Authenticity Of Products Research Articles

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration

This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable Leadership (CSL). The research focuses on how IoT enhances Environmental, Social, and Governance (ESG) practices within organizations, addressing key sustainability challenges and supporting Sustainable Development Goals (SDGs). Through quantitative and qualitative analyses, the study identifies three primary findings: (1) IoT applications directly enhance power source management in the environmental domain, improving Business Strategy and Long-term Growth (BSLG) and Measurement and Reporting (M&R) capabilities. This is achieved through real-time monitoring, smart building management, and predictive maintenance. In the social sphere, IoT strengthens supply chain transparency, bolstering Stakeholder Management (SM) through real-time tracking and blockchain-integrated systems for product authenticity verification. IoT comprehensively empowers performing risk management in the governance realm, improving Organizational Culture (OC) through early warning systems and predictive analytics for risk mitigation. (2) IoT integration facilitates Data-Driven Decision Making (DDDM), Real-Time Responsiveness (RTR), and Business Risk Management (BRM), key components of IoT-DMC. Furthermore, it supports the development of Effective Stakeholder Management (ESM), Resource Efficiency (RE), and Sustainable Competitive Outcomes (SCO) within IoT-CSL. (3) While highlighting the potential of IoT in advancing CSL, the research also acknowledges challenges such as initial investment costs, data privacy concerns, technological complexity, energy consumption of IoT devices, and electronic waste management. The study concludes that successful IoT implementation in CSL requires careful planning, robust data management, and a holistic approach considering both benefits and potential drawbacks.

Optimizing traceability scheme in a fresh product supply chain considering product competition in blockchain era

Fresh product traceability plays an integral role in ensuring food safety. Recently, many firms have introduced blockchain technology (BCT) for product traceability to guarantee product authenticity and consumer rights. However, introduction of the new technology has also intensified the competition among fresh products. Meanwhile, previous studies on traceability strategies only considered the impact of traceability cost and ignored the issue of increased product competition in blockchain era. Driven by these problems, we build a game-theoretical model to analyze the optimization of traceability schemes for both low-end fresh products (LEFP) and high-end fresh products (HEFP) in a fresh product supply chain (FPSC) in a competitive environment. Moreover, this paper investigates the impact of power structure on traceability schemes and identifies the conditions under which one scheme outperforms the others. We find that the cost per unit of product input has no effect on the traceability decision of the FPSC. Nonetheless, the introduction of BCT increases the number of consumers who choose LEFP and raises the selling price and wholesale price of corresponding products. Finally, from the supply chain perspective, it is best to adopt the same traceability scheme for both products. This study sheds light on the use of BCT in traceability can provide enhanced product authenticity and consumer rights while increasing product competition and demonstrates the significance of traceability schemes in the FPSC.

Rapid detection of murine-derived ingredients in meat products using real-time enzymatic recombinase amplification.

Accurate identification of meat species is critical to prevent economic fraud and safeguard public health. The use of inappropriate meat sources, such as murine, poses significant health risks because of potential contamination with pathogens and allergens, leading to foodborne illnesses. The present study aimed to develop a novel real-time enzymatic recombinase amplification (ERA) method for the rapid and specific detection of murine DNA in meat products. A novel ERA primer and probe set was designed, targeting a murine-specific single-copy nuclear gene identified through bioinformatics analysis. The assay demonstrates high specificity, showing no amplification in commonly consumed meats, other animals or major crops. Additionally, it exhibits remarkable sensitivity, detecting as few as five copies of murine genomic DNA. For practical application, the ERA method could effectively identify mouse DNA in laboratory-prepared samples at concentrations as low as 0.5% and also quantify samples with mouse DNA content as low as 5%. It also accurately detects the presence of murine-derived ingredients in commercially available meat products. The detection process is straightforward, utilizing a simple isothermal device for incubation, blue light excitation and a smartphone camera for result interpretation. This rapid analysis can be completed within 20 min. The newly developed real-time ERA method provides a valuable tool for standardizing meat trade practices, promoting food safety and enhancing consumer confidence in the authenticity of meat products. © 2024 Society of Chemical Industry.

Identifying Key Markers for Monofloral (Eucalyptus, Rosemary, and Orange Blossom) and Multifloral Honey Differentiation in the Spanish Market by UHPLC-Q-Orbitrap-High-Resolution Mass Spectrometry Fingerprinting and Chemometrics.

Honey differentiation based on the botanical origin is crucial to guarantee product authenticity, especially considering the increasing number of fraud cases. This study assessed the metabolomic differences arising from various botanical origins in honey products sold in Spanish markets, focusing on two goals: (1) discrimination within monofloral samples (eucalyptus, rosemary, and orange blossom honey) and (2) differentiation between multifloral vs. monofloral honey samples. An omics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap-high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) was applied for the reliable identification of specific honey markers selected by orthogonal partial least squares discriminant analysis (OPLS-DA) (R2Y = 0.929-0.981 and Q2 = 0.868-0.952), followed by the variable importance in projection (VIP) approach. Key amino acid, alkaloid, and trisaccharide markers were identified to distinguish between honey samples. Some Amadori compounds were highlighted as eucalyptus honey markers, suggesting their potential use for honey aging and botanical origin differentiation. L-phenylalanine and raffinose were markers of rosemary honey. Four markers (e.g., trigonelline, L-isoleucine, and N-(1-deoxy-1-fructosyl)isoleucine) were found in higher levels in multifloral samples, indicating a greater availability of amino acids, potentially increasing the Maillard reaction. This research is the first to address the botanical origin's impact on honey by identifying novel markers not previously described.

Lipid Marker ESI-MS Method for Quantification of Adulteration in Almond-Based Vegetable Beverage

ABSTRACT Almond-based beverages are valued for their functionalities. However, cases of adulteration are found in the literature aiming to reduce costs. In this study, almond (LA) and peanut (LP)-based beverages were used as investigational controls in commercial products analyzed by gas chromatography and direct infusion electrospray ionization mass spectrometry (ESI-MS) combined with principal component analysis (PCA). Thus, characteristic fatty acids were identified in plant-based beverages. The ESI-MS method combined with PCA indicated individualities regarding the disposition of triacylglycerol (TAG) composition, as higher intensities of TAG were found as a lipid marker at m/z 919 [TAG+NH4]+ of ions formed by arachidic (20:0), 8/heptadecenoic (17:1n-9) and oleic (18:1n-9) fatty acids in LP. The technique demonstrated efficiency in the detection of fraud due to its sensitivity, identifying peanut content in three almond-based commercial beverages (4.4–11.58%). This study highlights the importance of robust analytical methods to the authenticity and quality of food products.

Halal Food Development Strategy: Preliminary Study

Indonesia is the country with the largest Muslim population in the world and is ranked first for consumers of food and beverage products halal. The large Muslim population with the value of food and drink consumption The largest halal food in the world has not been able to become a driving force for Indonesia to increasing the production of halal food and drinks. The country's large Muslim population provides a market for halal products, creating strong domestic demand that can drive growth and innovation in the industry. Indonesia's commitment to halal certification and regulations also makes Indonesia a leader in ensuring the integrity and authenticity of halal products, thereby further enhancing its reputation in the global market. The aim of this research is to identify strengths, weaknesses, opportunities and threats facing the large-scale halal food and beverage industry Indonesian small and medium enterprises and formulate industrial development strategies Indonesian halal food and drinks. To identify strategies for developing halal food products in Indonesia using SWOT analysis. The approach taken is a desk study approach where secondary data is obtained from various journal articles, statistical report data, reports from government agencies related to food, electronic news and other secondary data.

Application of Steady-State and Time-Resolved Fluorescence Spectroscopy in Identification of Bee Products

In this study, steady-state and time-resolved fluorescence spectroscopy techniques have been applied to determine fluorescence characteristics and fluorescence decay kinetics parameters (fluorescence lifetimes and their amplitudes) of available on the Polish market bee products, including several nectar honeys, royal jelly, bee bread in honey and in liquid artificial honey. The fluorescence properties of the tested bee products arise from the presence of a unique composition of aromatic amino acids, vitamins, phenolic compounds and Maillard reaction products. In the 300–550 nm region of the emission spectra (excited at 280 nm), each of the tested bee products exhibited (showed) a specific and distinctive vibronic structure, which was not observed in the spectrum of artificial honey. Quantitative and qualitative composition as well as specific interactions between fluorescent constituents determine the specific fluorescence characteristics of a given bee product providing a unique fingerprint that can be used in the identification of bee products of different botanical origin. Combination of stationary and time-resolved fluorescence techniques seems to be a promising approach in the identification, authentication and quality control of bee products to verify their health-beneficial properties.

Species authentication and conservation challenges in Chinese fish maw market using Mini-DNA barcoding

Fish maws, derived from the swim bladders of fish, are valued for their high collagen content and nutritional benefits, leading to significant market demand, especially in Asia. This study investigates the species authenticity of fish maw products using mini-DNA barcoding technology to address issues of mislabeling and substitution. Out of 350 samples, 347 were successfully sequenced, revealing a high mislabeling rate of 51.01% among identified species. Species such as Diodon liturosus and Diodon hystrix were found, posing significant health risks due to the presence of tetrodotoxin, a potent neurotoxin. The study also highlights the conservation implications of this market, with endangered species like Totoaba macdonaldi being illicitly traded. These findings underscore the urgent need for accurate species identification, stringent labeling regulations, and enhanced fishery management to protect endangered species and ensure consumer safety. This research advocates for robust international cooperation and reporting to combat illegal fishing and trade, promoting the sustainable development of fish populations and marine ecosystem health.

Designing Innovative Quick Response Codes-Based Product Authenticity Safeguards: Risk Analysis

This paper examines the potential risks that may occur when designing and implementing innovative products’ authenticity safeguards based on quick response (QR) codes. The study identified ten key risks, which were then analyzed in two dimensions: the probability of their occurrence and the strength of their impact, should they occur. Using the matrix-based method, risks were visualized on an intensity scale in both dimensions and with adequate color notation. Subsequently, the identified risks were grouped thematically. This enabled the identification of four key areas containing the identified risks, namely: competition, external websites, technological integration and risks dependent on internal factors. After aggregating individual risk levels, for each key area the average risk level was calculated. The first three areas were characterized by an average low level of risk, while the area related to risks dependent on internal factors was characterized by an average medium level of risk. The study’s systematic approach to identify and evaluate risks related to the execution of QR code-based authenticity safeguards project can be applied by businesses to similar projects, as it enables scientific-based risk identification and management, and consideration of strategies to mitigate the potential issues, ensuring smoother project execution and more reliable product performance.

Enhanced data preprocessing with novel window function in Raman spectroscopy: Leveraging feature selection and machine learning for raspberry origin identification

In this study, a simple and accurate approach is proposed for enhancing the origin identification of raspberry samples using a combination of innovative Raman spectral preprocessing techniques, feature selection, and machine learning algorithms. Window function was creatively introduced and combined with baseline removal technique to preprocess the Raman spectral data, reducing the dimensionality of the raw data and ensuring the quality of the processed data. An optimization process was conducted to determine the optimal parameter for the window function, resulting in a binning window width of 5 that yielded the highest accuracy. After applying three feature selection techniques, it was found that the information gain model had the best performance in extracting discriminative spectral features. Finally, ten different machine learning algorithms were employed to construct predictive models, and the optimal models were selected. Linear Support Vector Classifier (LinearSVC), Multi-Layer Perceptron Classifier (MLPClassifier), and Linear Discriminant Analysis (LDA) achieve accuracy, precision, recall, and F1 values above 0.96, while the Random Vector Functional Link Network Classifier (RVFLClassifier) surpasses 0.93 for these performance metrics. These results demonstrate the effectiveness of the proposed approach in identifying the origin of raspberry samples with high accuracy and robustness, providing a valuable tool for agricultural product authentication and quality control.

Machine learning-enabled fatty acid quantification and classification of pork from autochthonous breeds using low-field 1H NMR spectroscopic data

Traditional pig breeds, known for their sustainability and superior meat quality, are experiencing growing consumer preference. The lipid fraction composition of these meats plays a fundamental role in their health benefits and excellent organoleptic properties. Accordingly, accurate characterisation of intramuscular fat is crucial for maintaining quality standards and combating fraudulent practices. This study employs benchtop nuclear magnetic resonance (NMR) spectroscopy to delineate the lipidic profiles of various cuts from two emblematic Spanish autochthonous pig breeds. The implementation of chemometric and machine learning models enabled the classification of pork samples based on cut and breed of origin. Moreover, this investigation pioneers the coupling of benchtop NMR with machine learning models for quantitative purposes, achieving precise quantification of polyunsaturated, monounsaturated and saturated fatty acids in intramuscular fat. This novel approach holds promise for enhancing the traceability and authentication of traditional pig products, fostering consumer confidence and promoting sustainable livestock practices.

Methodological approaches to gene identification of tea raw materials and raw material composition of tea-based soft drinks

Tea or tea shrub is a plant of the Camellia sinensis species, the leaves of which, previously prepared in a special way, are the traditional raw material for the production of tea products. Varietal gene identification of tea allows us to increase the level of assessment of the authenticity of tea raw materials and tea products. It is predominantly based on DNA technologies for the detection and interpretation of SNP markers (Single Nucleotide Polymorphism), represented by a wide arsenal of both expensive high-tech methods and publicly available laboratory approaches. Species gene identification of the raw material composition of tea-based soft drinks is an equally important area of research due to the risk of falsification of this type of product. The purpose of this study was to find methodological approaches to the varietal gene identification of tea raw materials and to the species gene identification of the raw material composition of tea-based soft drinks. As a result of a bioinformatics study to identify polymorphic restriction sites in the nucleotide sequences of Camellia sinensis genome loci, diagnostically significant restriction enzymes were selected that were capable of detecting SNPs and identifying tea genotypes using the analyzed markers. At the same time, 16 loci had potential for practical application, of which 11 belonged to the group of the most informative SNP markers. A post-analytical assessment of tea varieties was carried out with them regarding their genotypic affiliation and identifiability as part of solving the first task of the study. To achieve the second task, a molecular genetic approach to the species identification of the raw composition of soft drinks based on green tea was tested. The study included the analysis of experimental drinks (with natural flavoring “Lemon” and synthetic flavoring “Peach 716”), as well as commercial concentrates “TIAKVA” (based on extracts from the coarse stems of green or black tea). The methods used in the work were PCR (Polymerase Chain Reaction), RFLP (Restriction Fragment Length Polymorphism) and direct sequencing of the amplified chloroplast DNA locus. The combination of two methods (PCR and sequencing) showed its effectiveness in establishing the belonging of the analyzed nucleic acid samples to the Camellia sinensis species, the raw material base of the studied drinks and concentrates. However, to unlock the authentication potential of PCR with primers #1 and #2 combined with RFLP analysis, it will be necessary to select diagnostically significant restriction enzymes suitable for generating species-specific combinations of PCR-RFLP profiles of marker sequence.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Authentication of sushi and sashimi products from northeastern Pará, coastal Amazon, by DNA barcoding reveals commercial frauds and potential threats to human health and environment

The Japanese cuisine has become a huge success in Brazil over the last years, but little is known about the fisheries resources commercialized to supply this specific market. In the present study, we carried out the first molecular characterization of sushi and sashimi products available in northeastern Pará, coastal Amazon, in order to infer what species have been actually exploited in this trade. We sequenced two mitochondrial DNA regions from 172 samples (138 of fish and 34 of shrimp-derived products) that were divided into 28 haplotypes. The comparative analyses with public DNA barcode databases revealed eight species of fish and three of shrimps among the samples, including cases of commercial frauds. For instance, Ruvettus pretiosus and Lepidocybium flavobrunneum were traded as “anchova” (bluefish), a valid and specific commercial name of Pomatomus saltatrix. Both species can cause gastrointestinal issues, which has led to restrictions on their sale in several countries. All commercialized shrimp-derived products were labeled as “camarão” (shrimp), as generic trade name that could not be applied to identify each species. Furthermore, potentially threatened species have also been recorded in the commercial products, such as Salmo salar and Cynoscion acoupa, classified as “nearly threatened” and “vulnerable”, respectively. The later, popularly known as “pescada-amarela” (acoupa weakfish) has been commercialized as “peixe-branco” (white fish), a designation that is not recognized by the Brazilian regulatory agencies. Both DNA markers (COI and 16S rRNA) were highly efficient in identifying the species from all samples, providing the first list of exploited species for Japanese cuisine along the coastal Amazon. Besides revealing issues in the commercialization of Japanese food items, we recorded a largely overlooked diversity of traded species. These data represent a potential risk to public health and to the environmental conservation such as the presence of exotic species and the exploitation of threatened species that should be considered to support effective policies for the regional regulation of this market.

Development of Certified Reference Materials Set for Open Porosity of Mammoth Ivory

Mammoth ivory is in demand in the jewelry industry, folk crafts and in the manufacture of cosmetics. This unique natural resource is an alternative to the prohibited extraction of ivory. It is fundamentally important for consumers to identify the raw material unambiguously as mammoth ivory and to accurately determine the grade of the material.The article presents the results of a study, which resulted in the development of a reference materials set of open porosity of the mammoth ivory. The reference materials are cylinders with an outer diameter of 30 mm and a height of 30 mm. Certified values of open porosity were determined by gas pycnometry. Reference materials are intended for certification of measurement methods and control of the accuracy of measurement results of open porosity of mammoth ivory in laboratory conditions. Reference materials can be used for various types of metrological control if the metrological characteristics of reference materials correspond to the established requirements.The research materials are of an applied nature for a number of economic sectors where mammoth ivory is used as a raw material. Reference materials of mammoth ivory are in demand in materials science, biology, geology, and other natural sciences, and in expert activities to determine the authenticity of products made from mammoth ivory. The article may also be of interest to students and teachers of natural sciences in higher education, as well as anyone interested in artifacts of ancient fauna.

Pricing and blockchain adoption for competitive sellers on an e-commerce platform with different contracts

We examine the impact of a blockchain technology (BCT) adoption strategy on the decisions and profits of branded companies and counterfeiters, and then discuss how BCT helps counterfeiters transform on a platform offering blockchain services and commission contracts. Our results show that BCT can make the branded seller better off when a platform has more brand-conscious consumers, despite the high adoption fee. Due to the free-riding effect, the counterfeiter’s best choice is often not to adopt BCT. As the fraction of brand-unconscious consumers increases, even with low adoption fee, the branded seller will not use BCT, but the counterfeiter will adopt BCT to transform her business and become a legitimate seller by offering innovative products, which increases consumer surplus. As the counterfeit probability and blockchain added value increase, both sellers proactively adopt BCT to disclose product authenticity, which may worsen the platform and social welfare. Importantly, there is a win–win–win outcome for sellers, platforms and consumers when at least one seller adopts BCT, depending on the interaction among the abovementioned factors. Compared with the proportional-fee contract, the fixed-fee contract improves the cost affordability of both sellers. In the context of different consumer types on platforms, our findings offer guidelines for branded companies to solve the authenticity problem of products and for counterfeiters to transform their businesses.

Chemical fingerprint analysis for quality assessment and control of Curcuma longa L. rhizomes from Vietnam using a high-performance liquid chromatography-diode array detector (HPLC-DAD)

Turmeric, extensively cultivated across Southeast Asia, especially in Vietnam, harbors active polyphenols, primarily curcumin (2–5%), renowned for its diverse health benefits. Pharmacopoeias recognize turmeric, yet it lacks standardized quality assessments and encounters challenges in extraction and identification due to natural variations and adulteration. This analytical method is vital for verifying the authenticity, purity, and quality of turmeric products in both the pharmaceutical and nutraceutical industries. This study successfully developed an efficient extraction process for curcumin (CUR), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) from Curcuma longa L. rhizomes. The herbal powder was extracted with methanol (1:30, w/v) by the ultrasound-assisted method for 10 minutes, and this process was repeated three times. A high-performance liquid chromatography-diode array detector (HPLC-DAD) method was validated for the simultaneous quantification of three analytes, following the AOAC guideline and achieving a correlation coefficient (R2) value greater than 0.9950. Utilizing the HPLC-DAD method, the study developed a chemical fingerprint analysis for three analytes to identify the characteristic chemical components distinguishing turmeric from each region. Nineteen samples collected from various provinces across Vietnam were subjected to analysis. In all analyzed samples, the concentrations of CUR, DMC, and BDMC ranged from 0.77–10.30%, 0.33–6.92%, and 0.03–3.23%, respectively. CUR was determined to be the dominant compound in most samples, while BDMC consistently exhibited the lowest levels of content. Utilizing the findings derived from the analysis of RRT and RPA metrics, the research assessed variances across sample batches. It is suggested that this newly established approach can be applied to construct and develop raw material areas to serve the needs of each field.

Brand Authenticity: A 21-Year Bibliometric Review and Future Outlook

Brand Authenticity plays a crucial role in marketing activities. Over the past two decades, considerable progress has been made in the academic research on Brand Authenticity. However, a more comprehensive investigation on this subject still needs to be done. This paper aims to conduct a literature review using a bibliometric approach to summarize articles published between 2003 and 2023. The aim is to identify the publication trends, key authors, pivotal literature, significant journals, research clusters, intellectual base, and topic trends. We intend to establish a foundation for future agendas by consolidating existing achievements and identifying gaps. We employed two analysis tools, VOSviewer and Bibliometrix, to analyze and visualize 880 selected articles. The result indicates an overall increasing trend in publications; several authoritative scholars have emerged, and international collaborations are extensive. Several knowledge groups have also been formed, focusing on tourism, food and retail, and marketing/management. There need to be more significant breakthroughs, prompting the exploration of new topics to expand the scope of brand authenticity research. Some potential areas of investigation include exploring more antecedents and consequences variables, cross-cultural marketing, specific product authenticity, and the impact of corporate social responsibility on perceived authenticity. By addressing these directions, we can advance the understanding of brand authenticity and enrich the existing knowledge on this topic, thereby contributing to its future development.

Rapid and duplex detection of sweetpotato and maize components in starch products using Proofman-LMTIA method

To simultaneously detect sweetpotato and maize components in starch products, this study develops a rapid analysis method based on ladder-shape melting temperature isothermal amplification (LMTIA) coupled with proofreading enzyme-mediated probe cleavage (Proofman). Internal transcription spacer (ITS) gene sequences were targeted to design species-specific primers linked with Proofman probes. The detection of this duplex Proofman-LMTIA method could be completed in 20 min under the optimal temperature of 61 ℃. The specificities of sweetpotato and maize ITS primers were verified with seven other species. The sensitivities of sweetpotato and maize DNA extracts were 10 pg/μL and 1 pg/μL, respectively. The detection limits of sweetpotato and maize DNA samples extracted from artificially adulterated starch powders were both 1 %. To demonstrate the effectiveness of the established duplex Proofman-LMTIA method, a total of 19 commercial sweetpotato noodles and 14 starch products were collected and tested for sweetpotato and maize authentication. Taken together, the LMTIA method is suitable for rapid and accurate detection of sweetpotato and maize components in starch products. The popularization of this method will provide guidelines for ingredients authentication and market supervision of products.

Incentive for blockchain technology adoption in an online platform-based co-opetitive supply chain

This paper explores the incentive for blockchain technology (BT) adoption in a co-opetitive supply chain with an online platform from the perspectives of consumers’ sensitivities to products’ checking time cost and authenticity. Within this supply chain framework, the manufacturer and reseller are co-opetitive, and both of them sell their products via the same online platform by paying a commission fee. The adoption of BT can reduce consumers’ time cost of checking products and ensure product authenticity. We establish decision models without and with adopting BT and analytically compare the equilibrium outcomes. Results show that the online platform’s incentive to adopt BT hinges on the commission rate, additional production cost, and fixed cost with introducing BT. When both the fixed cost with introducing BT and additional production cost are low, and the commission rate falls in a moderate range, the online platform is willing to introduce BT. The manufacturer participates in blockchain when the competition intensity is high or when the competition intensity, commission rate and additional production cost are all low. Interestingly, adopting BT benefits the reseller with a free-riding behavior only if the additional production cost is low. Based on the basic model, we make some extensions to check the robustness of these results by relaxing our parameter settings and changing the sequence game. We verify that our main derived results remain true in these extensions.