Application of Biosensors for the Detection of Mycotoxins for the Improvement of Food Safety

Toxins, Год журнала: 2024, Номер 16(6), С. 249 - 249

Опубликована: Май 27, 2024

Mycotoxins, secondary metabolites synthesized by various filamentous fungi genera such as Aspergillus, Penicillium, Fusarium, Claviceps, and Alternaria, are potent toxic compounds. Their production is contingent upon specific environmental conditions during fungal growth. Arising byproducts of metabolic processes, mycotoxins exhibit significant toxicity, posing risks acute or chronic health complications. Recognized highly hazardous food contaminants, present a pervasive threat throughout the agricultural processing continuum, from plant cultivation to post-harvest stages. The imperative adhere principles good industrial practice underscored mitigate risk mycotoxin contamination in production. In domain safety, rapid efficient detection holds paramount significance. This paper delineates conventional commercial methodologies for ensuring encompassing techniques like liquid chromatography, immunoassays, test strips, with emphasis on role electrochemiluminescence (ECL) biosensors, which known their high sensitivity specificity. These categorized into antibody-, aptamer-based, well molecular imprinting methods. examines latest advancements biosensors testing, particular focus amplification strategies operating mechanisms.

Язык: Английский

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From Food Industry 4.0 to Food Industry 5.0: Identifying technological enablers and potential future applications in the food sector

Comprehensive Reviews in Food Science and Food Safety, Год журнала: 2024, Номер 23(6)

Опубликована: Окт. 22, 2024

Abstract Although several food‐related fields have yet to fully grasp the speed and breadth of fourth industrial revolution (also known as Industry 4.0), growing literature from other sectors shows that 5.0 (referring fifth revolution) is already underway. Food 4.0 has been characterized by fusion physical, digital, biological advances in food science technology, whereas future could be seen a more holistic, multidisciplinary, multidimensional approach. This review will focus on identifying potential enabling technologies harnessed shape coming years. We state‐of‐the‐art studies use innovative various agriculture applications over last 5 In addition, opportunities challenges highlighted, directions conclusions drawn. Preliminary evidence suggests outcome an evolutionary process not revolution, often claimed. Our results show regenerative and/or conversational artificial intelligence, Internet Everything, miniaturized nanosensors, 4D printing beyond, cobots advanced drones, edge computing, redactable blockchain, metaverse immersive techniques, cyber‐physical systems, digital twins, sixth‐generation wireless beyond are likely among main driving 5.0. framework, vision, value becoming popular research topics academic fields, agri‐food sector just started embrace some aspects dimensions

Язык: Английский

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Enhanced Food Quality by Digital Traceability in Food Processing Industry

Food Engineering Reviews, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Язык: Английский

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Recent Advances in Biosensor Technologies for Meat Production Chain

Foods, Год журнала: 2025, Номер 14(5), С. 744 - 744

Опубликована: Фев. 22, 2025

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity specificity for the rapid accurate point-of-care (POC) quantitative detection of selected biomolecules. In meat production chain, their application has gained attention due increasing demand enhanced food safety, quality assurance, fraud detection, regulatory compliance. can foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria indicators, contaminants (pesticides, dioxins, mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters stress hormones), metabolites (acute-phase proteins as inflammation markers) at different modules along from livestock farming packaging in farm-to-fork (F2F) continuum. By real-time data biosensors enable early interventions, reducing health risks (foodborne outbreaks) associated contaminated meat/meat products or sub-standard products. Recent advancements micro- nanotechnology, microfluidics, wireless communication have further sensitivity, specificity, portability, automation biosensors, making them suitable on-site field applications. The integration blockchain Internet Things (IoT) systems allows acquired management, while artificial intelligence (AI) machine learning (ML) enables processing, analytics, input risk assessment by competent authorities. This promotes transparency traceability within fostering consumer trust industry accountability. Despite biosensors' promising potential, challenges such scalability, reliability complexity matrices, approval still main challenges. review provides broad overview most relevant aspects current state-of-the-art development, challenges, opportunities prospective applications regular use safety monitoring, clarifying perspectives.

Язык: Английский

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Emerging applications of biorecognition elements-based optical biosensors for food safety monitoring

Опубликована: Фев. 14, 2025

Язык: Английский

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Strategies, mechanism, and prospects for wood biomass-based intelligent food packaging materials

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 105022 - 105022

Опубликована: Апрель 1, 2025

Язык: Английский

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Viral escape-inspired framework for structure-guided dual bait protein biosensor design

PLoS Computational Biology, Год журнала: 2025, Номер 21(4), С. e1012964 - e1012964

Опубликована: Апрель 15, 2025

A generalizable computational platform, CTRL-V (Computational TRacking of Likely Variants), is introduced to design selective binding (dual bait) biosensor proteins. The iteratively evolving receptor domain (RBD) SARS-CoV-2 spike protein has been construed as a model dual bait which evolved distinguish and selectively bind human entry receptors avoid neutralizing antibodies. Spike RBD prioritizes mutations that reduce antibody while enhancing/ retaining with the ACE2 receptor. CTRL-V’s through iterative cycles was shown pinpoint 20% (of 39) reported point across 30 circulating, infective strains responsible for immune escape from commercial LY-CoV1404. successfully identifies ~70% (five out seven) single (371F, 373P, 440K, 445H, 456L) in latest circulating KP.2 variant offers detailed structural insights mechanism. While other data-driven viral predictor tools have promise predicting potential future variants, they require massive amounts data bypass need physics explicit biochemical interactions. Consequently, cannot be generalized applications. publicly availably leveraged vivo anchors streamline workflow can tasks exemplified by identifying key mutational loci Raf kinase enables it Ras Rap1a GTP. We demonstrate three versions use combination integer optimization, stochastic sampling PyRosetta, deep learning-based ProteinMPNN structure-guided design.

Язык: Английский

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Modernization of digital food safety control

Advances in food and nutrition research, Год журнала: 2024, Номер unknown, С. 93 - 137

Опубликована: Янв. 1, 2024

Язык: Английский

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Enhancing Baby Food Safety - integrating Advanced Sensor Technology and Blockchain for Contaminant Detection and Transparency

European Journal of Nutrition & Food Safety, Год журнала: 2024, Номер 16(7), С. 313 - 316

Опубликована: Июль 3, 2024

The “Type of Article” this paper is “Letter to the Editor”. This discuses about: “Enhancing Baby Food Safety - integrating Advanced Sensor Technology and Blockchain for Contaminant Detection Transparency”. No formal abstract available. Readers are requested read full article.

Язык: Английский

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Advancing Food Safety Sensing through Artificial Intelligence: Machine Learning-Enhanced Biosensors in Action

Опубликована: Май 28, 2024

first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Size: Aa Line Spacing:  Column Width:  Background: Open AccessAbstract Advancing Food Safety Sensing through Artificial Intelligence: Machine Learning-Enhanced Biosensors in Action † by Paula BarcielaPaula Barciela SciProfiles Scilit Preprints.org Google Scholar 1, Ana Perez-VazquezAna Perez-Vazquez Aurora SilvaAurora Silva 1,2, M. Fatima BarrosoM. Barroso 2, Maria CarpenaMaria Carpena 1 and Miguel A. PrietoMiguel Prieto 1,* Universidade de Vigo, Nutrition Bromatology Group, Department of Analytical Chemistry Science, Instituto Agroecoloxía e Alimentación (IAA)–CITEXVI, 36310 Spain 2 REQUIMTE/LAQV, Superior Engenharia do Porto, Politécnico Rua Dr. António Bernardino Almeida 431, 4200-072 Portugal * Author to whom correspondence should be addressed. Presented at the 4th International Electronic Conference on Biosensors, 20–22 May 2024; Available online: https://sciforum.net/event/IECB2024 . Proceedings 2024, 104(1), 25; https://doi.org/10.3390/proceedings2024104025 Published: 28 2024 (This article belongs The Biosensors) keyboard_arrow_down with Cover XML Epub Supplementary Material Versions Notes Keywords: artificial intelligence; machine learning; food safety; nanotechnology; biosensors Current safety techniques equipment are struggling meet evolving demands industry. Traditional practices rely reactive measures, leading delays monitoring, early warnings, risk assessments, thereby impeding their effectiveness mitigation. integration nanotechnology into sensing offers significant advantages, including enhanced speed, cost-effectiveness, on-site detection, surpassing capabilities larger analytical tools. This is pivotal for detection pathogens, effective control fresh food, prevention food-borne illnesses identifying spoilage before it reaches consumers. Nevertheless, based antibodies or aptamers face limitations lifetime stability that impact commercial viability. To overcome these challenges, researchers turning intelligence as a groundbreaking solution. application learning, also known deep has potential transform conventional intelligent systems capable automated analyte prediction decision-making process. facilitates harmful substances during traceability processing. However, this innovative convergence raised ethical privacy concerns demand careful consideration [1,2,3,4,5]. review evaluates biosensors, aiming create cost-effective, real-time recognition devices identification contaminants matrices. MaterialsThe following available online https://www.mdpi.com/article/10.3390/proceedings2024104025/s1.Author ContributionsConceptualization, P.B.; methodology, P.B., M.C. A.S.; software, validation, A.P.-V. formal analysis, investigation, writing—original draft preparation, writing—review editing, A.P.-V., A.S., M.F.B., M.A.P.; visualization, supervision, M.A.P. All authors have read agreed published version manuscript.FundingThe research results was supported Xunta Galicia supporting pre-doctoral grant P. (ED481A-2024-230). (2020.03107.CEECIND) thanks FCT Investigator grant. thank Ibero-American Program Science Technology (CYTED—GENOPSYSEN, P222RT0117).Institutional Review Board StatementNot applicable.Informed Consent applicable.Data Availability StatementNo new data were created study.Conflicts InterestThe declare no conflict interest.ReferencesCui, F.; Yue, Y.; Zhang, Z.; Zhou, H.S. Learning. ACS Sens. 2020, 5, 3346–3364. [Google Scholar] [CrossRef] [PubMed]Chen, Wang, X.; C.; Cheng, N. Intelligent Promise Smarter Solutions 4.0. Foods 13, 235. [PubMed]Pampoukis, G.; Lytou, A.E.; Argyri, A.A.; Panagou, E.Z.; Nychas, G.-J.E. Recent Advances Applications Rapid Microbial Assessment from Perspective. Sensors 2022, 22, 2800. [PubMed]Kakkar, S.; Gupta, P.; Kumar, N.; Kant, K. Progress Fluorescence Biosensing towards Point-of-Detection (PoD) System. 2023, 249. [PubMed]Weston, M.; Geng, Chandrawati, R. Sensors: Challenges Opportunities. Adv. Mater. Technol. 2021, 6, 16. [CrossRef]Disclaimer/Publisher's Note: statements, opinions contained all publications solely those individual author(s) contributor(s) not MDPI and/or editor(s). editor(s) disclaim responsibility any injury people property resulting ideas, methods, instructions products referred content. © authors. Licensee MDPI, Basel, Switzerland. an open access distributed under terms conditions Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share Cite Style Barciela, Perez-Vazquez, A.; Silva, Barroso, M.F.; Carpena, Prieto, M.A. Action. 104, 25. AMA P, A, MF, M, MA. Proceedings. 104(1):25. Chicago/Turabian Paula, Prieto. 2024. "Advancing Action" no. 1: Metrics No Access Statistics Multiple requests same IP address counted one view.

Язык: Английский

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