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The main purpose of this paper was to evaluate the validation process of food safety control measures.

The validation of control measures has been analyzed at 50 food companies in Serbia. The sample included companies that produce food of both plant and animal origin and have certified food safety management systems. A total of 156 control measures that combat physical hazards (41.6%), followed by microbial hazards (34.0%) and chemical hazards (24.4%), have been analyzed. To enable quantification of the validation protocols, each control measure was assigned a score.

The validation scores showed that the highest level of validation was observed in large companies, as opposed to small and medium-sized companies (p < 0.05). The type of food safety hazards and the food sector did not reveal any statistical differences in-between the scores. The main approach to validating control measures was referring to the technical documentation of equipment used (52.6%), followed by scientific and legal requirements (30.7%). Less than 20% of the analyzed control measures were validated with operational data collected on-site. No mathematical modeling was observed for the sampled food companies. Future steps should include the development of validation guides for different types of control measures and training modules.

This study can serve as an improvement guide for food safety consultants, food safety auditors, certification bodies, inspection services, food technologists and food managers.

This study is one of the first to provide an insight into how food companies validate their control measures to combat microbial, chemical and physical food safety hazards.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Food insecurity is a major concern for developing economies. Operational inadequacies get introduced with unorganized interactions among stakeholders in the food supply chain, affecting social, economic, environmental and operational (SEEO) aspects of a nation. This study analyzes India's largest food safety net program, Public Distribution System (PDS) and develops a perception-based model, mapping the root causes (of food insecurity) with possible solutions. The novelty lies in leveraging blockchain in the implementation of food traceability system.

Soft system methodology (SSM) is used to identify and analyze problems in PDS, leveraging the learning and inquiry process. It relies on system thinking and action research to create a defendable and rational model, which helps in proposing recommendations for addressing the problem.

Blockchain-enabled food traceability system increases transparency, thus enabling the fulfillment of basic food necessities for beneficiaries.

The proposed model enables policymakers to build a profound understanding of existing operational issues and provides insightful recommendations for making informed decisions to deal with the grave issue of food insecurity.

Unlike previous studies, this research attempts to understand operational inefficiencies during interactions among stakeholders. It proposes a perception-based conceptual model for the final implementation at the ground level. It also reveals significance of three systems: a delivery system, an enabling system empowering delivery system and a criteria system to control and monitor processes. This study thus bridges an important gap in the literature by proposing a blockchain-driven traceability system, under the control of criteria system, through the integration of system-thinking and action-research approach.

This paper aims to uncover patterns of Mexican craft beer consumers and guide companies’ decisions in the creation of new products, marketing strategies, advertising and promotion to increase craft beer sales and contribute to faster growth.

This is a conjoint analysis with a selection of attributes for new or renewed products, marginal disposition to pay for particular characteristics through brand-specific choice-based design, and market simulation.

This paper clearly demonstrates consumers’ preferences and willingness to pay in Mexico, with a cutting-edge market research technique combining the prioritization of preferred craft beer characteristics, and the price consumers are willing to pay for such product characteristics.

The study's sample size of 501 responses is relatively small compared to the total number of craft beer consumers in Mexico. To enhance the validity and reliability of the findings, future studies should aim to obtain larger samples and compare their results with those of this study.

This study has important implications for craft beer producers, allowing them to develop targeted craft beers with appealing attributes for Mexican consumers, such as color, aroma intensity, alcohol degree intensity, bitterness, foam level and price.

This study's market forecasting simulation technique is based on assumptions of consumer behavior and market dynamics. Although relevant variables were considered, unanticipated external factors or market changes could impact the forecasts' accuracy. This will allow for a more comprehensive understanding of craft beer consumer preferences in different markets and enhance the reliability of forecasting techniques.

This paper informs craft beer producers by providing valuable knowledge on customers’ preferences and willingness to pay to enhance craft beer companies’ product development processes.

This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.