Preventing illegal seafood trade using machine-learning assisted microbiome analysis.

BACKGROUND: Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances. The study was designed to represent a real-world scenario. Clams were collected in different seasons across the most important production area in Europe (lagoons along the northern Adriatic coast) to cover the known seasonal variation in microbiome composition for the species. DNA extracted from samples underwent the same depuration process as commercial products (i.e. at least 12 h in open flow systems). RESULTS: Machine learning-based analysis of microbiome profiles was carried out using two completely independent sets of data (collected at the same locations but in different years), one for training the algorithm, and the other for testing its accuracy and assessing the temporal stability signal. Briefly, gills (GI) and digestive gland (DG) of clams were collected in summer and winter over two different years (i.e. from 2018 to 2020) in one banned area and four farming sites. 16S DNA metabarcoding was performed on clam tissues and the obtained amplicon sequence variants (ASVs) table was used as input for ML MP. The best-predicting performances were obtained using the combined information of GI and DG (consensus analysis), showing a Cohen K-score > 0.95 when the target was the classification of samples collected from the banned area and those harvested at farming sites. Classification of the four different farming areas showed slightly lower accuracy with a 0.76 score. CONCLUSIONS: We show here that MP coupled with ML is an effective tool to trace the origin of shellfish products. The tool is extremely robust against seasonal and inter-annual variability, as well as product depuration, and is ready for implementation in routine assessment to prevent the trade of illegally harvested or mislabeled shellfish.

Blockchain implementation for food safety in supply chain: A review.

Food safety has emerged as the topmost priority in the current fast-paced food industry era. According to the World Health Organization, around 600 million people, approximately 1 in 10 individuals worldwide, experience illness due to contaminated food consumption, resulting in nearly 0.42 million fatalities annually. The recent development in software and hardware sectors has created opportunities to improve the safety concerns in the food supply chain. The objective of this review is to explain the fundamentals of blockchain and its integration into the supply chain of various food commodities to enhance food safety. This paper presents the analysis of 31 conceptual works, 10 implementation works, 39 case studies, and other investigations in blockchain-based food supply chain from a total of 80 published papers. In this paper, the significance of adapting conceptual ideas into practical applications for effectively tracing food commodities throughout the supply chain has been discussed. This paper also describes the transformative role of blockchain platforms in the food industry, providing a decentralized and transparent ledger to access real-time and immutable records of a product's journey. In addition, both the positive impacts and challenges associated with implementing blockchain technology in the food supply chain have been evaluated. In summary, the blockchain-based food supply chains offer greater transparency, traceability, and trust, ultimately resulting in higher standards of food safety and quality.

Incidence of foliar treatments and geographical origin on the geochemical fingerprints of leaves and fruits in olive growing.

Recently, food quality and safety has become of great interest, with a consequent demand for geographical identification of agri-food products and eco-friendly agricultural practices. In this study geochemical analyses of soils, leaves and olives from two areas in the Emilia-Romagna Region (Italy), Montiano and San Lazzaro were performed aiming at identifying geochemical fingerprints able to (1) univocally determine the locality of provenance and (2) the effect of different foliar treatments (control, dimethoate, and alternating of natural zeolitite and dimethoate in MN; Spinosad + Spyntor fly, natural zeolitite and NH4+-enriched zeolitite in SL). PCA and PLS-DA (including VIP analysis) were used to discriminate between localities and different treatments. Bioaccumulation and Translocation Coefficients (BA and TC) were studied to evaluate differences in the uptake of trace elements by plants. The PCA performed on soil data highlighted a total variance of 88.81%, allowing a good distinction between the two sites. Leaves and olives PCA showed that using trace elements it is possible to discriminate different foliar treatments (total variance: 95.64% and 91.08% in MN; 71.31% and 85.33% in SL of leaves and olives, respectively) better than the identification of their geographical origin (87.46% of leaves and 83.50% of total variance of olives). PLS-DA of all samples gave the largest contribution to the discrimination of different treatments and geographical identification. Among all elements, only Lu and Hf were able to correlate soil, leaf, and olive for geographical identification through VIP analyses, but also Rb and Sr were significant in the plant uptake (BA and TC). For the discrimination of different foliar treatments, Sm and Dy were identified in MN site, whereas Rb, Zr, La and Th correlated leaves and olives from SL. Based on trace element analyses, it can be put forward that (1) the geographical origin could be discriminated and (2) different foliar treatments applied for crop protection can be recognized, which means, reversing the reasoning that each farmer can develop a method to pinpoint his own product.

Smart traceability for food safety.

Current food production faces a tremendous challenge due to the growing human population. The global population is estimated to reach 9 billion by 2050 with 70% more food being required. Safe food is an important dimension of food security, and food traceability across the supply chain is a key component of this. However, current food traceability systems are challenged by frequent occurrences of food safety incidents and food recalls that have damaged consumer confidence, caused huge economic loss, and put pressure on food safety agencies. This review focuses on smart food traceability that has the potential to significantly improve food safety in global food supply chains. The basic concepts and critical perspectives for various detection strategies for food safety are summarized, including portable detection devices, smart indicators and sensors integrated on food packages, and data-assisted whole-genome sequencing. In addition, new digital technologies, such as Internet-of-things (IoTs) and cloud computing, are discussed with the aim of providing readers with an overview of the exciting opportunities in smart food traceability systems.

A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability.

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO2. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted.

Blockchain-IoT Sensor (BIoTS): A Solution to IoT-Ecosystems Security Issues.

Sensor devices that act in the IoT architecture perception layer are characterized by low data processing and storage capacity. These reduced capabilities make the system ubiquitous and lightweight, but considerably reduce its security. The IoT-based Food Traceability Systems (FTS), aimed at ensuring food safety and quality, serve as a motivating scenario for BIoTS development and deployment; therefore, security challenges and gaps related with data integrity are analyzed from this perspective. This paper proposes the BIoTS hardware design that contains some modules built-in VHDL (SHA-256, PoW, and SD-Memory) and other peripheral electronic devices to provide capabilities to the perception layer by implementing the blockchain architecture's security requirements in an IoT device. The proposed hardware is implemented on FPGA Altera DE0-Nano. BIoTS can participate as a miner in the blockchain network through Smart Contracts and solve security issues related to data integrity and data traceability in an Blockchain-IoT system. Blockchain algorithms implemented in IoT hardware opens a path to IoT devices' security and ensures participation in data validation inside a food certification process.

Exploring Chinese consumers' attitudes toward traceable dairy products: A focus group study.

Dairy products are an essential part of a healthy diet, and dairy is an emerging food industry in China. With rapid economic development, Chinese consumers are increasingly health-conscious and are becoming more selective about the quality and safety of dairy products. Adopting a qualitative approach, we explored Chinese consumers' perception of dairy food safety and attitudes toward traceable dairy products through 9 focus group interviews administered in 4 urban locations in North and South China, with a total of 61 participants. Results showed that a prevalence of food safety incidents triggers consumers to lower their confidence in food safety and to pay more attention to the news about food safety incidents in the media, including social media. Chemical residues ranked as the first concern for food safety in the dairy industry. Meanwhile, traceable dairy products were not well known among consumers. Although the possibility to trace back all stages of the food supply chain in the dairy sector was considered important, respondents raised doubts about the truthfulness of traceability information.

Traceability of 'Mozzarella di Bufala Campana' production chain by means of carbon, nitrogen and oxygen stable isotope ratios.

BACKGROUND: New techniques are required to guarantee the authenticity of food, especially for PDO (Protected Designation of Origin) trademarks. The genuineness of a product is directly related to the raw material and to the production process used. In this article, the traceability of the Mozzarella di Bufala Campana PDO was investigated, using carbon, nitrogen and oxygen stable isotopes ratios, measured on buffalo feeding, milk and mozzarella, from Caserta and Salerno farms. Furthermore, 37 mozzarella brands were analyzed (carbon, nitrogen and oxygen isotopes) from the different production areas, to characterize their origin. RESULTS: The results of this work showed no changes in carbon and nitrogen isotopic ratios of milk and mozzarella, indicating no fractionation in the production process. The δ13 C of milk was influenced by feeding signal; while, milk δ15 N was regulated by fractionation occurring during ruminant metabolism. Mozzarella oxygen isotopic signal depleted with respect to the milk one. Regarding brand samples, it was found that the geographical differentiation is based more on carbon isotopes than on the nitrogen and oxygen ones. CONCLUSION: This work gives an important contribution to the knowledge regarding the traceability of such a particular cheese as mozzarella. © 2019 Society of Chemical Industry.

Hydrophobic Amino Acid Content in Onions as Potential Fingerprints of Geographical Origin: The Case of Rossa da Inverno sel. Rojo Duro.

In this study, we were interested in comparing the amino acid profile in a specific variety of onion, Rossa da inverno sel. Rojo Duro, produced in two different Italian sites: the Cannara (Umbria region) and Imola (Emilia Romagna region) sites. Onions were cultivated in a comparable manner, mostly in terms of the mineral fertilization, seeding, and harvesting stages, as well as good weed control. Furthermore, in both regions, the plants were irrigated by the water sprinkler method and subjected to similar temperature and weather conditions. A further group of Cannara onions that were grown by micro-irrigation was also evaluated. After the extraction of the free amino acid mixture, an ion-pairing reversed-phase (IP-RP) HPLC method allowed for the separation and the evaporative light scattering detection of almost all the standard proteinogenic amino acids. However, only the peaks corresponding to leucine (Leu), phenylalanine (Phe), and tryptophan (Trp), were present in all the investigated samples and they were unaffected from the matrix interfering peaks. The use of the beeswarm/box plots revealed that the content of Leu and Phe were markedly influenced by the geographical origin of the onions (with *** p.

Food Safety Evaluation Based on Near Infrared Spectroscopy and Imaging: A Review.

In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food hazards, food authentication, and traceability. Near infrared (NIR) spectroscopy and imaging techniques have gained wide acceptance in many fields because of their advantages over other analytical techniques. Following a brief introduction of NIR spectroscopy and imaging basics, this review mainly focuses on recent NIR spectroscopy and imaging applications for food safety evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; (3) physical hazards detection; (4) new technology-induced food safety concerns; and (5) food traceability. The review shows NIR spectroscopy and imaging to be effective tools that will play indispensable roles for food safety evaluation. In addition, on-line/real-time applications of these techniques promise to be a huge growth field in the near future.

Review: domestic animal forensic genetics - biological evidence, genetic markers, analytical approaches and challenges.

This review highlights the importance of domestic animal genetic evidence sources, genetic testing, markers and analytical approaches as well as the challenges this field is facing in view of the de facto 'gold standard' human DNA identification. Because of the genetic similarity between humans and domestic animals, genetic analysis of domestic animal hair, saliva, urine, blood and other biological material has generated vital investigative leads that have been admitted into a variety of court proceedings, including criminal and civil litigation. Information on validated short tandem repeat, single nucleotide polymorphism and mitochondrial DNA markers and public access to genetic databases for forensic DNA analysis is becoming readily available. Although the fundamental aspects of animal forensic genetic testing may be reliable and acceptable, animal forensic testing still lacks the standardized testing protocols that human genetic profiling requires, probably because of the absence of monetary support from government agencies and the difficulty in promoting cooperation among competing laboratories. Moreover, there is a lack in consensus about how to best present the results and expert opinion to comply with court standards and bear judicial scrutiny. This has been the single most persistent challenge ever since the earliest use of domestic animal forensic genetic testing in a criminal case in the mid-1990s. Crime laboratory accreditation ensures that genetic test results have the courts' confidence. Because accreditation requires significant commitments of effort, time and resources, the vast majority of animal forensic genetic laboratories are not accredited nor are their analysts certified forensic examiners. The relevance of domestic animal forensic genetics in the criminal justice system is undeniable. However, further improvements are needed in a wide range of supporting resources, including standardized quality assurance and control protocols for sample handling, evidence testing, statistical analysis and reporting that meet the rules of scientific acceptance, reliability and human forensic identification standards.

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.

Effect of Beverage Composition on Radio Frequency Identification (RFID) Performance Using Polyethylene Terephthalate (PET) Bottles for Smart Food Packaging Applications.

Radio frequency identification (RFID) technology is crucial in revolutionizing the food supply chain and combating global food waste. However, this technology faces challenges in full integration due to disruptive effects on tags caused by the dielectric properties of food and beverage ingredients, chemical constituents, and their packaging. This paper aims to demonstrate the effect of packaging and beverage contents on RFID tag performance. Three commercially available ultra-high frequency (UHF) RFID tags with different designs were tested on polyethylene terephthalate (PET) bottles, measuring tag performance through sensitivity, backscatter, and read range in the presence of various water-based solutions and commercially available beverages. The results highlight the substantial impact of the beverage type and tag design on RFID performance. The results of this study showed that tag 3 was the most consistent and readable tag amongst those tested in the presence of different beverage contents. Tag 3 resulted in a sensitivity ranging from -0.49 to -2.01 dBm, backscatter from -38.16 to 43.59 dBm, and read range from 1.58 to 1.88 m, while tag 1 performed the best in the presence of an empty PET bottle resulting in a sensitivity of -20.78 dBm, backscatter of -23.65 dBm, and read range of 16.34 m. The results of this study can be used for further investigations to develop a mathematical model that predicts the RFID tag performance based on the food composition. This model will be helpful for the design of the tags while facilitating the adoption of smart packaging for food traceability.

What the fish? Tracing the geographical origin of fish using NIR spectroscopy.

Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling.

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.

A Novel Blockchain and Internet of Things-Based Food Traceability System for Smart Cities.

Rapid urbanization has recently caused serious problems for cities all around the world. Smart cities have drawn much interest from researchers in the present research paradigm to manage the expanding urban population. Frameworks for smart cities are planned and implemented using platforms based on blockchain and the Internet of Things (BIOT). Smart cities may use the BIoT platform to provide improved transportation, food traceability, and healthcare services. Food safety is one of the sectors where less research has been done than the others. The importance of food safety is now more widely recognized, making it essential to improve the traceability and transparency of the food supply chain. In this paper, a novel BIOT-based layered framework using EOSIO has been proposed for effective food traceability. The proposed system first identifies the suitable traceability units to provide better transparency and traceability and then defines and implements a layered architecture using Ethereum and EOSIO blockchain platforms. The performance of the proposed EOSIO-based model is evaluated using the practicality of the consensus algorithm, block production rate, throughput, and block confirmation time. The proposed traceability system attains a block production rate of 0.5 s and a block confirmation time of 1 s, which is much lower than the Ethereum-based traceability system. Hence, from the experimental evidence, the superiority of the proposed EOSIO-based food traceability can be observed.

"Blockchain technology in food safety and traceability concern to livestock products".

Livestock products share more than fifteen percent of total agri-foods traded worldwide. A global increase in food demand has increased the risk to food safety. Improvements in food quality, cold chain transit, and preservation are required for safe livestock products. Though, the food safety and regulation authorities demand complete food traceability from farm to fork, but in traditional supply chain it is ignored by fiddling with the transit paperwork and bill invoices. The process of supply chain reformation and activities linked to food recalls during food safety issues are insanely expensive and challenging. Traceability-driven food supply chain management is likely to implement novel technologies like the Internet of Things (IoT). The capability of the Blockchain era within the food sector is emerging with use cases across different regions, as shown via the growing number of studies. Credibility, efficiency, and safety are all improved when food products can be instantly traced from their point of origin through all points of contact on their way to the consumer. Blockchain assures a tamper-proof and transparent system that allows an innovative business solution, together with smart contracts. However, there are significant difficulties with the implementation of blockchain technology for food traceability. It necessitates more and more training platforms as well as trainers, who can make understanding and operability of this technology easy among ground-level participants and food entities. For the tactical application of this technology, it is essential to comprehend the legal and regulatory framework.

Traceability of bilberries (Vaccinium myrtillus L.) of the Baltic-Nordic region using surface-enhanced Raman spectroscopy (SERS): DFT simulation-based DNA analysis.

Food traceability is a major issue in the industry. We investigated whether bilberries (Vaccinium myrtillus L.) from 4 different locations within the Baltic-Nordic region could be effectively differentiated using surface-enhanced Raman scattering (SERS) based spectral data and chemometric analyses. Furthermore, we aimed to determine if nucleobase (adenine and cytosine) methylation could be responsible for any observed variation. Our experiment was successful in that both principal component (PCA) and discriminant function analyses (DFA) showed differentiation between bilberry DNA from all 4 geographical regions. Density functional theory (DFT) based simulations allowed us to analyze whether DNA's spectral data dissimilarities may be due to nucleobase methylation. Although results were inconclusive on this, our investigation provides valuable data on simulated versus experimental DNA and DNA component spectra. Further research will be directed towards understanding what other epigenetic changes could be responsible for the observed DNA variation as well as determining the optimal parameters for using DFT simulations in upcoming projects.

Characterizing wine terroir using strontium isotope ratios: a review.

This paper presents a detailed review of the use of 87Sr/86Sr isotope systematics for wine provenance studies. The method is based on the principle that the Sr isotope ratio in wine reflects that of the labile fraction of the vineyard soil from which the wine is produced. The review encompasses 87Sr/86Sr data from wine samples published between 1993 and 2021 from terroirs in 22 different countries. The analytical procedures and techniques adopted by the different authors and the range of isotope ratios obtained in the different studies are discussed and evaluated. This study provides a bibliometric analysis of the 87Sr/86Sr isotope approach for wine authentication at different scales. Although limitations are evident when implemented at large (global) scales, we demonstrate that the 87Sr/86Sr isotope tracing technique remains a powerful and reliable tool for determining the geographical origin of wine when combined with detailed knowledge of the geological and soil characteristics of the substrata. For example, this combination of data allows the wines grown in the volcanic soils of Central and Southern Italy to be unambiguously fingerprinted. We present a detailed protocol for the application of the Sr isotope technique to wine authentication.

Consumers' Preferences for Apple Production Attributes: Results of a Choice Experiment.

Various food safety and environmental problems in China have raised consumer awareness of food safety issues and negative environmental impacts in various supply chains. This research assessed consumer preferences and willingness to pay (WTP) for food safety and ecosystem delivery attributes associated with apples, demonstrated through the application of different traceability systems. Research participants were recruited in Beijing (N = 384) and Shanghai (N = 320). Choice experiment methodology was applied. The data were analyzed using conditional logit, random parameter logit, and latent class models; the results indicated significant consumer preferences for traceability information, including in relation to lower pesticide usage and application of organic fertilizer during primary production. The results also indicated that participants in this research had a significant willingness-to-pay premium for apple products that had production information traceability, had reduced pesticide use, and were grown with organic fertilizers. The models demonstrated heterogeneous preferences among participants such that consumers could be divided into three classes: non-price-sensitive (53.5%), pesticide-sensitive (21.7%), and price-sensitive (24.8%).