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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]