Authenticity Of Products Research Articles

Brand extension or co-branding: The consumer's attribution of responsibility to the crossover strategies of heritage brands

Cross-over brand extension and co-branding are effective innovation strategies for heritage brands to expand into new markets. This research compares consumers' responses to these two strategies, with the aim to address the gap in the literature and provide actionable insights for heritage brand managers. Based on the conceptual link between responsibility attribution and authenticity, and across four experimental studies, this research uncovers the advantage of brand extension over co-branding and its underlying mechanism: the serial mediation of responsibility attribution and new product authenticity. Meanwhile, the consumer's mindsets (fixed vs. growth) are identified as a boundary condition. Specifically, compared to brand extension, consumers attribute less responsibility to the heritage brand in co-branding; this in turn weakens the perception of new product authenticity, and leads to lower purchase intention for the new product. However, the disadvantage of co-branding will be neutralized when the consumer's growth mindset (vs. fixed mindset) is activated.

Purchase Intention and Attitude Toward Community Products in Sing Buri Province: The Role of Perceived Authenticity from Local Wisdom for Sustainable Economic Growth And Reduced Inequalities

Objective: This comprehensive research aims to study the factors contributing to constructing a structural equation model for marketing, providing a reliable foundation for promoting consumers’ purchase intention towards community products in Sa Chaeng Subdistrict, Bang Rachan District, Sing Buri Province: The Role of Perceived Authenticity from Local Wisdom for Sustainable Economic Growth and Reduced Inequalities. Method: The study synthesizes relevant factors from a literature review, identifying key components in the structural equation model, including perceived authenticity of the product, attitudes towards the product, and the level of purchase intention. The study employs the structural equation modeling process for analysis and hypothesis testing. The study population comprises potential consumers interested in local community products and general consumers. Data was collected using a questionnaire, with a total sample size of 400 participants. Results: The study results indicate that the structural equation model aligns with empirical data (CMIN/df = 3.448, GFI = 0.955, NFI = 0.961, RFI = 0.915, IFI = 0.972, TLI = 0.958, CFI = 0.972, RMSEA = 0.078). Conclusions: Hypothesis testing reveals that the variables of perceived authenticity of community products and attitudes toward community products have a direct positive influence on the purchase intention of community products in Sa Chaeng Subdistrict, Bang Rachan District, Sing Buri Province, at a statistically significant level of 0.01.

Exploring the Impact of Handwriting vs. Keyboarding on L2 Assessments: Biases, Integrity, Authenticity, and Literacies

Is paper or computer better for assessing L2 students’ writing? The ineluctable transition to technology might suggest this question has already been answered. However, the technology divide in L2 assessments may have indeed widened since the pandemic: whereas some teachers have fully embraced technology in assessments as in instruction, others are reluctant to eliminate paper, owing to concerns about the reliability, integrity and authenticity of L2 production on computer. This article shares observations from several French classes at an American high school in which assessments that were otherwise identical were offered to students on both paper and computer. These observations revealed several overlapping areas of L2 research that merit further consideration, including instructor bias between media, academic integrity of student work, and the need to align the technological literacies between instructors and students. The reflection that follows points to specific directions for further empirical research on the effects of input medium on L2 learners in K-12 and higher education.

The Role of Perceived Authenticity in Increasing Green Purchase Intention: Systematic Literature Review

This study explored the relationship between perceived authenticity and consumer purchase intention in green products, especially in the context of increasing awareness of sustainability among consumers. Using the Systematic Literature Review (SLR) method, this research explores the dynamics of the relationship between perceived authenticity and consumer intention for green products. Analysis of key findings reveals that perceived authenticity is not just a concept but is a key element that forms the basis of consumer trust in brands and products. The results of this study highlight the important role of perceived authenticity as the main instrument in forming positive relationships between brands, consumers and green products. The implications are particularly relevant in a marketing context, where strategies that establish and maintain product authenticity are crucial. This study provides in-depth insight into how perceived authenticity not only influences consumers' perceptions of the sustainability of green products but also directly impacts purchase intention. This study also emphasizes the importance of marketing strategies that build and maintain perceived authenticity. This strategy not only helps brands win consumer trust but also becomes a catalyst in driving change towards more sustainable and responsible consumption. Thus, this study significantly contributes to business practitioners and academics in understanding the dynamics of the green product market amidst the shift in consumer values towards sustainability.

Challenges in building scholarly knowledge graphs for research assessment in open science

Abstract Open science has revolutionized scholarly communication and research assessment by introducing research data and software as first-class citizens. Scholarly knowledge graphs (SKGs) are expected to play a crucial role in generating research assessment indicators being able to aggregate bibliographic metadata records and semantic relationships describing all research products and their links (e.g., citations, affiliations, funding). However, the rapid advance of open science has led to publication workflows that do not adequately support and guarantee the authenticity of products and metadata quality required for research assessment. Additionally, the heterogeneity of research communities and the multitude of data sources and exchange formats complicate the provision of consistent and stable SKGs. This work builds upon the experience gained from pioneering and addressing these challenges in the OpenAIRE Graph SKG. The aim is twofold and broader. First, we identify obstacles to the creation of SKGs for research assessment caused by the state-of-the-art publishing workflows for publications, software, and data. Second, we describe repurposing SKGs as tools to monitor such workflows to identify and heal their shortcomings, taking advantage of tools, techniques, and practices that support the actors involved, namely research communities, scientists, organizations, data source providers, and SKG providers, to improve the Open Science scholarly publishing ecosystem.

Simultaneous Detection of Eight Dairy-Derived Components Using Double-Tube Multiplex qPCR Based TaqMan Probe.

The authentication of milk and dairy products has great significance for food fraud. The present investigation entailed the development of a novel method that amalgamates the double-tube approach with multiplex real-time polymerase chain reaction (PCR) technology, incorporating TaqMan probes, to facilitate the high-throughput screening and detection of animal-derived constituents within milk and dairy products. Eight dairy-derived animal-specific primers and probes were designed for the mitochondrial cytochrome b (Cytb) gene of eight dairy products, including cow, buffalo, yak, goat, sheep, horse, donkey, and camel. Through the developed double-tube detection assays, the above eight targets could be simultaneously identified with a detection limit of 0.00128-0.0064 ng/μL. The multiplex qPCR assay was effectively validated using simulated adulterated samples with different mixing ratios and demonstrated a detection limit of 0.1%. Upon analysis of 54 commercially available dairy products, a mislabeling rate of 33% was revealed. This method affords an efficacious means of detecting dairy product ingredients, thereby offering robust technical backing for market oversight and regulatory enforcement of milk and dairy products.

The Diversity of N-Glycans of Chlorella Food Supplements Challenges Current Species Classification.

N-glycans have recently emerged as highly varied elements of Chlorella strains and products. Four years and eighty samples later, the increasing N-glycan diversity calls for a re-examination in the light of concepts of species designations and product authenticity. N-glycans of commercial products were analyzed by matrix-assisted time-of-flight mass spectrometry (MALDI-TOF MS) supported by chromatography on porous graphitic carbon with mass spectrometric detection. Although 36% of 172 products were labeled C. vulgaris, only 9% presented what could be taken as a C. vulgaris type N-glycan pattern. Respectively, 5 and 20% of the products matched with C. sorokiniana strains SAG 211-8k and SAG 211-34, which, however, carry entirely different structures. Furthermore, 41% presented with one of four frequently occurring glyco-types while 26% of the samples showed unique or rare N-glycan patterns. These glycan signatures thus profoundly challenge the stated species designations. By no means do we want to question the presumed health benefits of the products or the sincerity of manufacturers. We rather aim to raise awareness of the fascinating but also concerning diversity of microalgal N-glycans and suggest it as a means for defining product identity and taxonomic classifications.

Blockchain-based food traceability system and pro-environmental consumption: A moderated mediation model of technology anxiety and trust in organic food product

Blockchain technology is increasingly recognized as a transformative tool for enhancing clarity and responsibility in the organic food supply chain, where concerns about product authenticity and safety are paramount. By offering provable and translucent information about the origination, production processes, and administration of organic items, blockchain helps to reduce information asymmetry and builds consumer trust, which is critical for purchase decisions in the organic food market. Drawing on insights from the trust theory and the stimulus-organism-response framework, this study uses a moderated mediation model to explore how technology anxiety negatively moderates the direct and indirect influences of blockchain-based food traceability systems on trust in organic food products and organic food purchase intention. Using a targeted sample of 5326 consumers in Vietnam, the PROCESS macro approach (models 8 and 4) is utilized to examine the formulated hypotheses. The results demonstrate that blockchain-based food traceability systems notably enhance consumer trust in organic food products and their intention to buy these products. Trust in organic food products serves as a mediator in the connection between blockchain-based traceability and purchase intention, while technology anxiety weakens the positive impacts of blockchain technology on trust and purchase intention when anxiety levels are high. This research makes crucial contributions to the literature by offering a mechanism-based explanation of how blockchain technology enhances consumer trust in organic food products and integrates technological anxiety as a moderator, providing a deeper understanding of how technological innovations shape consumer behavior. In light of the findings, several critical recommendations have been proposed for practitioners and policymakers.

Utilizing X-ray diffraction in conjunction with competitive adaptive reweighted sampling (CARS) and principal component analysis for the discrimination of medicinal pearl powder and nacre powder.

Nacre powder, often utilized to counterfeit medicinal pearl powder due to their similar chemical composition and appearance, poses a challenge in product authentication. This study introduces a rapid and efficient method for distinguishing between medicinal pearl powder and nacre powder using X-ray diffraction in conjunction with principal component analysis (PCA). The X-ray diffraction pattern underwent preprocessing techniques including smoothing denoising (Savitzky-Golay filter, 5-point) and second-order derivative analysis. Subsequently, PCA was employed for dimensionality reduction modeling. The CARS method was applied to select optimal variables for model refinement, determining the data preprocessing approach and key modeling variables. This method demonstrates the capability to accurately differentiate between pearl powder, nacre powder, and even counterfeit samples containing up to 90% pearl powder. With a high accuracy rate, swift operational speed, and potential for automation, this approach shows promise for practical implementation in the realm of pearl powder quality control.

Fuzzy logic-based barcode scanning system for food products halal identification

Halal status in food products is fundamental for Muslims and may be crucial for those who are allergic to non-halal ingredients when choosing suitable foods for consumption. However, with the rising demand for food products and the advancement of science and technology, contamination with non-halal substances is likely to occur in food production. Furthermore, some irresponsible food industry players tend to replace halal with non-halal ingredients to reduce costs. Therefore, it is crucial to identify the contents of halal food products to ensure compliance with the guidelines outlined by the Department of Islamic Development Malaysia (JAKIM). In this study, an integrated fuzzy logic barcode scanning system was developed to determine non-halal ingredients and traces in packaged food products. Although the information on food products can be easily accessed from the open-source web data bank, using the proposed fuzzy logic based-barcode scanning system to identify the authenticity of halal food products is an alternative option for providing accurate, reliable, and fast detection. The inputs for the fuzzy logic framework are halal logo, alcohol, animal fats, gelatin, ham, bacon, L-cysteine, lipase, mono-and diglycerides, pepsin, rennet, sodium stearoyl lactylate, vanilla extract, whey, and E-code of E120, E422, E441, E470, E542, and E904. On the other hand, the outputs are halal, uncertain, and non-halal. The fuzzy logic-based barcode system was able to identify the halal status for three different categories: samples with a halal logo, uncertain halal status samples, and non-halal samples.

The Situation of Counterfeited and Mislabeled Commercialized Edible Mushrooms in China and the Development of Possible Controls

Edible mushroom products, encompassing both cultivated and wild varieties, are highly favored by consumers due to their rich nutritional profiles, including significant levels of proteins and amino acids. These mushrooms have extensive applications across the food, pharmaceutical, and cosmetic industries, making the edible mushroom industry a vital component of global poverty alleviation efforts. Taking China as an example, the country produces over 45 million tons of edible mushrooms annually, accounting for 94.01% of the world’s total production, thereby establishing itself as the leading global producer of edible mushrooms. However, alongside the rapid expansion of this industry, concerns have emerged regarding counterfeit products and incidents of poisoning resulting from the consumption of toxic wild mushrooms. As follows, to advance the development and integrity of the mushroom production and processing industry: (1) This study presents the situation of counterfeit edible mushrooms and elucidates the factors contributing to the production of fraudulent products from both subjective and non-subjective perspectives. (2) We provide a detailed introduction to 22 varieties of freshly cultivated edible mushrooms and commonly encountered wild edible mushrooms in the Chinese consumer market, proposing the application of DNA barcoding, environmental DNA analysis, and other technologies for the future authentication of counterfeit mushroom products. (3) Concurrently, we present an overview of mushroom poisoning incidents in China from 2010 to 2023, emphasizing the challenges in mitigating the risks associated with wild mushroom consumption and preventing food poisoning, thereby necessitating heightened consumer caution. (4) Finally, we offer four recommendations aimed at ensuring the healthy, stable, and sustainable growth of the edible mushroom industry.

Authentication of edible herbal materials and food products using mass spectrometry based metabolites and inorganic constituents

Medicinal food homologous (MFH) substances not only provide nutrition but also serve as a traditional means to overcome many health issues. Authentication of these products verifies their efficacity and assures consumers of a genuine product. In this review paper, we focus the determination of MFH authenticity including geographical identification and adulteration detection using mass spectrometry (liquid and gas chromatography) based metabolites and inorganic constituents (muti-elements and stable isotopes). The application of these techniques to determine product identification characteristics combined with chemometrics are discussed, along with the limitations of these techniques. Multi-elements, stable isotopes, and metabolite analysis are shown to provide an effective combination of techniques to resolve the origin of various MFH products. Most organic compounds from MFH products are identified using chromatographic separation techniques (HPLC, GC) combined with different detection methods. Chemometric analysis of organic and inorganic fingerprints offers a robust method to detect and classify mislabeled and suspected fraudulent samples of different MFH products.

Forging a Lineage: Kinship, Mobility, and the Zildjian Cymbal

Abstract Renowned for its 400-year-old Ottoman/Turkish/Armenian past and produced by “America's oldest family-run business,” the Zildjian cymbal is paradoxically rendered an unremarkable “humble object” in its assumed inclusion in orchestras and bands around the world. Tracing the lineages of the Zildjians and their cymbals through historical documentation, ethnography, and the materiality of the instruments themselves, I first discuss the cymbal's shifting musical contexts and functions in Ottoman Janissary mehter bands, European orchestras, American jazz bands, and many other ensembles over the past four centuries, as well as the role of the Zildjians in this musical expansion. Then, I examine how twentieth-century negotiations of Zildjian kinship emerged in contentions over the authenticity and ownership of cymbal production. Finally, I consider how the assimilatory pressures of nation-states shaped narratives of cymbal production as well as the Zildjians’ mobilities, particularly in the context of the ethnoracialization of minority populations in the late Ottoman Empire and Turkish Republic as well as the struggle of Armenian migrants to the United States to be recognized as valid U.S. American citizens at the turn of the twentieth century. By approaching the cymbal itself as the main interlocutor of this exploration, I aim to foreground the ways in which cymbals have sounded and resounded the mobility and kinships of its human creators. In doing so, I regard musical instruments as essential mediators of histories of cultural and musicological development as well as constructions of human identity and relationship, glimpsing how such objects may both reify and unsettle our epistemologies and the institutions of modern life.

Multi-Elemental Analysis and Geographical Discrimination of Greek "Gigantes Elefantes" Beans Utilizing Inductively Coupled Plasma Mass Spectrometry and Machine Learning Models.

Greek giant beans, also known as "Gigantes Elefantes" (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek "Gigantes Elefantes" beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model's effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek "Gigantes Elefantes" beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition.

Use of a novel combination of multiplex PCR and DNA barcoding in assessing authenticity of ginseng products

Ginseng (Panax sp.) is a medicinal plant used for its purported health benefits, primarily in East Asian countries. The COVID-19 pandemic led to a significant increase in sales of dietary supplements, including ginseng supplements, to purportedly support immune health and provide other health benefits. However, this heightened demand has subsequently increased the risk of adulteration in these dietary supplements. This study aimed to determine the efficacy of a novel combination of DNA barcoding and multiplex PCR to identify species in ginseng supplements. A total of 50 commercial ginseng supplements containing Panax ginseng, Panax quinquefolius, or Panax notoginseng were obtained from an online vendor. Two composite samples per product were subjected to DNA extraction followed by multiplex PCR and DNA barcoding with three genetic targets (i.e., rbcL, matK, and ITS2). Of the three DNA barcoding markers, ITS2 had the highest amplification success at 74%, and matK had the highest sequencing success at 60%. DNA barcoding alone identified species in 68% of products, while multiplex PCR had an overall 60% identification rate. The combination of both methods resulted in a 72% species identification rate. In addition to improving the overall identification rate, the combination of both methods allowed for greater species resolution and the detection of undeclared plant species. Future research should explore combining DNA-based methods with chemical-based approaches to improve ginseng detection capabilities and enable the quantification of undeclared adulterants.

Artificial Intelligence based techniques for authenticity of food products in Food fraud

Artificial Intelligence based techniques for authenticity of food products in Food fraud

Innovative QR code-based product authenticity safeguards: Case study of design considerations and technological challenges

Innovative QR code-based product authenticity safeguards: Case study of design considerations and technological challenges

Wavelength-dependent photoelectrochemical sensor array based on Bi2WO6/TiO2 electrospun nanoheterojunction for multiple antioxidants identification and total antioxidant capacity analysis in natural tea

Evaluating total antioxidant capacity (TAC) have been playing a vital role in health guidelines, the analysis of nutritional composition in foods, and the diagnosis and treatment of oxidative stress-induced disease. However, numerous antioxidants with similar physicochemical properties make them very difficult to be discriminated because of the similarly analytical signal output. In this work, a novel wavelength-dependent and three-channel photoelectrochemical (PEC) sensor array based on Bi2WO6/Vo-TiO2 electrospun nanostructures was established for the identification of antioxidants in natural teas. The formation of heterojunction interfaces between Bi2WO6 nanosheet and Vo-TiO2 NFs, and the introduction of the oxygen vacancies in TiO2 NFs promoted the visible light absorption ability and electron transfer rate of Bi2WO6/Vo-TiO2 NFs, causing the differences of photocurrent response to different antioxidants under different wavelengths. Furthermore, excited wavelengths, antioxidant types, and photocurrent were regarded as three-factors of fingerprint to accurately discriminate five antioxidants via linear discriminant analysis (LDA). Finally, the PEC sensor array was able to estimate TAC in five actual tea samples (green, yellow, black, white, and dark teas). This development of LDA-assisted multiplex PEC fingerprinting sensor array showed a good promise for the identification and authentication of agricultural products, and the establishment of the database.

A novel analytical approach in Amazon fish chain: Using infrared-spectroscopy with chemometric tools to identify non-typhoid Salmonella

The tambaqui (Colossoma macropomum) is one of the main native fish species in the Amazon region of Brazil, with economic, nutritional, cultural and environmental importance for the country. However, the presence of pathogens such as Salmonella in this matrix poses a threat to the safety and potential of this food chain. In response, this study aimed to develop a rapid, non-destructive approach to detecting Salmonella enterica serovar Schwarzengrund in tambaqui samples, using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) spectral data combined with the Data-Driven - Soft Independent Modeling of Class Analogy (DD-SIMCA) method. For this purpose, seventy-two samples of tambaqui ribs were purchased from supermarkets. Two groups of samples were used to build the model: control and contaminated with Salmonella. FT-MIR spectra were checked and four relevant regions were analyzed: all spectrum (4000–550 cm-1), region 1 (1490–500 cm-1), region 2 (1500–1730 cm-1), and region 3 (2835–4000 cm-1). The results revealed that region 1 proved to be the best for classifying contaminated samples from those not contaminated with Salmonella, with the best predictive performance with an accuracy of 94.2%. Our model exhibited the potential to be applied to the identification of Salmonella in tambaqui and to be a valuable tool for guaranteeing the safety and authenticity of fish products in the Brazilian Amazon region and, potentially, beyond. However, the use of FT-MIR combined with DD-SIMCA could be further explored in the future with a larger sample database, in order to verify the model's performance when the entire spectrum, regions 2 and 3 are used.

Combating Counterfeit Products with Blockchain Technology to Enhance Trust and Authenticity in Consumer Markets

Objectives: Counterfeit products are becoming a global problem where consumers are fooled into buying fake goods without being able to verify their authenticity. By taking advantage of the secure and transparent nature of blockchain, this work aims to establish a system for consumers to verify the authenticity of products. Methods: Recently, blockchain has become more popular because it fosters trust between untrusted participants. A blockchain is a decentralized, distributed, and digital ledger that records transactions by means of blocks. It is a security technology that does not allow anyone to change or hack the block. Using blockchain technology, customers do not have to rely on third parties to confirm product originality. Findings: This work will show the effectiveness of blockchain. Blockchain creates a more secure and transparent system for product authentication. It helps in identifying counterfeit products entering the market and increases consumer confidence during purchase. The comparison of the transaction send rate and the average latency for each type of transaction is elaborated. The CPU consumption for each transaction type Add, Query, and Query-all are 1.81%, 1.11% and 1.27% respectively. The memory consumption by add, Query and Query all are respectively 53MB, 45MB and 92 MB. Novelty: Blockchain technology helps manufacturers to add real product serial numbers into the ledgers. The serial number stored in the ledger is protected from tampering. The stored serial number helps consumers check the authenticity of a product before buying it. To verify that a manufacturer or trader is genuine, consumers may also submit reviews of the products they purchase. The deeper integration of consumer reviews within the system offers a novel approach to enhancing trust and transparency in the verification process. We have also developed a user-friendly interface for manufacturers, sellers, and customers, enhancing the novelty of our solution. Keywords: Counterfeit product, Blockchain, Supply chain, Product Authenticity, Trust