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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]

Bacteroides dorei is a promising candidate for the next-generation probiotics (NGP), but its accurate identification remains challenging due to its close taxonomic relationship with other Bacteroides species. This study aimed to mine the novel specific molecular target for detecting Bacteroides dorei. Using pan-genome analysis and PCR validation, species-specific target genes with a size of 193 bp were identified, and the specificity of the designed primer pair (30 bp) for this molecular target was confirmed. The PCR assay demonstrated high applicability for detecting target and/or nontarget strains in three simulated food systems, yielding no false-positive or false-negative results. Additionally, a qPCR method was developed for quantifying Bacteroides dorei , with a linear detection range from 1 × 108 to 1 × 101 CFU/mL and an R2 value greater than 0.98. This method exhibited accurate and sensitive detection capabilities suitable for both food and feces samples. Overall, our study established PCR and qPCR assays for the rapid identification of Bacteroides dorei , advancing the exploration of Bacteroides species and the application of detection for NGPs in functional foods across various product forms. [Display omitted] • New species-specific molecular target for Bacteroides dorei was identified. • PCR assay were developed for rapid identification of Bacteroides dorei. • qPCR assay were established for sensitive quantification of Bacteroides dorei. • These assays showed applicability for detecting in simulated or food samples. [ABSTRACT FROM AUTHOR]

Nitrite (NO 2 −) is present in a variety of foods, but the excessive intake of NO 2 − can indirectly lead to carcinogenic, teratogenic, mutagenicity and other risks to the human body. Therefore, the detection of NO 2 − is crucial for maintaining human health. In this study, an integrated array sensor for NO 2 − detection is developed based on molybdenum single atom material (IMS Mo-SAC) using high-resolution electrohydrodynamic (EHD) printing technology. The sensor comprises three components: a printed electrode array, multichannels designed on polydimethylsiloxane (PDMS) and an electronic signal process device with bluetooth. By utilizing Mo-SAC to facilitate electron transfer during the redox reaction, rapid and efficient detection of NO 2 − can be achieved. The sensor has a wide linear range of 0.1 μM–107.8 mM, a low detection limit of 33 nM and a high sensitivity of 0.637 mA−1mM−1 cm−2. Furthermore, employing this portable array sensor allows simultaneously measurements of NO 2 − concentrations in six different foods samples with acceptable recovery rates. This array sensor holds great potential for detecting of small molecules in various fields. [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]

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]

Objective: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.Design: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.Results: A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.Conclusions: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns. [ABSTRACT FROM AUTHOR]