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This review paper delves into the global agricultural food supply chains through the lens of African perspectives, examining the role of blockchain and Internet of Things (IoT) technologies in transforming food traceability. It assesses the applicability and efficacy of these innovations in addressing critical issues such as food fraud, contamination, and systemic inefficiencies from an African viewpoint. By engaging in an in-depth analysis of relevant studies, this work dissects the technical, economic, legal, and operational facets of employing blockchain and IoT in the agri-food sector. The findings illuminate the transformative potential these technologies hold for enhancing food safety and transparency across supply chains. However, the review also brings to light significant hurdles related to scalability, cost-effectiveness, and regulatory frameworks that must be surmounted. Advocating for a context-sensitive application of blockchain and IoT, the paper highlights the importance of adapting these technologies to fit the diverse socio-economic and infrastructural realities prevalent in African countries. Offering valuable insights to stakeholders in agricultural technology and food safety, this comprehensive review outlines a roadmap for future research and strategic implementation efforts aimed at leveraging blockchain and IoT for the development of secure, sustainable food systems.

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Among pets, cats are the most popular in Europe. Despite the fact, the interest in the safety and quality of their food is much lower compared to the interest of caregivers in the nutrition of dogs. In this research, 27 commercial cat foods were analyzed for mislabeled component composition. Cat foods were divided into a control group, a group of fish foods and a group of other foods with alternative sources of animal protein. Chicken and pig DNA detection was performed using real-time PCR. In this research, 100% of the cat foods contained chicken DNA and 96% of the foods - pig DNA, despite the lack of declaration of these ingredients on the product label. The results indicate that cat food appear to be mislabeled to an even greater extent than dog food. Moreover, manufacturers' declarations in terms of ingredient composition do not reflect the actual composition of commercial products available on the market and intended for everyday feeding of animals. Mislabeling of these products also poses a risk for animals suffering from food allergies.

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Omics technologies, such as genomics, proteomics, metabolomics, isotopolomics, and metallomics, are important tools for analytical verification of food authenticity. However, in many cases, their application requires the use of high-resolution technological platforms as well as careful consideration of sample collection, storage, preparation and, in particular, extraction. In this overview, the individual steps and disciplines are explained against the background of the term "Green Chemistry," and the various instrumental procedures for the respective omics disciplines are discussed. Furthermore, new approaches and developments are presented on how such analyses can be made sustainable in the future.

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The emergence and development of food fraud are closely related to a country's economic system and social development. It has distinct characteristics in different historical stages, and an inherent historical logic links different historical stages. Following the thread of "what", "why", and "what to do", this study uses a broad perspective and comparative historical approach to examine the evolution of the basic characteristics, underlying causes, and management tools of food fraud in China at different historical stages over 70 years from 1949 to 2022. This study argues that the historical evolution of food fraud in China has characteristics unique to China as well as features similar to those in other countries. It provides a window for academics to understand the historical evolution of food fraud in China. It also provides valuable insights for other countries, especially developing countries, for objectively understanding the evolution of food fraud during their economic development process, and how to address it.

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Local village chicken, or "Ayam kampung" as it's known in Malaysia, is considered a premium chicken breed with a higher price than other chicken breeds. As a result of their comparable appearances and sizes, colored broiler chickens are often sold as village chickens, which is a form of food fraud that can result in a 3- to 4-fold rise in profit. Therefore, developing a breed-specific authentication method is crucial for preventing food fraud in the poultry industry. This study aims to investigate the genetic diversity of village chickens from other commercial chicken breed populations available in the market (broiler [Cobb], colored broiler [Hubbard], and layer [DeKalb]) to identify breed-specific DNA fragments as biomarkers for village chicken authentication. The Whole-genome sequencing and mutation calling of 12 chickens (3 chickens/breed) led to the identification of a total of 73,454,654 single nucleotide polymorphisms (SNP) and 8,762,338 insertion and deletions (InDel) variants, with more variants detected in the village chicken population (6,346,704 SNPs; 752,408 InDels) compared to commercial breeds. Therefore, this study revealed that village chickens were more genetically variable compared to other breeds in Malaysia. Furthermore, the breed-specific genomic region located on chromosome 1 (1:84,405,652) harboring SNP (C-T) with high discrimination power was discovered and validated which can be considered as a novel breed-specific biomarker to develop a method for accurate authentication of village chickens in Malaysia. This authentication method offers potentialw applications in the chicken industry and food safety.

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Snow Lotus Seed (SLS), esteemed for its nutritional and market value, faces challenges of authentication due to the absence of appropriate testing standards and methods. This results in frequent adulteration of SLS sourced from Gleditsia sinensis (G. sinensis) with other plant seeds endosperm. Traditional chloroplast DNA barcoding methods are inadequate for species identification due to the absence of chloroplasts in G. sinensis seeds endosperm. In this study, the homology of 11 ITS genes among 6 common Gleditsia species was analyzed. Universal primers suitable for these species were designed and screened. A DNA barcoding method for distinguishing SLS species was developed using Sanger sequencing technology, leveraging existing GenBank and Barcode of Life Data System (BOLD) databases. Optimized sample pretreatment facilitated effective DNA extraction from phytopolysaccharide-rich SLS. Through testing of commercial SLS products, the species origin has been successfully identified. Additionally, a novel instance of food fraud was uncovered, where the Caesalpinia spinosa endosperm was used to counterfeit SLS for the first time. The study established that the developed DNA barcoding method is effective for authenticating SLS species. It is of great significance for combating food fraud related to SLS, ensuring food safety, and promoting the healthy development of the SLS industry.

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The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

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Food adulteration with toxic chemicals is a global public health threat. Lead chromate adulterated spices have been linked with lead poisoning in many countries, from Bangladesh to the United States. This study systematically assessed lead chromate adulteration in turmeric, a spice that is consumed daily across South Asia. Our study focused on four understudied countries that produce >80 % of the world's turmeric and collectively include 1.7 billion people, 22 % of the world's population. Turmeric samples were collected from wholesale and retail bazaars from 23 major cities across India, Pakistan, Sri Lanka, and Nepal between December 2020 and March 2021. Turmeric samples were analyzed for lead and chromium concentrations and maximum child blood lead levels were modeled in regions where samples had detectable lead. A total of 356 turmeric samples were collected, including 180 samples of dried turmeric roots and 176 samples of turmeric powder. In total, 14 % of the samples (n = 51) had detectable lead above 2 µg/g. Turmeric samples with lead levels greater than or equal to 18 µg/g had molar ratios of lead to chromium near 1:1, suggestive of lead chromate adulteration. Turmeric lead levels exceeded 1000 µg/g in Patna (Bihar, India) as well as Karachi and Peshawar (Pakistan), resulting in projected child blood lead levels up to 10 times higher than the CDC's threshold of concern. Given the overwhelmingly elevated lead levels in turmeric from these locations, urgent action is needed to halt the practice of lead chromate addition in the turmeric supply chain.

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Food fraud is a problematic yet common phenomenon in the food industry. It impacts numerous sectors, including the market of edible mushrooms. Morel mushrooms are prized worldwide for their culinary and medicinal use. They represent a taxonomically complex group in which food fraud has already been reported. Among the methods to evaluate food fraud, some rely on comparisons of genetic sequences obtained from a sample to existing databases. However, the quality and usefulness of the results are limited by the type of comparison tool and the quality of the database used. The Centroid-based approach is applied by SmartGene in a proprietary artificial intelligence-based method for the generation of automatically curated reference databases that can be further expert curated. In this study, using sequences of the ribosomal internal transcribed spacer (ITS) of the genus Morchella (true morels), we compared this approach to the traditional pairwise alignment tool using two other databases: UNITE and Mycobank (MLST). The Centroid-based approach using an expert-curated database was more performant for the identification of 53 representative ITS sequences corresponding to validated species (83% accuracy, compared to 36% and 47% accuracy for UNITE and MLST, respectively). The Centroid method also revealed an inaccurate taxonomic annotation for sequences of commercial cultivars submitted to public databases. Combined with the web-based commercial software IDNS® available at Smartgene, the Centroid-based approach constitutes a valuable tool to ensure the quality of morel products on the market for actors of the food industry. PRACTICAL APPLICATION: The Centroid-based approach can be used by agri-food actors who need to identify true morels down to the species level without any prior taxonomical knowledge. These include routine laboratories of the food industry, food distributors, and public surveillance agencies. This is a reliable method that requires minimal skills and resources, therefore being particularly adapted for nonspecialists.

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Food fraud (often called fake food in South Africa) the deliberate misrepresentation or adulteration of food products for financial gain, is a growing problem in South Africa (SA) with severe public health and financial consequences for consumers and businesses. The recent public outcry against food fraud practices especially in communities that have lost loved ones due to the consumption of allegedly adulterated foodstuffs, highlights the grave danger that food fraud poses to consumers and the potential for significant reputational damage to food manufacturers. Despite the risks, food fraud often goes undetected, as perpetrators are becoming increasingly sophisticated. The precise magnitude of food fraud remains obscure, as incidents that do not cause consumer illnesses are frequently unreported and, as a result, are not investigated. Food fraud costs the global economy billion annually. This cost is borne by consumers, businesses, and the government. Food fraud can occur at any stage of the food supply chain, from production to processing to retailing or distribution. This is due in part to the limitations of current analytical methods, which are not always able to detect food fraud. This review of food fraud in SA looks at several factors that may be contributing to epidemic of food fraud, including inadequate penalties, inadequate government commitment, a complex labelling regulation, emerging threats such as e-commerce, and shortage of inspectors and laboratories. The review recommends establishing a single food control/safety authority, developing more food safety laboratories, and adopting innovative technologies to detect and prevent food fraud. SA faces a serious food fraud crises unless decisive action is taken.

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Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.

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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.

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Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.

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The adulteration of goat milk powder occurs frequently; cattle-derived and soybean-derived ingredients are common adulterants in goat milk powder. However, simultaneously and rapidly detecting cattle-derived and soybean-derived components is still a challenge. An efficient, high-throughput screening method for adulteration detection is needed. In this study, a rapid method was developed to detect the adulteration of common cattle-derived and soybean-derived components simultaneously in goat milk powder by combining the CRISPR/Cas12a system with recombinant polymerase amplification (RPA). A dual DNA extraction method was employed. Primers and crRNA for dual detection were designed and screened, and a series of condition optimizations were carried out in this experiment. The optimized assay rapidly detected cattle-derived and soybean-derived components in 40 min. The detection limits of both cattle-derived and soybean-derived components were 1% (w/w) for the mixed adulteration models. The established method was applied to a blind survey of 55 commercially available goat milk powder products. The results revealed that 36.36% of the samples contained cattle-derived or soybean-derived ingredients, which revealed the noticeable adulteration situation in the goat milk powder market. This study realized a fast flow of dual extraction, dual amplification, and dual detection of cattle-derived and soybean-derived components in goat milk powder for the first time. The method developed can be used for high-throughput and high-efficiency on-site primary screening of goat milk powder adulterants, and provides a technical reference for combating food adulteration.

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Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

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This paper discusses the development of rapid, reliable, and accurate polymerase chain reaction (PCR) assays for detecting opium poppy (Papaver somniferum L.) in food. Endpoint, quantitative, and digital PCRs were compared based on the amplification of a newly developed DNA marker targeting the NADPH-dependent codeinone reductase (COR) gene. Designed assays were shown to be highly specific and sensitive in discriminating opium poppy from other plant species, even in heat-treated and food samples. Digital PCR was the most sensitive, with a detection limit of up to 5 copies, i.e., approximately 14 pg of target DNA per reaction. Quantitative and digital PCR further allowed the quantification of opium poppy in up to 1.5 ng and 42 pg (15 copies) of target DNA in a sample, respectively. In addition, two duplex PCRs have been developed for the simultaneous detection of opium poppy DNA and representatives of (i) the Papaveraceae family or (ii) the Plantae kingdom. Finally, all designed assays were successfully applied for analysis of 15 commercial foodstuffs; two were suspected of being adulterated. The study results have an important impact on addressing food fraud and ensuring the safety and authenticity of food products. Beyond food adulteration, the study may also have significant implications for forensics and law enforcement.

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Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

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Fish from the pike (Esox) genus are valued in gastronomy for their superior meat quality. However, they can cause allergic reactions in sensitive consumers. This work aimed to fill the gap in the detection of pike allergens using molecular-biological techniques. New, fast, and accurate loop-mediated isothermal amplification (LAMP) and real-time PCR (qPCR) assays were designed to detect pike DNA using the parvalbumin gene as a marker. LAMP was assessed by electrophoresis, SYBR green optical detection, and real-time fluorescence detection. The latter was the most sensitive, detecting as little as 0.78 ng of pike DNA; the qPCR detection limit was 0.1 ng. The LAMP analysis took 20-70 min, which is significantly faster than qPCR. The study provides reliable detection and quantification of the parvalbumin gene in both fresh and processed samples and further highlights the versatility of the use of the parvalbumin gene for the authentication of food products and consumer protection via refined allergen risk assessment that is independent of the type of tissue or food processing method used.

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Food fraud refers to deceptive practices conducted for economic gain, and incidents of such fraud are often reported in the media and scientific literature. However, little is known about how European consumers perceive food fraud. To address this gap, a study explored Portuguese consumers' knowledge and perceptions of food fraud using qualitative methods such as free word association and semi-structured interviews. For this research, 340 participants were recruited, providing 911 valid words, classified into categories, major categories, and dimensions. Differences between consumers' previous exposure to food fraud and sociodemographic characteristics were explored. Additionally, other thirty-six participants were selected and interviewed, following a semi-structured guide. Interviews were transcribed, coded, and analyzed using a thematic analysis procedure. The results suggest that Portuguese consumers view food fraud as a morally reprehensible deception and are aware of its causes and impacts. However, not all consumers know the different forms of food fraud or the types of products vulnerable to fraud. Among the most repeated words were "deception", "expiration date", and "falsification". Despite this food fraud awareness, most consumers believed they were not exposed to food fraud and stated that they do not conduct daily practices to reduce exposure to it. Following the chi-square and Mann-Whitney tests, significant differences (p ≤ 0.05) were identified between participants exposed and not exposed to food fraud. The study also found that consumers with higher education and self-reported exposure to food fraud had a better understanding of the issue. This study provides insights for quantitative research on consumer perceptions and beliefs about food fraud to explore further vulnerable food categories and types of food fraud in real-world scenarios.

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Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

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