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Humans need food, and the food detection system is a fascinating research topic and a complex weight loss mechanism. Eating healthy and balanced is crucial. Over the last few years, studies on food-related tasks have progressed, but the existing system deals only with single- and multi-food items by using convolution neural networks (CNN), and the recent development of Transformers in computer-vision has outperformed famous networks like ResNet 50, VGG 16, etc., but Transformer-based methods are limited in food-related tasks. For this issue, we improve Swin Transformer-based method by taking the dominance of transformer and CNN, we propose a novel multi-food detection utilizing a modified Swin-Transfomer and Recursive Feature Pyramid Network called (MFD-MST) and Swin-Transformer with spatial extraction block (STSE) as backbone to recognize multi-food item in images to improve local and structural information of image and RFP as neck to enhance This feature representation recognizes multi-food items. STSE solves transformer positional encoding, and RFP can look at feature map twice, helping the model build powerful representations. A prominent dataset, UEC-FOOD 100, Indian Food 28, UEC-FOOD 256 was widely tested to construct a detection system that can distinguish various objects in food photos and classify them into food categories. Our model's evaluation measures vary. MFD-MST outperforms Swin-Transformer by 2.7% at AP[0.50] and 3.3%, 1.4% on three food datasets respectively. Test results suggest that our system accurately detects food items. [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]

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]

Hypochloric acid (HClO) is a reactive oxygen species (ROS) that functions as a bacteriostatic and disinfectant in food production. Excessive levels of ClO−, however, have been linked to various health issues, including cardiovascular diseases (Halliwell and Gutteridge in Oxford University press, USA, 2015), arthritis, and neurodegenerative diseases (Heinzelmann and Bauer in Biol Chem. 391(6):675–693, 2010). Therefore, synthesizing highly selective and sensitive probes for rapidly detecting endogenous ClO− in daily foods is currently a popular research topic (Kalyanaraman et al. in Redox Biol. 15:347–362, 2018; Winterbourn in Nat Chem Biol. 4(5):278–286, 2008; Turrens in J Physiol. 552(2):335–344, 2003). Thus, we have developed two highly selective ratiometric fluorescent probes (Probe1 and Probe2) based on indole-phenothiazine to detect ClO− in common vegetables, fruits and beverages qualitatively and quantitatively. Moreover, Both Probe1 and Probe2 have shown good specificity and stability, with high fluorescence intensity and long duration (Feng et al. in Adv Sci. 5:1800397, 2018; Wei et al. in Angew Chem. 131(14):4595–4599, 2019; Baruah et al. in J Mater Chem B, 2022). [ABSTRACT FROM AUTHOR]

A novel fluorescent probe L was designed for rapid detection of Al3+ with high selectivity in water-acetonitrile medium based on aggregation-induced emission (AIE). The recognition mechanism was investigated by fluorescence spectra, SEM, FI-IR, 1H NMR, and mass spectra. The detection limit of Al3+ was 5.3 × 10−8 M. The recoveries were within the range of 84.5–103.3% and the relative standard deviation (RSD) values were within the range of 1.7–6.4%. The AIE behavior could be attributed to the J-aggregates formation and the chelation-enhanced fluorescence (CHEF) in water-acetonitrile medium. The probe L can be used to detect Al3+ in food and environmental samples. What's more interesting is that the L could detect intracellular Al3+ in live cells by bioimaging. [ABSTRACT FROM AUTHOR]

Salmonella is one of the most common pathogenic bacterium, which causes food spoilage and human diseases. The national standard detection method (GB4789.4) and other fast detection methods for Salmonella cannot satisfy the requirements in the present climate because of their inefficiency, low accuracy, high equipment requirements, or high cost. In this study, we combine isothermal recombinase polymerase amplification (RPA) with unmodified gold nanoparticles (AuNPs) to detect Salmonella in milk. A rapid, effective, visualized, and low-cost RPA-AuNP assay was developed, with a detection limit of 50 CFU for milk samples after enrichment for 6 h or 1 pg for DNA within 15 min at 37 °C using simple water bath equipment. Furthermore, it exhibited no cross-reactions with other pathogens by using highly specific region of invA of Salmonella as the target detection gene. And it may apply to the local food inspection and detection center whose equipment conditions are poor. [ABSTRACT FROM AUTHOR]

Vespula germanica wasps typically make consecutive trips between an undepleted food source and their nest. Hence, relocating a rewarded location is a recurrent activity during a wasp's lifetime. While foraging, wasps continue searching over a previously rewarded location even when food is no longer available. Moreover, when food is displaced to a location nearby, wasps still visit the previously rewarded location. We aimed to study how the modification of the local context would affect the discovery of the novel food site. By displacing food and changing the local context after wasps had learned a certain task, we evaluated whether wasps found a new food location more rapidly than if contextual conditions remained unaltered. We found that when previously associated cues were modified, wasps were more likely to 'give up' searching at the old location than if these cues remained unaltered. Furthermore, we observed that a higher number of rewarding trials at the initial location resulted in greater time taken to detect the new feeder location. [ABSTRACT FROM AUTHOR]

In this study we investigate associative learning related to food location in Vespula germanica social wasps. Wasps were trained to associate a certain location with food, and their behaviour after food displacement was recorded. The effect of a different number of previous feeding experiences on the detection of the novel food source was evaluated. When these wasps associated a certain location with food, they returned to the original site despite the fact that food was no longer available. This occurred even when a new food source was placed very close to the learned location. The results demonstrate that this learning seems to temporarily impair the detection of new food sources. We discuss how previous experience seems to condition perception, delaying the detection of more rewarding contexts. [ABSTRACT FROM AUTHOR]

Abilities of detection and preference of food by a Japanese whelk,Buccinum isaotakii,were investigated under laboratory conditions. Consumption per food item and the corresponding assimilation efficiency were also determined. Whelks were more sensitive to fresh foods than boiled foods, detecting them in a short period of time varying between 3.8 (±0.8) and 23.1 (±3.1) minutes.B. isaotakiipreferred fish, polychate and bivalves and the time spent on these foods was more than 10 min as compared with those of shrimp (2.0 min) and squid (1.0 min). The highest daily consumption rate was observed on sardine, followed by scallop, polychaete and oyster. The relationship between shell length and consumption of different food items was positive. The coefficients of determination were relatively high for sardine, scallop, polychaete and oyster (r 2, between 0.46 and 0.51) but not for clam, mussel shrimp and squid (r2, between 0.003 and 0.176), suggesting that the former could be predators of consumption forB. isaotakii. Observed were relatively high assimilation efficiencies on sardine, scallop, polychaete, and oyster, ranging from 30 to 70%. The present results are the intial information pertaining to the feeding biology of this whelk, thus, in-depth studies like an energy budget are recommended to deepen the understanding about seed production and resource management. [ABSTRACT FROM AUTHOR]

Mechanism of food detection inRana temporalistadpoles was studied using a rectangular choice tank with end compartments (stimulus zones) providing exclusively visual and/or chemical food cues. Boiled spinach served as the food. The test tadpoles were starved for 24 h before use. They were released from the center of the choice tank (n=24) after 5 min of acclimation to test for end bias and food-detecting mechanism. The number of tadpoles in the two stimulus zones was recorded at 5-min intervals from 10 to 30 min. In the end-bias tests (without food cues) tadpole distribution was comparable at all times in the two compartments of the choice tank, exhibiting no end bias. In tests with the visual food cues provided in one of the stimulus zones, the tadpole distribution was also random. On the other hand, in experiments involving chemical cues emanating from food the tadpoles preferentially associated with the food source in significantly greater numbers compared to the zone lacking food or providing only a visual cue. The experiments with individual test tadpoles also revealed that they detect food based on chemical cues and ruled out “copycat” behavior. These findings onR. temporalistadpoles reveal that chemical senses predominate over the visual senses in detection of food and foraging. [ABSTRACT FROM AUTHOR]

Knowing how far away animals can detect food has important consequences for understanding their foraging and social behaviors. As part ofa broader set of field experiments on primate foraging behavior, we set out artificial feeding platforms (90 W 90 cm or 50 W 50 cm) throughout the home range of one group of 22 brown capuchin monkeys, at sites where they had not seen such platforms previously. Whenever the group approached such a new platform to within 100 m, we recorded the group's direction and speed of approach, and the identity and distance from the platform of the group member that detected the platform orcame closest to it without detecting it. We used logistic regressionon these data to examine the effects of group movement speed, platform size and height, and focal individual age and sex on the probability of detecting the platform as a function of distance. Likelihood ofdetecting a platform decreased significantly at greater distances - the probability of detecting a platform reached 0.5 at 41 m from the group's center and 25.5 m from the nearest group member. These results show that detectability of platforms by the entire group (9 adults,13 juveniles) was less than twice that for single group members. Detectability at a given distance decreased severely as the group moved faster; at their fastest speed, individuals had to approach a platform to within less than 10 m to find it. The large platforms were significantly more likely to be detected than the small ones, suggesting that increased use of larger food patches by wild primates may not necessarily reflect foraging preferences. [ABSTRACT FROM AUTHOR]