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The ability of anuran larvae to efficiently locate food is essential for their growth and development, but little is known about the sensory modalities they use to find food. We experimentally assessed the role of visual and chemical cues in finding food in tadpoles of the invasive species Lithobates catesbeianus. In the two‐choice experiment, using rectangular containers that offer visual and/or chemical food cues on opposite ends, tadpoles chose the side where the chemical cues of the food were present consistently. The visual cues alone showed an initial transient attraction of tadpoles, which disappeared after a few minutes, suggesting that tadpoles are potentially capable of seeing contrasting objects, at least, at a close range and in clear waters and daylight conditions. We also evaluated the foraging behaviour and the capability of tadpoles to find food in an open field experiment under two different lighting conditions: natural daylight and darkness. Results showed that tadpoles find food faster in dark conditions, confirming that chemical cues alone are sufficient for tadpoles of this species to find food. Moreover, the time spent feeding and the number of tadpoles simultaneously exploiting the food patch were greater in darkness suggesting that environments with low visibility conditions could favour the establishment of L. catesbeianus. Understanding the sensory modalities used by tadpoles of this invasive species to find food could help to understand the potential abilities to expand and establish in novel environments. [ABSTRACT FROM AUTHOR]

Food is closely related to human life. With the development of the times, the human demand for food has changed dramatically. People pay closer attention to the safety, health, composition, brand, origin, and processing method of food, which is precisely inseparable from food testing technology. Currently, there are many food inspection technologies, and the electronic nose (E-nose), as an efficient, fast, non-destructive, and promising technology, has been successfully applied in many aspects of the food industry and has shown promising results. This paper first introduces the basic principle and composition of the E-nose. Then it describes in detail the key elements, including gas sensor selection, sampling method design, data acquisition and information processing. Further summarizes the various typical applications of E-nose technology in the food industry in recent years, including six application directions: freshness assessment, process monitoring, flavor evaluation, authenticity evaluation, quality control, origin traceability and pesticide residue detection. Finally, the critical problems that need to be solved in the current application of E-nose technology in the food industry are discussed, and the potential future research directions in this field are foreseen. [ABSTRACT FROM AUTHOR]

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]

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.)

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]

Fipronil (FIP), a broad-spectrum phenylpyrazole insecticide, is highly toxic and threatens human health and ecological balance. Developing convenient, rapid, portable analytical technology for on-site and high-frequency testing of FIP is essential to reduce its damage. Herein, a monoclonal antibody (Clone F-3F6) against FIP, with high affinity and specificity, was produced using a novel immunogen, FIP-BSA, which was simply and directly synthesized by conjugating FIP with bovine serum albumin (BSA). Among the previously reported antibodies, F-3F6 acts more specifically against FIP. The FIP metabolites fipronil desulfinyl, fipronil sulfide, and fipronil sulfone showed lower cross-reactivity, and other pesticides were not recognized. To achieve high-frequency and on-site measurements of FIP, an evanescent wave fluorescence biosensor was built by integrating evanescent wave fluorescence technology, a functionalized fiber bioprobe, and a fluorescence-labeled F-3F6 antibody. The detection limit of FIP was 0.032 μg/L. The detection results of real milk and water samples showed that all the coefficients of variation were less than 10%, and the recovery ranged from 90 to 120%. The high reusability and stability of functionalized fiber bioprobe enables the accurate, cost-effective, high-frequency, and facile quantitative detection of FIP. This highly specific and reliable evanescent wave fluorescence biosensor will be well suited to the sensitive and high-frequency on-site analysis of only FIP in food. [ABSTRACT FROM AUTHOR]

Automatic food classification systems have several interesting applications ranging from detecting eating habits, to waste food management and advertisement. When a food image has multiple food items, the food detection step is necessary before classification. This work challenges the food detection issue and it introduces to the research community the Segmented UEC Food-100 dataset, which expands the original UEC Food-100 database with segmentation masks. In the semantic segmentation experiment, the performance of YOLAC and DeeplabV3+ has been compared and YOLAC reached the best accuracy of 64.63% mIoU. In the instance segmentation experiment, YOLACT has been used due to its speed and high accuracy. The benchmark performance on the newly released Segmented UEC Food-100 dataset is 68.83% mAP. For comparison purpose, experiments have been run also on the UEC FoodPix Complete dataset of Okamoto et al. The database and the code will be available after publication. [ABSTRACT FROM AUTHOR]

“Lab on paper” is a paper-based microanalytical system with unique advantages such as high sensitivity, low cost, portability, and mass production, which breaks through the limitations of traditional detection technologies and shows great potential in the field of rapid on-site food detection. In this paper, the typical “labs on paper”, including chromatographic paper, chemical test chip, lateral flow assay (LFA), microfluidic paper-based analytical devices (μPADs), and synthetic biological paper, are systematically reviewed. Special emphasis is put on the application of “lab on paper” in the field of food detection. Finally, the advantages, challenges, and future prospects of paper-based analytic methods are discussed, which may provide theoretical support for better application of “lab on paper” in the field of food detection.

Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the instance segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran. [ABSTRACT FROM AUTHOR]

As a testing technology for food, fluorescence spectrometry has the advantages of convenient, sensitive and efficient detection of complex and trace components in food. This article introduces the basic principles of fluorescence spectrum analysis technology, and systematically summarizes several fluorescence spectrum analysis technologies currently used in the field of food inspection, including atomic fluorescence spectrometry, two dimensional fluorescence correlation spectroscopy, excitation-emission matrices，front-face fluorescence spectroscopy，(total) synchronous fluorescence spectroscopy and fluorescence spectroscopy imaging technology, etc. The fluorescence spectrum analysis technology is used in the detection of different types of food such as grain and oil, livestock and poultry, aquatic products, dairy products, fruits and vegetables. The application methods and effects of fluorescence spectroscopy are summarized, and the existing problems and future development trends of fluorescence spectroscopy are analyzed.

Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture interference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecular composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Raman spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new progresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, respectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil colony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organisms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, portable and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the characterization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wider range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more inspiration to future agricultural production and research.

Carbon quantum dots (CQDs) are category of novel carbon nanomaterials, which have gradually attracted attention due to their unique properties. CQDs have obvious advantages such as water solubility, modifiability, photochemical property, easy surface modification, antibacterial activity and good biocompatibility. In this paper, the formation mechanism and doping synthesis methods of CQDs are summarized. This review also highlights the biological toxicity, antibacterial properties and application of CQDs in the detection of heavy metals, antibiotics, pathogens, pesticide residues and prohibited additives in food, nutritional functional components, and fresh-keeping of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture inter‐ ference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecu‐ lar composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Ra‐ man spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new pro‐ gresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, re‐ spectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil col‐ ony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organ‐ isms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, por‐ table and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the character‐ ization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wid‐ er range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more in‐ spiration to future agricultural production and research. [ABSTRACT FROM AUTHOR]

Genetically modified crops (GMCs) have been discussed due to unknown safety, and thus, it is imperative to develop an effective detection technology. CRISPR/Cas is deemed a burgeoning technology for nucleic acid detection. Herein, we developed a novel detection method for the first time, which combined thermostable Cas12b with loop-mediated isothermal amplification (LAMP), to detect genetically modified (GM) soybeans in a customized one-pot vessel. In our method, LAMP-specific primers were used to amplify the cauliflower mosaic virus 35S promoter (CaMV35S) of the GM soybean samples. The corresponding amplicons activated the trans -cleavage activity of Cas12b, which resulted in the change of fluorescence intensity. The proposed bioassay was capable of detecting synthetic plasmid DNA samples down to 10 copies/μL, and as few as 0.05% transgenic contents could be detected in less than 40 min. This work presented an original detection method for GMCs, which performed rapid, on-site, and deployable detection.

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.)

In the present study, the electrochemiluminescence (ECL) properties of Gd(OH)3 nanocrystals with K2S2O8 as the cathode coreactant were studied for the first time. Based on the prominent ECL behavior of this material and the excellent specificity of the aptamer technique, an ECL aptasensor for the detection of ochratoxin A (OTA) was formulated successfully. Over an OTA concentration range of 0.01 pg mL−1 to 10 ng mL−1, the change in the ECL signal was highly linear with the OTA concentration, and the limit of detection (LOD) was 0.0027 pg mL−1. Finally, the ECL aptasensor was further used to detect OTA in real samples (grapes and corn) and satisfactory results were obtained, which indicated that the built method is expected to be applied in food detection. [ABSTRACT FROM AUTHOR]

Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.

In the present study, the electrochemiluminescence (ECL) properties of Gd(OH)3 nanocrystals with K2S2O8 as the cathode coreactant were studied for the first time. Based on the prominent ECL behavior of this material and the excellent specificity of the aptamer technique, an ECL aptasensor for the detection of ochratoxin A (OTA) was formulated successfully. Over an OTA concentration range of 0.01 pg mL−1 to 10 ng mL−1, the change in the ECL signal was highly linear with the OTA concentration, and the limit of detection (LOD) was 0.0027 pg mL−1. Finally, the ECL aptasensor was further used to detect OTA in real samples (grapes and corn) and satisfactory results were obtained, which indicated that the built method is expected to be applied in food detection.

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance. [ABSTRACT FROM AUTHOR]

The extraction of near‐infrared (NIR) spectral discrimination information is important for the NIR spectral classification task. Some discriminant information extraction algorithms such as linear discriminant analysis (LDA), uncorrelated discriminant transform (UDT), and fuzzy uncorrelated discriminant transform (FUDT) use the sample mean to calculate the total scattering matrix. However, the calculation of the sample mean will be affected by abnormal samples, which will affect the extraction of discriminant information. To solve this problem, this article proposes an improved discriminant information extraction algorithm called weighted global fuzzy uncorrelated discriminant transform (WGFUDT). The algorithm uses the Euclidean distance between the training samples to weight the training samples and assigns a smaller weight to the abnormal data to reduce its effect on the sample mean. The algorithm was used for the grade identification of two green teas (Huangshan Maofeng tea and Mee tea). The results show that the classification accuracies of WGFUDT on Mee tea and Huangshan Maofeng tea are 97.22% and 99.07%, respectively. Compared with LDA, UDT and FUDT, WGFUDT can obtain more spectral discriminant information and has higher accuracy in grade identification of Huangshan Maofeng tea and Mee tea. Practical Applications: Some traditional discriminant information extraction algorithms sometimes cannot extract enough discriminant information, which will affect the recognition rate of tea grades. WGFUDT can extract more discriminant information in the face of green tea spectral information and achieve higher accuracy. WGFUDT for green tea grade identification is fast and effective. [ABSTRACT FROM AUTHOR]

The detection of food is crucial for our survival and health. Earlier experimental psychological studies have demonstrated that participants detect food more rapidly than non-food stimuli. However, it remains unknown whether color, which was shown to have various influences on food processing, can modulate the detection of food. To address this issue, a psychological experiment was conducted using a visual search paradigm in which photographs of food (fast food and Japanese food) and kitchen utensils were presented alongside images of non-food distractors (cars), with both color and gray images used. Participants used a key to indicate whether one item was different from the rest, and their reaction times (RTs) were measured. RTs for the detection of both food types were shorter than for the kitchen utensils when color images were used, but not when gray images were used; moreover, the RTs were slower for gray images than for color images for both food types but not for kitchen utensils. These results indicate that color facilitates rapid detection of food in the environment.

There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energy-dense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ϵ [380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system.

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance.

The development of techniques and methods for rapidly and reliably detecting and analysing food quality and safety products is of significance for the food industry. Traditional machine learning algorithms based on handcrafted features normally have poor performance due to their limited representation capacity for complex food characteristics. Recently, the convolutional neural network (CNN) emerges as an effective and potential tool for feature extraction, which is considered the most popular architecture of deep learning and has been increasingly applied for the detection and analysis of complex food matrices. In the current review, the structure of CNN, the method of feature extraction based on 1-D, 2-D and 3-D CNN models, and multi-feature aggregation methods are introduced. Applications of CNN as a depth feature extractor for detecting and analyzing complex food matrices are discussed, including meat and aquatic products, cereals and cereal products, fruits and vegetables, and others. In addition, data sources, model architecture and overall performance of CNN with other existing methods are compared, and trends of future studies on applying CNN for food detection and analysis are also highlighted. CNN combined with nondestructive detection techniques and computer vision system show great potential for effectively and efficiently detecting and analysing complex food matrices, and the features based on CNN show better performance and outperform the features handcrafted or those extracted by machine learning algorithms. Although there still remains some challenges in using CNN, it is expected that CNN models will be deployed on mobile devices for real-time detection and analysis of food matrices in future. • The principle and architectures of convolutional neural network are presented. • Feature extraction methods based on convolutional neural network are investigated. • Recent applications of convolutional neural network in food are summarized. • Challenges and future work of convolutional neural network are discussed. [ABSTRACT FROM AUTHOR]

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 Food Research & Development is the property of Food Research & Development 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 Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology 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.)

In this study, the objective was to develop Au nanorod arrays (AuNRAs) using two-dimensional (2D) graphene oxide (GO)-supported Au nanoparticles (AuNPs). Graphene oxide (GO) and AuNPs in different ratios were prepared, and the coupling performances for AuNRAs-GO/AuNPs were studied to optimize the AuNRAs-GO/AuNP substrate using 1,2-bis-(4-pyridyl)-ethylene (BPE). To further prove the Raman performance of AuNRAs-GO/AuNPs, Raman molecular BPE was used to assess the uniformity of Raman signaling across the substrate. The addition of GO/AuNPs endows AuNRAs with universally high adsorption of analytes and the electromagnetic mechanism from the multidimensional couplings of AuNRAs-GO/AuNPs enhances Raman performance; therefore, the AuNRAs-GO/AuNP preparation achieves sensitivity and repeatability. The optimized substrate was then used to detect melamine. Ultrasensitive detection of melamine reached a limit of detection of 10–9 M. To demonstrate the application potential of AuNPs/GO/AuNRAs, melamine was detected in liquid milk and its limit of detection was determined to be 1 ppm, which meets the maximum limit for the melamine content in infant milk as proposed by the World Health Organization, thus revealing the application potential of our method. [ABSTRACT FROM AUTHOR]

[Display omitted] • 3a and 3c are screened out from five D-A type dicyanoisophorone derivatives, which show high selectivity towards H 2 O 2. • 3a and 3c express colorimetric and fluorescent dual-signals towards H 2 O 2 with low detection limits (0.20 μM and 0.14 μM). • 3a and 3c are capable of H 2 O 2 detection in milk samples. • Low cytotoxicity allows 3a to be utilized as a H 2 O 2 fluorescence sensor in living HepG-2 cells and zebrafish. • A smartphone-based analysis method is utilized for the rapid quantitative detection of H 2 O 2 by 3c. Excessive residual hydrogen peroxide (H 2 O 2) disinfectant in food is harmful to human health. Therefore, it is necessary to develop efficient detection methods for H 2 O 2 detection. In this work, we designed and synthesized five D-A molecules 3a - 3e by introducing electron-donor substituents (–OCH 3 and –CH 3) to the electron-acceptor dicyanoisophorone skeleton in order to find out the suitable probes for H 2 O 2 detection. Among them, two promising probes, 3a and 3c , are screened out according to structure-property relationships. Based on the principle of intramolecular charge transfer (ICT), 3a and 3c express colorimetric and fluorescent dual-signals towards H 2 O 2 with low detection limits (0.20 μM and 0.14 μM) and rapid response (within 20 mins). The reaction mechanism between probes and H 2 O 2 is determined by 1H NMR and HRMS. Density functional theory (DFT) calculations are measured to study the regulation mechanism of structure adjustment on probs performance. Furthermore, a smartphone RGB analysis is utilized as a portable platform for the quantitative detection of H 2 O 2 without complicated instruments, indicating a high efficiency and on-site detection method for H 2 O 2. In addition, probes are applied to detect H 2 O 2 in milk samples, HepG-2 cells and zebrafish, suggesting the promising applications in food samples and physiological systems. [ABSTRACT FROM AUTHOR]

The detection of food is crucial for our survival and health. Earlier experimental psychological studies have demonstrated that participants detect food more rapidly than non-food stimuli. However, it remains unknown whether color, which was shown to have various influences on food processing, can modulate the detection of food. To address this issue, a psychological experiment was conducted using a visual search paradigm in which photographs of food (fast food and Japanese food) and kitchen utensils were presented alongside images of non-food distractors (cars), with both color and gray images used. Participants used a key to indicate whether one item was different from the rest, and their reaction times (RTs) were measured. RTs for the detection of both food types were shorter than for the kitchen utensils when color images were used, but not when gray images were used; moreover, the RTs were slower for gray images than for color images for both food types but not for kitchen utensils. These results indicate that color facilitates rapid detection of food in the environment. [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.)

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.)

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.)

Based on the demonstration of molecular imprinting technology, photonic crystal technology and the technology of molecularly imprinted photonic crystal, the relevant researches on the application of molecularly imprinted photonic crystal technology in the detection of toxic and harmful substances in food (veterinary drug residues, pesticide residues, biological toxins, endocrine disruptors, illegal additives) and nutrients was combed. It pointed out that molecularly imprinted photonic crystal technology combined the high selectivity of molecular imprinting technology to the target and the unique optical properties of photonic crystals, and was suitable for food detection as a new optical sensor. Compared with the traditional food detection methods, molecularly imprinted photonic crystal technology greatly improved the detection efficiency, shortened the detection time, improved the visual detection degree, and was more conducive to the development of field detection. However, molecularly imprinted photonic crystal technology also had the disadvantages of complex synthesis process, large amount of organic reagents, narrow detection range and poor repeatability. Researchers could make further research in the future on developing environment-friendly green synthesis methods, enhancing the stability and controllability of photonic crystals, reducing the nonspecific adsorption of molecularly imprinted photonic crystals, developing high-throughput detection methods suitable for simultaneous detection of multiple targets, and expanding the application in the field of microbial detection to optimize and develop molecularly imprinted photonic crystal technology. [ABSTRACT FROM AUTHOR]

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.)

Most of existing methods mainly focus on the food image recognition which assumes that one food image contains only one food item. However, in this paper, we present a system to detect a diversity of foods in refrigerator where multiple food items may exist. In view of the refrigerator environment, we propose a food detection framework based on the supervised transformer network. More specifically, the supervised transformer network, dotted as RectNet, is first proposed to automatically select the irregular food regions and transform them to the frontal views. Then, based on the rectified food images, we further propose an end-to-end detection network that predicts the categories and locations of food items. The proposed detection network, called Lite Fully Convolutional Network (LiteFCN), is evolved from the advanced object detection algorithm Faster R-CNN while several significant improvements are tailored to achieve a higher accuracy and keep inference time efficiency. To validate the effectiveness of each component of our method, we build a real-world refrigerator dataset with 80 classes. Extensive experiments demonstrate that our methods achieve the state-of-the-art results, which improves the baseline by a large margin, e.g. , 3–5% in terms of F-measure. We also show that the proposed detection network achieve a competitive result on the public PASCAL VOC2007 dataset, which outperforms the Faster R-CNN by 2.3% with a higher speed. [ABSTRACT FROM AUTHOR]