of plastic products and MPs in road dust were compared, and several road dust-associated MPs had similar additive profiles to road paints, braille blocks, road marking sheets, and reflectors. This suggested that the MPs were originated from these plastics on the road surface. Road paint was the most important contributor of MPs in road dust (60 % of the MPs), followed by braille block (23 %), road marking sheet (8.3 %), and reflector (2.4 %). These results indicated that organic/inorganic plastic additives in plastic products can be used as chemical indicators to trace the sources of MPs in road dust.