Search Results (232)

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems. Full article

L-thyroxine (T4) is an important hormone for diagnosing and evaluating thyroid function disorders. As outlined in ISO17511, having a certified reference material (CRM) is crucial for ensuring that the results of clinical tests are traceable to the SI-unit. This study employed two principal methods to evaluate the purity of T4, mass balance (MB) and quantitative nuclear magnetic resonance (qNMR), both of which are SI-traceable (International System of Units) approaches. The MB method involved a detailed analysis of impurities, including water, structurally related compounds, and volatile and non-volatile substances. A variety of techniques were employed to characterize T4 and its impurities, including liquid-phase tandem high-resolution mass spectrometry, ultraviolet spectrophotometry, infrared spectroscopy, and both ¹H-NMR and ¹³C-NMR. Additionally, impurities were quantified using Karl Fischer coulometric titration, ion chromatography, gas chromatography–mass spectrometry, and inductively coupled plasma–mass spectrometry. In qNMR, ethylparaben was used as the internal standard for direct value assignment. The results showed T4 purities of 94.92% and 94.88% for the MB and qNMR methods, respectively. The water content was determined to be 3.563% (n = 6), representing the highest impurity content. Ten structurally related organic impurities were successfully separated, and five of them were quantified. Ultimately, a purity of 94.90% was assigned to T4 CRM, with an expanded uncertainty of 0.34% (k = 2). Full article

The development of reversible animal models for the study of intestinal pathologies is essential to reduce the number of animals used in research and to better understand disease mechanisms. In this study, we present a reversible model of intestinal malabsorption through the administration of sublethal doses of ebulin-f, a ribosome-inactivating protein, and validate its usefulness by monitoring vitamin C absorption. The scientific community increasingly recognizes the importance of rationalizing experimental designs, optimizing treatment protocols, and minimizing the use of animals in research models. Thus, new methodologies are needed to minimize invasive sampling and to develop reversible animal models that recover physiologically post-study. Such models are essential for in vivo studies of human pathologies. Sublethal doses of ebulin-f (2.5 mg/kg) administered intraperitoneally to female Swiss CD1 mice (n = 6 per group) can cause reversible intestinal alterations in the small intestine, which offer the possibility of having a valuable reversible study model of malabsorption for the investigation of this syndrome. To verify whether nutrient absorption is altered, we used vitamin C as a traceable nutrient that can be quantified in the blood. Peripheral blood samples were collected through the retro-orbital area at 30, 80, 120, 180, and 1440 min post-administration, treated with DTT and MPA, and analyzed using a validated UV/Vis–HPLC method to indirectly determine vitamin C absorption by enterocytes. Pharmacokinetic analysis revealed significantly increased vitamin C absorption on days 1 and 3 post-treatment (AUC values of 3.65 × 10⁴ and 7.10 × 10⁴, respectively) compared to control (0.94 × 10⁴), with partial recovery by day 22 (3.27 × 10⁴). Blood concentration profiles indicate that intestinal damage peaks at day 3, followed by significant regeneration by day 22, establishing this as a viable reversible model for inflammatory bowel disease research. Full article

Growing concern over food safety and adulteration has thrust milk traceability technologies to the forefront of agrifood supply chains. This qualitative study explores the technological, organisational, and environmental (TOE) determinants of traceability technology adoption in Saudi Arabia’s dairy sector. In-depth semi-structured interviews with nine senior managers from small-, medium-, and large-scale dairy farms were analysed thematically in NVivo. Thematic analysis revealed that technological cost and compatibility played crucial role, while contrary to the prior literature, respondents downplayed technological complexity, arguing that training could offset it. Organisational culture and employee resistance were the primary inhibitors within dairy firms. Saudi Vision 2030, post COVID-19 consumer pressure and competitor pressure emerged as the dominant environmental factors. The findings offer insights for managers and policymakers on how to improve supply chain transparency, operational efficiency, product quality, and consumer trust while advancing several UN SDGs. Full article

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification. Full article

In current evaluations of intelligent system capabilities, there is a problem with the difficulty of unifying quantitative scoring results with qualitative grading standards, which severely limits cross-platform and cross-task intelligent comparisons and grading certifications. To address this issue, this paper proposes a hierarchical parameter partitioning equivalent mapping method (HPP-EM) to construct a mapping mechanism from quantitative scores to qualitative grades. The method comprises three modules: Nonlinear Interval Partitioning (NIP) constructs intelligent capability intervals based on the principle of diminishing capability margins; Quantification-to-Grade Mapping (QGM) introduces a set of criteria to determine mapping rules; and Dynamic Threshold Generation (DTA) calculates the lower score thresholds and total score thresholds for each grade based on indicator weights. The model is interpretable and supports mapping across multiple metrics and multi-level systems. Experimental validation was conducted using evaluation data from a deep-space unmanned system, demonstrating that HPP-EM accurately reflects system intelligence levels and exhibits good adaptability and scalability. This paper provides a traceable, structured mapping scheme for unified intelligence level assessment. Full article

A rapid, traceable, and highly sensitive method was developed for the simultaneous separation and quantification of 24 water-soluble synthetic colorants in premade cocktails, utilizing ultra-performance liquid chromatography coupled with diode array detection (UPLC-DAD). The purity of each colorant was individually confirmed through multi-wavelength analysis. Chromatographic conditions, including mobile phase composition and gradient elution, were meticulously optimized, achieving the separation of the 24 colorants on a BEH C18 column using a linear gradient elution within 16 min. The mobile phase consisted of an ammonium acetate solution (100 mmol/L, pH 6.25) and a mixed organic solvent of methanol and acetonitrile (2:8, v/v). The method exhibited excellent linearity across the concentration range of 0.005–10 μg/mL, with limits of detection (LODs) ranging from 0.66 to 27.78 μg/L for all 24 colorants. The method also demonstrated good precision (0.1–4.9%) at various concentration levels and recoveries ranging from 87.8% to 104.5% at spiked concentrations of 0.1, 0.5, and 1.0 μg/mL. A comparison with other published methods for colorant determination in food samples using HPLC-DAD and LC-MS (2014–2024) revealed that the proposed method offers superior performance in terms of the number of analytes detected, lower limits of detection, and reduced analytical time. Finally, the method was successfully applied to the analysis of colorants in premade cocktails from different sources. Full article

During ginseng selection, marketing promotion, and sales, it is imperative to expeditiously differentiate the overall quality grades, identify the geographic traces and determine the growth ages. This facilitates the selection of the most appropriate quality grade for each product, thereby ensuring the most efficacious marketing strategy. In this study, a new method is proposed and developed for the classification of ginsengs with diverse geographical traceability and with various growth ages by combining an electronic nose (E-nose) system and machine learning with Fourier-transform infrared spectroscopy (FTIR) as a calibration technology. An investigation has been carried out to discover the differences in the secondary metabolites and odor of three types of ginseng with different geographic traceability and three growth ages of ginseng from the same geographic traceability site. In the proposed method, five types of ginseng samples have been successfully tested. The optimal Mean-SVM model combined with an E-nose system classified ginseng samples with different geographic traceability and different growth years with accuracies of 100% and 82% in the training and test sets, respectively. These results have significant implications for ginseng’s geographic traceability, growth age determination, and overall quality control. It is believed that the future implementation of the proposed method would significantly protect the health and economic interests of consumers as well as promoting the use of an E-nose in the market surveillance of consumable products such as ginseng and other foods. Full article

Lycium barbarum L. is a shrub native to China. It produces berries that are high in nutraceutical value. Its commercial appeal has led to the development of new plantations in Italy over recent decades. The majority of cultivated goji plants are derived from local nursery seedlings without the selection of varieties or clones. This study used nine simple sequence repeats (SSRs) from Lycium chinense L. to analyze the genetic structure and variability of heterogeneous, seed-derived accessions cultivated in an orchard located in central Italy (from here on referred to as field). The results were compared to accessions of known origin (LB, Lycium barbarum; LC, Lycium chinense). The study aimed to determine the genetic origin of seedlings and assess the feasibility of using microsatellite markers for individual fingerprinting. It also aimed to propagate the most adapted, productive plants while ensuring traceability and protection of potential clones throughout the production chain. The SSR markers used revealed that the field accessions were genetically distinct from both the L. barbarum and L. chinense accessions, whose seeds came from different European Botanical Gardens. The mean observed heterozygosity (H_o) across the three groups was 0.356, higher than the mean expected heterozygosity (H_e) of 0.314. The values of the inbreeding coefficient (F_IS) ranged from −0.25 (field) to 0.05 (LC), confirming the high genetic variability in our dataset. The fixation index (F_ST) was 0.234, indicating medium to high genetic differentiation. The Bayesian analysis revealed three distinct clusters, indicating that three gene pools influenced the genetic structure of the studied populations. The orchard accessions form a distinct population, most likely a L. barbarum landrace, descended from two distinct ancestral populations that differ from the two known species. Our findings preliminarily lay the groundwork for the protection of some clonal lines of goji accessions for use in future planting more suited to the Mediterranean climate. This study also serves as a foundation for a more thorough characterization of cultivated L. barbarum, allowing for traceability and sustainable management of the genetic resource. Full article

This paper aims to define a model for measuring the localness of a company in an innovative and reliable way, motivated by the growing consumer interest in purchasing local products and supporting local economies. The proposed Expenses Localness Indicators (ELI) model uses existing data from information systems to define Localness Indicators, and incorporates Localness Levels based on geographic and economic criteria. It can be applied to any type of financial entity and overcomes the difficulty of defining localness in e-commerce companies or digital businesses in general. Previous studies have examined the impact of localness and investigated its effectiveness as a branding strategy for managers, mainly through product traceability. The ELI model uses as data the expenses of a company paid to other financial entities. The Expenses Localness Indicators are determined based on the distribution of these payments combined with the localness of the paid financial entities. These Indicators represent the degree of localness as a percentage, ranging from 0% (non-local) to 100% (fully local), and may vary over time. The results of the presented examples indicate that a company’s localness increases as it spends more of its expenses on local financial entities and vice versa. Specific strategies have been tested using synthetic data that demonstrate the correct functioning of the model’s indicators. The ELI model could be used to provide reliable and certifiable information to consumers who want to know where their money goes when they buy products. Implementing the proposed model on a large scale would require acceptance by as many companies and states as possible. However, by making the necessary adjustments, the model could be applied on a smaller scale, supported by consumers and local governments interested in uncovering knowledge about localness. It could also be established as a valid indicator of localness to provide information that researchers, government agencies and professionals can use to promote local entrepreneurship. Full article

Calibration is crucial in quantitative analysis, ensuring the traceability of standards for an accurate comparison of results. In mercury determinations, a gas calibrator unit containing liquid mercury is used for calibration by injecting headspace volumes via syringe. The Dumarey equation has been used for over 35 years to calculate mercury headspace concentration, aligning closely with saturated vapor pressure equations. However, the 2006 Huber equation yields different values, creating discrepancies. This paper compares calibrations using the Dumarey equations against NIST 3133 certified reference material, with detection by a cold vapor atomic fluorescence spectrophotometer (CV-AFS). The gas standard was injected directly, while Hg^II in NIST 3133 was reduced to Hg⁰ and captured on gold traps. Across 10–24 °C, the Hg⁰ concentration was determined, with uncertainties ranging from 2.9% to 8.4% for a coverage factor of two. No significant differences were found between calibrations using NIST 3133 and the Dumarey equation. These findings provide crucial insights into the traceability and accuracy of mercury calibration methods, ensuring the reliability of measurements used for environmental monitoring and regulatory compliance. Full article

Mezcal is a distilled beverage with a complex chemical profile defined by volatile organic compounds and physicochemical properties that determine its sensory attrib-utes. This study analyzed nine artisanal mezcals produced from four wild agave species in Oaxaca using solid-phase microextraction (SPME) and gas chromatography–mass spectrometry (GC-MS) to identify key volatile compounds for traceability and quality control. A total of 82 volatile compounds were identified, with esters, terpenes, and higher alcohols being the most abundant. Eight key compounds, including ethyl acetate, acetic acid, 1-butanol, furfural, methanol, and 2-methyl-1-propanol, were quantified due to their significant impact on mezcal’s quality and authenticity. Additionally, 1,2,3-trimethyl-benzene, nerolidol, and terpinolene were identified as exclusive compounds for differentiating mezcal by agave species and storage duration. The findings highlight the influence of fermentation, distillation, and storage conditions on mezcal’s chemical profile and demonstrate the importance of standardized analytical methods for product authentication. Proper management of variables during fermentation and optimization of the final distillation cuts is necessary to fully comply with regulatory parameters and ensure product quality. By establishing a catalog of compounds that characterize the mezcals, this study provides a scientific basis for improving quality control, ensuring regulatory compliance, and enhancing the traceability of mezcal in high-value markets. The next step is to validate the key volatile compounds with a larger sample and evaluate their reproducibility under different production and storage conditions. Full article

Background: Alcoholic beverages have been popular for thousands of years due to their unique flavors and cultural significance. However, the industry’s high profit margins have led to increasingly sophisticated counterfeiting practices. Stable isotope analysis has emerged as one of the most promising techniques for addressing authenticity and traceability challenges in alcoholic beverages. Scope and approach: This review presents a comprehensive summary of the principles and recent advancements in the application of stable isotope techniques for authenticity assessment. It examines their use in detecting fraud (e.g., identifying edible alcohol, exogenous water, carbonylation, and trace compounds), vintage identification, and geographical origin determination across various alcoholic beverages, with a particular focus on wine, Chinese baijiu, and beer. Conclusions: Stable isotope analysis is a powerful tool for verifying the authenticity of alcoholic beverages, offering effective solutions to combat counterfeiting, mislabeling, and adulteration. Future studies should focus on understanding the ecological, biological, and hydrometeorological factors influencing isotope signatures and develop advanced multi-isotope and chemometric approaches to improve reliability. Expanding global databases and integrating emerging technologies such as artificial intelligence (AI) and machine learning will further enhance the effectiveness and accessibility of stable isotope techniques, ensuring safer and higher-quality alcoholic beverages for consumers worldwide. Full article

Background: The supply chain refers to the full process of creating and providing a good or service, starting with the raw materials and ending with the final customer. It requires cooperation and coordination between many parties, including the suppliers, manufacturers, distributors, retailers, and customers. Methods: In the medicinal supply chain (MSC), the critical nature of these processes becomes more complicated. It requires strict regulation, quality control, and traceability to ensure patient safety and compliance with regulatory standards. This study is conducted to suggest a smooth channel to deal with the challenges and adaptability of the MSC. Different MSC challenges are considered as criteria which deal with various adaptation plans. Multi-criteria decision-making (MCDM) methodologies are taken as optimization tools and probabilistic linguistic term sets (PLTSs) are considered for express uncertainty. Results: The subscript degree function (SDF) and deviation degree function (DDF) are introduced to evaluate the crisp value of the PLTSs. An MSC model is constructed to optimize the sustainable medicinal supply chain and overcome various barriers to MSC problems. Conclusions: Additionally, sensitivity analysis and comparative analysis were conducted to check the robustness and flexibility of the system. Finally, the conclusion section determines the optimal weighted criteria for the MSC problem and identifies the best possible solutions for MSC using PLTS-based MCDM methodologies. Full article