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Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring., (Copyright © 2024. Published by Elsevier Inc.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important part of food quality management, the detection of food hazard factors and safety risk prediction have always been a research hotspot. In order to better carry out related work in the future, this article reviews research progress on conventional methods for the detection of food hazard factors, and introduces new methods for the detection of food hazard factors such as nanozymes, clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins (CRISPR/Cas) sensors and nuclear magnetic resonance (NMR) spectroscopy and the application of new materials such as magnetic covalent organic frameworks in this field. Then, it summarizes the application of subjective weighting, machine learning and deep learning in food safety risk prediction. Finally, the advantages and disadvantages of each method are analyzed, and future research directions are discussed.

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

From farm to plate, food meets various challenges and pollution. Food detection is essential to ensure food safety. Although detection techniques have obtained great development, the accuracy, sensitivity and operating conditions still have room for improvement. Composed of dielectric media with different refractive indices to form a periodic structure, an optical material named photonic crystal (PC) can offer a breakthrough. Owing to the periodic structure, Bragg diffraction happens with optical waves propagating in a PC and photonic band gap (PBG), a characteristic parameter of the PC, will form. The properties of PBG are widely used in the field of detection. Combined with the techniques of biology, immunology, optics and fluorescence etc., PCs can play the role of specific, efficient, sensitive and convenient sensors in food detection. At present, PCs have been successfully applied to analyze hazardous substances in food, such as microorganisms, biotoxins, veterinary drug residues, pesticide residues, excessive additives, illegal additives, environmental pollutants and heavy metal composition. This article introduces PCs about the fabrication methods, principles and techniques applied in detection and research progress in food detection. [ABSTRACT FROM AUTHOR]

In recent years, developing rapid, simple, and effective new methods to monitor food safety is standing out enough to be noticed. Electrospinning is an easy, versatile, and low-cost technique to fabricate multifunctional micro-and nanomaterials. The combination of electrospinning and biosensors is one of the most promising strategies and is expected to replace traditional detection materials. This paper reviews the research progress of electrospinning in food detection, including electrospinning materials generally used in the food field with their benefits of high aspect ratio, large specific surface area, tunable porosity, easy functionalization, easy handling, mechanical stability, such as natural polymers, synthetic polymers or the mixture of both. Besides, we also emphatically introduced the application of their intrinsic characteristics combined with biosensing platforms and list the sensitivity and detection limit of various methods and materials in detection, as well as their application potential, and the challenges faced by the on-site detection and the large-scale commercial applications in the food industry as future exploration headings, giving new thinking of nanomaterial applications for food safety detection. [ABSTRACT FROM AUTHOR]

The quality of food has a crucial relationship with human health, so the rapid and accurate detection of food quality has always attracted much attention. Olfactory visualization technology is a new type of food testing technology, which not only has the advantages of being fast, sensitive, and not easily affected by the environment, but also can intuitively judge the quality of food through color changes. However, due to the influence of complex surrounding factors, olfactory visualization technology cannot be used as a standard food quality testing method. In this paper, we review the basic principles and components of olfactory visualization techniques and their applications in the detection of meat, spices, beverages, grains and other foodstuffs at home and abroad. The impact of the technology on them is analyzed in depth from a multidimensional perspective, the main problems faced are summarized, and the future development trend is foreseen. It provides theoretical references for expanding its application scope and realizing the functionalization, intelligence and informatization of food safety and quality testing. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Food contains a variety of poisonous and harmful substances that have an impact on human health. Therefore, food safety is a worldwide public concern. Food detection approaches must ensure the safety of food at every step of the food supply chain by monitoring and evaluating all hazards from every single step of food production. Therefore, early detection and determination of trace-level contaminants in food are one of the most crucial measures for ensuring food safety and safeguarding consumers’ health. In recent years, various methods have been introduced for food safety analysis, including classical methods and biomolecules-based sensing methods. However, most of these methods are laboratory-dependent, time-consuming, costly, and require well-trained technicians. To overcome such problems, developing rapid, simple, accurate, low-cost, and portable food sensing techniques is essential. Metal-organic frameworks (MOFs), a type of porous materials that present high porosity, abundant functional groups, and tunable physical and chemical properties, demonstrates promise in large-number applications. In this regard, MOF-based sensing techniques provide a novel approach in rapid and efficient sensing of pathogenic bacteria, heavy metals, food illegal additives, toxins, persistent organic pollutants (POPs), veterinary drugs, and pesticide residues. This review focused on the rapid screening of MOF-based sensors for food safety analysis. Challenges and future perspectives of MOF-based sensors were discussed. MOF-based sensing techniques would be useful tools for food safety evaluation owing to their portability, affordability, reliability, sensibility, and stability. The present review focused on research published up to 7 years ago. We believe that this work will help readers understand the effects of food hazard exposure, the effects on humans, and the use of MOFs in the detection and sensing of food hazards.

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A novel, integrated, real‐time electrochemical loop‐mediated isothermal amplification (LAMP) device for pathogen detection in food was developed. The electrochemical LAMP device for detection of the vt gene in E. coli O157 : H7 and invA gene in Salmonella enterica was implemented with DNA intercalating probes‐methylene blue. To improve assay efficiency, the optimal concentration of MB was determined to be 30 μM. The real‐time electrochemical LAMP results of gene serial dilutions were compared to the fluorescence‐based detection method. For E. coli O157 and Salmonella, the assay detection limits were 10 DNA copies and 1 DNA copy, respectively, in a reaction time of less than 30 min. The electrochemical and optical LAMP method analyses were realized with 10 % milk to simulate the turbid environment of real food samples. In real‐time LAMP of Salmonella in a turbid environment, the detection limit of 10 copies without pretreatment was possible using the electrochemical method only. Our electrochemical device was used for bacterial detection in 150 turbid food samples, including juice, milk, and soy milk. This electrochemical device has many advantages, including size, portability, high specificity, and high sensitivity, while also being a user‐friendly device that provides stable signals. Thus, to the best of our knowledge, this new electrochemical device demonstrates the best performance for bacterial detection in the rapid contaminated food detection category. [ABSTRACT FROM AUTHOR]

Food contains a variety of poisonous and harmful substances that have an impact on human health. Therefore, food safety is a worldwide public concern. Food detection approaches must ensure the safety of food at every step of the food supply chain by monitoring and evaluating all hazards from every single step of food production. Therefore, early detection and determination of trace-level contaminants in food are one of the most crucial measures for ensuring food safety and safeguarding consumers' health. In recent years, various methods have been introduced for food safety analysis, including classical methods and biomolecules-based sensing methods. However, most of these methods are laboratory-dependent, time-consuming, costly, and require well-trained technicians. To overcome such problems, developing rapid, simple, accurate, low-cost, and portable food sensing techniques is essential. Metal-organic frameworks (MOFs), a type of porous materials that present high porosity, abundant functional groups, and tunable physical and chemical properties, demonstrates promise in large-number applications. In this regard, MOF-based sensing techniques provide a novel approach in rapid and efficient sensing of pathogenic bacteria, heavy metals, food illegal additives, toxins, persistent organic pollutants (POPs), veterinary drugs, and pesticide residues. This review focused on the rapid screening of MOF-based sensors for food safety analysis. Challenges and future perspectives of MOF-based sensors were discussed. MOF-based sensing techniques would be useful tools for food safety evaluation owing to their portability, affordability, reliability, sensibility, and stability. The present review focused on research published up to 7 years ago. We believe that this work will help readers understand the effects of food hazard exposure, the effects on humans, and the use of MOFs in the detection and sensing of food hazards. [ABSTRACT FROM AUTHOR]

[Display omitted] • A unique SERS substrate processed by one-step femtosecond laser ablation in Argon. • S-Ag-Ar can detect 10-8mol/L R6G with enhancement factor up to 5.3 × 106. • S-Ag-Ar has high uniformity with relative standard deviation less than 11.97%. • S-Ag-Ar can be stored for more than seven months and shows good universality. • This unique SERS substrate demonstrates good potentials for food safety evaluation. Surface Enhanced Raman Scattering (SERS) is a reliable tool for detection of food additives due to its advantages including rapid detection, non-destructive and excellent sensitivity. Currently, the commercial SERS substrates are not yet satisfactory referring to cost, sensitivity, uniformity and stability. Herein, we report a unique SERS substrate processed by one-step femtosecond laser ablation in Argon. The substrate shows high sensitivity with limit of detection up to 10-8mol/L for Rhodamine 6G (R6G) and enhancement factor up to 5.3 × 106 due to laser induced more plentiful nanostructures and less oxidation in Argon, in addition to high uniformity with relative standard deviation less than 11.97% and long storage time more than seven months. This SERS substrate is capable of detecting the common food additives including malachite green (1 ppm), crystal violet (10-7mol/L), Sudan I (1 ppm), melamin (1 ppm), brilliant blue (1 ppm) and tartrazine (1 ppm). This unique Ag SERS substrate demonstrates good potentials for food safety evaluation. [ABSTRACT FROM AUTHOR]